From 159bc4fe54d5fea44a5b0598d5a8930ed98a6531 Mon Sep 17 00:00:00 2001 From: wyattz23 Date: Mon, 29 Sep 2025 21:46:58 -0700 Subject: [PATCH 1/9] new --- .DS_Store | Bin 0 -> 6148 bytes .github/workflows/ci.yml | 99 + .pre-commit-config.yaml | 54 + DEVELOPMENT.md | 178 + README.md | 233 +- XuanjiNovo/XuanjiNovo.py | 231 +- XuanjiNovo/components/transformers.py | 54 +- XuanjiNovo/config.yaml | 13 +- XuanjiNovo/config/__init__.py | 21 + XuanjiNovo/config/logger.py | 133 + XuanjiNovo/config/model.py | 116 + XuanjiNovo/denovo/ctc_beam_search.py | 25 +- XuanjiNovo/denovo/db_dataloader.py | 8 +- XuanjiNovo/denovo/db_dataset.py | 1 - XuanjiNovo/denovo/db_index.py | 14 +- XuanjiNovo/denovo/mass_con.py | 279 - XuanjiNovo/denovo/model.py | 745 +- XuanjiNovo/denovo/model_runner.py | 392 +- XuanjiNovo/denovo/parser2.py | 2 - XuanjiNovo/denovo/pmc.py | 137 + XuanjiNovo/utils/__init__.py | 54 + XuanjiNovo/utils/metadata.py | 125 + XuanjiNovo/utils2.py | 62 +- checksums.txt | 4 + denovo.tsv | 49021 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zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 1: config["train_batch_size"] = config["train_batch_size"] // n_gpus + + logger.info(f"Number of available GPUs: {n_gpus}") + if n_gpus > 0: + gpu_names = [torch.cuda.get_device_name(i) for i in range(n_gpus)] + logger.info(f"GPU devices: {gpu_names}") import random if(config["random_seed"]==-1): diff --git a/XuanjiNovo/components/transformers.py b/XuanjiNovo/components/transformers.py index e1f4ca4..3d5e0e3 100644 --- a/XuanjiNovo/components/transformers.py +++ b/XuanjiNovo/components/transformers.py @@ -6,7 +6,7 @@ from .encoders import MassEncoder, PeakEncoder, PositionalEncoder from ..masses import PeptideMass -from .. import utils +from ..utils2 import listify class SpectrumEncoder(torch.nn.Module): @@ -112,7 +112,12 @@ def forward(self, spectra, precursors): #latent_spectra = self.latent_spectrum.expand(peaks.shape[0], -1, -1) peaks = torch.cat([precursors, peaks], dim=1) - return self.transformer_encoder(peaks, src_key_padding_mask=mask), mask + # Suppress nested tensor prototype warning + import warnings + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="The PyTorch API of nested tensors is in prototype stage") + output = self.transformer_encoder(peaks, src_key_padding_mask=mask) + return output, mask @property def device(self): @@ -383,7 +388,7 @@ def forward(self, sequences, precursors): mem_mask : torch.Tensor The memory mask specifying which elements were padding in X. """ - sequences = utils.listify(sequences) + sequences = listify(sequences) tokens = [self.tokenize(s) for s in sequences] tokens = torch.nn.utils.rnn.pad_sequence(tokens, batch_first=True, padding_value = len(self._amino_acids)) encoded = self.aa_encoder(tokens) @@ -402,7 +407,11 @@ def forward(self, sequences, precursors): encoded = self.pos_encoder(encoded) # Run through the model: - latent = self.transformer_encoder(encoded, src_key_padding_mask=mask) + # Suppress nested tensor prototype warning + import warnings + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="The PyTorch API of nested tensors is in prototype stage") + latent = self.transformer_encoder(encoded, src_key_padding_mask=mask) return latent, mask @@ -525,17 +534,14 @@ def forward(self, sequences, precursors, memory, memory_key_padding_mask, prev = # Prepare sequences if sequences is not None: - sequences = utils.listify(sequences) + sequences = listify(sequences) tokens = [self.tokenize(s) for s in sequences] tokens = torch.nn.utils.rnn.pad_sequence(tokens, batch_first=True, padding_value = self.get_pad_idx()) else: tokens = torch.tensor([[]]).to(self.device) - # Prepare mass and charge - #mass_tensor = torch.tensor([ 57.021464, 71.037114, 87.032028, 97.052764, 99.068414, 101.04767, 160.030649, 113.084064, 113.084064, 114.042927, 115.026943, 128.058578, 128.094963, 129.042593, 131.040485, 137.058912, 147.068414, 156.101111, 163.063329, 186.079313, 147.0354, 115.026943, 129.042594, 42.010565, 43.005814, -17.026549, 25.980265, 0.0]).to(self.device) - #look_up_feature = self.mass_lookup_ln(mass_tensor)[None, None, :] #(1, 1, dim) - #look_up_feature = look_up_feature.repeat(precursors.shape[0], 1, 1) # bz, max_len, dim + masses = self.mass_encoder(precursors[:, None, [0]]) #(bz, 1, dim) #print("charges:", precursors[:, 1].int() - 1) @@ -581,25 +587,17 @@ def forward(self, sequences, precursors, memory, memory_key_padding_mask, prev = output_list = [] - for mod in self.layers: - output = mod(output, memory, tgt_mask=None, - - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask) - preds = self.final(output) #bz, token_len, dic_size - output_list.append(preds) - #pred_tokens = torch.argmax(preds, axis=2) #bz, token_len - #encoded_tokens = self.aa_encoder(pred_tokens) #bz, token_len, dim_model - #-------------not useful------- - #cum_mass = self.demass(pred_tokens) #bz, token_len - #cum_mass = cum_mass[:, :, None] #bz, token_len, 1 - #encoded_mass = self.mass_ln(cum_mass) #bz, token_len, dim_model - #aa_encoded = encoded_mass + encoded_tokens #bz, token_len, dim_model - #-------------------------------- - #total_encod = torch.cat([output, encoded_tokens], dim=2) # bz, token_len ,dim_model * 2 - #output = self.layer_ln(total_encod) - - #output = torch.nn.functional.dropout(output, p=self.dropout) + # Suppress nested tensor prototype warning + import warnings + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="The PyTorch API of nested tensors is in prototype stage") + for mod in self.layers: + output = mod(output, memory, tgt_mask=None, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask) + preds = self.final(output) # bz, token_len, dic_size + output_list.append(preds) + diff --git a/XuanjiNovo/config.yaml b/XuanjiNovo/config.yaml index df9af41..0cbbf89 100644 --- a/XuanjiNovo/config.yaml +++ b/XuanjiNovo/config.yaml @@ -15,7 +15,6 @@ mass_control_tol: 0.1 #unit: Da, eveything decoded will be within 0.1 Da of MS/ #-------- load_file_name: "./NON351/epoch=5-step=65000.ckpt" # random_seed: -1 # Random -custom_ctc: False # Spectrum processing options. n_peaks: 800 min_mz: 1 @@ -32,6 +31,13 @@ n_head: 8 #8 dim_feedforward: 1024 n_layers: 9 dropout: 0.18 + +# Refinement and masking options +refine_iters: 3 # Number of refinement iterations in forward() +mask_schedule: + initial_peek: 0.93 # Initial peek factor + epoch_decay: 0.01 # How much to reduce peek factor per epoch + min_peek: 0.00 # Minimum peek factor dim_intensity: custom_encoder: max_length: 40 #70 #NON73: 32 @@ -68,6 +74,9 @@ residues: n_log: 1 tb_summarywriter: +# Logging configuration +log_level: "CRITICAL" # One of: DEBUG, INFO, WARNING, ERROR, CRITICAL + # Neptune logger enable_neptune: True neptune_project: @@ -102,7 +111,7 @@ train_from_scratch: False n_nodes: 1 save_model: False train_from_resume: False -model_save_folder_path: "./NON355" +model_save_folder_path: "./saved_models" save_weights_only: False every_n_train_steps: 2500 temp_dir_auto: True diff --git a/XuanjiNovo/config/__init__.py b/XuanjiNovo/config/__init__.py new file mode 100644 index 0000000..271594b --- /dev/null +++ b/XuanjiNovo/config/__init__.py @@ -0,0 +1,21 @@ +"""Configuration management for XuanjiNovo.""" + +from .model import ( + XuanjiNovoConfig, + ModelConfig, + TrainingConfig, + DecodingConfig, + DataConfig, + MaskSchedule +) +from .logger import ConfigLogger + +__all__ = [ + 'XuanjiNovoConfig', + 'ModelConfig', + 'TrainingConfig', + 'DecodingConfig', + 'DataConfig', + 'MaskSchedule', + 'ConfigLogger' +] diff --git a/XuanjiNovo/config/logger.py b/XuanjiNovo/config/logger.py new file mode 100644 index 0000000..7370520 --- /dev/null +++ b/XuanjiNovo/config/logger.py @@ -0,0 +1,133 @@ +"""Configuration logging utilities.""" + +import json +import os +from datetime import datetime +from typing import Dict, Optional + +import yaml +from .model import XuanjiNovoConfig + +class ConfigLogger: + """Logger for configuration management and validation.""" + + def __init__(self, output_dir: str): + """ + Initialize configuration logger. + + Parameters + ---------- + output_dir : str + Directory where configuration logs will be saved + """ + self.output_dir = output_dir + self.config_dir = os.path.join(output_dir, "config_logs") + os.makedirs(self.config_dir, exist_ok=True) + + def log_config(self, config: XuanjiNovoConfig, run_id: Optional[str] = None) -> Dict[str, str]: + """ + Log configuration with validation. + + Parameters + ---------- + config : XuanjiNovoConfig + Configuration to log + run_id : Optional[str] + Unique identifier for this run. If None, timestamp will be used. + + Returns + ------- + Dict[str, str] + Paths to the saved configuration files + """ + if run_id is None: + run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + + # Create run-specific directory + run_dir = os.path.join(self.config_dir, run_id) + os.makedirs(run_dir, exist_ok=True) + + # Save configurations in different formats + paths = {} + + # JSON format (full configuration) + json_path = os.path.join(run_dir, "config_full.json") + with open(json_path, 'w') as f: + json.dump(config.dict(), f, indent=2, sort_keys=True) + paths['json'] = json_path + + # YAML format (human-readable) + yaml_path = os.path.join(run_dir, "config.yaml") + with open(yaml_path, 'w') as f: + yaml.dump(config.dict(), f, default_flow_style=False) + paths['yaml'] = yaml_path + + # Device configuration + device_path = os.path.join(run_dir, "device_config.json") + with open(device_path, 'w') as f: + json.dump(config.get_device_config(), f, indent=2) + paths['device'] = device_path + + # Summary file (key configurations) + summary_path = os.path.join(run_dir, "config_summary.txt") + with open(summary_path, 'w') as f: + f.write(f"XuanjiNovo Configuration Summary\n") + f.write(f"Generated: {datetime.now().isoformat()}\n\n") + f.write(f"Model Architecture:\n") + f.write(f"- Dimensions: {config.model.dim_model}\n") + f.write(f"- Heads: {config.model.n_head}\n") + f.write(f"- Layers: {config.model.n_layers}\n\n") + f.write(f"Training Configuration:\n") + f.write(f"- Batch Size: {config.training.train_batch_size}\n") + f.write(f"- Learning Rate: {config.training.learning_rate}\n") + f.write(f"- Max Epochs: {config.training.max_epochs}\n\n") + f.write(f"Decoding Configuration:\n") + f.write(f"- PMC Enabled: {config.decoding.PMC_enable}\n") + f.write(f"- Beam Size: {config.decoding.n_beams}\n") + f.write(f"- Refinement Iterations: {config.decoding.refine_iters}\n") + paths['summary'] = summary_path + + return paths + + def load_config(self, run_id: str) -> XuanjiNovoConfig: + """ + Load configuration from logs. + + Parameters + ---------- + run_id : str + Run identifier to load configuration from + + Returns + ------- + XuanjiNovoConfig + Loaded configuration + """ + json_path = os.path.join(self.config_dir, run_id, "config_full.json") + with open(json_path) as f: + config_dict = json.load(f) + return XuanjiNovoConfig.from_dict(config_dict) + + def list_runs(self) -> Dict[str, Dict]: + """ + List all logged configuration runs. + + Returns + ------- + Dict[str, Dict] + Dictionary of run IDs and their metadata + """ + runs = {} + for run_id in os.listdir(self.config_dir): + run_dir = os.path.join(self.config_dir, run_id) + if os.path.isdir(run_dir): + summary_path = os.path.join(run_dir, "config_summary.txt") + if os.path.exists(summary_path): + with open(summary_path) as f: + summary = f.read() + runs[run_id] = { + "timestamp": run_id, + "summary_path": summary_path, + "summary": summary + } + return runs diff --git a/XuanjiNovo/config/model.py b/XuanjiNovo/config/model.py new file mode 100644 index 0000000..a126fd0 --- /dev/null +++ b/XuanjiNovo/config/model.py @@ -0,0 +1,116 @@ +"""Configuration validation models for XuanjiNovo.""" + +from typing import Dict, List, Optional, Tuple, Union +from pydantic import BaseModel, Field, validator +import torch + +class MaskSchedule(BaseModel): + """Mask schedule configuration.""" + initial_peek: float = Field(0.93, ge=0.0, le=1.0) + epoch_decay: float = Field(0.01, ge=0.0) + min_peek: float = Field(0.00, ge=0.0, le=1.0) + + @validator('min_peek') + def min_peek_less_than_initial(cls, v, values): + if 'initial_peek' in values and v > values['initial_peek']: + raise ValueError('min_peek must be less than or equal to initial_peek') + return v + +class TrainingConfig(BaseModel): + """Training-specific configuration.""" + train_batch_size: int = Field(2800, gt=0) + max_epochs: int = Field(150, gt=0) + warm_up_epochs: int = Field(1, ge=0) + learning_rate: float = Field(0.00035, gt=0) + weight_decay: float = Field(8e-5, ge=0) + gradient_clip_val: float = Field(2.5, gt=0) + gradient_clip_algorithm: str = Field("norm", regex="^(norm|value)$") + accumulate_grad_batches: int = Field(1, gt=0) + sync_batchnorm: bool = False + SWA: bool = False # StochasticWeightAveraging + +class ModelConfig(BaseModel): + """Model architecture configuration.""" + dim_model: int = Field(400, gt=0) + n_head: int = Field(8, gt=0) + dim_feedforward: int = Field(1024, gt=0) + n_layers: int = Field(9, gt=0) + dropout: float = Field(0.18, ge=0.0, le=1.0) + max_length: int = Field(40, gt=0) + +class DecodingConfig(BaseModel): + """Decoding configuration.""" + PMC_enable: bool = True + mass_control_tol: float = Field(0.1, gt=0) + n_beams: int = Field(5, gt=0) + refine_iters: int = Field(3, gt=0) + +class DataConfig(BaseModel): + """Data processing configuration.""" + n_peaks: int = Field(800, gt=0) + min_mz: float = Field(1.0, ge=0) + max_mz: float = Field(6500.0, gt=0) + min_intensity: float = Field(0.0, ge=0) + remove_precursor_tol: float = Field(1.0, gt=0) + max_charge: int = Field(10, gt=0) + precursor_mass_tol: float = Field(50.0, gt=0) + isotope_error_range: Tuple[int, int] = Field((0, 1)) + + @validator('isotope_error_range') + def validate_isotope_range(cls, v): + if v[0] > v[1]: + raise ValueError('isotope_error_range[0] must be <= isotope_error_range[1]') + return v + +class XuanjiNovoConfig(BaseModel): + """Complete configuration for XuanjiNovo.""" + # Basic settings + random_seed: int = -1 + log_level: str = Field("INFO", regex="^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$") + + # Component configurations + training: TrainingConfig + model: ModelConfig + decoding: DecodingConfig + data: DataConfig + mask_schedule: MaskSchedule + + # Residues configuration + residues: Dict[str, float] + + # Optional configurations + neptune_config: Optional[Dict] = None + custom_metadata: Optional[Dict] = None + + @validator('model') + def validate_model_heads(cls, v): + if v.dim_model % v.n_head != 0: + raise ValueError('dim_model must be divisible by n_head') + return v + + def get_device_config(self) -> Dict: + """Get device-specific configuration.""" + return { + "cuda_available": torch.cuda.is_available(), + "gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0, + "gpu_names": [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())] + if torch.cuda.is_available() else [] + } + + def save_to_file(self, filepath: str) -> None: + """Save configuration to a file.""" + with open(filepath, 'w') as f: + f.write(self.json(indent=2)) + + @classmethod + def from_yaml(cls, yaml_path: str) -> 'XuanjiNovoConfig': + """Create configuration from YAML file.""" + import yaml + with open(yaml_path) as f: + config_dict = yaml.safe_load(f) + return cls(**config_dict) + + @classmethod + def from_dict(cls, config_dict: Dict) -> 'XuanjiNovoConfig': + """Create configuration from dictionary.""" + return cls(**config_dict) diff --git a/XuanjiNovo/denovo/ctc_beam_search.py b/XuanjiNovo/denovo/ctc_beam_search.py index 367b93d..e1f449e 100644 --- a/XuanjiNovo/denovo/ctc_beam_search.py +++ b/XuanjiNovo/denovo/ctc_beam_search.py @@ -19,18 +19,7 @@ class CTCBeamSearchDecoder(CTCDecoderBase): def __init__(self, decoder, decoder_parameters: Dict) -> None: super().__init__(decoder) self.attn_decoder = decoder - ''' - self.decoder = CTCBeamDecoder(decoder.get_symbols(), model_path=decoder_parameters["model_path"], - alpha=decoder_parameters["alpha"], beta=decoder_parameters["beta"], - cutoff_top_n=decoder_parameters["cutoff_top_n"], - cutoff_prob=decoder_parameters["cutoff_prob"], - beam_width=decoder_parameters["beam_width"], - num_processes=decoder_parameters["num_processes"], - blank_id=decoder.get_blank_idx(), - log_probs_input=False) # This is true since our criteria script returns log_prob. - print("cut_off", self.decoder.cutoff_top_n) - print("ctc_beam_width", self.decoder._beam_width) - ''' + self.decoder = CTCBeamDecoder(decoder.get_symbols(), model_path= None, alpha=0, beta=0, cutoff_top_n=30, @@ -39,7 +28,6 @@ def __init__(self, decoder, decoder_parameters: Dict) -> None: num_processes=4, blank_id=decoder.get_blank_idx(), log_probs_input=False) # This is true since our criteria script returns log_prob. - print("notice here: ", self.decoder._beam_width) def decode(self, log_prob: TensorType, **kwargs) -> List[List[int]]: """ @@ -57,14 +45,5 @@ def decode(self, log_prob: TensorType, **kwargs) -> List[List[int]]: repeat(top_beam_len.size(0), 1).lt(top_beam_len.unsqueeze(1)) top_beam_tokens[~mask] = self.attn_decoder.get_pad_idx() # mask out nonsense index with pad index. #top_beam_tokens = top_beam_tokens.cpu().tolist() - ''' - for i in range(0, len(top_beam_tokens)): - current_summary_index = top_beam_tokens[i] - # Since ctc beam search decoder does post-process for us, we don't need to call post-processing again. - - if self.truncate: - current_summary_index = current_summary_index[:self.desired_length] - - top_beam_tokens[i] = current_summary_index - ''' + return top_beam_tokens, beam_scores[:,0] # then use truth decoding from decoder to get the results diff --git a/XuanjiNovo/denovo/db_dataloader.py b/XuanjiNovo/denovo/db_dataloader.py index 3cb1842..bfe7a66 100644 --- a/XuanjiNovo/denovo/db_dataloader.py +++ b/XuanjiNovo/denovo/db_dataloader.py @@ -20,7 +20,7 @@ def num_samples(self): return self.N_1 + self.N_2 def __iter__(self): offset = self.data_source.offset - print("offset in sampler: ", offset) + rand_tensor1 = torch.randint(low = 0, high = offset[0]-1, size = (self.N_1,) ).tolist() rand_tensor2 = torch.randint(low = offset[0], high = offset[1]-1, size = (self.N_2,)).tolist() rand_tensor1.extend(rand_tensor2) @@ -47,8 +47,7 @@ def num_samples(self): return self.N_1 + self.N_2 def __iter__(self): offset = self.data_source.offset - #print("offset in sampler: ", offset) - #rand_tensor1 = torch.randint(low = 0, high = offset[0]-1, size = (self.N_1,) ).tolist() + rand_tensor2 = torch.randint(low = offset[0], high = offset[1]-1, size = (self.N_2,)).tolist() self.N_1_range.extend(rand_tensor2) random.shuffle(self.N_1_range) @@ -204,8 +203,7 @@ def prepare_data(self) -> None: ''' - print("prepare_data ing.....") - + if self.train_index == None and self.mode == "fit": #prepare train_index diff --git a/XuanjiNovo/denovo/db_dataset.py b/XuanjiNovo/denovo/db_dataset.py index 9985359..fcdb292 100644 --- a/XuanjiNovo/denovo/db_dataset.py +++ b/XuanjiNovo/denovo/db_dataset.py @@ -116,7 +116,6 @@ def n_spectra(self) -> int: """The total number of spectra.""" total = 0 for each in self.indexs: - print("each data size: ", each.n_spectra) total += each.n_spectra return total @property diff --git a/XuanjiNovo/denovo/db_index.py b/XuanjiNovo/denovo/db_index.py index a02d6bf..a31c5cb 100644 --- a/XuanjiNovo/denovo/db_index.py +++ b/XuanjiNovo/denovo/db_index.py @@ -29,7 +29,6 @@ def __init__(self, db_path, filenames=None, ms_level = 2, valid_charge = None, a index_path = Path(db_path) self.db_path = index_path self.lock = lock - print("what lock is this:", lock) @@ -43,16 +42,12 @@ def __init__(self, db_path, filenames=None, ms_level = 2, valid_charge = None, a #create a new if not is_exist: - # print("I am not exist") txn = self.env.begin(write=True) txn.put( "ms_level".encode(), str(self.ms_level).encode()) txn.put("n_spectra".encode(), str(0).encode()) txn.put("n_peaks".encode(), str(0).encode()) self.n_spectra = 0 - # print("Before Writing:", self.annotated) - # txn.put("annotated".encode(), str(int(self.annotated)).encode()) - # annotated_read = bool(txn.get("annotated".encode()).decode()) - # print("jinzhi:",annotated_read,self.annotated) + txn.commit() else: txn=self.env.begin() @@ -76,15 +71,12 @@ def write_to_db(self, parser, n ): txn = self.env.begin(write=True) assert n == len(parser.precursor_charge) for i in range(len(parser.precursor_charge)): - #print("write in %i th spectrum,", i) - #remember to round the charge - #precusor m/z, precusor charge, peaks number, m/z1, .... , m/zn, i_1, ...., i_n + collection_data = {"precursor_mz": parser.precursor_mz[i], "precursor_charge": parser.precursor_charge[i], "mz_array": parser.mz_arrays[i], "intensity_array": parser.intensity_arrays[i]} collection_data["pep"] = parser.annotations[i] - # print(parser.annotations[i]) - + buffers = pickle.dumps(collection_data) txn.put(str(self.n_spectra).encode(), buffers) diff --git a/XuanjiNovo/denovo/mass_con.py b/XuanjiNovo/denovo/mass_con.py deleted file mode 100644 index 6d48853..0000000 --- a/XuanjiNovo/denovo/mass_con.py +++ /dev/null @@ -1,279 +0,0 @@ -import cupy as cp -import numpy as np -import time -import torch -import sys - -# inference_kernel = cp.RawKernel( -# r''' -# extern "C" __global__ -# void inference(float* prob, int* ans, float* aa_mass, float* dp, float* dpMass ,int* lock,float premass,int length) { -# const int AA_num = 28; -# const float tol = 0.1; -# int w = blockIdx.x; -# int dim = blockDim.x; -# int h = threadIdx.x; -# if(w == 0 || h == 0)return; -# int maxMass = int(aa_mass[AA_num - 8] * h); -# if(w > maxMass){ -# lock[w * dim + h] = 1; -# return; -# } -# if(h == 1){ -# for(int i = 1; i < AA_num; i ++){ -# if(w == int(aa_mass[i])){ -# if(dp[w * dim + h] < prob[(h-1) * AA_num + i]){ -# dp[w * dim + h] = prob[(h-1) * AA_num + i]; -# dpMass[w * dim + h] = aa_mass[i]; -# ans[w * (dim * dim) + 1 * dim + 1] = i; -# } -# } -# } -# lock[w * dim + h] = 1; -# return; -# } -# while(!atomicCAS(lock + w * dim + h - 1, 1, 1)); -# __threadfence(); -# if(dp[w * dim + h - 1] > 0){ -# dp[w * dim + h] = dp[w * dim + h - 1] + prob[(h - 1) * AA_num]; -# dpMass[w * dim + h] = dpMass[w * dim + h - 1]; -# for(int l = 1; l < h + 1; l++){ -# ans[w * (dim * dim) + h * dim + l] = ans[w * (dim * dim) + (h - 1) * dim + l]; -# } -# int preid = ans[w * (dim * dim) + (h - 1) * dim + h - 1]; -# if(preid != 0){ -# float tempProb = dp[w * dim + h - 1] + prob[(h - 1) * AA_num + preid]; -# if(dp[w * dim + h] < tempProb){ -# dpMass[w * dim + h] = dpMass[w * dim + h - 1]; -# dp[w * dim + h] = tempProb; -# for(int l = 1; l < h; l++){ -# ans[w * (dim * dim) + h * dim + l] = ans[w * (dim * dim) + (h - 1) * dim + l]; -# } -# ans[w * (dim * dim) + h * dim + h] = preid; -# } -# } -# } -# __threadfence(); -# for(int i = 1; i < AA_num; i ++){ -# int minw = int(w - aa_mass[i]); -# if(minw >= 0){ -# while(!atomicCAS(lock + minw * dim + h - 1, 1, 1)); -# __threadfence(); -# int preid = ans[minw * (dim * dim) + (h-1) * dim + h - 1]; -# if(preid != i){ -# float temp = dpMass[minw * dim + h - 1] + aa_mass[i]; -# if(temp >= w && temp < w + 1 && w != length-1 || w == length - 1 && temp >= premass - tol && temp <= premass + tol){ -# float tempProb = dp[minw * dim + h - 1] + prob[(h-1) * AA_num + i]; -# if(tempProb > dp[w * dim + h]){ -# dp[w * dim + h] = tempProb; -# dpMass[w * dim + h] = temp; -# for(int l = 1; l < h; l++){ -# ans[w * (dim * dim) + h * dim + l] = ans[minw * (dim * dim) + (h - 1) * dim + l]; -# } -# ans[w * (dim * dim) + h * dim + h] = i; -# } -# } -# } -# } -# minw = minw + 1; -# if(minw >= 0){ -# while(!atomicCAS(lock + minw * dim + h - 1, 1, 1)); -# __threadfence(); -# int preid = ans[minw * (dim * dim) + (h-1) * dim + h - 1]; -# if(preid != i){ -# float temp = dpMass[minw * dim + h - 1] + aa_mass[i]; -# if(temp >= w && temp < w + 1 && w != length-1 || w == length - 1 && temp >= premass - tol && temp <= premass + tol){ -# float tempProb = dp[minw * dim + h - 1] + prob[(h-1) * AA_num + i]; -# if(tempProb > dp[w * dim + h]){ -# dp[w * dim + h] = tempProb; -# dpMass[w * dim + h] = temp; -# for(int l = 1; l < h; l++){ -# ans[w * (dim * dim) + h * dim + l] = ans[minw * (dim * dim) + (h - 1) * dim + l]; -# } -# ans[w * (dim * dim) + h * dim + h] = i; -# } -# } -# } -# } -# } -# __threadfence(); -# lock[w * dim + h] = 1; -# return; -# } -# ''', 'inference') - -inference_kernel = cp.RawKernel( -r''' -extern "C" __global__ -void inference(float* prob, int* ans, float* aa_mass, float* dp, float* dpMass ,int* lock,float premass,int length,float grid_size, float tol2) { - const int AA_num = 28; - const float tol = tol2; - int w = blockIdx.x; - int dim = blockDim.x; - int h = threadIdx.x; - if(w == 0 || h == 0)return; - float maxMass = aa_mass[AA_num - 8] * h; - int maxW = int(maxMass / grid_size); - if(w > maxW){ - lock[w * dim + h] = 1; - return; - } - if(h == 1){ - for(int i = 1; i < AA_num; i ++){ - if(w == int(aa_mass[i] / grid_size)){ - if(dp[w * dim + h] < prob[(h-1) * AA_num + i]){ - dp[w * dim + h] = prob[(h-1) * AA_num + i]; - dpMass[w * dim + h] = aa_mass[i]; - ans[w * (dim * dim) + 1 * dim + 1] = i; - } - } - } - lock[w * dim + h] = 1; - return; - } - while(!atomicCAS(lock + w * dim + h - 1, 1, 1)); - __threadfence(); - if(dp[w * dim + h - 1] > 0){ - dp[w * dim + h] = dp[w * dim + h - 1] + prob[(h - 1) * AA_num]; - dpMass[w * dim + h] = dpMass[w * dim + h - 1]; - for(int l = 1; l < h + 1; l++){ - ans[w * (dim * dim) + h * dim + l] = ans[w * (dim * dim) + (h - 1) * dim + l]; - } - int preid = ans[w * (dim * dim) + (h - 1) * dim + h - 1]; - if(preid != 0){ - float tempProb = dp[w * dim + h - 1] + prob[(h - 1) * AA_num + preid]; - if(dp[w * dim + h] < tempProb){ - dpMass[w * dim + h] = dpMass[w * dim + h - 1]; - dp[w * dim + h] = tempProb; - for(int l = 1; l < h; l++){ - ans[w * (dim * dim) + h * dim + l] = ans[w * (dim * dim) + (h - 1) * dim + l]; - } - ans[w * (dim * dim) + h * dim + h] = preid; - } - } - } - for(int i = 1; i < AA_num; i ++){ - int minw = int((w * grid_size - aa_mass[i]) / grid_size); - if(minw >= 0){ - while(!atomicCAS(lock + minw * dim + h - 1, 1, 1)); - __threadfence(); - int preid = ans[minw * (dim * dim) + (h-1) * dim + h - 1]; - if(preid != i){ - float temp = dpMass[minw * dim + h - 1] + aa_mass[i]; - if(temp >= w * grid_size && temp < (w + 1) * grid_size && w != length-1 || w == length - 1 && temp >= premass - tol && temp <= premass + tol){ - float tempProb = dp[minw * dim + h - 1] + prob[(h-1) * AA_num + i]; - if(tempProb > dp[w * dim + h]){ - dp[w * dim + h] = tempProb; - dpMass[w * dim + h] = temp; - for(int l = 1; l < h; l++){ - ans[w * (dim * dim) + h * dim + l] = ans[minw * (dim * dim) + (h - 1) * dim + l]; - } - ans[w * (dim * dim) + h * dim + h] = i; - } - } - } - } - minw = minw + 1; - if(minw >= 0){ - while(!atomicCAS(lock + minw * dim + h - 1, 1, 1)); - __threadfence(); - int preid = ans[minw * (dim * dim) + (h-1) * dim + h - 1]; - if(preid != i){ - float temp = dpMass[minw * dim + h - 1] + aa_mass[i]; - if(temp >= w * grid_size && temp < (w + 1) * grid_size && w != length-1 || w == length - 1 && temp >= premass - tol && temp <= premass + tol){ - float tempProb = dp[minw * dim + h - 1] + prob[(h-1) * AA_num + i]; - if(tempProb > dp[w * dim + h]){ - dp[w * dim + h] = tempProb; - dpMass[w * dim + h] = temp; - for(int l = 1; l < h; l++){ - ans[w * (dim * dim) + h * dim + l] = ans[minw * (dim * dim) + (h - 1) * dim + l]; - } - ans[w * (dim * dim) + h * dim + h] = i; - } - } - } - } - } - lock[w * dim + h] = 1; - return; -} -''', 'inference') - -def knapDecode(prob, preMass, tol): - grid_size = 1 - AAmasses = [0.0, 57.021464, 71.037114, 87.032028, 97.052764, 99.068414, 101.04767, 160.030649, 113.084064, 113.084064, 114.042927, 115.026943, 128.058578, 128.094963, 129.042593, 131.040485, 137.058912, 147.068414, 156.101111, 163.063329, 186.079313, 147.0354, 115.026943, 129.042594, 42.010565, 43.005814, 10000.0, 25.980265] - AAmasses = np.array(AAmasses,dtype=np.float32) - prob = torch.roll(prob[0],1,dims = -1) - AAmasses = cp.array(AAmasses,dtype=cp.float32) - preMass = float(preMass[0].item() - 18.01) - preMass = cp.float32(preMass) - grid_size = cp.float32(grid_size) - tol = cp.float32(tol) - # print(preMass) - prob = torch.where(prob < -20.0, -20.0, prob) - #print(prob) - #print(prob.min()) - prob = prob - prob.min() + 0.1 - prob = prob.cpu().numpy().astype(np.float32) - # print(prob) - prob = cp.array(prob,dtype=cp.float32) - #print(prob) - word_num = 40 - length = int(preMass / grid_size + 1) - ans = cp.zeros((length, word_num + 1, word_num + 1),dtype = cp.int32) - dpProb = cp.zeros((length,word_num + 1),dtype = cp.float32) - dpMass = cp.zeros((length,word_num + 1),dtype = cp.float32) - dpLock = cp.zeros((length,word_num + 1),dtype = cp.int32) - dpLock[0,:] = 1 - inference_kernel((length,),(word_num + 1,),(prob,ans,AAmasses,dpProb,dpMass,dpLock,preMass,length,grid_size, tol)) - ans = ans.get() - dpMass = dpMass.get() - # # print(ans[:,4,1:]) - # print(ans[length-1,word_num,1:]) - #print("desired mass:", preMass) - #print("decoded mass: ", dpMass[length-1,word_num]) - - - # print("time:", end - start) - result = ans[length-1,word_num,1:] - result = np.where(result == 0, 27, result - 1) - #print("result:", result) - sys.stdout.flush() - - return result.tolist() - - - -# cp.random.seed(3407) - -# word_num = 40 -# aa_num = 28 -# prob = cp.random.uniform(-15.0,1.0,size=(word_num,aa_num),dtype=cp.float32) -# print(prob) -# preMass = cp.random.uniform(2800,3000,dtype=cp.float32) -# print(preMass) -# length = int(preMass + 1) -# ans = cp.zeros((length, word_num + 1, word_num + 1),dtype = cp.int32) - -# AAmasses = [0.0, 57.021464, 71.037114, 87.032028, 97.052764, 99.068414, 101.04767, 160.030649, 113.084064, 113.084064, 114.042927, 115.026943, 128.058578, 128.094963, 129.042593, 131.040485, 137.058912, 147.068414, 156.101111, 163.063329, 186.079313, 147.0354, 115.026943, 129.042594, 10000.0, 10000.0, 10000.0, 10000.0] -# AAmasses = np.array(AAmasses,dtype=np.float32) -# AAmasses = cp.array(AAmasses,dtype=cp.float32) -# # AAmasses = cp.sort(AAmasses) -# print(AAmasses) - -# ans = cp.zeros((length, word_num + 1, word_num + 1),dtype = cp.int32) -# dpProb = cp.zeros((length,word_num + 1),dtype = cp.float32) -# dpMass = cp.zeros((length,word_num + 1),dtype = cp.float32) -# dpLock = cp.zeros((length,word_num + 1),dtype = cp.int32) -# dpLock[0,:] = 1 - -# preMass = cp.float32(preMass.get()) -# start = time.time() -# inference_kernel((length,),(word_num + 1,),(prob,ans,AAmasses,dpProb,dpMass,dpLock,preMass,length)) -# end = time.time() -# ans = ans.get() -# dpMass = dpMass.get() -# # print(ans[:,4,1:]) -# print(ans[length-1,word_num,1:]) -# print(dpMass[length-1,word_num]) -# print("time:", end - start) diff --git a/XuanjiNovo/denovo/model.py b/XuanjiNovo/denovo/model.py index 7b6cf2a..e1bfd0f 100644 --- a/XuanjiNovo/denovo/model.py +++ b/XuanjiNovo/denovo/model.py @@ -1,31 +1,82 @@ -"""A de novo peptide sequencing model.""" -#model for XuanjiNovo -from captum.attr import IntegratedGradients -import heapq +"""A de novo peptide sequencing model with configurable logging.""" + import threading import logging import re import sys -from torch_imputer.imputer import best_alignment -from .ctc_customized_mass_control import CTCMassControl -from lion_pytorch import Lion import operator +import os +from datetime import datetime import torch.nn.functional as F import random from typing import Any, Dict, List, Optional, Set, Tuple, Union -from .customized_ctc import ctc_loss as custom_ctc_loss import depthcharge.masses -import einops import numpy as np import pytorch_lightning as pl import torch from torch.utils.tensorboard import SummaryWriter -from . import mass_con -from .ctc_mass_control import CTCScopeSearchCharLengthControlDecoder -from depthcharge.components import ModelMixin, PeptideDecoder, SpectrumEncoder +#from torch_imputer.imputer import best_alignment +from . import pmc # Precise Mass Control module +from ..components import ModelMixin, PeptideDecoder, SpectrumEncoder from .ctc_beam_search import CTCBeamSearchDecoder from . import evaluate -from ..data import ms_io + +# Configure logging +def setup_logger(verbosity: str = "INFO") -> logging.Logger: + """ + Set up the logger with the specified verbosity level. + + Parameters + ---------- + verbosity : str + The logging level to use. One of: "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" + Default is "INFO". + + Returns + ------- + logging.Logger + Configured logger instance + """ + # Create logs directory if it doesn't exist + log_dir = "logs" + os.makedirs(log_dir, exist_ok=True) + + # Create a unique log file name with timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_file = os.path.join(log_dir, f"xuanjinovo_{timestamp}.log") + + # Set up logging format + log_format = "%(asctime)s [%(levelname)s] %(name)s - %(message)s" + date_format = "%Y-%m-%d %H:%M:%S" + + # Map string level to logging constant + level_map = { + "DEBUG": logging.DEBUG, + "INFO": logging.INFO, + "WARNING": logging.WARNING, + "ERROR": logging.ERROR, + "CRITICAL": logging.CRITICAL + } + level = level_map.get(verbosity.upper(), logging.INFO) + + # Configure logging + logging.basicConfig( + level=level, + format=log_format, + datefmt=date_format, + handlers=[ + logging.FileHandler(log_file), + logging.StreamHandler(sys.stdout) + ] + ) + + logger = logging.getLogger("xuanjinovo") + logger.setLevel(level) + + return logger + +# Initialize default logger +logger = setup_logger() aa2mas = { 'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': 100000, '+43.006-17.027': 25.980265, "_":0} logger = logging.getLogger("casanovo") @@ -68,9 +119,7 @@ def ctc_post_processing( sentence_index: List[int]) -> List[int]: for each in sentence_index: if each != 27: temp.append(each) - # print("temp.", temp) return temp - #sentence_index = list(filter((self.attn_decoder.get_pad_idx()).__ne__, sentence_index)) class Spec2Pep(pl.LightningModule, ModelMixin): """ @@ -138,7 +187,8 @@ class Spec2Pep(pl.LightningModule, ModelMixin): def __init__( self, - custom_ctc_loss = True, + mass_control_tol = 0.1, + PMC_enable = True, dim_model: int = 512, n_head: int = 8, dim_feedforward: int = 1024, @@ -157,13 +207,28 @@ def __init__( torch.utils.tensorboard.SummaryWriter] = None, warmup_iters: int = 100_000, max_iters: int = 600_000, - out_writer: Optional[ms_io.MztabWriter] = None, + out_writer : str = None, ctc_dic: dict = {}, + refine_iters: int = 3, + log_level: str = "INFO", + mask_schedule: Dict[str, float] = { + "initial_peek": 0.93, + "epoch_decay": 0.01, + "min_peek": 0.00 + }, **kwargs: Dict, ): super().__init__() self.save_hyperparameters() + + # Configure logger + self.model_logger = setup_logger(log_level) # Renamed to avoid conflict with pl.LightningModule's logger + self.model_logger.debug("Initializing Spec2Pep model") + self.ctc_dic = ctc_dic + self.PMC_enable = PMC_enable + self.model_logger.info(f"PMC enabled: {PMC_enable}, Mass control tolerance: {mass_control_tol}") + self.mass_control_tol = mass_control_tol self.ctc_dic["beam"] = n_beams @@ -191,7 +256,7 @@ def __init__( ) self.n_layers = n_layers - self.ctc_customized_mass_control = CTCMassControl(self.decoder ) + self.ctc_decoder = CTCBeamSearchDecoder(self.decoder, self.ctc_dic) self.calctime = 0.0 #print("ctc_decoder:", self.ctc_decoder) @@ -209,7 +274,7 @@ def __init__( "scaling_factor": getattr(args, 'bucket_size'), } ''' - self.ctc_length_control_decoder = CTCScopeSearchCharLengthControlDecoder(self.decoder, {} ) + #self.ctc_length_control_decoder = CTCScopeSearchCharLengthControlDecoder(self.decoder, {} ) self.softmax = torch.nn.Softmax(2) self.celoss = torch.nn.CrossEntropyLoss(ignore_index=0) self.ctcloss = torch.nn.CTCLoss(blank = self.decoder.get_blank_idx(), zero_infinity=True) #to do @@ -217,7 +282,6 @@ def __init__( self.warmup_iters = warmup_iters self.max_iters = max_iters self.opt_kwargs = kwargs - self.custom_ctc_loss = custom_ctc_loss # Data properties. self.max_length = max_length @@ -266,193 +330,67 @@ def forward( aa_scores : torch.Tensor of shape (n_spectra, length, n_amino_acids) The individual amino acid scores for each prediction. """ - output_decoded_saved = [] + self.model_logger.debug(f"Forward pass started with batch size: {spectra.shape[0]}") + start_time = datetime.now() prev = None - for i in range(3): + for i in range(self.hparams.refine_iters): output_logits, _, output_list = self.decoder(None, precursors, *self.encoder(spectra, precursors), prev) - prev = output_logits.argmax(-1) - - - - #explainitybility - - # for each in output_list: - # top_tokensss, _ = self.ctc_decoder.decode(F.softmax(each, -1)) - - # output_decoded_saved.append(torch.flip(top_tokensss, dims= [-1])) - # layer_pep_true = [[] for i in range( len(output_decoded_saved))] - # layer_pep_pred = [[] for i in range( len(output_decoded_saved))] - # for i in range( output_logits.shape[0]): # for each batch data - - # for j in range( len(output_decoded_saved)) : #for each layer - # #print(output_decoded_saved[j][i]) - - # #print(i,"th data", j + 1, " th layer : ", "".join([self.decoder._idx2aa[each.item()] for each in output_decoded_saved[j][i] if each != -1 ])) - # #print("mass of above is: ", mass_cal("".join([self.decoder._idx2aa[each.item()] for each in output_decoded_saved[j][i] if each != -1 ]))[0]) - # #print("jj", j ) - # #print("len", len(layer_pep_pred)) - # layer_pep_pred[j].append("".join([self.decoder._idx2aa[each.item()] for each in output_decoded_saved[j][i] if each != -1 ])) - # layer_pep_true[j].append(true_peps[i]) - # for i in range(len(output_decoded_saved)): - # aa_precision, aa_recall, pep_recall = evaluate.aa_match_metrics( - # *evaluate.aa_match_batch(layer_pep_pred[i], layer_pep_true[i], - # self.decoder._peptide_mass.masses)) - # log_args = dict(on_step=True, on_epoch=True, sync_dist=True, add_dataloader_idx=False) - # self.log("{} layer/aa_precision".format(i), aa_precision, **log_args) - # self.log("{} layer/aa_recall".format(i), aa_recall, **log_args) - # self.log("{} layer/pep_recall".format(i), pep_recall, **log_args) - - # with open("./layer_samples.txt",'a') as f: - # f.write(str(int(i+1)) + ","+ str(float(aa_precision))+"\n") - - - #----------explain_-------- - - #output_logits, _, _ = self.decoder(None, precursors, *self.encoder(spectra)) top_tokens, beamscores = self.ctc_decoder.decode(F.softmax(output_logits, -1)) batchscores = beamscores.tolist() batchscores = 1 / torch.exp(beamscores) top_tokens_beam = top_tokens.tolist() - #input_lengths = torch.full(size=(output_logits.size()[0],), fill_value=output_logits.size()[1]) - #top_tokens = self.ctc_customized_mass_control.decode(F.log_softmax(output_logits, -1), precursors[:, 0]) - '''''' - # --- here is multithreading----- - batch_size = output_logits.shape[0] - - + batch_size = output_logits.shape[0] self.mass_offset_total = 0 self.mass_offset_count = 0 top_tokens = [[] for i in range(batch_size)] - def worker (logits, mass, i , output_logits): - #print("worker ", i , "started") + def worker(logits, mass, i, output_logits): mass = mass.clone().detach() - if mass[0].item() > 1000000: - ''' - sequence = list(filter((self.decoder.get_pad_idx()).__ne__, top_tokens_beam[i])) - token_true = [self.decoder._idx2aa[each] for each in sequence] - mass_true = mass[0].item() - 18.01 - mass_true_cal, seq = mass_cal("".join(token_true)) - if abs(mass_true_cal-mass_true) > 0.1: - print("not consistent here!!!!!!!, " , abs(mass_true_cal-mass_true)) - self.mass_offset_total+=abs(mass_true_cal-mass_true) - self.mass_offset_count+=1 - ''' + if not self.PMC_enable: + top_tokens[i] = top_tokens_beam[i] + return + + mass_true = mass[0].item() - 18.01 + sequence = list(filter((self.decoder.get_pad_idx()).__ne__, top_tokens_beam[i])) + token_true = [self.decoder._idx2aa[each] for each in sequence] + + + + pred_mass, seq = mass_cal("".join(token_true)) + if abs(mass_true - pred_mass) < 0.1: top_tokens[i] = top_tokens_beam[i] - #print("skip ctc since mass greater than threshold") - #sequence = list(filter((self.decoder.get_pad_idx()).__ne__, top_tokens_beam[i])) - #token_true = [self.decoder._idx2aa[each] for each in sequence] - #print("".join(reversed(token_true))) else: - mass_true = mass[0].item() - 18.01 - #print(top_tokens_beam) - sequence = list(filter((self.decoder.get_pad_idx()).__ne__, top_tokens_beam[i])) - token_true = [self.decoder._idx2aa[each] for each in sequence] - #if "+43.006" in "".join(token_true) or "+42.011" in "".join(token_true) or "-17.027" in "".join(token_true): - if "hello" in "".join(token_true): - #ctc_customized_mass_control = CTCMassControl(self.decoder ) - #top_tokens[i] = ctc_customized_mass_control.decode(logits, mass)[0] - temp = mass_con.knapDecode(logits, mass) - temp = ctc_post_processing(temp) - if temp: - top_tokens[i] = temp - token_temp = [self.decoder._idx2aa[each] for each in temp] - token_temp = list(reversed(token_temp)) - print("truth: ", token_true) - #print("truth: ", true_peps[i]) - print("inf: " , token_temp) - # sys.stdout.flush() - else: - top_tokens[i] = top_tokens_beam[i] - else: #will always execute - #if True: - # correct mismatch mass, can only be used when true token is given - #---------------------- - cal_mass, seq= mass_cal(true_peps[i]) - if abs(cal_mass- mass_true ) > 0.9: - mass[0] = mass[0] - 1 - print("error after correction:", mass[0].item()-cal_mass, " ", ) - #---------------------- - #batchscores[i] = 1 / torch.exp(beamscores[i]) #commonted score - pred_mass, seq = mass_cal("".join(token_true)) - #print("True mass:", mass_true) - #print("Decoded_mass: ", pred_mass) - #print("aminos: ", seq) - # pred_mass = 0.0 - if abs(mass_true - pred_mass) < 0.1: - top_tokens[i] = top_tokens_beam[i] - # if beamscores[i] < 0.0: - # print(beamscores[i]) - # print("I am CTCBeam") - # batchscores[i] = 1 / torch.exp(beamscores[i]) - # if batchscores[i] > 1.0: - # print("xianggebuxing:",beamscores[i]) - - # print("beamscore:",beamscores[i]) - #print("skip CTC length control") - - else: - #ctc_customized_mass_control = CTCMassControl(self.decoder ) - print("I am CUDA program") - temp = mass_con.knapDecode(logits, mass) - # knapscores = torch.exp(_) - # indTemp = torch.tensor(temp) - # knapscores = torch.softmax(output_logits,-1)[0] - # knapscores = knapscores[torch.arange(40),indTemp] - # scoreTemp = 1.0 - # for x in knapscores: - # scoreTemp *= x - # if scoreTemp == 0.0: - # print("I am CUDA program") - # print(knapscores) - # knapscores = scoreTemp - # knapscore = torch.sum(knapscores) - # knapscores = torch.exp(knapscore) - # print("knapscore:",torch.exp(_)) - temp = ctc_post_processing(temp) - if temp: - top_tokens[i] = temp - # batchscores[i] = knapscores - token_temp = [self.decoder._idx2aa[each] for each in temp] - token_temp = list(reversed(token_temp)) - # print("truth: ", true_peps[i]) - # print("inf: " , token_temp) - # sys.stdout.flush() - else: - top_tokens[i] = top_tokens_beam[i] - #batchscores[i] = 1 / torch.exp(beamscores[i]) #commented score - #sequence0 = list(filter((self.decoder.get_pad_idx()).__ne__, top_tokens[i])) - #token_true = [self.decoder._idx2aa[each] for each in sequence0] - #print("".join(reversed(token_true))) - #print("woker ", i , "stopped") + temp = pmc.knapDecode(logits, mass, self.mass_control_tol) + temp = ctc_post_processing(temp) + if temp: + top_tokens[i] = temp + token_temp = [self.decoder._idx2aa[each] for each in temp] + token_temp = list(reversed(token_temp)) + else: + top_tokens[i] = top_tokens_beam[i] threads = [] - #log_prob = F.log_softmax(output_logits, -1) - log_prob = F.log_softmax(output_logits, -1) #to change back + log_prob = F.log_softmax(output_logits, -1) for i in range(batch_size): t = threading.Thread(target=worker, args=(log_prob[[i], :, :], precursors[[i], 0], i, output_logits)) threads.append(t) t.start() - log_args = dict(on_step=True, on_epoch=True, sync_dist=True, add_dataloader_idx=False) - #self.log("val/wrong_offset_average",self.mass_offset_total/self.mass_offset_count, **log_args) - for t in threads: t.join() - #------------------- - #top_tokens = top_tokens.tolist() - return [self.decoder.detokenize_truth(t, True) for t in top_tokens], batchscores - - ''' - aa_scores, tokens = self.beam_search_decode( #to do - spectra.to(self.encoder.device), - precursors.to(self.decoder.device), - ) - ''' - #return [self.decoder.detokenize(t) for t in tokens], aa_scores + end_time = datetime.now() + processing_time = (end_time - start_time).total_seconds() + self.model_logger.debug(f"Forward pass completed in {processing_time:.2f} seconds") + + # Log prediction statistics + peptides = [self.decoder.detokenize_truth(t, True) for t in top_tokens] + avg_peptide_length = sum(len(p) for p in peptides) / len(peptides) + self.model_logger.info(f"Average predicted peptide length: {avg_peptide_length:.2f}") + + return peptides, batchscores def _forward_step( self, @@ -508,19 +446,35 @@ def training_step( ------- torch.Tensor The loss of the training step. - """ - # import time + self.model_logger.debug(f"Starting {mode} step with batch size: {len(batch[0])}") + step_start_time = datetime.now() + """ + A single training step. - # begintime=time.time() - - + Parameters + ---------- + batch : Tuple[torch.Tensor, torch.Tensor, List[str]] + A batch of (i) MS/MS spectra, (ii) precursor information, (iii) + peptide sequences as torch Tensors. + mode : str + Logging key to describe the current stage. + + Returns + ------- + torch.Tensor + The loss of the training step. + + """ glat_prev = None - #chnage the peak factor with-respect to epoch here - peek_factor = max(0.93 - self.current_epoch * 0.01, 0.00 ) #max(0.9 - self.current_epoch * 0.025, 0.05 ) #max(0.9 - self.current_epoch * 0.0025, 0.05 ) # max(0.85 - self.current_epoch * 0.006, 0 ) - # begintime=time.time() - #----curriculum learning sampling--- + # Calculate peek factor based on mask schedule + peek_factor = max( + self.hparams.mask_schedule["initial_peek"] - + self.current_epoch * self.hparams.mask_schedule["epoch_decay"], + self.hparams.mask_schedule["min_peek"] + ) + if mode == "train": with torch.no_grad(): word_ins_out, tgt_tokens, _ = self._forward_step(*batch) @@ -540,16 +494,11 @@ def training_step( keep_prob = ((seq_lens - same_num) / seq_lens * peek_factor ).unsqueeze(-1) keep_word_mask = (torch.rand(pred_tokens.shape, device=word_ins_out.device) < keep_prob).bool() glat_prev = oracle_empty.masked_fill(~keep_word_mask, self.decoder.get_mask_idx()) - #print("got it") + total_loss = torch.tensor([0]).to(self.device) - #_------- + for i in range(2): - pred, truth, output_list = self._forward_step(*batch, glat_prev) - - - - pred_temp = pred.permute(1, 0, 2) input_lengths = torch.full(size=(pred_temp.size()[1],), fill_value=pred_temp.size()[0]) @@ -557,106 +506,52 @@ def training_step( loss = self.ctcloss( torch.nn.functional.log_softmax(pred_temp, dim=-1), truth, input_lengths, target_lengths ) glat_prev = pred.argmax(-1) total_loss = total_loss + loss + assert len(output_list) == self.n_layers - - # Compute AA recall, AA precision and Pep recall in training step - # Author: Sheng Xu - # Date: 20230220 tokens = torch.argmax(pred, axis=2) peptides_pred = [] peptides_true = [] for idx in range(tokens.size()[0]): tokens_true = truth[idx,:] tokens_true = self.decoder.detokenize_truth(tokens_true) - #print("tokens_true: ", ''.join(tokens_true)) - #print("tokens_batch: ", batch[2][idx]) peptides_true.append(''.join(tokens_true)) tokens_pred = tokens[idx,:] tokens_pred = self.decoder.detokenize(tokens_pred) peptides_pred.append(tokens_pred) - #print("token_predict: ", ''.join(tokens_pred)) aa_precision, aa_recall, pep_recall = evaluate.aa_match_metrics( *evaluate.aa_match_batch(peptides_pred, peptides_true, self.decoder._peptide_mass.masses)) rand = random.random() - sampling_factor=0.8 - if(mode == "train"): - sampling_factor=0.3 - if (rand < sampling_factor): - peptides_pred_sample = [] - for tokenlist in peptides_pred: - peptides_pred_sample.append(''.join(tokenlist)) - peptides_true_sample = peptides_true - peptides_pair_list = list(zip(batch[1].cpu().numpy().tolist(),peptides_true_sample, peptides_pred_sample)) + sampling_factor = 0.3 if mode == "train" else 0.8 + + if rand < sampling_factor: + peptides_pred_sample = [''.join(tokenlist) for tokenlist in peptides_pred] + peptides_pair_list = list(zip(batch[1].cpu().numpy().tolist(), peptides_true, peptides_pred_sample)) peptides_pair = random.choices(peptides_pair_list, k=15) - # print("peptides_pred",peptides_pred_sample) - # print("peptides_true",peptides_true_sample) - # print(aa_precision, aa_recall, pep_recall) - if(not self.logger==None): + if self.logger is not None: self.logger.experiment[mode+"/peptides_pair"].append("Epoch: "+str(self.trainer.current_epoch)+str(peptides_pair)) - - #self.log(mode+"/peptides_true", str(peptides_true_sample)) - #self.log(mode+"/peptides_pred", str(peptides_pred_sample)) - # endtime = time.time() - # calctime = (endtime - begintime) / len(peptides_pred) - if (mode == "train"): - log_args = dict(on_step=True, on_epoch=True, sync_dist=True, add_dataloader_idx=False) + log_args = dict(on_step=True, on_epoch=True, sync_dist=True, add_dataloader_idx=False) + if mode == "train": self.log("train/aa_precision", aa_precision, **log_args) self.log("train/aa_recall", aa_recall, **log_args) self.log("train/pep_recall", pep_recall, **log_args) - if (mode == "valid" and self.n_beams==0): - log_args = dict(on_step=False, on_epoch=True, sync_dist=True, add_dataloader_idx=False) - # self.log("valid/spec/second", calctime, **log_args) + elif mode == "valid" and self.n_beams == 0: + log_args.update(on_step=False) self.log("valid/aa_precision", aa_precision, **log_args) self.log("valid/aa_recall", aa_recall, **log_args) self.log("valid/pep_recall", pep_recall, **log_args) - if (mode == "test" and self.n_beams==0): - log_args = dict(on_step=False, on_epoch=True, sync_dist=True, add_dataloader_idx=False) + elif mode == "test" and self.n_beams == 0: + log_args.update(on_step=False) self.log("test/aa_precision", aa_precision, **log_args) self.log("test/aa_recall", aa_recall, **log_args) self.log("test/pep_recall", pep_recall, **log_args) - - # for idx, pred in enumerate(output_list): - # #if idx <= 0: #5 - # #continue - # total_loss = torch.tensor([0]).to(self.device) - # pred = pred.permute(1, 0, 2) - - # input_lengths = torch.full(size=(pred.size()[1],), fill_value=pred.size()[0]) - # target_lengths = (truth != self.decoder.get_pad_idx()).sum(axis=1) - # if self.custom_ctc_loss == True : - # loss = custom_ctc_loss(torch.nn.functional.log_softmax(pred, dim=-1).float(), truth, input_lengths, target_lengths, blank=self.decoder.get_blank_idx(), - # reduction="mean" ) - # loss = torch.stack([a / b for a, b in zip(loss, target_lengths)]) - # loss = torch.clip(loss, min=0, max=100) # Clip the loss to avoid overflow - # loss = loss.mean() - # total_loss = total_loss + loss * (idx+1) * 0.2 - # else: - - # loss = self.ctcloss( torch.nn.functional.log_softmax(pred, dim=-1), truth, input_lengths, target_lengths ) - # ''' - # if self.trainer.current_epoch <= 50: - # idx_ratio = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1, 1] - # else:x - # idx_ratio = [ 0 for _ in range(self.n_layers-1)] - # idx_ratio.append(1) - # ''' - # #idx_ratio = [ 0 for _ in range(self.n_layers-1)] - # #idx_ratio.append(1) - # #idx_ratio = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1, 1] - # #idx_ratio = [0.3, 0.5, 0.7, 0.8, 1, 1] - # idx_ratio = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1, 1] - # #idx_ratio = [0, 0, 0, 0, 0.1, 0.1, 0,1, 0,1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1, 1] - # idx_ratio = [ 0 for _ in range(self.n_layers-1)] - # idx_ratio.append(1) - # total_loss = total_loss + loss *idx_ratio[idx] - # #loss = self.celoss(pred, truth.flatten()) - loss = total_loss # to change + + if (mode == "train"): if(not self.logger==None): @@ -670,7 +565,20 @@ def training_step( add_dataloader_idx=False ) - return loss + step_end_time = datetime.now() + step_duration = (step_end_time - step_start_time).total_seconds() + + # Log detailed metrics + self.model_logger.debug(f"{mode} step completed in {step_duration:.2f} seconds") + self.model_logger.debug(f"Total loss: {total_loss.item():.4f}") + + if mode == "train": + self.model_logger.debug(f"Current peek factor: {peek_factor:.4f}") + self.model_logger.debug(f"Amino acid precision: {aa_precision:.4f}") + self.model_logger.debug(f"Amino acid recall: {aa_recall:.4f}") + self.model_logger.debug(f"Peptide recall: {pep_recall:.4f}") + + return total_loss def validation_step(self, batch, batch_idx=None, dataloader_idx=None) -> torch.Tensor: """ @@ -681,23 +589,25 @@ def validation_step(self, batch, batch_idx=None, dataloader_idx=None) -> torch.T batch : Tuple[torch.Tensor, torch.Tensor, List[str]] A batch of (i) MS/MS spectra, (ii) precursor information, (iii) peptide sequences. + batch_idx : Optional[int] + The index of the current batch + dataloader_idx : Optional[int] + The index of the dataloader Returns ------- torch.Tensor The loss of the validation step. """ - #print("validation ssssss") - # import time - - # begintime = time.time() + val_start_time = datetime.now() + self.model_logger.debug(f"Starting validation step {batch_idx if batch_idx is not None else 'N/A'}") + if dataloader_idx is None: + dataloader_idx = 0 + key = "valid" if dataloader_idx == 0 else "test" - if (dataloader_idx==None): dataloader_idx=0 - key = "valid" if dataloader_idx==0 else "test" - # Record the loss. loss = self.training_step(batch, mode=key) self.log( - "valid/CELoss" if dataloader_idx==0 else "test/CELoss", + "valid/CELoss" if dataloader_idx == 0 else "test/CELoss", loss.detach(), on_step=False, on_epoch=True, @@ -705,34 +615,28 @@ def validation_step(self, batch, batch_idx=None, dataloader_idx=None) -> torch.T add_dataloader_idx=False ) - # Calculate and log amino acid and peptide match evaluation metrics from - # the predicted peptides. - - if(self.n_beams>0): - # print("Beam Search: 5") + if self.n_beams > 0: peptides_pred_raw, inferscores = self.forward(batch[0], batch[1], batch[2]) - # print("inferscores:",inferscores) - # FIXME: Temporary fix to skip predictions with multiple stop tokens. peptides_pred, peptides_true = [], [] + + # Process predictions for peptide_pred, peptide_true in zip(peptides_pred_raw, batch[2]): if len(peptide_pred) > 0: if peptide_pred[0] == "$": - peptide_pred = peptide_pred[1:] # Remove stop token. + peptide_pred = peptide_pred[1:] # Remove stop token if "$" not in peptide_pred and len(peptide_pred) > 0: peptides_pred.append(peptide_pred) peptides_true.append(peptide_true) - - + + # Calculate per-amino-acid precision targets = ["M+15.995", "F", "Q", "K"] counts = {char: {"numChar": 1e-13, "numTrue": 0} for char in targets} - for i in range(len(peptides_pred)): - pred = peptides_pred[i] - true = peptides_true[i] - if isinstance(peptides_pred[i], str): - pred = re.split(r"(?<=.)(?=[A-Z])", peptides_pred[i]) - if isinstance(peptides_true[i], str): - true = re.split(r"(?<=.)(?=[A-Z])", peptides_true[i]) + for pred, true in zip(peptides_pred, peptides_true): + if isinstance(pred, str): + pred = re.split(r"(?<=.)(?=[A-Z])", pred) + if isinstance(true, str): + true = re.split(r"(?<=.)(?=[A-Z])", true) for j, c in enumerate(true): if c in counts: @@ -740,120 +644,44 @@ def validation_step(self, batch, batch_idx=None, dataloader_idx=None) -> torch.T if j < len(pred) and pred[j] == c: counts[c]["numTrue"] += 1 - # 计算精度并记录日志 + # Log per-amino-acid precision for char, data in counts.items(): precision = data["numTrue"] / data["numChar"] - self.log(f"{key}/{char}_precision", precision, on_step=False, on_epoch=True, sync_dist=True, add_dataloader_idx=False) - - # with open("./denovo_random.txt",'a') as f: - # for i in range(len(peptides_pred_raw)): - - # print("label:",batch[2][i], ":" , peptides[i] , "\n") - - - # f.write("label: " + batch[2][i] + " prediction: " + "".join(peptides[i]) + " charge: " + str(int(batch[1][i][1])) +"\n") - - # f.write("label: " + batch[2][i] + " prediction : " + "".join(peptides_pred_raw[i]) + "\n") - - # AA_sim_sum = {"M+15.995" : 0.0000001, 'Q' : 0.0000001, 'F' : 0.0000001, "K": 0.0000001} - # AA_sim_true = {"M+15.995" : 0, 'Q' : 0, 'F' : 0, "K": 0} - # #---------xiang: write the scores out--------- - for i in range(len(peptides_pred)): - pred = peptides_pred[i] - true = peptides_true[i] - if isinstance(peptides_pred[i], str): - pred = re.split(r"(?<=.)(?=[A-Z])", peptides_pred[i]) - if isinstance(peptides_true[i], str): - true = re.split(r"(?<=.)(?=[A-Z])", peptides_true[i]) - matches , pep_match = evaluate.aa_match(pred,true,self.decoder._peptide_mass.masses) - import os - - # species_name = os.environ.get('SPECIES_NAME_GL') - # with open("glance_"+species_name,'a') as f: - # sum = np.sum(matches) - # aaPre = sum / len(pred) - # f.write(str(float(aaPre))+ " " + str(float(inferscores[i])) + " " + str("".join(true)) + " " + str("".join(pred)) +"\n") - # f.flush() - #f.write(str(float(aaPre)) + " " + str(float(inferscores[i])) + " " + str("".join(pred)) + " " + str("".join(true)) +"\n") - #f.write(str(float(pep_match)) + " " + str(float(inferscores[i])) + "\n") - - - #-- + self.log( + f"{key}/{char}_precision", + precision, + on_step=False, + on_epoch=True, + sync_dist=True, + add_dataloader_idx=False + ) - # targets = ["M+15.995", "F", "Q", "K"] - #             counts = {char: {"numChar": 1e-13, "numTrue": 0} for char in targets} - - #             for i in range(len(peptides_pred)): - #                 pred = peptides_pred[i] - #                 true = peptides_true[i] - #                 if isinstance(peptides_pred[i], str): - #                     pred = re.split(r"(?<=.)(?=[A-Z])", peptides_pred[i]) - #                 if isinstance(peptides_true[i], str): - #                     true = re.split(r"(?<=.)(?=[A-Z])", peptides_true[i]) - - #                 for j, c in enumerate(true): - #                     if c in counts: - #                         counts[c]["numChar"] += 1 - #                         if j < len(pred) and pred[j] == c: - #                             counts[c]["numTrue"] += 1 - - #             # 计算精度并记录日志 - #             for char, data in counts.items(): - #                 precision = data["numTrue"] / data["numChar"] - #                 self.log(f"{key}/{char}_precision", precision, on_step=False, on_epoch=True, sync_dist=True, add_dataloader_idx=False) - # #------------------------------ - - # pred_len = len(pred) - # true_len = len(true) - # min_len = min(pred_len, true_len) - # for x in range(min_len): - # if pred[x] in AA_sim_sum: - # AA_sim_sum[pred[x]] += 1 - # if matches[x]: - # AA_sim_true[pred[x]] += 1 - - # endtime = time.time() - # calctime = (endtime - begintime) / len(peptides_pred_raw) - # for i in range(len(peptides_pred)): - # pred = peptides_pred[i] - # true = peptides_true[i] - # if isinstance(peptides_pred[i], str): - # pred = re.split(r"(?<=.)(?=[A-Z])", peptides_pred[i]) - # if isinstance(peptides_true[i], str): - # true = re.split(r"(?<=.)(?=[A-Z])", peptides_true[i]) - - # matches,_ = evaluate.aa_match(pred,true,self.decoder._peptide_mass.masses) - # with open("./NATNovo/mouse.txt",'a') as f: - # sum = np.sum(matches) - # aaPre = sum / len(pred) - # f.write(str(float(aaPre)) + " " + str(float(inferscores[i])) + " " + str(len(pred)) + "\n") - + # Calculate overall metrics aa_precision, aa_recall, pep_recall = evaluate.aa_match_metrics( *evaluate.aa_match_batch(peptides_pred, peptides_true, - self.decoder._peptide_mass.masses)) + self.decoder._peptide_mass.masses)) + + # Log overall metrics log_args = dict(on_step=True, on_epoch=True, sync_dist=True, add_dataloader_idx=False) - # self.log("{}/spec/second".format(key), calctime, **log_args) - # self.log("{}/M+0.984_precision".format(key), AA_sim_true["M+15.995"] / AA_sim_sum["M+15.995"], **log_args) - # self.log("{}/Q_precision".format(key), AA_sim_true["Q"] / AA_sim_sum["Q"], **log_args) - # self.log("{}/F_precision".format(key), AA_sim_true["F"] / AA_sim_sum["F"], **log_args) - # self.log("{}/K_precision".format(key), AA_sim_true["K"] / AA_sim_sum["K"], **log_args) - - self.log("{}/aa_precision".format(key), aa_precision, **log_args) - self.log("{}/aa_recall".format(key), aa_recall, **log_args) - self.log("{}/pep_recall".format(key), pep_recall, **log_args) - # print("{}/pep_recall".format(key), pep_recall) - # sys.stdout.flush() - # with open("./denovo_prime_medium_bacillus.txt",'a') as f: - # for i in range(len(peptides_pred_raw)): - # # print("label:",batch[2][i], ":" , peptides[i] , "\n") - # if batch[2][i].replace("$", "").replace("N+0.984", "D").replace("Q+0.984", "E").replace("L","I") == "".join(peptides_pred_raw[i]).replace("$", "").replace("N+0.984", "D").replace("Q+0.984", "E").replace("L","I"): - # answer_is_correct = "correct" - # else: - # answer_is_correct = "incorrect" - - # #f.write("label: " + batch[2][i] + " prediction: " + "".join(peptides[i]) + " charge: " + str(int(batch[1][i][1])) + " score: " + str(float(inferscores[i]))+"\n") - - # f.write("label: " + batch[2][i] + " prediction : " + "".join(peptides_pred_raw[i]) + " " + answer_is_correct + "\n") + self.log(f"{key}/aa_precision", aa_precision, **log_args) + self.log(f"{key}/aa_recall", aa_recall, **log_args) + self.log(f"{key}/pep_recall", pep_recall, **log_args) + + # Validation metrics are already logged through self.log + + val_end_time = datetime.now() + val_duration = (val_end_time - val_start_time).total_seconds() + + # Log validation metrics + self.model_logger.debug(f"Validation step completed in {val_duration:.2f} seconds") + self.model_logger.debug(f"Validation loss: {loss.item():.4f}") + + if self.n_beams > 0: + self.model_logger.debug(f"Validation metrics for {key}:") + self.model_logger.debug(f"AA precision: {aa_precision:.4f}") + self.model_logger.debug(f"AA recall: {aa_recall:.4f}") + self.model_logger.debug(f"Peptide recall: {pep_recall:.4f}") + return loss def predict_step( @@ -879,59 +707,49 @@ def predict_step( aa_scores : torch.Tensor of shape (n_spectra, length, n_amino_acids) The individual amino acid scores for each prediction. """ - peptides , inferscores = self.forward(batch[0], batch[1], batch[2]) import os - - with open("./denovo_LAUD.txt",'a') as f: + file_path = "./denovo.tsv" + headers = "label\tprediction\tcharge\tscore\n" + + # Check if the file exists and whether it contains headers + if not os.path.exists(file_path) or open(file_path, 'r').readline().strip() != headers.strip(): + with open(file_path, 'a') as f: + f.write(headers) + + # Append data + with open(file_path,'a') as f: for i in range(len(peptides)): + sequence = "" + for el in peptides[i]: + if len(el) > 1: + if el[0] in ('+', '-'): + sequence += '[' + el + ']' + elif el[0].isdigit(): + sequence += '[' + el + ']' + else: + sequence += el[0] + '[' + el[1:] + ']' + else: + sequence += el + # print("label:",batch[2][i], ":" , peptides[i] , "\n") if batch[2][i].replace("$", "").replace("N+0.984", "D").replace("Q+0.984", "E").replace("L","I") == "".join(peptides[i]).replace("$", "").replace("N+0.984", "D").replace("Q+0.984", "E").replace("L","I"): answer_is_correct = "correct" else: answer_is_correct = "incorrect" - - f.write("label: " + batch[2][i] + " prediction: " + "".join(peptides[i]) + " charge: " + str(int(batch[1][i][1])) + " score: " + str(float(inferscores[i]))+"\n") + #each line output this: label (title if label is none), predictions, charge, and confidence score + f.write(batch[2][i].replace("\t", " ") + "\t" + sequence + "\t" + str(int(batch[1][i][1])) + "\t" + str(float(inferscores[i])) + "\n") + - #f.write("label: " + batch[2][i] + " prediction : " + "".join(peptides[i]) + " " + answer_is_correct + "\n") return batch[2], batch[1], peptides #batch[2]: identifier - # def on_train_epoch_end(self) -> None: - # """ - # Log the training loss at the end of each epoch. - # """ - # train_loss = self.trainer.callback_metrics["train/CELoss"].detach() - - # #self._history[-1]["train"] = train_loss - # #self._log_history() - - def on_validation_epoch_end(self) -> None: """ Log the validation metrics at the end of each epoch. """ - ''' - print("calculation:", self.calctime) - callback_metrics = self.trainer.callback_metrics - logger.info(callback_metrics) - metrics = { - "epoch": self.trainer.current_epoch, - "valid": callback_metrics["valid/CELoss"].detach(), - "valid_aa_precision": - callback_metrics["valid/aa_precision"].detach(), - "valid_aa_recall": callback_metrics["valid/aa_recall"].detach(), - "valid_pep_recall": callback_metrics["valid/pep_recall"].detach(), - "test": callback_metrics["test/CELoss"].detach(), - "test_aa_precision": - callback_metrics["test/aa_precision"].detach(), - "test_aa_recall": callback_metrics["test/aa_recall"].detach(), - "test_pep_recall": callback_metrics["test/pep_recall"].detach(), - } - self._history.append(metrics) - self._log_history() - ''' + pass def on_predict_epoch_end( self, results: List[List[Tuple[np.ndarray, List[str], @@ -940,58 +758,7 @@ def on_predict_epoch_end( Write the predicted peptide sequences and amino acid scores to the output file. """ - - - # if self.out_writer is None: - # return - # for batch in results: - # for step in batch: - # for spectrum_i, precursor, aa_tokens in zip(*step): - # # Get peptide sequence, amino acid and peptide-level - # # confidence scores to write to output file. - # ( - # peptide, - # aa_tokens, - # peptide_score, - # aa_scores, - # ) = self._get_output_peptide_and_scores( - # aa_tokens, 1) - # # Compare the experimental vs calculated precursor m/z. - # _, precursor_charge, precursor_mz = precursor - # precursor_charge = int(precursor_charge.item()) - # precursor_mz = precursor_mz.item() - # try: - # calc_mz = self.peptide_mass_calculator.mass( - # aa_tokens, precursor_charge) - # delta_mass_ppm = [ - # _calc_mass_error( - # calc_mz, - # precursor_mz, - # precursor_charge, - # isotope, - # ) for isotope in range( - # self.isotope_error_range[0], - # self.isotope_error_range[1] + 1, - # ) - # ] - # is_within_precursor_mz_tol = any( - # abs(d) < self.precursor_mass_tol - # for d in delta_mass_ppm) - # except KeyError: - # calc_mz, is_within_precursor_mz_tol = np.nan, False - # # Subtract one if the precursor m/z tolerance is violated. - # if not is_within_precursor_mz_tol: - # peptide_score -= 1 - - # self.out_writer.psms.append(( - # peptide, - # spectrum_i, - # peptide_score, - # precursor_charge, - # precursor_mz, - # calc_mz, - # aa_scores, - # ), ) + pass def _get_output_peptide_and_scores( self, aa_tokens: List[str], diff --git a/XuanjiNovo/denovo/model_runner.py b/XuanjiNovo/denovo/model_runner.py index 7c3171e..1e4a132 100644 --- a/XuanjiNovo/denovo/model_runner.py +++ b/XuanjiNovo/denovo/model_runner.py @@ -5,6 +5,7 @@ import operator import os import tempfile +import time import uuid from typing import Any, Dict, Iterable, List, Optional, Union @@ -15,7 +16,7 @@ from pytorch_lightning.strategies import DDPStrategy from pytorch_lightning.profiler import SimpleProfiler -from .. import utils +from ..utils import record_metadata, n_workers from .db_dataloader import DeNovoDataModule from .model import Spec2Pep @@ -44,7 +45,7 @@ def predict( out_writer : ms_io.MztabWriter The mzTab writer to export the prediction results. """ - _execute_existing(peak_path, model_filename, config, False, out_writer) + _execute_existing(peak_path, model_filename, config, False, out_writer, "denovo") def evaluate(peak_path: str, model_filename: str, config: Dict[str, @@ -70,7 +71,20 @@ def _execute_existing( config: Dict[str, Any], annotated: bool, out_writer = None, + mode: str = "eval", ) -> None: + """Execute model with existing checkpoint. + + The config parameter can be either a Dict or a XuanjiNovoConfig object. + If it's a Dict, it will be validated if pydantic is available. + """ + try: + from ..config import XuanjiNovoConfig + if isinstance(config, dict): + config = XuanjiNovoConfig.from_dict(config) + logger.info("Configuration validated via pydantic") + except ImportError: + logger.warning("Pydantic not available, skipping additional config validation") """ Predict peptide sequences with a trained XuanjiNovo model with/without evaluation. @@ -89,18 +103,17 @@ def _execute_existing( out_writer : Optional[ms_io.MztabWriter] The mzTab writer to export the prediction results. """ - # Load the trained model. if not os.path.isfile(model_filename): logger.error( "Could not find the trained model weights at file %s", model_filename, ) raise FileNotFoundError("Could not find the trained model weights") + model = Spec2Pep().load_from_checkpoint( model_filename, PMC_enable=config["PMC_enable"], mass_control_tol=config["mass_control_tol"], - dim_model=config["dim_model"], n_head=config["n_head"], dim_feedforward=config["dim_feedforward"], @@ -116,35 +129,43 @@ def _execute_existing( n_beams=config["n_beams"], n_log=config["n_log"], out_writer=out_writer, + log_level=config.get("log_level", "INFO"), # Get log_level from config with default "INFO" + refine_iters=config.get("refine_iters", 3), + mask_schedule=config.get("mask_schedule", { + "initial_peek": 0.93, + "epoch_decay": 0.01, + "min_peek": 0.00 + }), ) - # Read the MS/MS spectra for which to predict peptide sequences. + if annotated: peak_ext = (".mgf", ".h5", ".hdf5") else: peak_ext = (".mgf", ".mzml", ".mzxml", ".h5", ".hdf5") - print("peak file iss: ", peak_path ) + if len(peak_filenames := _get_peak_filenames(peak_path, peak_ext)) == 0: logger.error("Could not find peak files from %s", peak_path) raise FileNotFoundError("Could not find peak files") + peak_is_not_index = any( [os.path.splitext(fn)[1] in (".mgf", ".mzxml", ".mzml") for fn in peak_filenames]) + class MyDirectory: def __init__(self, sdir=None): self.name = sdir - if not config["temp_dir_auto"]: # if we don't use auto-generated temp dir - + + if not config["temp_dir_auto"]: tmp_dir = MyDirectory("/mnt/petrelfs/zhangxiang/dump6/") else: tmp_dir = tempfile.TemporaryDirectory() + if peak_is_not_index: index_path = [os.path.join(tmp_dir.name, f"eval_{uuid.uuid4().hex}")] else: index_path = peak_filenames peak_filenames = None - print("is peak not index?, ", peak_is_not_index) - - #SpectrumIdx = AnnotatedSpectrumIndex if annotated else SpectrumIndex valid_charge = np.arange(1, config["max_charge"] + 1) + dataloader_params = dict( batch_size=config["predict_batch_size"], n_peaks=config["n_peaks"], @@ -153,26 +174,22 @@ def __init__(self, sdir=None): min_intensity=config["min_intensity"], remove_precursor_tol=config["remove_precursor_tol"], n_workers=config["n_workers"], - train_filenames = None, - val_filenames = None, - test_filenames = peak_filenames, - - - train_index_path = None, #always a list, either a list containing one index path file or a list containing multiple db files - val_index_path = None, - test_index_path = index_path, - annotated = annotated, - valid_charge = valid_charge , - mode = "test" - + train_filenames=None, + val_filenames=None, + test_filenames=peak_filenames, + train_index_path=None, + val_index_path=None, + test_index_path=index_path, + annotated=annotated, + valid_charge=valid_charge, + mode="test" ) - # Initialize the data loader. + dataModule = DeNovoDataModule(**dataloader_params) dataModule.prepare_data() dataModule.setup(stage="test") test_dataloader = dataModule.test_dataloader() - # Create the Trainer object. trainer = pl.Trainer( enable_model_summary=True, accelerator="auto", @@ -183,12 +200,36 @@ def __init__(self, sdir=None): num_sanity_val_steps=config["num_sanity_val_steps"], strategy=_get_strategy(), ) - # Run the model with/without validation. + + # Record metadata before prediction/validation + # Use model_save_folder_path from config if available + if isinstance(config, dict): + output_dir = config.get("model_save_folder_path") + else: + output_dir = config.model_save_folder_path if hasattr(config, 'model_save_folder_path') else None + + if not output_dir: + # If no output directory specified, use model directory + output_dir = os.path.dirname(os.path.abspath(model_filename)) + logger.info(f"Using model directory for metadata: {output_dir}") + + # Create a run-specific subdirectory for metadata + metadata_dir = os.path.join(output_dir, "run_metadata") + os.makedirs(metadata_dir, exist_ok=True) + logger.info(f"Recording metadata in: {metadata_dir}") + + metadata_file, summary_file = record_metadata( + metadata_dir, + config, + model_name=os.path.basename(model_filename) + ) + + if metadata_file: + logger.info(f"Run metadata recorded in {metadata_file}") + logger.info(f"Quick summary available in {summary_file}") + run_trainer = trainer.validate if annotated else trainer.predict - pytorch_total_params = sum(p.numel() for p in model.parameters()) - print("model size is : ", pytorch_total_params) - run_trainer(model,test_dataloader ) - # Clean up temporary files. + run_trainer(model, test_dataloader) tmp_dir.cleanup() @@ -199,6 +240,18 @@ def train( model_filename: str, config: Dict[str, Any], ) -> None: + """Train a model with configuration validation. + + The config parameter can be either a Dict or a XuanjiNovoConfig object. + If it's a Dict, it will be validated if pydantic is available. + """ + try: + from ..config import XuanjiNovoConfig + if isinstance(config, dict): + config = XuanjiNovoConfig.from_dict(config) + logger.info("Training configuration validated via pydantic") + except ImportError: + logger.warning("Pydantic not available, skipping additional config validation") """ Train a XuanjiNovo model. @@ -218,18 +271,15 @@ def train( config : Dict[str, Any] The configuration options. """ - # Read the MS/MS spectra to use for training and validation. ext = (".mgf", ".h5", ".hdf5") - print("entering modelrunner_train") - print("peak path iis : ", peak_path) - #check if input peak files are valid and exist + if len(train_filenames := _get_peak_filenames(peak_path, ext)) == 0: - print(train_filenames) logger.error("Could not find training peak files from %s", peak_path) raise FileNotFoundError("Could not find training peak files") + train_is_not_index = any([ os.path.splitext(fn)[1] in (".mgf", ".mzxml", ".mzml") for fn in train_filenames - ]) #check if training peak files are raw files or lmdb_index files + ]) if (peak_path_val is None or len(val_filenames := _get_peak_filenames(peak_path_val, ext)) @@ -237,6 +287,7 @@ def train( logger.error("Could not find validation peak files from %s", peak_path_val) raise FileNotFoundError("Could not find validation peak files") + val_is_not_index = any( [os.path.splitext(fn)[1] in (".mgf", ".mzxml", ".mzml") for fn in val_filenames]) @@ -246,20 +297,18 @@ def train( logger.error("Could not find testing peak files from %s", peak_path_test) raise FileNotFoundError("Could not find testing peak files") + test_is_not_index = any( [os.path.splitext(fn)[1] in (".mgf", ".mzxml", ".mzml") for fn in test_filenames]) - - class MyDirectory: def __init__(self, sdir=None): self.name = sdir - if not config["temp_dir_auto"]: # if we don't use auto-generated temp dir - + + if not config["temp_dir_auto"]: tmp_dir = MyDirectory("/mnt/petrelfs/zhangxiang/dump6/") else: tmp_dir = tempfile.TemporaryDirectory() - if train_is_not_index: train_index_path = [os.path.join(tmp_dir.name, f"Train_{uuid.uuid4().hex}")] @@ -267,40 +316,20 @@ def __init__(self, sdir=None): train_index_path = train_filenames train_filenames = None - - if val_is_not_index: - val_index_path = [os.path.join(tmp_dir.name, f"valid_{uuid.uuid4().hex}")] + val_index_path = [os.path.join(tmp_dir.name, f"valid_{uuid.uuid4().hex}")] else: val_index_path = val_filenames val_filenames = None + if test_is_not_index: - test_index_path = [os.path.join(tmp_dir.name, f"test_{uuid.uuid4().hex}")] + test_index_path = [os.path.join(tmp_dir.name, f"test_{uuid.uuid4().hex}")] else: test_index_path = test_filenames test_filenames = None valid_charge = np.arange(1, config["max_charge"] + 1) - ''' - train_index = AnnotatedSpectrumIndex(train_idx_fn, - train_filenames, - valid_charge=valid_charge) - if val_is_index: - val_idx_fn, val_filenames = val_filenames[0], None - else: - val_idx_fn = os.path.join(tmp_dir.name, f"Valid_{uuid.uuid4().hex}.hdf5") - val_index = AnnotatedSpectrumIndex(val_idx_fn, - val_filenames, - valid_charge=valid_charge) - if test_is_index: - test_idx_fn, test_filenames = test_filenames[0], None - else: - test_idx_fn = os.path.join(tmp_dir.name, f"Test_{uuid.uuid4().hex}.hdf5") - test_index = AnnotatedSpectrumIndex(test_idx_fn, - test_filenames, - valid_charge=valid_charge) - ''' - # Initialize the data loaders. + dataloader_params = dict( batch_size=config["train_batch_size"], n_peaks=config["n_peaks"], @@ -309,53 +338,38 @@ def __init__(self, sdir=None): min_intensity=config["min_intensity"], remove_precursor_tol=config["remove_precursor_tol"], n_workers=config["n_workers"], - train_filenames = train_filenames, - val_filenames = val_filenames, - test_filenames = test_filenames, - - - train_index_path = train_index_path, #always a list, either a list containing one index path file or a list containing multiple db files - val_index_path = val_index_path, - test_index_path = test_index_path, - annotated = True, - valid_charge = valid_charge , - mode = "fit" - + train_filenames=train_filenames, + val_filenames=val_filenames, + test_filenames=test_filenames, + train_index_path=train_index_path, + val_index_path=val_index_path, + test_index_path=test_index_path, + annotated=True, + valid_charge=valid_charge, + mode="fit" ) + dataModule = DeNovoDataModule(**dataloader_params) dataModule.prepare_data() dataModule.setup() - train_dataloader=dataModule.train_dataloader() - #train_loader = DeNovoDataModule(train_index=train_index, - # **dataloader_params) - #train_loader.setup() - #train_dataloader=train_loader.train_dataloader() - - - #val_loader = DeNovoDataModule(valid_index=val_index, **dataloader_params) - #val_loader.setup() - - #test_loader = DeNovoDataModule(valid_index=test_index, **dataloader_params) - #test_loader.setup() - - # Set warmup_iters & max_iters - # Author: Sheng Xu - # Date: 20230202 - config["warmup_iters"] = int(len(train_dataloader)/(torch.cuda.device_count()*config["accumulate_grad_batches"])) * config["warm_up_epochs"] + train_dataloader = dataModule.train_dataloader() + + config["warmup_iters"] = int(len(train_dataloader)/(torch.cuda.device_count()*config["accumulate_grad_batches"])) * config["warm_up_epochs"] config["max_iters"] = int(len(train_dataloader)/(torch.cuda.device_count()*config["accumulate_grad_batches"])) * int(config["max_epochs"]) - print("max_iter", int(len(train_dataloader))) - # Initialize the model. - ctc_params = dict(model_path=None, #to change - alpha=0, beta=0, - cutoff_top_n=100, - cutoff_prob= 1.0, - beam_width=config["n_beams"], - num_processes=4, - log_probs_input = False) + + ctc_params = dict( + model_path=None, + alpha=0, + beta=0, + cutoff_top_n=100, + cutoff_prob=1.0, + beam_width=config["n_beams"], + num_processes=4, + log_probs_input=False + ) model_params = dict( - PMC_enable = config["PMC_enable"], - mass_control_tol = config["mass_control_tol"], - custom_ctc_loss = config["custom_ctc"], + PMC_enable=config["PMC_enable"], + mass_control_tol=config["mass_control_tol"], dim_model=config["dim_model"], n_head=config["n_head"], dim_feedforward=config["dim_feedforward"], @@ -375,10 +389,16 @@ def __init__(self, sdir=None): max_iters=config["max_iters"], lr=config["learning_rate"], weight_decay=config["weight_decay"], - ctc_dic = ctc_params + ctc_dic=ctc_params, + log_level=config.get("log_level", "INFO"), # Get log_level from config with default "INFO" + refine_iters=config.get("refine_iters", 3), + mask_schedule=config.get("mask_schedule", { + "initial_peek": 0.93, + "epoch_decay": 0.01, + "min_peek": 0.00 + }) ) - print("what is training from?? ", config["train_from_scratch"]) if config["train_from_scratch"]: model = Spec2Pep(**model_params) else: @@ -386,35 +406,29 @@ def __init__(self, sdir=None): model_filename = config["load_file_name"] if not os.path.isfile(model_filename): logger.error( - "Could not find the model weights at file %s to continue " - "training", + "Could not find the model weights at file %s to continue training", model_filename, ) - raise FileNotFoundError( - "Could not find the model weights to continue training") + raise FileNotFoundError("Could not find the model weights to continue training") model = Spec2Pep().load_from_checkpoint(model_filename, **model_params) - # Create the Trainer object and (optionally) a checkpoint callback to - # periodically save the model. + + callbacks = [] if config["save_model"]: - callbacks = [ + callbacks.append( pl.callbacks.ModelCheckpoint( dirpath=config["model_save_folder_path"], save_top_k=-1, save_weights_only=False, every_n_train_steps=config["every_n_train_steps"], ) - ] - else: - callbacks = [] - - (path, filename) = os.path.split(val_index_path[0]) - import time - + ) + if config["SWA"]: callbacks.append(pl.callbacks.StochasticWeightAveraging(swa_lrs=1e-2)) - + if config["enable_neptune"]: callbacks.append(pl.callbacks.LearningRateMonitor(logging_interval='epoch')) + (path, filename) = os.path.split(val_index_path[0]) neptune_logger = pl.loggers.NeptuneLogger( project=config["neptune_project"], api_token=config["neptune_api_token"], @@ -423,73 +437,69 @@ def __init__(self, sdir=None): name=filename + str(time.time()), tags=config["tags"] ) - neptune_logger.log_hyperparams({ "train_batch_size": config["train_batch_size"], "n_cards": torch.cuda.device_count(), "random_seed": config["random_seed"], - "train_filename":peak_path, - "val_filename":peak_path_val, - "test_filename":peak_path_test, - "gradient_clip_val":config["gradient_clip_val"], + "train_filename": peak_path, + "val_filename": peak_path_val, + "test_filename": peak_path_test, + "gradient_clip_val": config["gradient_clip_val"], "accumulate_grad_batches": config["accumulate_grad_batches"], - "sync_batchnorm":config["sync_batchnorm"], - "SWA":config["SWA"], - "gradient_clip_algorithm":config["gradient_clip_algorithm"] + "sync_batchnorm": config["sync_batchnorm"], + "SWA": config["SWA"], + "gradient_clip_algorithm": config["gradient_clip_algorithm"] }) - print("num avaiable devices" , torch.cuda.device_count()) - if config["val_interval"] == 1: - trainer = pl.Trainer( - - - reload_dataloaders_every_n_epochs=1, - enable_model_summary= True, - accelerator="auto", - auto_select_gpus=True, - callbacks=callbacks, - devices=_get_devices(), - num_nodes=config["n_nodes"], - - logger=neptune_logger if config["enable_neptune"] else None, - max_epochs=config["max_epochs"], - num_sanity_val_steps=config["num_sanity_val_steps"], - strategy= _get_strategy(), - gradient_clip_val=config["gradient_clip_val"], - gradient_clip_algorithm=config["gradient_clip_algorithm"], - accumulate_grad_batches=config["accumulate_grad_batches"], - sync_batchnorm=config["sync_batchnorm"], - ) + + trainer_params = dict( + reload_dataloaders_every_n_epochs=1, + enable_model_summary=True, + accelerator="auto", + auto_select_gpus=True, + callbacks=callbacks, + devices=_get_devices(), + num_nodes=config["n_nodes"], + logger=neptune_logger if config["enable_neptune"] else None, + max_epochs=config["max_epochs"], + num_sanity_val_steps=config["num_sanity_val_steps"], + strategy=_get_strategy(), + gradient_clip_val=config["gradient_clip_val"], + gradient_clip_algorithm=config["gradient_clip_algorithm"], + accumulate_grad_batches=config["accumulate_grad_batches"], + sync_batchnorm=config["sync_batchnorm"], + ) + + if config["val_interval"] != 1: + trainer_params["val_check_interval"] = config["val_interval"] + + trainer = pl.Trainer(**trainer_params) + + # Record metadata before training + # Use model_save_folder_path from config if available, otherwise use a default + if isinstance(config, dict): + output_dir = config.get("model_save_folder_path") else: - trainer = pl.Trainer( - val_check_interval=config["val_interval"], - - reload_dataloaders_every_n_epochs=1, - enable_model_summary= True, - accelerator="auto", - auto_select_gpus=True, - callbacks=callbacks, - devices=_get_devices(), - num_nodes=config["n_nodes"], - - logger=neptune_logger if config["enable_neptune"] else None, - max_epochs=config["max_epochs"], - num_sanity_val_steps=config["num_sanity_val_steps"], - strategy= _get_strategy(), - gradient_clip_val=config["gradient_clip_val"], - gradient_clip_algorithm=config["gradient_clip_algorithm"], - accumulate_grad_batches=config["accumulate_grad_batches"], - sync_batchnorm=config["sync_batchnorm"], - ) - - pytorch_total_params = sum(p.numel() for p in model.parameters()) - print("model size is : ", pytorch_total_params) - # Train the model. - if config["train_from_resume"] == True and config["train_from_scratch"] == False: - trainer.fit(model, datamodule=dataModule,ckpt_path=config['load_file_name']) + output_dir = config.model_save_folder_path if hasattr(config, 'model_save_folder_path') else None + + if not output_dir: + logger.warning("model_save_folder_path not specified in config, using default 'outputs' directory") + output_dir = "outputs" + + # Ensure output directory exists + os.makedirs(output_dir, exist_ok=True) + logger.info(f"Recording metadata in: {output_dir}") + + metadata_file, summary_file = record_metadata(output_dir, config) + + if metadata_file: + logger.info(f"Run metadata recorded in {metadata_file}") + logger.info(f"Quick summary available in {summary_file}") + + if config["train_from_resume"] and not config["train_from_scratch"]: + trainer.fit(model, datamodule=dataModule, ckpt_path=config['load_file_name']) else: - trainer.fit(model, - datamodule=dataModule) - # Clean up temporary files. + trainer.fit(model, datamodule=dataModule) + tmp_dir.cleanup() @@ -519,16 +529,12 @@ def _get_peak_filenames( for path in paths: path = os.path.expanduser(path) path = os.path.expandvars(path) - files += [fn for fn in glob.glob(path, recursive=True)] + files += glob.glob(path, recursive=True) return files path = os.path.expanduser(path) path = os.path.expandvars(path) - - return [ - fn for fn in glob.glob(path, recursive=True) - #if os.path.splitext(fn.lower())[1] in supported_ext - ] + return glob.glob(path, recursive=True) def _get_strategy() -> Optional[DDPStrategy]: @@ -560,12 +566,18 @@ def _get_devices() -> Union[int, str]: The number of GPUs/CPUs to use, or "auto" to let PyTorch Lightning determine the appropriate number of devices. """ + # Check if specific GPUs were selected via CUDA_VISIBLE_DEVICES + if "CUDA_VISIBLE_DEVICES" in os.environ: + gpu_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",") + if gpu_ids and gpu_ids[0]: # Non-empty string + return len(gpu_ids) + # Default behavior if any( operator.attrgetter(device + ".is_available")(torch)() for device in ["cuda", "backends.mps"]): - return -1 - elif not (n_workers := utils.n_workers()): + return -1 # Use all available GPUs + elif not (n_workers := n_workers()): return "auto" else: return n_workers diff --git a/XuanjiNovo/denovo/parser2.py b/XuanjiNovo/denovo/parser2.py index 62d87f3..4d3118e 100644 --- a/XuanjiNovo/denovo/parser2.py +++ b/XuanjiNovo/denovo/parser2.py @@ -274,8 +274,6 @@ def parse_spectrum(self, spectrum): if self.annotationsLabel: self.annotations.append(spectrum["params"].get("seq")) else: - # print(spectrum) - # print((spectrum["params"]["title"])) self.annotations.append(spectrum["params"]["title"]) if self.valid_charge is None or precursor_charge in self.valid_charge: diff --git a/XuanjiNovo/denovo/pmc.py b/XuanjiNovo/denovo/pmc.py new file mode 100644 index 0000000..25d52ee --- /dev/null +++ b/XuanjiNovo/denovo/pmc.py @@ -0,0 +1,137 @@ +"""Precise Mass Control (PMC) module for mass-constrained decoding.""" +import cupy as cp +import numpy as np +import time +import torch +import sys + +inference_kernel = cp.RawKernel( +r''' +extern "C" __global__ +void inference(float* prob, int* ans, float* aa_mass, float* dp, float* dpMass ,int* lock,float premass,int length,float grid_size, float tol2) { + const int AA_num = 28; + const float tol = tol2; + int w = blockIdx.x; + int dim = blockDim.x; + int h = threadIdx.x; + if(w == 0 || h == 0)return; + float maxMass = aa_mass[AA_num - 8] * h; + int maxW = int(maxMass / grid_size); + if(w > maxW){ + lock[w * dim + h] = 1; + return; + } + if(h == 1){ + for(int i = 1; i < AA_num; i ++){ + if(w == int(aa_mass[i] / grid_size)){ + if(dp[w * dim + h] < prob[(h-1) * AA_num + i]){ + dp[w * dim + h] = prob[(h-1) * AA_num + i]; + dpMass[w * dim + h] = aa_mass[i]; + ans[w * (dim * dim) + 1 * dim + 1] = i; + } + } + } + lock[w * dim + h] = 1; + return; + } + while(!atomicCAS(lock + w * dim + h - 1, 1, 1)); + __threadfence(); + if(dp[w * dim + h - 1] > 0){ + dp[w * dim + h] = dp[w * dim + h - 1] + prob[(h - 1) * AA_num]; + dpMass[w * dim + h] = dpMass[w * dim + h - 1]; + for(int l = 1; l < h + 1; l++){ + ans[w * (dim * dim) + h * dim + l] = ans[w * (dim * dim) + (h - 1) * dim + l]; + } + int preid = ans[w * (dim * dim) + (h - 1) * dim + h - 1]; + if(preid != 0){ + float tempProb = dp[w * dim + h - 1] + prob[(h - 1) * AA_num + preid]; + if(dp[w * dim + h] < tempProb){ + dpMass[w * dim + h] = dpMass[w * dim + h - 1]; + dp[w * dim + h] = tempProb; + for(int l = 1; l < h; l++){ + ans[w * (dim * dim) + h * dim + l] = ans[w * (dim * dim) + (h - 1) * dim + l]; + } + ans[w * (dim * dim) + h * dim + h] = preid; + } + } + } + for(int i = 1; i < AA_num; i ++){ + int minw = int((w * grid_size - aa_mass[i]) / grid_size); + if(minw >= 0){ + while(!atomicCAS(lock + minw * dim + h - 1, 1, 1)); + __threadfence(); + int preid = ans[minw * (dim * dim) + (h-1) * dim + h - 1]; + if(preid != i){ + float temp = dpMass[minw * dim + h - 1] + aa_mass[i]; + if(temp >= w * grid_size && temp < (w + 1) * grid_size && w != length-1 || w == length - 1 && temp >= premass - tol && temp <= premass + tol){ + float tempProb = dp[minw * dim + h - 1] + prob[(h-1) * AA_num + i]; + if(tempProb > dp[w * dim + h]){ + dp[w * dim + h] = tempProb; + dpMass[w * dim + h] = temp; + for(int l = 1; l < h; l++){ + ans[w * (dim * dim) + h * dim + l] = ans[minw * (dim * dim) + (h - 1) * dim + l]; + } + ans[w * (dim * dim) + h * dim + h] = i; + } + } + } + } + minw = minw + 1; + if(minw >= 0){ + while(!atomicCAS(lock + minw * dim + h - 1, 1, 1)); + __threadfence(); + int preid = ans[minw * (dim * dim) + (h-1) * dim + h - 1]; + if(preid != i){ + float temp = dpMass[minw * dim + h - 1] + aa_mass[i]; + if(temp >= w * grid_size && temp < (w + 1) * grid_size && w != length-1 || w == length - 1 && temp >= premass - tol && temp <= premass + tol){ + float tempProb = dp[minw * dim + h - 1] + prob[(h-1) * AA_num + i]; + if(tempProb > dp[w * dim + h]){ + dp[w * dim + h] = tempProb; + dpMass[w * dim + h] = temp; + for(int l = 1; l < h; l++){ + ans[w * (dim * dim) + h * dim + l] = ans[minw * (dim * dim) + (h - 1) * dim + l]; + } + ans[w * (dim * dim) + h * dim + h] = i; + } + } + } + } + } + lock[w * dim + h] = 1; + return; +} +''', 'inference') + +def knapDecode(prob, preMass, tol): + """CUDA-accelerated knapsack algorithm for Precise Mass Control (PMC) decoding.""" + grid_size = 1 + AAmasses = [0.0, 57.021464, 71.037114, 87.032028, 97.052764, 99.068414, 101.04767, 160.030649, 113.084064, 113.084064, 114.042927, 115.026943, 128.058578, 128.094963, 129.042593, 131.040485, 137.058912, 147.068414, 156.101111, 163.063329, 186.079313, 147.0354, 115.026943, 129.042594, 42.010565, 43.005814, 10000.0, 25.980265] + AAmasses = np.array(AAmasses, dtype=np.float32) + prob = torch.roll(prob[0], 1, dims=-1) + AAmasses = cp.array(AAmasses, dtype=cp.float32) + preMass = float(preMass[0].item() - 18.01) + preMass = cp.float32(preMass) + grid_size = cp.float32(grid_size) + tol = cp.float32(tol) + + prob = torch.where(prob < -20.0, -20.0, prob) + prob = prob - prob.min() + 0.1 + prob = prob.cpu().numpy().astype(np.float32) + prob = cp.array(prob, dtype=cp.float32) + + word_num = 40 + length = int(preMass / grid_size + 1) + ans = cp.zeros((length, word_num + 1, word_num + 1), dtype=cp.int32) + dpProb = cp.zeros((length, word_num + 1), dtype=cp.float32) + dpMass = cp.zeros((length, word_num + 1), dtype=cp.float32) + dpLock = cp.zeros((length, word_num + 1), dtype=cp.int32) + dpLock[0,:] = 1 + + inference_kernel((length,), (word_num + 1,), (prob, ans, AAmasses, dpProb, dpMass, dpLock, preMass, length, grid_size, tol)) + ans = ans.get() + dpMass = dpMass.get() + + result = ans[length-1, word_num, 1:] + result = np.where(result == 0, 27, result - 1) + + return result.tolist() diff --git a/XuanjiNovo/utils/__init__.py b/XuanjiNovo/utils/__init__.py new file mode 100644 index 0000000..74a45ce --- /dev/null +++ b/XuanjiNovo/utils/__init__.py @@ -0,0 +1,54 @@ +"""Utility functions and modules for XuanjiNovo.""" + +import os +import multiprocessing +from typing import Any, List, Union + +from .metadata import record_metadata, get_git_info, get_hardware_info, get_software_info + +def n_workers() -> int: + """ + Get the number of worker processes to use. + + This is determined by the environment variable "SLURM_CPUS_ON_NODE" if running + on a SLURM cluster, otherwise by the number of CPU cores available. + + Returns + ------- + int + The number of worker processes to use. + """ + if "SLURM_CPUS_ON_NODE" in os.environ: + return int(os.environ["SLURM_CPUS_ON_NODE"]) + return multiprocessing.cpu_count() + +def listify(x: Any) -> List: + """ + Convert input to a list if it isn't already. + + Parameters + ---------- + x : Any + Input to convert to a list. + + Returns + ------- + List + The input as a list. If input was None, returns empty list. + If input was already a list, returns it unchanged. + Otherwise, returns a single-element list containing the input. + """ + if x is None: + return [] + elif isinstance(x, list): + return x + return [x] + +__all__ = [ + 'record_metadata', + 'get_git_info', + 'get_hardware_info', + 'get_software_info', + 'n_workers', + 'listify' +] diff --git a/XuanjiNovo/utils/metadata.py b/XuanjiNovo/utils/metadata.py new file mode 100644 index 0000000..3b843d5 --- /dev/null +++ b/XuanjiNovo/utils/metadata.py @@ -0,0 +1,125 @@ +"""Utilities for recording software and hardware metadata for reproducibility.""" + +import json +import os +import platform +import sys +import time +from datetime import datetime +import torch +import psutil +import pkg_resources +import git +import logging + +logger = logging.getLogger("xuanjinovo") + +def get_git_info(): + """Get git repository information if available.""" + try: + repo = git.Repo(search_parent_directories=True) + return { + "git_commit": repo.head.object.hexsha, + "git_branch": repo.active_branch.name, + "git_dirty": repo.is_dirty(), + "git_remote_url": list(repo.remotes[0].urls)[0] if repo.remotes else None + } + except (git.InvalidGitRepositoryError, git.NoSuchPathError): + return {"git_info": "Not a git repository"} + +def get_python_packages(): + """Get list of installed Python packages and versions.""" + return {pkg.key: pkg.version for pkg in pkg_resources.working_set} + +def get_hardware_info(): + """Get hardware information.""" + gpu_info = [] + if torch.cuda.is_available(): + for i in range(torch.cuda.device_count()): + gpu_info.append({ + "name": torch.cuda.get_device_name(i), + "total_memory": f"{torch.cuda.get_device_properties(i).total_memory / 1024**3:.2f} GB", + "compute_capability": torch.cuda.get_device_capability(i) + }) + + return { + "cpu": { + "processor": platform.processor(), + "physical_cores": psutil.cpu_count(logical=False), + "total_cores": psutil.cpu_count(logical=True), + "max_frequency": f"{psutil.cpu_freq().max:.2f}MHz" if psutil.cpu_freq() else "Unknown" + }, + "memory": { + "total": f"{psutil.virtual_memory().total / (1024**3):.2f} GB", + "available": f"{psutil.virtual_memory().available / (1024**3):.2f} GB" + }, + "gpus": gpu_info + } + +def get_software_info(): + """Get software environment information.""" + return { + "python": { + "version": sys.version, + "executable": sys.executable, + "platform": platform.platform() + }, + "pytorch_version": torch.__version__, + "cuda_version": torch.version.cuda if torch.cuda.is_available() else "Not available", + "cudnn_version": torch.backends.cudnn.version() if torch.cuda.is_available() else "Not available", + "installed_packages": get_python_packages() + } + +def record_metadata(output_dir, config, model_name="XuanjiNovo"): + """ + Record comprehensive metadata about the software and hardware environment. + + Parameters + ---------- + output_dir : str + Directory where to save the metadata + config : dict + Model configuration dictionary + model_name : str + Name of the model being run + """ + try: + os.makedirs(output_dir, exist_ok=True) + + metadata = { + "timestamp": datetime.now().isoformat(), + "model_name": model_name, + "config": config, + "git_info": get_git_info(), + "hardware_info": get_hardware_info(), + "software_info": get_software_info() + } + + # Save detailed metadata + metadata_file = os.path.join(output_dir, "run_metadata.json") + with open(metadata_file, "w") as f: + json.dump(metadata, f, indent=2, sort_keys=True) + + # Save a quick summary + summary = { + "timestamp": metadata["timestamp"], + "git_commit": metadata["git_info"].get("git_commit", "N/A"), + "python_version": sys.version.split()[0], + "pytorch_version": torch.__version__, + "gpu_count": len(metadata["hardware_info"]["gpus"]), + "gpu_names": [gpu["name"] for gpu in metadata["hardware_info"]["gpus"]], + "cuda_version": metadata["software_info"]["cuda_version"] + } + + summary_file = os.path.join(output_dir, "run_summary.txt") + with open(summary_file, "w") as f: + for key, value in summary.items(): + f.write(f"{key}: {value}\n") + + logger.info(f"Metadata recorded in {output_dir}") + return metadata_file, summary_file + + except Exception as e: + logger.error(f"Failed to record metadata: {str(e)}") + return None, None + diff --git a/XuanjiNovo/utils2.py b/XuanjiNovo/utils2.py index 81cf7a2..5913392 100644 --- a/XuanjiNovo/utils2.py +++ b/XuanjiNovo/utils2.py @@ -1,4 +1,64 @@ -"""Common utility functions""" +"""Small utility functions""" +import os +import platform +import re +from typing import Tuple + +import psutil +import torch + + +def n_workers() -> int: + """ + Get the number of workers to use for data loading. + + This is the maximum number of CPUs allowed for the process, scaled for the + number of GPUs being used. + + On Windows and MacOS, we only use the main process. See: + https://discuss.pytorch.org/t/errors-when-using-num-workers-0-in-dataloader/97564/4 + https://github.com/pytorch/pytorch/issues/70344 + + Returns + ------- + int + The number of workers. + """ + # Windows or MacOS: no multiprocessing. + if platform.system() in ["Windows", "Darwin"]: + return 0 + # Linux: scale the number of workers by the number of GPUs (if present). + try: + n_cpu = len(psutil.Process().cpu_affinity()) + except AttributeError: + n_cpu = os.cpu_count() + return ( + n_cpu // n_gpu if (n_gpu := torch.cuda.device_count()) > 1 else n_cpu + ) + + +def split_version(version: str) -> Tuple[str, str, str]: + """ + Split the version into its semantic versioning components. + + Parameters + ---------- + version : str + The version number. + + Returns + ------- + major : str + The major release. + minor : str + The minor release. + patch : str + The patch release. + """ + version_regex = re.compile(r"(\d+)\.(\d+)\.*(\d*)(?:.dev\d+.+)?") + return tuple(g for g in version_regex.match(version).groups()) + + import pandas as pd from tensorboard.backend.event_processing.event_accumulator import ( EventAccumulator, diff --git a/checksums.txt b/checksums.txt new file mode 100644 index 0000000..7e2657e --- /dev/null +++ b/checksums.txt @@ -0,0 +1,4 @@ +# SHA-256 checksums for model checkpoints and datasets +XuanjiNovo_100M_massnet.ckpt=59469325807a2d1b666daad6b3b5c05a5d3b66a489d43b0486485a55a3bde3e6 +XuanjiNovo_130M_massnet_massivekb.ckpt=d909f907aeaa9b215d343b95c6694d85809265ccaabc9f90b1e1b58942cbdcec +Bacillus.10k.mgf=47eba8c23197214ca7f40200c53ca2abe96a7824b613377738526353a67264df \ No newline at end of file diff --git a/denovo.tsv b/denovo.tsv new file mode 100644 index 0000000..3fa4aad --- /dev/null +++ b/denovo.tsv @@ -0,0 +1,49021 @@ +label prediction charge score +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_2327 YNHHNSTYPTK 3 0.00817840825766325 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_3391 NHSDSEEQPQAEEEEHPSSSDGAK 4 6.738844898768193e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_3623 LSGEVPEEK 2 0.9370234608650208 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4001 ALTETDGDMDK 2 0.33475425839424133 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4306 ALTETDGDNDK 2 0.4052024781703949 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4588 ALAETADANLK 2 0.9735389351844788 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4933 LQHLDEAYNK 2 0.9977360963821411 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5068 DVLGQAQTSQK 2 0.4362994432449341 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5588 SDQESFGEVK 2 0.9952811598777771 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5709 KELVEKQQAEE 3 0.0011585972970351577 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5815 DLQAEVDR 2 0.9894288182258606 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6024 GGALHPGMVLGK 4 0.5375634431838989 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6256 EGMPLGAK 2 0.9900884032249451 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6549 TYNFPQNR 2 0.9950031638145447 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6986 VPVDQVTETAK 2 0.9760689735412598 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7152 HFYTDDVR 2 0.8641716837882996 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7340 TALPFLK 2 0.00015221313515212387 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7432 ALMEEYK 2 0.928252637386322 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7596 TLQDALDR 2 0.6518266201019287 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7789 VGGGGGK 2 0.006237287074327469 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7917 EFDDSEK 2 0.00062800373416394 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8226 KEELEVPK 2 0.463377445936203 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8288 QMVLETKPN 2 0.093741774559021 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8358 TTDRDSWPMLSDM[+15.995]HM[+15.995]GMYTK 3 8.001627065823413e-06 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8788 PATVLVQTASK 2 0.8498216867446899 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9136 AKPELEEEEEQMDEEYK 4 2.3354898530669743e-06 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9245 ANEDALTSLLHK 3 1.0145466148969717e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9686 SNSDKVVLVK 3 0.0008224076591432095 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9761 LENWNHLEYPTK 3 0.003499164478853345 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9890 TAFDGMK 2 0.23526784777641296 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10056 TMHHDVTYNR 3 0.007241277955472469 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10158 NLLHGSDSLESAER 2 0.9481425881385803 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10319 AVVVNLQR 2 0.9822706580162048 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10530 AAHELHAPLLR 4 1.5626435924787074e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10775 LGGLGFR 2 0.0023134120274335146 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10941 M[+15.995]HAFSGR 2 8.306624658871442e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_11199 VASPNYLGVR 2 0.9970025420188904 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LLFVGTKK 3 0.9946191310882568 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_10981 ALSSSSLTLAK 2 0.6540493369102478 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_11370 GGGGGGK 2 0.026526164263486862 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_11875 EGGGTYDLYTTTR 2 0.6873912215232849 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_11973 KLELENAEQYEGK 2 0.9980425834655762 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_12369 ESSYLEAAVQDGKR 3 0.010291892103850842 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_12544 C[+57.021]SESAEVATEEVK 2 0.003800358157604933 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1.0743650591393816e-07 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_40117 GLDEPFTGVFLR 2 0.9962550401687622 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_40360 ELEPSEWKPDEFHLDLDQEFK 4 7.535928131519043e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_40800 VLELHMGMAAR 2 0.6912415623664856 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_41088 LEELLPYNSGAMSLR 3 8.07134426850098e-07 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_41381 LSELNVDPDEEFLAPVLSVK 3 7.627866580151021e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_41533 VFLVYLVPLAPFGGDASVHTVK 3 2.646597385691507e-10 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9.200386980046105e-12 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_47647 VYQFYESLLELEQLEYDALKK 3 5.332559976306084e-16 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_47784 GFPYTLLLEENFLNDSPEYLELFK 3 8.596005851568833e-13 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_47879 DLLSEYDFPGDDVPVVK 2 0.033683136105537415 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_48545 ELADVVLVDLPQLENPTK 2 0.9519573450088501 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_48845 TFDDLQQLPSPELLQLLWLLK 3 5.903578320642922e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_48986 TLWPEVHETSENEQGSPFLK 3 1.8234947996020878e-09 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_49259 FGSGGAAYLVVDVPALLELTSTPNQDSPLSEGK 3 2.8575354008353315e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_33810 YPHLLPL 2 0.15129266679286957 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_34082 GLAPLGEEYASLLK 2 0.9966331124305725 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_34802 TAFGDTLAFVNTQLGK 3 0.741371750831604 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_35447 KLEEEETLWQVDQFAMSHEDLGPK 3 7.892624331873321e-09 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_36034 GVPHLPL 2 0.11682044714689255 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+C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_48502 VEVVLPEEYMGDLMGDLTSR 2 0.7752214670181274 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_48623 DKYGLTLHGWWNEGFEQLPL 3 0.00017686728097032756 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_48886 TVELQVTQPEYLEEEEDEVEYC[+57.021]K 3 6.731035173679345e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49418 FVFVTPLWNFSYPPVM[+15.995]K 2 0.723372220993042 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49588 AVGLVNLMTLFGAGAVR 3 0.17310011386871338 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49748 PATTLYVQPGGATLYVNPLELTKNPFYVDVL 3 1.713238020784047e-06 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49897 SMQTVFYETLLALVK 2 0.9966296553611755 diff --git a/download_data.py b/download_data.py new file mode 100644 index 0000000..9477abc --- /dev/null +++ b/download_data.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +""" +Download and verify model checkpoints and datasets. +Supports both Google Drive and direct HTTPS downloads. +""" + +import os +import hashlib +import argparse +from pathlib import Path +import requests +from tqdm import tqdm + +# Data URLs and their corresponding checksums +DOWNLOADS = { + "XuanjiNovo_100M_massnet.ckpt": { + "gdrive_id": "1BtEYZ9FuWvQub2YQEHYMy5l2Y7bcmQDr", + "mirror_url": "https://huggingface.co/Wyattz23/XuanjiNovo/resolve/main/XuanjiNovo_100M_massnet.ckpt", + "sha256": "59469325807a2d1b666daad6b3b5c05a5d3b66a489d43b0486485a55a3bde3e6" + }, + "XuanjiNovo_130M_massnet_massivekb.ckpt": { + "gdrive_id": "1dcbdn5tV5x2tmUKT7nJe8deqMwGzpx4E", + "mirror_url": "https://huggingface.co/Wyattz23/XuanjiNovo/resolve/main/XuanjiNovo_130M_massnet_massivekb.ckpt", + "sha256": "d909f907aeaa9b215d343b95c6694d85809265ccaabc9f90b1e1b58942cbdcec" + }, + "Bacillus.10k.mgf": { + "gdrive_id": "1HqfCETZLV9ZB-byU0pqNNRXbaPbTAceT", + "mirror_url": "https://huggingface.co/Wyattz23/XuanjiNovo/resolve/main/bacillus.10k.mgf", + "sha256": "47eba8c23197214ca7f40200c53ca2abe96a7824b613377738526353a67264df" + } +} + +def calculate_sha256(filepath): + """Calculate SHA-256 hash of a file.""" + sha256_hash = hashlib.sha256() + with open(filepath, "rb") as f: + for byte_block in iter(lambda: f.read(4096), b""): + sha256_hash.update(byte_block) + return sha256_hash.hexdigest() + +def verify_checksum(filepath, expected_sha256): + """Verify file integrity using SHA-256.""" + if not os.path.exists(filepath): + return False + actual_sha256 = calculate_sha256(filepath) + return actual_sha256.lower() == expected_sha256.lower() + +def download_from_gdrive(file_id, destination): + """Download a file from Google Drive.""" + try: + import gdown + url = f"https://drive.google.com/uc?id={file_id}" + gdown.download(url, destination, quiet=False) + return True + except Exception as e: + print(f"Error downloading from Google Drive: {e}") + return False + +def download_from_mirror(url, destination): + """Download a file from HTTPS mirror.""" + try: + response = requests.get(url, stream=True) + response.raise_for_status() + total_size = int(response.headers.get('content-length', 0)) + + with open(destination, 'wb') as f, tqdm( + desc=destination, + total=total_size, + unit='iB', + unit_scale=True, + unit_divisor=1024, + ) as pbar: + for data in response.iter_content(chunk_size=1024): + size = f.write(data) + pbar.update(size) + return True + except Exception as e: + print(f"Error downloading from mirror: {e}") + return False + +def main(): + parser = argparse.ArgumentParser(description="Download and verify model checkpoints and datasets") + parser.add_argument('--output-dir', type=str, default='.', + help='Directory to save downloaded files') + parser.add_argument('--prefer-mirror', action='store_true', + help='Prefer HTTPS mirror over Google Drive') + parser.add_argument('--files', nargs='+', choices=list(DOWNLOADS.keys()), + help='Specific files to download. If not specified, downloads all.') + args = parser.parse_args() + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + files_to_download = args.files if args.files else DOWNLOADS.keys() + + for filename in files_to_download: + file_info = DOWNLOADS[filename] + destination = output_dir / filename + + # Check if file exists and is valid + if verify_checksum(destination, file_info['sha256']): + print(f"{filename} already exists and checksum matches.") + continue + + print(f"\nDownloading {filename}...") + success = False + + if args.prefer_mirror: + # Try mirror first, then fallback to Google Drive + if download_from_mirror(file_info['mirror_url'], destination): + success = True + elif download_from_gdrive(file_info['gdrive_id'], destination): + success = True + else: + # Try Google Drive first, then fallback to mirror + if download_from_gdrive(file_info['gdrive_id'], destination): + success = True + elif download_from_mirror(file_info['mirror_url'], destination): + success = True + + if not success: + print(f"Failed to download {filename}") + continue + + # Verify downloaded file + if verify_checksum(destination, file_info['sha256']): + print(f"Successfully downloaded and verified {filename}") + else: + print(f"Checksum verification failed for {filename}") + os.remove(destination) + +if __name__ == "__main__": + main() diff --git a/imputer-pytorch/torch_imputer/__pycache__/__init__.cpython-311.pyc b/imputer-pytorch/torch_imputer/__pycache__/__init__.cpython-311.pyc index fec37e121526ca488883d2fa892d5366882492fc..55533f821e019edd724c728ab2a5132a510cf0c9 100644 GIT binary patch delta 91 zcmX@Z^pc5ZIWI340}woGzmYMKXNhx;er{fgesM-(QEG~AaY=qrVtT4>X>n?ieo|^; tu6{~tUVd4=esFSPPG(-ZeuQUYUb;(WqLIFDVsWuwYKg9ki{r#=WdOU7AkhE- delta 62 zcmaFKbcTs%IWI340}xa^*_k?#XNj7>er{fgenDzUQEEZ@hpO8IWI340}woGzmc(#XCI4mwtjA2iGFcLVo_>}ZgEL|QDSU>zJ73WVoqjWx_*RbVqUsSW}=b4Z(?z=Uuuc2i;Lst4=j=D08$kp{r~^~ delta 65 zcmaDBaWaBuIWI340}xa^*_pbLXCI52pMGv$iGD$9Nl|J}TCsjrMq*xiMJABYFDNL< S)vqWm&PdHm-^|MzsSW`CSQmN# diff --git a/logs/xuanjinovo_20250930_043835.log b/logs/xuanjinovo_20250930_043835.log new file mode 100644 index 0000000..82e70fc --- /dev/null +++ b/logs/xuanjinovo_20250930_043835.log @@ -0,0 +1,66 @@ +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930043835 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - random_seed = 2717 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:38:36 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:38:36 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:38:36 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:38:36 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 diff --git a/logs/xuanjinovo_20250930_043836.log b/logs/xuanjinovo_20250930_043836.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_044032.log b/logs/xuanjinovo_20250930_044032.log new file mode 100644 index 0000000..4736392 --- /dev/null +++ b/logs/xuanjinovo_20250930_044032.log @@ -0,0 +1,181 @@ +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044032 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 7973 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044032 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 862 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:40:51 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 04:41:25 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 12.02 +2025-09-30 04:41:27 [INFO] xuanjinovo - Average predicted peptide length: 11.97 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.77 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.53 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 11.98 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.28 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.50 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.17 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.69 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.94 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.72 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.50 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.67 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 17.12 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.70 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.27 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.95 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.14 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.94 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.55 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.42 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.77 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.88 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.25 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.55 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.34 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 13.83 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.70 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.19 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.97 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 15.19 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 15.17 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 12.83 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.81 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.84 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.28 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.72 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.12 +2025-09-30 04:41:32 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 18.90 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 19.30 diff --git a/logs/xuanjinovo_20250930_044033.log b/logs/xuanjinovo_20250930_044033.log new file mode 100644 index 0000000..93ac930 --- /dev/null +++ b/logs/xuanjinovo_20250930_044033.log @@ -0,0 +1,430 @@ +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 4234 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 4151 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 3000 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 4597 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 9428 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 11.31 +2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 12.16 +2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 11.58 +2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 11.20 +2025-09-30 04:41:27 [INFO] xuanjinovo - Average predicted peptide length: 12.33 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.67 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.64 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 17.23 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 17.25 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 17.00 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.69 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 13.28 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.52 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.58 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.45 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.97 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.94 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.47 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.69 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.83 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.33 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.44 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.70 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.53 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.98 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.92 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 13.16 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.39 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.39 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.52 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.67 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.53 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.31 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.31 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.73 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.47 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.38 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.31 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.89 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.31 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.50 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.12 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.23 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.41 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.19 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.06 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.16 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.39 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.30 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.42 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.72 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.66 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.25 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.86 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.09 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.77 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.23 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.00 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.36 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.73 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.11 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.25 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 13.92 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.91 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.58 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.91 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.75 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.95 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 13.20 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.33 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.58 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.16 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 17.19 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 17.08 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 17.14 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.80 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.23 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.84 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 15.31 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.94 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.41 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.67 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.80 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.53 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.70 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.88 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.64 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.73 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.41 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.70 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.97 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.33 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.56 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.61 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.30 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 19.90 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 21.00 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 19.80 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 21.50 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 20.70 diff --git a/logs/xuanjinovo_20250930_044036.log b/logs/xuanjinovo_20250930_044036.log new file mode 100644 index 0000000..80ec5cb --- /dev/null +++ b/logs/xuanjinovo_20250930_044036.log @@ -0,0 +1,86 @@ +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044036 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - random_seed = 9387 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - log_level = INFO +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:40:37 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:40:37 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:40:37 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:40:38 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 +2025-09-30 04:41:27 [INFO] xuanjinovo - Average predicted peptide length: 11.77 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.69 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 13.25 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.09 +2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.34 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.78 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.02 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.41 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.27 +2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.28 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.27 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.58 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.22 +2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.00 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.67 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.70 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 12.98 +2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.75 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.69 +2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 20.40 diff --git a/logs/xuanjinovo_20250930_044037.log b/logs/xuanjinovo_20250930_044037.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_044038.log b/logs/xuanjinovo_20250930_044038.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_044146.log b/logs/xuanjinovo_20250930_044146.log new file mode 100644 index 0000000..3932fd7 --- /dev/null +++ b/logs/xuanjinovo_20250930_044146.log @@ -0,0 +1,65 @@ +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044146 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - random_seed = 6573 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:46 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:46 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt diff --git a/logs/xuanjinovo_20250930_044147.log b/logs/xuanjinovo_20250930_044147.log new file mode 100644 index 0000000..f170c80 --- /dev/null +++ b/logs/xuanjinovo_20250930_044147.log @@ -0,0 +1,464 @@ +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 2394 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 3206 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 6278 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 614 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 4889 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 479 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 6479 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 04:42:04 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 04:42:37 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 04:42:42 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_050709.log b/logs/xuanjinovo_20250930_050709.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_050901.log b/logs/xuanjinovo_20250930_050901.log new file mode 100644 index 0000000..035544d --- /dev/null +++ b/logs/xuanjinovo_20250930_050901.log @@ -0,0 +1,571 @@ +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 9261 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 5458 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 8629 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 8842 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 9461 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 6169 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 3213 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 2571 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:07 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:07 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:07 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:19 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + diff --git a/logs/xuanjinovo_20250930_050948.log b/logs/xuanjinovo_20250930_050948.log new file mode 100644 index 0000000..e033163 --- /dev/null +++ b/logs/xuanjinovo_20250930_050948.log @@ -0,0 +1,571 @@ +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - random_seed = 9420 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:48 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:48 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - random_seed = 8900 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:48 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:48 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 5423 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 2162 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 3854 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 1811 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 9764 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 5398 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:54 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:54 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:54 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:54 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:10:06 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + diff --git a/logs/xuanjinovo_20250930_050949.log b/logs/xuanjinovo_20250930_050949.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_051203.log b/logs/xuanjinovo_20250930_051203.log new file mode 100644 index 0000000..4a233a2 --- /dev/null +++ b/logs/xuanjinovo_20250930_051203.log @@ -0,0 +1,71 @@ +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051203 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - random_seed = 2937 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_051204.log b/logs/xuanjinovo_20250930_051204.log new file mode 100644 index 0000000..bc87d39 --- /dev/null +++ b/logs/xuanjinovo_20250930_051204.log @@ -0,0 +1,500 @@ +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 2496 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 8559 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 4215 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 9581 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 278 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 6674 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 685 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:12:22 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + diff --git a/logs/xuanjinovo_20250930_051503.log b/logs/xuanjinovo_20250930_051503.log new file mode 100644 index 0000000..bf435fa --- /dev/null +++ b/logs/xuanjinovo_20250930_051503.log @@ -0,0 +1,574 @@ +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 5594 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 1900 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 471 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 9613 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 2174 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 1966 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 5978 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 9143 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 +2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:15:21 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 05:15:28 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_051504.log b/logs/xuanjinovo_20250930_051504.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_051747.log b/logs/xuanjinovo_20250930_051747.log new file mode 100644 index 0000000..eb8254f --- /dev/null +++ b/logs/xuanjinovo_20250930_051747.log @@ -0,0 +1,577 @@ +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 1216 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 5205 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 4618 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 3571 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 4879 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 7617 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 1487 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 6533 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:18:06 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 05:18:41 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 05:18:46 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_051748.log b/logs/xuanjinovo_20250930_051748.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_051859.log b/logs/xuanjinovo_20250930_051859.log new file mode 100644 index 0000000..a3b0318 --- /dev/null +++ b/logs/xuanjinovo_20250930_051859.log @@ -0,0 +1,577 @@ +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 469 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 1407 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 5743 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 9967 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 2658 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 9177 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 6165 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 6028 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:19:17 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 05:19:49 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 05:19:55 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_051900.log b/logs/xuanjinovo_20250930_051900.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052157.log b/logs/xuanjinovo_20250930_052157.log new file mode 100644 index 0000000..7d41bcc --- /dev/null +++ b/logs/xuanjinovo_20250930_052157.log @@ -0,0 +1,577 @@ +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 5248 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 2232 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 4746 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 1735 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 8131 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 3047 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 2641 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 4999 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 +2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:22:16 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 05:22:50 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 05:22:55 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_052158.log b/logs/xuanjinovo_20250930_052158.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052159.log b/logs/xuanjinovo_20250930_052159.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052318.log b/logs/xuanjinovo_20250930_052318.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052319.log b/logs/xuanjinovo_20250930_052319.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052326.log b/logs/xuanjinovo_20250930_052326.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052625.log b/logs/xuanjinovo_20250930_052625.log new file mode 100644 index 0000000..573907a --- /dev/null +++ b/logs/xuanjinovo_20250930_052625.log @@ -0,0 +1,518 @@ +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 1510 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 3242 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 1208 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 8890 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 4285 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 2273 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 3120 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_052626.log b/logs/xuanjinovo_20250930_052626.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052628.log b/logs/xuanjinovo_20250930_052628.log new file mode 100644 index 0000000..f3eec52 --- /dev/null +++ b/logs/xuanjinovo_20250930_052628.log @@ -0,0 +1,74 @@ +2025-09-30 05:26:28 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:28 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:28 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052628 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - random_seed = 601 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:28 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:28 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:29 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:29 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:29 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_052629.log b/logs/xuanjinovo_20250930_052629.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052657.log b/logs/xuanjinovo_20250930_052657.log new file mode 100644 index 0000000..2868f6f --- /dev/null +++ b/logs/xuanjinovo_20250930_052657.log @@ -0,0 +1,157 @@ +2025-09-30 05:26:57 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:57 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] +2025-09-30 05:26:58 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:58 [INFO] XuanjiNovo - Number of available GPUs: 2 +2025-09-30 05:26:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052657 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - random_seed = 2088 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052657 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - random_seed = 5178 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_batch_size = 1400 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:26:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_workers = 96 +2025-09-30 05:26:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:26:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:26:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:26:59 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:59 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:26:59 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:26:59 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:27:04 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 05:27:33 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 05:27:54 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 38. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_052658.log b/logs/xuanjinovo_20250930_052658.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052806.log b/logs/xuanjinovo_20250930_052806.log new file mode 100644 index 0000000..fe8659a --- /dev/null +++ b/logs/xuanjinovo_20250930_052806.log @@ -0,0 +1,305 @@ +2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 6193 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 6107 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 7726 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 4215 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 05:28:19 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 05:28:48 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 05:29:01 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 19. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_052807.log b/logs/xuanjinovo_20250930_052807.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_052808.log b/logs/xuanjinovo_20250930_052808.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_104735.log b/logs/xuanjinovo_20250930_104735.log new file mode 100644 index 0000000..6489bd6 --- /dev/null +++ b/logs/xuanjinovo_20250930_104735.log @@ -0,0 +1,305 @@ +2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 9700 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 2425 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 +2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 8714 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 3519 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. +2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 10:47:48 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-09-30 10:48:21 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-09-30 10:48:29 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 19. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + diff --git a/logs/xuanjinovo_20250930_104736.log b/logs/xuanjinovo_20250930_104736.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_110305.log b/logs/xuanjinovo_20250930_110305.log new file mode 100644 index 0000000..66b7adc --- /dev/null +++ b/logs/xuanjinovo_20250930_110305.log @@ -0,0 +1,296 @@ +2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 865 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 5242 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 4410 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 7393 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_110306.log b/logs/xuanjinovo_20250930_110306.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_110307.log b/logs/xuanjinovo_20250930_110307.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_110706.log b/logs/xuanjinovo_20250930_110706.log new file mode 100644 index 0000000..65039f3 --- /dev/null +++ b/logs/xuanjinovo_20250930_110706.log @@ -0,0 +1,304 @@ +2025-09-30 11:07:06 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:07:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:07:06 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:07:06 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:07:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:07:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:07:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:07:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 726 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 5094 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 6938 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 2828 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_110707.log b/logs/xuanjinovo_20250930_110707.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_111048.log b/logs/xuanjinovo_20250930_111048.log new file mode 100644 index 0000000..476007d --- /dev/null +++ b/logs/xuanjinovo_20250930_111048.log @@ -0,0 +1,304 @@ +2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 6113 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 7307 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 6720 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 2785 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt +2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json +2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_111049.log b/logs/xuanjinovo_20250930_111049.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_111520.log b/logs/xuanjinovo_20250930_111520.log new file mode 100644 index 0000000..59fbb82 --- /dev/null +++ b/logs/xuanjinovo_20250930_111520.log @@ -0,0 +1,308 @@ +2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 6395 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 2242 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 9835 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 5364 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_111521.log b/logs/xuanjinovo_20250930_111521.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_111522.log b/logs/xuanjinovo_20250930_111522.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_112055.log b/logs/xuanjinovo_20250930_112055.log new file mode 100644 index 0000000..7eea681 --- /dev/null +++ b/logs/xuanjinovo_20250930_112055.log @@ -0,0 +1,308 @@ +2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 7463 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 2296 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 9371 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 4497 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_112056.log b/logs/xuanjinovo_20250930_112056.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_112408.log b/logs/xuanjinovo_20250930_112408.log new file mode 100644 index 0000000..6c08d73 --- /dev/null +++ b/logs/xuanjinovo_20250930_112408.log @@ -0,0 +1,308 @@ +2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 6446 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 7287 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 4149 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 7087 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:24:10 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:24:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:24:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:24:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_112409.log b/logs/xuanjinovo_20250930_112409.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_112410.log b/logs/xuanjinovo_20250930_112410.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_113215.log b/logs/xuanjinovo_20250930_113215.log new file mode 100644 index 0000000..1f4f9c0 --- /dev/null +++ b/logs/xuanjinovo_20250930_113215.log @@ -0,0 +1,308 @@ +2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 11:32:15 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:32:15 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:32:15 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:32:16 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 2335 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 200 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 1968 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 6930 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_113216.log b/logs/xuanjinovo_20250930_113216.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_120240.log b/logs/xuanjinovo_20250930_120240.log new file mode 100644 index 0000000..1c970cd --- /dev/null +++ b/logs/xuanjinovo_20250930_120240.log @@ -0,0 +1,284 @@ +2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 4397 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 5492 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 1921 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 6134 +2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf +2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf diff --git a/logs/xuanjinovo_20250930_120241.log b/logs/xuanjinovo_20250930_120241.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_120307.log b/logs/xuanjinovo_20250930_120307.log new file mode 100644 index 0000000..8917b68 --- /dev/null +++ b/logs/xuanjinovo_20250930_120307.log @@ -0,0 +1,312 @@ +2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 4540 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 9648 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 541 +2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 4913 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_120308.log b/logs/xuanjinovo_20250930_120308.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_121032.log b/logs/xuanjinovo_20250930_121032.log new file mode 100644 index 0000000..9849a44 --- /dev/null +++ b/logs/xuanjinovo_20250930_121032.log @@ -0,0 +1,312 @@ +2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 3206 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 3555 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 933 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 6683 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121033.log b/logs/xuanjinovo_20250930_121033.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_121309.log b/logs/xuanjinovo_20250930_121309.log new file mode 100644 index 0000000..b4e743a --- /dev/null +++ b/logs/xuanjinovo_20250930_121309.log @@ -0,0 +1,312 @@ +2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 3020 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 6016 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 1671 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 2610 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:13:15 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:13:15 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:13:15 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:13:15 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:13:15 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:13:15 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:13:16 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:13:16 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121310.log b/logs/xuanjinovo_20250930_121310.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_121504.log b/logs/xuanjinovo_20250930_121504.log new file mode 100644 index 0000000..8d779aa --- /dev/null +++ b/logs/xuanjinovo_20250930_121504.log @@ -0,0 +1,312 @@ +2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 901 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 2679 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 8299 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 1184 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121505.log b/logs/xuanjinovo_20250930_121505.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_121925.log b/logs/xuanjinovo_20250930_121925.log new file mode 100644 index 0000000..5b81414 --- /dev/null +++ b/logs/xuanjinovo_20250930_121925.log @@ -0,0 +1,312 @@ +2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 2789 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 5962 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 7193 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 1896 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121926.log b/logs/xuanjinovo_20250930_121926.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_122127.log b/logs/xuanjinovo_20250930_122127.log new file mode 100644 index 0000000..9852162 --- /dev/null +++ b/logs/xuanjinovo_20250930_122127.log @@ -0,0 +1,312 @@ +2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 7420 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 3470 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 2576 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 982 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_122128.log b/logs/xuanjinovo_20250930_122128.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_122402.log b/logs/xuanjinovo_20250930_122402.log new file mode 100644 index 0000000..561397e --- /dev/null +++ b/logs/xuanjinovo_20250930_122402.log @@ -0,0 +1,312 @@ +2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 1254 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 5015 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 6444 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 9607 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_122403.log b/logs/xuanjinovo_20250930_122403.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_123732.log b/logs/xuanjinovo_20250930_123732.log new file mode 100644 index 0000000..4703af1 --- /dev/null +++ b/logs/xuanjinovo_20250930_123732.log @@ -0,0 +1,312 @@ +2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 763 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 6687 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 7034 +2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 8661 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_123733.log b/logs/xuanjinovo_20250930_123733.log new file mode 100644 index 0000000..e69de29 diff --git a/logs/xuanjinovo_20250930_124220.log b/logs/xuanjinovo_20250930_124220.log new file mode 100644 index 0000000..6a7e7cb --- /dev/null +++ b/logs/xuanjinovo_20250930_124220.log @@ -0,0 +1,312 @@ +2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 9906 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 2653 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 6821 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 625 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 +2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] +2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 +2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-09-30 12:42:26 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:26 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:42:27 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:42:27 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt +2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json +2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_124221.log b/logs/xuanjinovo_20250930_124221.log new file mode 100644 index 0000000..e69de29 diff --git a/output.txt b/output.txt deleted file mode 100644 index ba28a4a..0000000 --- a/output.txt +++ /dev/null @@ -1,2733 +0,0 @@ -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -notice here: 5 -notice here: 5 -peak file iss: ./bacillus.10k.mgf -is peak not index?, True -prepare_data ing..... -what lock is this: True -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 - Validation: 0it [00:00, ?it/s]what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -what lock is this: False -model size is : 32478860 -each data size: 9804 -each data size: 9804 -each data size: 9804 -each data size: 9804 -each data size: 9804 -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -each data size: 9804 -each data size: 9804 -each data size: 9804 -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -each data size: 9804 -each data size: 9804 -each data size: 9804 -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -each data size: 9804 -each data size: 9804 -each data size: 9804 -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -I am CUDA program -each data size: 9804 -each data size: 9804 -each data size: 9804 - Validation: 0%| | 0/20 [00:00=3.6.0 +black>=24.1.1 +isort>=5.13.2 +flake8>=7.0.0 +flake8-docstrings>=1.7.0 +flake8-bugbear>=24.1.17 +flake8-comprehensions>=3.14.0 +mypy>=1.8.0 +types-all>=1.0.0 diff --git a/requirements.txt b/requirements.txt index b2baf67..e08a8ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ absl-py==1.3.0 +pydantic>=2.5.0 aiohttp==3.8.3 aiosignal==1.3.1 appdirs==1.4.4 @@ -47,8 +48,6 @@ gitdb==4.0.10 GitPython==3.1.30 google-auth==2.6.0 google-auth-oauthlib==0.4.4 -grpcio==1.42.0 -h5py==3.8.0 idna==3.4 ipykernel==6.19.2 ipython==8.7.0 @@ -64,8 +63,6 @@ jupyter_core==5.1.1 kiwisolver==1.4.4 lark==1.1.5 lightning-utilities==0.5.0 -llvmlite==0.39.1 -lxml==4.9.2 Markdown==3.4.1 MarkupSafe==2.1.1 matchms==0.18.0 @@ -75,8 +72,8 @@ multidict==6.0.4 neptune-client==0.16.16 nest-asyncio==1.5.6 networkx==3.1 -numba==0.56.4 -numpy==1.23.5 +numba +numpy oauthlib==3.2.1 packaging==22.0 pandas==1.5.2 @@ -84,8 +81,8 @@ parso==0.8.3 pexpect==4.8.0 pickleshare==0.7.5 pickydict==0.4.0 -Pillow==9.3.0 -pip==22.3.1 +#Pillow==9.3.0 +#pip==22.3.1 platformdirs==2.5.2 prompt-toolkit==3.0.36 protobuf==3.19.6 @@ -154,3 +151,4 @@ wurlitzer==3.0.3 yapf==0.32.0 yarl==1.8.2 lmdb +ninja diff --git a/requirements_all.txt b/requirements_all.txt new file mode 100644 index 0000000..53cb143 --- /dev/null +++ b/requirements_all.txt @@ -0,0 +1,8 @@ + +-r requirements.txt + +# GitHub repo (ctcdecode) +git+https://github.com/WayenVan/ctcdecode.git@master#egg=ctcdecode + +# Local repo (editable install) +-e ./imputer-pytorch \ No newline at end of file diff --git a/run_eval.sh b/run_eval.sh new file mode 100644 index 0000000..96079a3 --- /dev/null +++ b/run_eval.sh @@ -0,0 +1,41 @@ +species_list=( + "test_DDA_18species_Acinetobacter_calcoaceticus" + "test_DDA_18species_Aedes_aegypti" + "test_DDA_18species_Arabidopsis_thaliana" + "test_DDA_18species_Aspergillus_fumigatus" + "test_DDA_18species_Bacteroides_thetaiotaomicron" + "test_DDA_18species_Caenorhabditis_elegans" + "test_DDA_18species_Drosophila_melanogaster" + "test_DDA_18species_Escherichia_coli" + "test_DDA_18species_Helicobacter_pylori" + "test_DDA_18species_Homo_sapiens" + "test_DDA_18species_Marchantia_polymorpha" + "test_DDA_18species_Mus_musculus" + "test_DDA_18species_Pyrococcus_furiosus" + "test_DDA_18species_Saccharomyces_cerevisiae" + "test_DDA_18species_Toxoplasma_gondii" + "test_DDA_18species_Trypanosoma_brucei" + "test_DDA_18species_Zea_mays" + "test_DDA_18species_Solanum_lycopersicum" +) +XuanjiNovo_100M=/mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +GlanceNovo_30M=/mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/NON944/epoch=23-step=20000.ckpt +test_path_l=/mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 +bacillus=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +# for species in "${species_list[@]}"; do +# export SPECIES_NAME="$species" +# full_path="$base_dir/$species" +# echo "Running evaluation on $full_path" +# torchrun --nproc_per_node=8 -m XuanjiNovo.XuanjiNovo --config=./inf3.yaml \ +# --mode=eval \ +# --model=$XuanjiNovo_100M \ +# --peak_path=$test_path_l +# done + +MASTER_PORT=${MASTER_PORT:-$(( ( RANDOM % 10000 ) + 20000 ))} +echo "Using MASTER_PORT=$MASTER_PORT" + +torchrun --nproc_per_node=4 --master_port=${MASTER_PORT} -m XuanjiNovo.XuanjiNovo \ + --mode=denovo \ + --model=$XuanjiNovo_100M \ + --peak_path=$bacillus --gpu=0,1,2,3 \ No newline at end of file diff --git a/saved_models/run_metadata/run_metadata.json b/saved_models/run_metadata/run_metadata.json new file mode 100644 index 0000000..3f6264c --- /dev/null +++ b/saved_models/run_metadata/run_metadata.json @@ -0,0 +1,340 @@ +{ + "config": { + "PMC_enable": true, + "SWA": false, + "accumulate_grad_batches": 1, + "custom_encoder": null, + "dim_feedforward": 1024, + "dim_intensity": null, + "dim_model": 400, + "dropout": 0.18, + "enable_neptune": true, + "every_n_train_steps": 2500, + "gradient_clip_algorithm": "norm", + "gradient_clip_val": 2.5, + "isotope_error_range": [ + 0, + 1 + ], + "learning_rate": 0.00035, + "load_file_name": "./NON351/epoch=5-step=65000.ckpt", + "log_level": "CRITICAL", + "logger": null, + "mask_schedule": { + "epoch_decay": 0.01, + "initial_peek": 0.93, + "min_peek": 0.0 + }, + "mass_control_tol": 0.1, + "max_charge": 10, + "max_epochs": 150, + "max_iters": null, + "max_length": 40, + "max_mz": 6500.0, + "min_intensity": 0.0, + "min_mz": 1.0, + "model_save_folder_path": "./saved_models", + "n_beams": 5, + "n_head": 8, + "n_layers": 9, + "n_log": 1, + "n_nodes": 1, + "n_peaks": 800, + "n_workers": 48, + "neptune_api_token": null, + "neptune_project": null, + "num_sanity_val_steps": 0, + "precursor_mass_tol": 50.0, + "predict_batch_size": 64, + "random_seed": 9906, + "refine_iters": 3, + "remove_precursor_tol": 1.0, + "residues": { + "+42.011": 42.010565, + "+43.006": 43.005814, + "+43.006-17.027": 25.980265, + "-17.027": -17.026549, + "A": 71.037114, + "C+57.021": 160.030649, + "D": 115.026943, + "E": 129.042593, + "F": 147.068414, + "G": 57.021464, + "H": 137.058912, + "I": 113.084064, + "K": 128.094963, + "L": 113.084064, + "M": 131.040485, + "M+15.995": 147.0354, + "N": 114.042927, + "N+0.984": 115.026943, + "P": 97.052764, + "Q": 128.058578, + "Q+0.984": 129.042594, + "R": 156.101111, + "S": 87.032028, + "T": 101.04767, + "V": 99.068414, + "W": 186.079313, + "Y": 163.063329 + }, + "save_model": false, + "save_weights_only": false, + "sync_batchnorm": false, + "tags": [], + "tb_summarywriter": null, + "temp_dir_auto": true, + "train_batch_size": 700, + "train_from_resume": false, + "train_from_scratch": false, + "warm_up_epochs": 1, + "warmup_iters": null, + "weight_decay": 8e-05 + }, + "git_info": { + "git_branch": "main", + "git_commit": "07a5329977fcacf2617354f689fd276e73ab8406", + "git_dirty": true, + "git_remote_url": "https://github.com/guomics-lab/MassNet-DDA.git" + }, + "hardware_info": { + "cpu": { + "max_frequency": "4000.00MHz", + "physical_cores": 96, + "processor": "x86_64", + "total_cores": 1 + }, + "gpus": [ + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + } + ], + "memory": { + "available": "289.11 GB", + "total": "292.97 GB" + } + }, + "model_name": "epoch=695-step=324000.ckpt", + "software_info": { + "cuda_version": "12.1", + "cudnn_version": 8902, + "installed_packages": { + "absl-py": "1.3.0", + "aiohttp": "3.8.3", + "aiosignal": "1.3.1", + "appdirs": "1.4.4", + "arrow": "1.2.3", + "asttokens": "2.0.5", + "async-timeout": "4.0.2", + "attrs": "22.2.0", + "backcall": "0.2.0", + "biopython": "1.81", + "blinker": "1.4", + "boto3": "1.26.53", + "botocore": "1.29.53", + "bravado": "11.0.3", + "bravado-core": "5.17.1", + "brotlipy": "0.7.0", + "cachetools": "4.2.2", + "captum": "0.8.0", + "casanovo": "3.2.0", + "certifi": "2022.12.7", + "cffi": "1.15.1", + "charset-normalizer": "2.0.4", + "click": "8.1.3", + "comet-ml": "3.31.22", + "comm": "0.1.2", + "configobj": "5.0.6", + "contourpy": "1.0.6", + "cryptography": "38.0.1", + "ctcdecode": "1.0.3", + "cupy-cuda12x": "13.6.0", + "cycler": "0.11.0", + "debugpy": "1.5.1", + "decorator": "5.1.1", + "deprecated": "1.2.13", + "depthcharge-ms": "0.1.0", + "dulwich": "0.20.50", + "einops": "0.6.0", + "entrypoints": "0.4", + "everett": "3.1.0", + "executing": "0.8.3", + "fastobo": "0.12.2", + "fastrlock": "0.8.3", + "filelock": "3.19.1", + "flit-core": "3.6.0", + "fonttools": "4.38.0", + "fqdn": "1.5.1", + "frozenlist": "1.3.3", + "fsspec": "2022.11.0", + "future": "0.18.3", + "gitdb": "4.0.10", + "gitpython": "3.1.30", + "google-auth": "2.6.0", + "google-auth-oauthlib": "0.4.4", + "grpcio": "1.74.0", + "h5py": "3.8.0", + "idna": "3.4", + "ipykernel": "6.19.2", + "ipython": "8.7.0", + "isoduration": "20.11.0", + "jedi": "0.18.1", + "jinja2": "3.1.6", + "jmespath": "1.0.1", + "joblib": "1.2.0", + "jsonpointer": "2.3", + "jsonref": "1.1.0", + "jsonschema": "4.17.3", + "jupyter-client": "7.4.8", + "jupyter-core": "5.1.1", + "kiwisolver": "1.4.4", + "lark": "1.1.5", + "lightning-utilities": "0.5.0", + "llvmlite": "0.45.0", + "lmdb": "1.7.3", + "lxml": "6.0.2", + "markdown": "3.4.1", + "markupsafe": "2.1.1", + "matchms": "0.18.0", + "matplotlib": "3.10.6", + "matplotlib-inline": "0.1.7", + "monotonic": "1.6", + "mpmath": "1.3.0", + "msgpack": "1.0.4", + "multidict": "6.0.4", + "neptune-client": "0.16.16", + "nest-asyncio": "1.5.6", + "networkx": "3.1", + "ninja": "1.13.0", + "numba": "0.62.0", + "numpy": "1.26.4", + "nvidia-cublas-cu12": "12.1.3.1", + "nvidia-cuda-cupti-cu12": "12.1.105", + "nvidia-cuda-nvrtc-cu12": "12.1.105", + "nvidia-cuda-runtime-cu12": "12.1.105", + "nvidia-cudnn-cu12": "8.9.2.26", + "nvidia-cufft-cu12": "11.0.2.54", + "nvidia-curand-cu12": "10.3.2.106", + "nvidia-cusolver-cu12": "11.4.5.107", + "nvidia-cusparse-cu12": "12.1.0.106", + "nvidia-nccl-cu12": "2.18.1", + "nvidia-nvjitlink-cu12": "12.9.86", + "nvidia-nvtx-cu12": "12.1.105", + "oauthlib": "3.2.1", + "packaging": "22.0", + "pandas": "1.5.2", + "parso": "0.8.3", + "pexpect": "4.8.0", + "pickleshare": "0.7.5", + "pickydict": "0.4.0", + "pillow": "11.3.0", + "pip": "25.2", + "platformdirs": "2.5.2", + "prompt-toolkit": "3.0.36", + "protobuf": "3.19.6", + "psutil": "5.9.4", + "ptyprocess": "0.7.0", + "pure-eval": "0.2.2", + "pyasn1": "0.4.8", + "pyasn1-modules": "0.2.8", + "pycparser": "2.21", + "pygithub": "1.57", + "pygments": "2.11.2", + "pyjwt": "2.6.0", + "pynacl": "1.5.0", + "pyopenssl": "22.0.0", + "pyparsing": "3.0.9", + "pyrsistent": "0.19.3", + "pysocks": "1.7.1", + "pyteomics": "4.5.6", + "python-dateutil": "2.8.2", + "pytorch-lightning": "1.8.6", + "pytorch-ranger": "0.1.1", + "pytz": "2022.7", + "pyyaml": "6.0", + "pyzmq": "23.2.0", + "requests": "2.28.1", + "requests-oauthlib": "1.3.0", + "requests-toolbelt": "0.10.1", + "rfc3339-validator": "0.1.4", + "rfc3987": "1.3.8", + "rsa": "4.7.2", + "s3transfer": "0.6.0", + "scikit-learn": "1.2.0", + "scipy": "1.10.0", + "seaborn": "0.12.2", + "semantic-version": "2.10.0", + "sentry-sdk": "1.12.1", + "setuptools": "65.5.0", + "simplejson": "3.18.1", + "six": "1.16.0", + "smmap": "5.0.0", + "sparsestack": "0.4.1", + "spectrum-utils": "0.4.1", + "stack-data": "0.2.0", + "swagger-spec-validator": "3.0.3", + "sympy": "1.14.0", + "tensorboard": "2.10.0", + "tensorboard-data-server": "0.6.1", + "tensorboard-plugin-wit": "1.8.1", + "tensorboardx": "2.5.1", + "threadpoolctl": "3.1.0", + "torch": "2.1.0", + "torch-imputer": "0.1.0", + "torch-optimizer": "0.3.0", + "torchmetrics": "0.11.0", + "tornado": "6.2", + "tqdm": "4.64.1", + "traitlets": "5.7.1", + "triton": "2.1.0", + "typing-extensions": "4.4.0", + "uri-template": "1.2.0", + "urllib3": "1.26.13", + "wcwidth": "0.2.5", + "webcolors": "1.12", + "websocket-client": "1.3.3", + "werkzeug": "2.2.2", + "wheel": "0.37.1", + "wrapt": "1.14.1", + "wurlitzer": "3.0.3", + "yapf": "0.32.0", + "yarl": "1.8.2" + }, + "python": { + "executable": "/home/weijiaqi/miniconda3/envs/XuanjiNovo/bin/python3.11", + "platform": "Linux-5.15.0-89-generic-x86_64-with-glibc2.35", + "version": "3.11.13 (main, Jun 5 2025, 13:12:00) [GCC 11.2.0]" + }, + "pytorch_version": "2.1.0+cu121" + }, + "timestamp": "2025-09-30T12:42:27.044401" +} \ No newline at end of file diff --git a/saved_models/run_metadata/run_summary.txt b/saved_models/run_metadata/run_summary.txt new file mode 100644 index 0000000..dacebb5 --- /dev/null +++ b/saved_models/run_metadata/run_summary.txt @@ -0,0 +1,7 @@ +timestamp: 2025-09-30T12:42:27.044401 +git_commit: 07a5329977fcacf2617354f689fd276e73ab8406 +python_version: 3.11.13 +pytorch_version: 2.1.0+cu121 +gpu_count: 4 +gpu_names: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +cuda_version: 12.1 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..224fa07 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,33 @@ +import pytest +import torch + +@pytest.fixture(autouse=True) +def cuda_init(): + """Initialize CUDA for tests if available""" + if torch.cuda.is_available(): + torch.cuda.init() + yield + if torch.cuda.is_available(): + torch.cuda.empty_cache() + +@pytest.fixture +def dummy_spectrum_batch(): + """Create a dummy spectrum batch for testing""" + batch_size = 2 + seq_len = 10 + feature_dim = 256 + return { + 'peaks': torch.randn(batch_size, seq_len, feature_dim), + 'precursors': torch.randn(batch_size, 2), # m/z and charge + 'mask': torch.ones(batch_size, seq_len, dtype=torch.bool) + } + +@pytest.fixture +def dummy_peptide_batch(): + """Create a dummy peptide batch for testing""" + batch_size = 2 + seq_len = 10 + return { + 'sequence': torch.randint(0, 26, (batch_size, seq_len)), + 'mask': torch.ones(batch_size, seq_len, dtype=torch.bool) + } diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..a897644 --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,92 @@ +import pytest +import torch +from XuanjiNovo.denovo.model import Spec2Pep +from XuanjiNovo.components.transformers import SpectrumEncoder, PeptideEncoder, PeptideDecoder + +def test_model_initialization(): + """Test basic model initialization with default parameters""" + model = Spec2Pep( + vocab_size=26, + hidden_dim=256, + n_head=4, + dim_feedforward=1024, + n_layers=3, + dropout=0.1, + max_length=50, + refine_iters=3, + mask_schedule={ + "initial_peek": 0.93, + "epoch_decay": 0.01, + "min_peek": 0.00 + } + ) + assert isinstance(model, Spec2Pep) + assert model.hparams.vocab_size == 26 + assert model.hparams.refine_iters == 3 + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_model_cuda(): + """Test model CUDA compatibility""" + model = Spec2Pep( + vocab_size=26, + hidden_dim=256, + n_head=4, + dim_feedforward=1024, + n_layers=3 + ) + model = model.cuda() + assert next(model.parameters()).is_cuda + +def test_spectrum_encoder(): + """Test spectrum encoder with dummy input""" + encoder = SpectrumEncoder( + d_model=256, + n_head=4, + dim_feedforward=1024, + n_layers=3, + dropout=0.1 + ) + batch_size = 2 + seq_len = 10 + feature_dim = 256 + x = torch.randn(batch_size, seq_len, feature_dim) + mask = torch.ones(batch_size, seq_len, dtype=torch.bool) + output = encoder(x, mask) + assert output.shape == (batch_size, seq_len, feature_dim) + +def test_peptide_decoder(): + """Test peptide decoder with dummy input""" + decoder = PeptideDecoder( + vocab_size=26, + d_model=256, + n_head=4, + dim_feedforward=1024, + n_layers=3, + dropout=0.1 + ) + batch_size = 2 + seq_len = 10 + feature_dim = 256 + tgt = torch.randint(0, 26, (batch_size, seq_len)) + memory = torch.randn(batch_size, seq_len, feature_dim) + tgt_mask = torch.ones(batch_size, seq_len, dtype=torch.bool) + memory_mask = torch.ones(batch_size, seq_len, dtype=torch.bool) + output, _, _ = decoder(tgt, None, memory, tgt_mask, memory_mask) + assert output.shape == (batch_size, seq_len, 26) + +def test_config_parameters(): + """Test model configuration parameter handling""" + model = Spec2Pep( + vocab_size=26, + hidden_dim=256, + refine_iters=5, + mask_schedule={ + "initial_peek": 0.95, + "epoch_decay": 0.02, + "min_peek": 0.10 + } + ) + assert model.hparams.refine_iters == 5 + assert model.hparams.mask_schedule["initial_peek"] == 0.95 + assert model.hparams.mask_schedule["epoch_decay"] == 0.02 + assert model.hparams.mask_schedule["min_peek"] == 0.10 From ba531442031d7d63aaee3fe59bfd475b2e94cc42 Mon Sep 17 00:00:00 2001 From: niezongxiang Date: Tue, 30 Sep 2025 16:24:10 +0800 Subject: [PATCH 2/9] update output dir --- XuanjiNovo/XuanjiNovo.py | 5 +++++ XuanjiNovo/denovo/model.py | 4 +++- XuanjiNovo/denovo/model_runner.py | 4 +++- 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/XuanjiNovo/XuanjiNovo.py b/XuanjiNovo/XuanjiNovo.py index ea07947..4885a3c 100644 --- a/XuanjiNovo/XuanjiNovo.py +++ b/XuanjiNovo/XuanjiNovo.py @@ -141,6 +141,7 @@ def main( else: output = os.path.splitext(os.path.abspath(output))[0] + os.makedirs(output, exist_ok=True) # Configure logging. logging.captureWarnings(True) root = logging.getLogger() @@ -275,6 +276,10 @@ def main( import random if(config["random_seed"]==-1): config["random_seed"]=random.randint(1, 9999) + + if not 'result_output_dir' in config: + config["result_output_dir"] = output + LightningLite.seed_everything(seed=config["random_seed"], workers=True) # Log the active configuration. diff --git a/XuanjiNovo/denovo/model.py b/XuanjiNovo/denovo/model.py index e1bfd0f..a679308 100644 --- a/XuanjiNovo/denovo/model.py +++ b/XuanjiNovo/denovo/model.py @@ -216,6 +216,7 @@ def __init__( "epoch_decay": 0.01, "min_peek": 0.00 }, + result_output_dir: str = None, **kwargs: Dict, ): super().__init__() @@ -303,6 +304,7 @@ def __init__( # Output writer during predicting. self.out_writer = out_writer + self.result_output_dir = result_output_dir def forward( @@ -710,7 +712,7 @@ def predict_step( peptides , inferscores = self.forward(batch[0], batch[1], batch[2]) import os - file_path = "./denovo.tsv" + file_path = os.path.join(self.result_output_dir, "denovo.tsv") headers = "label\tprediction\tcharge\tscore\n" # Check if the file exists and whether it contains headers diff --git a/XuanjiNovo/denovo/model_runner.py b/XuanjiNovo/denovo/model_runner.py index 1e4a132..4d4935e 100644 --- a/XuanjiNovo/denovo/model_runner.py +++ b/XuanjiNovo/denovo/model_runner.py @@ -136,6 +136,7 @@ def _execute_existing( "epoch_decay": 0.01, "min_peek": 0.00 }), + result_output_dir=config.get('result_output_dir') ) if annotated: @@ -396,7 +397,8 @@ def __init__(self, sdir=None): "initial_peek": 0.93, "epoch_decay": 0.01, "min_peek": 0.00 - }) + }), + result_output_dir=config.get('result_output_dir') ) if config["train_from_scratch"]: From c11fb48c76be33aed8b0e6c9dad2d91ed74dfec6 Mon Sep 17 00:00:00 2001 From: niezongxiang Date: Tue, 30 Sep 2025 16:27:36 +0800 Subject: [PATCH 3/9] add docker file --- Dockerfile_cuda11 | 19 ++++ Dockerfile_cuda12 | 19 ++++ requirements-docker-cuda11.txt | 156 +++++++++++++++++++++++++++++++++ requirements-docker-cuda12.txt | 156 +++++++++++++++++++++++++++++++++ 4 files changed, 350 insertions(+) create mode 100644 Dockerfile_cuda11 create mode 100644 Dockerfile_cuda12 create mode 100644 requirements-docker-cuda11.txt create mode 100644 requirements-docker-cuda12.txt diff --git a/Dockerfile_cuda11 b/Dockerfile_cuda11 new file mode 100644 index 0000000..4aec2e8 --- /dev/null +++ b/Dockerfile_cuda11 @@ -0,0 +1,19 @@ +FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime + +ENV CUDA_HOME=/usr/local/cuda +ENV PATH=$CUDA_HOME/bin:$PATH +ENV LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +ENV GIT_PYTHON_REFRESH=quiet + +WORKDIR /app + +COPY . /app/ + +RUN apt update && apt install -y gcc g++ ninja-build + +RUN cd /app/ctcdecode-master && pip install . + +RUN pip install -r /app/requirements-docker-cuda11.txt -i https://mirrors.aliyun.com/pypi/simple/ +RUN cd /app/imputer-pytorch && pip install -e . + +ENTRYPOINT ["python", "-m", "XuanjiNovo.XuanjiNovo"] \ No newline at end of file diff --git a/Dockerfile_cuda12 b/Dockerfile_cuda12 new file mode 100644 index 0000000..f13ba3f --- /dev/null +++ b/Dockerfile_cuda12 @@ -0,0 +1,19 @@ +FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime + +ENV CUDA_HOME=/usr/local/cuda +ENV PATH=$CUDA_HOME/bin:$PATH +ENV LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH +ENV GIT_PYTHON_REFRESH=quiet + +WORKDIR /app + +COPY . /app/ + +RUN apt update && apt install -y gcc g++ ninja-build + +RUN cd /app/ctcdecode-master && pip install . + +RUN pip install -r /app/requirements-docker-cuda12.txt -i https://mirrors.aliyun.com/pypi/simple/ +RUN cd /app/imputer-pytorch && pip install -e . + +ENTRYPOINT ["python", "-m", "XuanjiNovo.XuanjiNovo"] \ No newline at end of file diff --git a/requirements-docker-cuda11.txt b/requirements-docker-cuda11.txt new file mode 100644 index 0000000..79685f4 --- /dev/null +++ b/requirements-docker-cuda11.txt @@ -0,0 +1,156 @@ +absl-py==1.3.0 +aiohttp==3.8.3 +aiosignal==1.3.1 +appdirs==1.4.4 +arrow==1.2.3 +asttokens==2.0.5 +async-timeout==4.0.2 +attrs==22.2.0 +backcall==0.2.0 +biopython==1.81 +blinker==1.4 +boto3==1.26.53 +botocore==1.29.53 +bravado==11.0.3 +bravado-core==5.17.1 +brotlipy==0.7.0 +cachetools==4.2.2 +captum==0.8.0 +casanovo==3.2.0 +certifi==2022.12.7 +cffi==1.15.1 +charset-normalizer==2.0.4 +click==8.1.3 +comet-ml==3.31.22 +comm==0.1.2 +configobj==5.0.6 +contourpy==1.0.6 +cryptography==38.0.1 +cycler==0.11.0 +debugpy==1.5.1 +decorator==5.1.1 +Deprecated==1.2.13 +depthcharge-ms==0.1.0 +dulwich==0.20.50 +einops==0.6.0 +entrypoints==0.4 +everett==3.1.0 +executing==0.8.3 +fastobo==0.12.2 +flit_core==3.6.0 +fonttools==4.38.0 +fqdn==1.5.1 +frozenlist==1.3.3 +fsspec==2022.11.0 +future==0.18.3 +gitdb==4.0.10 +GitPython==3.1.30 +google-auth==2.6.0 +google-auth-oauthlib==0.4.4 +#grpcio==1.42.0 +h5py==3.8.0 +idna==3.4 +ipykernel==6.19.2 +ipython==8.7.0 +isoduration==20.11.0 +jedi==0.18.1 +jmespath==1.0.1 +joblib==1.2.0 +jsonpointer==2.3 +jsonref==1.1.0 +jsonschema==4.17.3 +jupyter_client==7.4.8 +jupyter_core==5.1.1 +kiwisolver==1.4.4 +lark==1.1.5 +lightning-utilities==0.5.0 +#llvmlite==0.39.1 +#lxml==4.9.2 +Markdown==3.4.1 +MarkupSafe==2.1.1 +matchms==0.18.0 +monotonic==1.6 +msgpack==1.0.4 +multidict==6.0.4 +neptune-client==0.16.16 +nest-asyncio==1.5.6 +networkx==3.1 +numba +numpy +oauthlib==3.2.1 +packaging==22.0 +pandas==1.5.2 +parso==0.8.3 +pexpect==4.8.0 +pickleshare==0.7.5 +pickydict==0.4.0 +#Pillow==9.3.0 +#pip==22.3.1 +platformdirs==2.5.2 +prompt-toolkit==3.0.36 +protobuf==3.19.6 +psutil==5.9.4 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.21 +PyGithub==1.57 +Pygments==2.11.2 +PyJWT==2.6.0 +PyNaCl==1.5.0 +pyOpenSSL==22.0.0 +pyparsing==3.0.9 +pyrsistent==0.19.3 +PySocks==1.7.1 +pyteomics==4.5.6 +python-dateutil==2.8.2 +pytorch-lightning==1.8.6 +pytorch-ranger==0.1.1 +pytz==2022.7 +PyYAML==6.0 +pyzmq==23.2.0 +requests==2.28.1 +requests-oauthlib==1.3.0 +requests-toolbelt==0.10.1 +rfc3339-validator==0.1.4 +rfc3987==1.3.8 +rsa==4.7.2 +s3transfer==0.6.0 +scikit-learn==1.2.0 +scipy==1.10.0 +seaborn==0.12.2 +semantic-version==2.10.0 +sentry-sdk==1.12.1 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+blinker==1.4 +boto3==1.26.53 +botocore==1.29.53 +bravado==11.0.3 +bravado-core==5.17.1 +brotlipy==0.7.0 +cachetools==4.2.2 +captum==0.8.0 +casanovo==3.2.0 +certifi==2022.12.7 +cffi==1.15.1 +charset-normalizer==2.0.4 +click==8.1.3 +comet-ml==3.31.22 +comm==0.1.2 +configobj==5.0.6 +contourpy==1.0.6 +cryptography==38.0.1 +cycler==0.11.0 +debugpy==1.5.1 +decorator==5.1.1 +Deprecated==1.2.13 +depthcharge-ms==0.1.0 +dulwich==0.20.50 +einops==0.6.0 +entrypoints==0.4 +everett==3.1.0 +executing==0.8.3 +fastobo==0.12.2 +flit_core==3.6.0 +fonttools==4.38.0 +fqdn==1.5.1 +frozenlist==1.3.3 +fsspec==2022.11.0 +future==0.18.3 +gitdb==4.0.10 +GitPython==3.1.30 +google-auth==2.6.0 +google-auth-oauthlib==0.4.4 +#grpcio==1.42.0 +h5py==3.8.0 +idna==3.4 +ipykernel==6.19.2 +ipython==8.7.0 +isoduration==20.11.0 +jedi==0.18.1 +jmespath==1.0.1 +joblib==1.2.0 +jsonpointer==2.3 +jsonref==1.1.0 +jsonschema==4.17.3 +jupyter_client==7.4.8 +jupyter_core==5.1.1 +kiwisolver==1.4.4 +lark==1.1.5 +lightning-utilities==0.5.0 +#llvmlite==0.39.1 +#lxml==4.9.2 +Markdown==3.4.1 +MarkupSafe==2.1.1 +matchms==0.18.0 +monotonic==1.6 +msgpack==1.0.4 +multidict==6.0.4 +neptune-client==0.16.16 +nest-asyncio==1.5.6 +networkx==3.1 +numba +numpy +oauthlib==3.2.1 +packaging==22.0 +pandas==1.5.2 +parso==0.8.3 +pexpect==4.8.0 +pickleshare==0.7.5 +pickydict==0.4.0 +#Pillow==9.3.0 +#pip==22.3.1 +platformdirs==2.5.2 +prompt-toolkit==3.0.36 +protobuf==3.19.6 +psutil==5.9.4 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.21 +PyGithub==1.57 +Pygments==2.11.2 +PyJWT==2.6.0 +PyNaCl==1.5.0 +pyOpenSSL==22.0.0 +pyparsing==3.0.9 +pyrsistent==0.19.3 +PySocks==1.7.1 +pyteomics==4.5.6 +python-dateutil==2.8.2 +pytorch-lightning==1.8.6 +pytorch-ranger==0.1.1 +pytz==2022.7 +PyYAML==6.0 +pyzmq==23.2.0 +requests==2.28.1 +requests-oauthlib==1.3.0 +requests-toolbelt==0.10.1 +rfc3339-validator==0.1.4 +rfc3987==1.3.8 +rsa==4.7.2 +s3transfer==0.6.0 +scikit-learn==1.2.0 +scipy==1.10.0 +seaborn==0.12.2 +semantic-version==2.10.0 +sentry-sdk==1.12.1 +setuptools==65.5.0 +simplejson==3.18.1 +six==1.16.0 +smmap==5.0.0 +sparsestack==0.4.1 +spectrum-utils==0.4.1 +stack-data==0.2.0 +swagger-spec-validator==3.0.3 +tensorboard==2.10.0 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 +tensorboardX==2.5.1 +threadpoolctl==3.1.0 +torch-optimizer==0.3.0 +torchmetrics==0.11.0 +tornado==6.2 +tqdm==4.64.1 +traitlets==5.7.1 +typing_extensions==4.4.0 +uri-template==1.2.0 +urllib3==1.26.13 +wcwidth==0.2.5 +webcolors==1.12 +websocket-client==1.3.3 +Werkzeug==2.2.2 +wheel==0.37.1 +wrapt==1.14.1 +wurlitzer==3.0.3 +yapf==0.32.0 +yarl==1.8.2 +lmdb +cupy-cuda12x \ No newline at end of file From 2049c570de0a04c06d5df78c886ffcc0f7beda2b Mon Sep 17 00:00:00 2001 From: niezongxiang Date: Tue, 30 Sep 2025 16:29:28 +0800 Subject: [PATCH 4/9] add dep ctcdecode-master --- ctcdecode-master/.gitignore | 139 + ctcdecode-master/.gitmodules | 6 + ctcdecode-master/.travis.yml | 30 + ctcdecode-master/LICENSE | 21 + ctcdecode-master/README.md | 124 + ctcdecode-master/ctcdecode/__init__.py | 272 + ctcdecode-master/ctcdecode/src/binding.cpp | 303 + ctcdecode-master/ctcdecode/src/binding.h | 55 + .../ctcdecode/src/ctc_beam_search_decoder.cpp | 317 + .../ctcdecode/src/ctc_beam_search_decoder.h | 134 + .../ctcdecode/src/decoder_utils.cpp | 193 + .../ctcdecode/src/decoder_utils.h | 102 + ctcdecode-master/ctcdecode/src/output.h | 11 + ctcdecode-master/ctcdecode/src/path_trie.cpp | 174 + ctcdecode-master/ctcdecode/src/path_trie.h | 70 + ctcdecode-master/ctcdecode/src/scorer.cpp | 230 + ctcdecode-master/ctcdecode/src/scorer.h | 112 + ctcdecode-master/requirements.txt | 1 + ctcdecode-master/setup.py | 143 + ctcdecode-master/tests/test.arpa | 124 + ctcdecode-master/tests/test_decode.py | 215 + .../third_party/ThreadPool/COPYING | 20 + .../third_party/ThreadPool/README.md | 17 + .../third_party/ThreadPool/ThreadPool.h | 98 + .../third_party/ThreadPool/example.cpp | 29 + 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+# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +ctcdecode/ctcdecode.xcodeproj/ diff --git a/ctcdecode-master/.gitmodules b/ctcdecode-master/.gitmodules new file mode 100644 index 0000000..8a98f78 --- /dev/null +++ b/ctcdecode-master/.gitmodules @@ -0,0 +1,6 @@ +[submodule "third_party/kenlm"] + path = third_party/kenlm + url = https://github.com/kpu/kenlm.git +[submodule "third_party/ThreadPool"] + path = third_party/ThreadPool + url = https://github.com/progschj/ThreadPool.git diff --git a/ctcdecode-master/.travis.yml b/ctcdecode-master/.travis.yml new file mode 100644 index 0000000..a801f37 --- /dev/null +++ b/ctcdecode-master/.travis.yml @@ -0,0 +1,30 @@ +dist: bionic +language: python +python: + - '3.5' + - '3.6' + - '3.7' + - '3.8' +install: + - pip install -r requirements.txt + - pip install . +script: + - pytest tests/ + +jobs: + include: + - stage: deploy + python: 3.8 + install: + - pip install -r requirements.txt + - pip install . + script: + - pytest tests/ + deploy: + provider: pypi + username: __token__ + password: + secure: 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 + on: + tags: true + skip_cleanup: true \ No newline at end of file diff --git a/ctcdecode-master/LICENSE b/ctcdecode-master/LICENSE new file mode 100644 index 0000000..3b4f84f --- /dev/null +++ b/ctcdecode-master/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Ryan Leary + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/ctcdecode-master/README.md b/ctcdecode-master/README.md new file mode 100644 index 0000000..b61004a --- /dev/null +++ b/ctcdecode-master/README.md @@ -0,0 +1,124 @@ +# ctcdecode + +ctcdecode is an implementation of CTC (Connectionist Temporal Classification) beam search decoding for PyTorch. +C++ code borrowed liberally from Paddle Paddles' [DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech). +It includes swappable scorer support enabling standard beam search, and KenLM-based decoding. If you are new to the concepts of CTC and Beam Search, please visit the Resources section where we link a few tutorials explaining why they are needed. + +## *Adapting for newer Pytorch and C++17 + +It seems like no one is maintaining the old repo [here](https://github.com/parlance/ctcdecode). I just want to fix some compatibility issue in third-party packages. It was successfully installed with Pytorch 2.1.0. I want share this for anyone who is struggling with the installation. If you met any other compatibility problem, feel free to raise issues! + +The modification is very simple, the comilation failed because dynamic exception specifications are not permitted since C++17. I just replace all `throw(xxx)` with `noexept(false)`. + +**Note that the function of this repo is not GUARANTEED, I don't have time to test them all, though I think it would work properly:)** + +## Installation +The library is largely self-contained and requires only PyTorch. +Building the C++ library requires gcc or clang. +KenLM language modeling support is also optionally included, and enabled by default. + +The below installation also works for Google Colab. + +```bash +# get the code +git clone --recursive https://github.com/WayenVan/ctcdecode.git +cd ctcdecode && pip install . +``` + +## How to Use + +```python +from ctcdecode import CTCBeamDecoder + +decoder = CTCBeamDecoder( + labels, + model_path=None, + alpha=0, + beta=0, + cutoff_top_n=40, + cutoff_prob=1.0, + beam_width=100, + num_processes=4, + blank_id=0, + log_probs_input=False +) +beam_results, beam_scores, timesteps, out_lens = decoder.decode(output) +``` + +### Inputs to `CTCBeamDecoder` + - `labels` are the tokens you used to train your model. They should be in the same order as your outputs. For example + if your tokens are the english letters and you used 0 as your blank token, then you would + pass in list("_abcdefghijklmopqrstuvwxyz") as your argument to labels + - `model_path` is the path to your external kenlm language model(LM). Default is none. + - `alpha` Weighting associated with the LMs probabilities. A weight of 0 means the LM has no effect. + - `beta` Weight associated with the number of words within our beam. + - `cutoff_top_n` Cutoff number in pruning. Only the top cutoff_top_n characters with the highest probability in the vocab will be used in beam search. + - `cutoff_prob` Cutoff probability in pruning. 1.0 means no pruning. + - `beam_width` This controls how broad the beam search is. Higher values are more likely to find top beams, but they also + will make your beam search exponentially slower. Furthermore, the longer your outputs, the more time large beams will take. + This is an important parameter that represents a tradeoff you need to make based on your dataset and needs. + - `num_processes` Parallelize the batch using num_processes workers. You probably want to pass the number of cpus your computer has. You can find this in python with `import multiprocessing` then `n_cpus = multiprocessing.cpu_count()`. Default 4. + - `blank_id` This should be the index of the CTC blank token (probably 0). + - `log_probs_input` If your outputs have passed through a softmax and represent probabilities, this should be false, if they passed through a LogSoftmax and represent negative log likelihood, you need to pass True. If you don't understand this, run `print(output[0][0].sum())`, if it's a negative number you've probably got NLL and need to pass True, if it sums to ~1.0 you should pass False. Default False. + +### Inputs to the `decode` method + - `output` should be the output activations from your model. If your output has passed through a SoftMax layer, you shouldn't need to alter it (except maybe to transpose), but if your `output` represents negative log likelihoods (raw logits), you either need to pass it through an additional `torch.nn.functional.softmax` or you can pass `log_probs_input=False` to the decoder. Your output should be BATCHSIZE x N_TIMESTEPS x N_LABELS so you may need to transpose it before passing it to the decoder. Note that if you pass things in the wrong order, the beam search will probably still run, you'll just get back nonsense results. + +### Outputs from the `decode` method + +4 things get returned from `decode` + 1. `beam_results` - Shape: BATCHSIZE x N_BEAMS X N_TIMESTEPS A batch containing the series of characters (these are ints, you still need to decode them back to your text) representing results from a given beam search. Note that the beams are almost always shorter than the total number of timesteps, and the additional data is non-sensical, so to see the top beam (as int labels) from the first item in the batch, you need to run `beam_results[0][0][:out_len[0][0]]`. + 1. `beam_scores` - Shape: BATCHSIZE x N_BEAMS A batch with the approximate CTC score of each beam (look at the code [here](https://github.com/parlance/ctcdecode/blob/master/ctcdecode/src/ctc_beam_search_decoder.cpp#L191-L192) for more info). If this is true, you can get the model's confidence that the beam is correct with `p=1/np.exp(beam_score)`. + 1. `timesteps` - Shape: BATCHSIZE x N_BEAMS The timestep at which the nth output character has peak probability. Can be used as alignment between the audio and the transcript. + 1. `out_lens` - Shape: BATCHSIZE x N_BEAMS. `out_lens[i][j]` is the length of the jth beam_result, of item i of your batch. + +### Online decoding + +```python +from ctcdecode import OnlineCTCBeamDecoder + +decoder = OnlineCTCBeamDecoder( + labels, + model_path=None, + alpha=0, + beta=0, + cutoff_top_n=40, + cutoff_prob=1.0, + beam_width=100, + num_processes=4, + blank_id=0, + log_probs_input=False +) + +state1 = ctcdecode.DecoderState(decoder) + +probs_seq = torch.FloatTensor([probs_seq]) +beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq[:, :2], [state1], [False]) +beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq[:, 2:], [state1], [True]) + +``` + +The Online decoder is copying CTCBeamDecoder interface, but it requires states and is_eos_s sequences. + +States are used to accumulate sequences of chunks, each corresponding to one data source. Is_eos_s tells the decoder whether the chunks have stopped being pushed to the corresponding state. + + ### More examples + +Get the top beam for the first item in your batch +`beam_results[0][0][:out_len[0][0]]` + +Get the top 50 beams for the first item in your batch +```python +for i in range(50): + print(beam_results[0][i][:out_len[0][i]]) +``` + +Note, these will be a list of ints that need decoding. You likely already have a function to decode from int to text, but if not you can do something like. +`"".join[labels[n] for n in beam_results[0][0][:out_len[0][0]]]` using the labels you passed in to `CTCBeamDecoder` + + +## Resources + +- [Distill Guide to CTC](https://distill.pub/2017/ctc/) +- [Beam Search Video by Andrew Ng](https://www.youtube.com/watch?v=RLWuzLLSIgw) +- [An Intuitive Explanation of Beam Search](https://towardsdatascience.com/an-intuitive-explanation-of-beam-search-9b1d744e7a0f) diff --git a/ctcdecode-master/ctcdecode/__init__.py b/ctcdecode-master/ctcdecode/__init__.py new file mode 100644 index 0000000..1715b77 --- /dev/null +++ b/ctcdecode-master/ctcdecode/__init__.py @@ -0,0 +1,272 @@ +import torch + +from ._ext import ctc_decode + + +class CTCBeamDecoder(object): + """ + PyTorch wrapper for DeepSpeech PaddlePaddle Beam Search Decoder. + Args: + labels (list): The tokens/vocab used to train your model. + They should be in the same order as they are in your model's outputs. + model_path (basestring): The path to your external KenLM language model(LM) + alpha (float): Weighting associated with the LMs probabilities. + A weight of 0 means the LM has no effect. + beta (float): Weight associated with the number of words within our beam. + cutoff_top_n (int): Cutoff number in pruning. Only the top cutoff_top_n characters + with the highest probability in the vocab will be used in beam search. + cutoff_prob (float): Cutoff probability in pruning. 1.0 means no pruning. + beam_width (int): This controls how broad the beam search is. Higher values are more likely to find top beams, + but they also will make your beam search exponentially slower. + num_processes (int): Parallelize the batch using num_processes workers. + blank_id (int): Index of the CTC blank token (probably 0) used when training your model. + log_probs_input (bool): False if your model has passed through a softmax and output probabilities sum to 1. + """ + + def __init__( + self, + labels, + model_path=None, + alpha=0, + beta=0, + cutoff_top_n=40, + cutoff_prob=1.0, + beam_width=100, + num_processes=4, + blank_id=0, + log_probs_input=False, + ): + self.cutoff_top_n = cutoff_top_n + self._beam_width = beam_width + self._scorer = None + self._num_processes = num_processes + self._labels = list(labels) # Ensure labels are a list + self._num_labels = len(labels) + self._blank_id = blank_id + self._log_probs = 1 if log_probs_input else 0 + if model_path: + self._scorer = ctc_decode.paddle_get_scorer( + alpha, beta, model_path.encode(), self._labels, self._num_labels + ) + self._cutoff_prob = cutoff_prob + + def decode(self, probs, seq_lens=None): + """ + Conducts the beamsearch on model outputs and return results. + Args: + probs (Tensor) - A rank 3 tensor representing model outputs. Shape is batch x num_timesteps x num_labels. + seq_lens (Tensor) - A rank 1 tensor representing the sequence length of the items in the batch. Optional, + if not provided the size of axis 1 (num_timesteps) of `probs` is used for all items + + Returns: + tuple: (beam_results, beam_scores, timesteps, out_lens) + + beam_results (Tensor): A 3-dim tensor representing the top n beams of a batch of items. + Shape: batchsize x num_beams x num_timesteps. + Results are still encoded as ints at this stage. + beam_scores (Tensor): A 3-dim tensor representing the likelihood of each beam in beam_results. + Shape: batchsize x num_beams x num_timesteps + timesteps (Tensor): A 2-dim tensor representing the timesteps at which the nth output character + has peak probability. + To be used as alignment between audio and transcript. + Shape: batchsize x num_beams + out_lens (Tensor): A 2-dim tensor representing the length of each beam in beam_results. + Shape: batchsize x n_beams. + + """ + probs = probs.cpu().float() + batch_size, max_seq_len = probs.size(0), probs.size(1) + if seq_lens is None: + seq_lens = torch.IntTensor(batch_size).fill_(max_seq_len) + else: + seq_lens = seq_lens.cpu().int() + output = torch.IntTensor(batch_size, self._beam_width, max_seq_len).cpu().int() + timesteps = torch.IntTensor(batch_size, self._beam_width, max_seq_len).cpu().int() + scores = torch.FloatTensor(batch_size, self._beam_width).cpu().float() + out_seq_len = torch.zeros(batch_size, self._beam_width).cpu().int() + if self._scorer: + ctc_decode.paddle_beam_decode_lm( + probs, + seq_lens, + self._labels, + self._num_labels, + self._beam_width, + self._num_processes, + self._cutoff_prob, + self.cutoff_top_n, + self._blank_id, + self._log_probs, + self._scorer, + output, + timesteps, + scores, + out_seq_len, + ) + else: + ctc_decode.paddle_beam_decode( + probs, + seq_lens, + self._labels, + self._num_labels, + self._beam_width, + self._num_processes, + self._cutoff_prob, + self.cutoff_top_n, + self._blank_id, + self._log_probs, + output, + timesteps, + scores, + out_seq_len, + ) + + return output, scores, timesteps, out_seq_len + + def character_based(self): + return ctc_decode.is_character_based(self._scorer) if self._scorer else None + + def max_order(self): + return ctc_decode.get_max_order(self._scorer) if self._scorer else None + + def dict_size(self): + return ctc_decode.get_dict_size(self._scorer) if self._scorer else None + + def reset_params(self, alpha, beta): + if self._scorer is not None: + ctc_decode.reset_params(self._scorer, alpha, beta) + + def __del__(self): + if self._scorer is not None: + ctc_decode.paddle_release_scorer(self._scorer) + + +class OnlineCTCBeamDecoder(object): + """ + PyTorch wrapper for DeepSpeech PaddlePaddle Beam Search Decoder with interface for online decoding. + Args: + labels (list): The tokens/vocab used to train your model. + They should be in the same order as they are in your model's outputs. + model_path (basestring): The path to your external KenLM language model(LM) + alpha (float): Weighting associated with the LMs probabilities. + A weight of 0 means the LM has no effect. + beta (float): Weight associated with the number of words within our beam. + cutoff_top_n (int): Cutoff number in pruning. Only the top cutoff_top_n characters + with the highest probability in the vocab will be used in beam search. + cutoff_prob (float): Cutoff probability in pruning. 1.0 means no pruning. + beam_width (int): This controls how broad the beam search is. Higher values are more likely to find top beams, + but they also will make your beam search exponentially slower. + num_processes (int): Parallelize the batch using num_processes workers. + blank_id (int): Index of the CTC blank token (probably 0) used when training your model. + log_probs_input (bool): False if your model has passed through a softmax and output probabilities sum to 1. + """ + def __init__( + self, + labels, + model_path=None, + alpha=0, + beta=0, + cutoff_top_n=40, + cutoff_prob=1.0, + beam_width=100, + num_processes=4, + blank_id=0, + log_probs_input=False, + ): + self._cutoff_top_n = cutoff_top_n + self._beam_width = beam_width + self._scorer = None + self._num_processes = num_processes + self._labels = list(labels) # Ensure labels are a list + self._num_labels = len(labels) + self._blank_id = blank_id + self._log_probs = 1 if log_probs_input else 0 + if model_path: + self._scorer = ctc_decode.paddle_get_scorer( + alpha, beta, model_path.encode(), self._labels, self._num_labels + ) + self._cutoff_prob = cutoff_prob + + def decode(self, probs, states, is_eos_s, seq_lens=None): + """ + Conducts the beamsearch on model outputs and return results. + Args: + probs (Tensor) - A rank 3 tensor representing model outputs. Shape is batch x num_timesteps x num_labels. + states (Sequence[DecoderState]) - sequence of decoding states with lens equal to batch_size. + is_eos_s (Sequence[bool]) - sequence of bool with lens equal to batch size. + Should have False if havent pushed all chunks yet, and True if you pushed last cank and you want to get an answer + seq_lens (Tensor) - A rank 1 tensor representing the sequence length of the items in the batch. Optional, + if not provided the size of axis 1 (num_timesteps) of `probs` is used for all items + + Returns: + tuple: (beam_results, beam_scores, timesteps, out_lens) + + beam_results (Tensor): A 3-dim tensor representing the top n beams of a batch of items. + Shape: batchsize x num_beams x num_timesteps. + Results are still encoded as ints at this stage. + beam_scores (Tensor): A 3-dim tensor representing the likelihood of each beam in beam_results. + Shape: batchsize x num_beams x num_timesteps + timesteps (Tensor): A 2-dim tensor representing the timesteps at which the nth output character + has peak probability. + To be used as alignment between audio and transcript. + Shape: batchsize x num_beams + out_lens (Tensor): A 2-dim tensor representing the length of each beam in beam_results. + Shape: batchsize x n_beams. + + """ + probs = probs.cpu().float() + batch_size, max_seq_len = probs.size(0), probs.size(1) + if seq_lens is None: + seq_lens = torch.IntTensor(batch_size).fill_(max_seq_len) + else: + seq_lens = seq_lens.cpu().int() + scores = torch.FloatTensor(batch_size, self._beam_width).cpu().float() + out_seq_len = torch.zeros(batch_size, self._beam_width).cpu().int() + + decode_fn = ctc_decode.paddle_beam_decode_with_given_state + res_beam_results, res_timesteps = decode_fn( + probs, + seq_lens, + self._num_processes, + [state.state for state in states], + is_eos_s, + scores, + out_seq_len + ) + res_beam_results = res_beam_results.int() + res_timesteps = res_timesteps.int() + + return res_beam_results, scores, res_timesteps, out_seq_len + + def character_based(self): + return ctc_decode.is_character_based(self._scorer) if self._scorer else None + + def max_order(self): + return ctc_decode.get_max_order(self._scorer) if self._scorer else None + + def dict_size(self): + return ctc_decode.get_dict_size(self._scorer) if self._scorer else None + + def reset_state(state): + ctc_decode.paddle_release_state(state) + + +class DecoderState: + """ + Class using for maintain different chunks of data in one beam algorithm corresponding to one unique source. + Note: after using State you should delete it, so dont reuse it + Args: + decoder (OnlineCTCBeamDecoder) - decoder you will use for decoding. + """ + def __init__(self, decoder): + self.state = ctc_decode.paddle_get_decoder_state( + decoder._labels, + decoder._beam_width, + decoder._cutoff_prob, + decoder._cutoff_top_n, + decoder._blank_id, + decoder._log_probs, + decoder._scorer, + ) + + def __del__(self): + ctc_decode.paddle_release_state(self.state) diff --git a/ctcdecode-master/ctcdecode/src/binding.cpp b/ctcdecode-master/ctcdecode/src/binding.cpp new file mode 100644 index 0000000..c14a822 --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/binding.cpp @@ -0,0 +1,303 @@ +#include +#include +#include +#include +#include +#include +#include "scorer.h" +#include "ctc_beam_search_decoder.h" +#include "utf8.h" +#include "boost/shared_ptr.hpp" +#include "boost/python.hpp" +#include "boost/python/stl_iterator.hpp" + +using namespace std; + +template +inline +std::vector< T > py_list_to_std_vector( const boost::python::object& iterable ) +{ + return std::vector< T >( boost::python::stl_input_iterator< T >( iterable ), + boost::python::stl_input_iterator< T >( ) ); +} + +template +inline +boost::python::list std_vector_to_py_list(std::vector vector) { + typename std::vector::iterator iter; + boost::python::list list; + for (iter = vector.begin(); iter != vector.end(); ++iter) { + list.append(*iter); + } + return list; +} + +int beam_decode(at::Tensor th_probs, + at::Tensor th_seq_lens, + std::vector new_vocab, + int vocab_size, + size_t beam_size, + size_t num_processes, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + bool log_input, + void *scorer, + at::Tensor th_output, + at::Tensor th_timesteps, + at::Tensor th_scores, + at::Tensor th_out_length) +{ + Scorer *ext_scorer = NULL; + if (scorer != NULL) { + ext_scorer = static_cast(scorer); + } + const int64_t max_time = th_probs.size(1); + const int64_t batch_size = th_probs.size(0); + const int64_t num_classes = th_probs.size(2); + + std::vector>> inputs; + auto prob_accessor = th_probs.accessor(); + auto seq_len_accessor = th_seq_lens.accessor(); + + for (int b=0; b < batch_size; ++b) { + // avoid a crash by ensuring that an erroneous seq_len doesn't have us try to access memory we shouldn't + int seq_len = std::min((int)seq_len_accessor[b], (int)max_time); + std::vector> temp (seq_len, std::vector(num_classes)); + for (int t=0; t < seq_len; ++t) { + for (int n=0; n < num_classes; ++n) { + float val = prob_accessor[b][t][n]; + temp[t][n] = val; + } + } + inputs.push_back(temp); + } + + + std::vector>> batch_results = + ctc_beam_search_decoder_batch(inputs, new_vocab, beam_size, num_processes, cutoff_prob, cutoff_top_n, blank_id, log_input, ext_scorer); + auto outputs_accessor = th_output.accessor(); + auto timesteps_accessor = th_timesteps.accessor(); + auto scores_accessor = th_scores.accessor(); + auto out_length_accessor = th_out_length.accessor(); + + + for (int b = 0; b < batch_results.size(); ++b){ + std::vector> results = batch_results[b]; + for (int p = 0; p < results.size();++p){ + std::pair n_path_result = results[p]; + Output output = n_path_result.second; + std::vector output_tokens = output.tokens; + std::vector output_timesteps = output.timesteps; + for (int t = 0; t < output_tokens.size(); ++t){ + outputs_accessor[b][p][t] = output_tokens[t]; // fill output tokens + timesteps_accessor[b][p][t] = output_timesteps[t]; + } + scores_accessor[b][p] = n_path_result.first; + out_length_accessor[b][p] = output_tokens.size(); + } + } + return 1; +} + +int paddle_beam_decode(at::Tensor th_probs, + at::Tensor th_seq_lens, + std::vector labels, + int vocab_size, + size_t beam_size, + size_t num_processes, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + at::Tensor th_output, + at::Tensor th_timesteps, + at::Tensor th_scores, + at::Tensor th_out_length){ + + return beam_decode(th_probs, th_seq_lens, labels, vocab_size, beam_size, num_processes, + cutoff_prob, cutoff_top_n, blank_id, log_input, NULL, th_output, th_timesteps, th_scores, th_out_length); +} + +int paddle_beam_decode_lm(at::Tensor th_probs, + at::Tensor th_seq_lens, + std::vector labels, + int vocab_size, + size_t beam_size, + size_t num_processes, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + void *scorer, + at::Tensor th_output, + at::Tensor th_timesteps, + at::Tensor th_scores, + at::Tensor th_out_length){ + + return beam_decode(th_probs, th_seq_lens, labels, vocab_size, beam_size, num_processes, + cutoff_prob, cutoff_top_n, blank_id, log_input, scorer, th_output, th_timesteps, th_scores, th_out_length); +} + + +void* paddle_get_scorer(double alpha, + double beta, + const char* lm_path, + vector new_vocab, + int vocab_size) { + Scorer* scorer = new Scorer(alpha, beta, lm_path, new_vocab); + return static_cast(scorer); +} + + +std::pair beam_decode_with_given_state(at::Tensor th_probs, + at::Tensor th_seq_lens, + size_t num_processes, + std::vector &states, + const std::vector &is_eos_s, + at::Tensor th_scores, + at::Tensor th_out_length) +{ + const int64_t max_time = th_probs.size(1); + const int64_t batch_size = th_probs.size(0); + const int64_t num_classes = th_probs.size(2); + + std::vector>> inputs; + auto prob_accessor = th_probs.accessor(); + auto seq_len_accessor = th_seq_lens.accessor(); + + for (int b=0; b < batch_size; ++b) { + // avoid a crash by ensuring that an erroneous seq_len doesn't have us try to access memory we shouldn't + int seq_len = std::min((int)seq_len_accessor[b], (int)max_time); + std::vector> temp (seq_len, std::vector(num_classes)); + for (int t=0; t < seq_len; ++t) { + for (int n=0; n < num_classes; ++n) { + float val = prob_accessor[b][t][n]; + temp[t][n] = val; + } + } + inputs.push_back(temp); + + } + + std::vector>> batch_results = + ctc_beam_search_decoder_batch_with_states(inputs, num_processes, states, is_eos_s); + + int max_result_size = 0; + int max_output_tokens_size = 0; + for (int b = 0; b < batch_results.size(); ++b){ + std::vector> results = batch_results[b]; + if (batch_results[b].size() > max_result_size) { + max_result_size = batch_results[b].size(); + } + for (int p = 0; p < results.size();++p){ + std::pair n_path_result = results[p]; + Output output = n_path_result.second; + std::vector output_tokens = output.tokens; + + if (output_tokens.size() > max_output_tokens_size) { + max_output_tokens_size = output_tokens.size(); + } + } + } + + torch::Tensor output_tokens_tensor = torch::randint(1, {batch_results.size(), max_result_size, max_output_tokens_size}); + torch::Tensor output_timesteps_tensor = torch::randint(1, {batch_results.size(), max_result_size, max_output_tokens_size}); + + + auto scores_accessor = th_scores.accessor(); + auto out_length_accessor = th_out_length.accessor(); + + + for (int b = 0; b < batch_results.size(); ++b){ + std::vector> results = batch_results[b]; + for (int p = 0; p < results.size();++p){ + std::pair n_path_result = results[p]; + Output output = n_path_result.second; + std::vector output_tokens = output.tokens; + std::vector output_timesteps = output.timesteps; + for (int t = 0; t < output_tokens.size(); ++t) { + output_tokens_tensor[b][p][t] = output_tokens[t]; // fill output tokens + output_timesteps_tensor[b][p][t] = output_timesteps[t]; + } + scores_accessor[b][p] = n_path_result.first; + out_length_accessor[b][p] = output_tokens.size(); + } + } + + return {output_tokens_tensor, output_timesteps_tensor}; +} + + +std::pair paddle_beam_decode_with_given_state(at::Tensor th_probs, + at::Tensor th_seq_lens, + size_t num_processes, + std::vector states, + std::vector is_eos_s, + at::Tensor th_scores, + at::Tensor th_out_length){ + + return beam_decode_with_given_state(th_probs, th_seq_lens, num_processes, states,is_eos_s, th_scores, th_out_length); +} + + + + +void* paddle_get_decoder_state(const std::vector &vocabulary, + size_t beam_size, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + void* scorer) +{ + // DecoderState state(vocabulary, beam_size, cutoff_prob, cutoff_top_n, blank_id, log_input, ext_scorer); + Scorer *ext_scorer = NULL; + if (scorer != NULL) { + ext_scorer = static_cast(scorer); + } + DecoderState* state = new DecoderState(vocabulary, beam_size, cutoff_prob, cutoff_top_n, blank_id, log_input, ext_scorer); + return static_cast(state); +} + +void paddle_release_state(void* state) { + delete static_cast(state); +} + +void paddle_release_scorer(void* scorer) { + delete static_cast(scorer); +} + +int is_character_based(void *scorer){ + Scorer *ext_scorer = static_cast(scorer); + return ext_scorer->is_character_based(); +} +size_t get_max_order(void *scorer){ + Scorer *ext_scorer = static_cast(scorer); + return ext_scorer->get_max_order(); +} +size_t get_dict_size(void *scorer){ + Scorer *ext_scorer = static_cast(scorer); + return ext_scorer->get_dict_size(); +} + +void reset_params(void *scorer, double alpha, double beta){ + Scorer *ext_scorer = static_cast(scorer); + ext_scorer->reset_params(alpha, beta); +} + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("paddle_beam_decode", &paddle_beam_decode, "paddle_beam_decode"); + m.def("paddle_beam_decode_lm", &paddle_beam_decode_lm, "paddle_beam_decode_lm"); + m.def("paddle_get_scorer", &paddle_get_scorer, "paddle_get_scorer"); + m.def("paddle_release_scorer", &paddle_release_scorer, "paddle_release_scorer"); + m.def("is_character_based", &is_character_based, "is_character_based"); + m.def("get_max_order", &get_max_order, "get_max_order"); + m.def("get_dict_size", &get_dict_size, "get_max_order"); + m.def("reset_params", &reset_params, "reset_params"); + m.def("paddle_get_decoder_state", &paddle_get_decoder_state, "paddle_get_decoder_state"); + m.def("paddle_beam_decode_with_given_state", &paddle_beam_decode_with_given_state, "paddle_beam_decode_with_given_state"); + m.def("paddle_release_state", &paddle_release_state, "paddle_release_state"); + //paddle_beam_decode_with_given_state +} diff --git a/ctcdecode-master/ctcdecode/src/binding.h b/ctcdecode-master/ctcdecode/src/binding.h new file mode 100644 index 0000000..e6bd2eb --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/binding.h @@ -0,0 +1,55 @@ +int paddle_beam_decode(THFloatTensor *th_probs, + THIntTensor *th_seq_lens, + std::vector labels, + int vocab_size, + size_t beam_size, + size_t num_processes, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + THIntTensor *th_output, + THIntTensor *th_timesteps, + THFloatTensor *th_scores, + THIntTensor *th_out_length); + + +int paddle_beam_decode_lm(THFloatTensor *th_probs, + THIntTensor *th_seq_lens, + std::vector labels, + int vocab_size, + size_t beam_size, + size_t num_processes, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + bool log_input, + int *scorer, + THIntTensor *th_output, + THIntTensor *th_timesteps, + THFloatTensor *th_scores, + THIntTensor *th_out_length); + +void* paddle_get_scorer(double alpha, + double beta, + const char* lm_path, + std::vector labels, + int vocab_size); + + +void* paddle_get_decoder_state(const std::vector &vocabulary, + size_t beam_size, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + void* scorer); + +void paddle_release_scorer(void* scorer); +void paddle_release_state(void* state); + + +int is_character_based(void *scorer); +size_t get_max_order(void *scorer); +size_t get_dict_size(void *scorer); +void reset_params(void *scorer, double alpha, double beta); diff --git a/ctcdecode-master/ctcdecode/src/ctc_beam_search_decoder.cpp b/ctcdecode-master/ctcdecode/src/ctc_beam_search_decoder.cpp new file mode 100644 index 0000000..0d0365d --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/ctc_beam_search_decoder.cpp @@ -0,0 +1,317 @@ +#include "ctc_beam_search_decoder.h" + +#include +#include +#include +#include +#include +#include + +#include "decoder_utils.h" +#include "ThreadPool.h" +#include "fst/fstlib.h" +#include "path_trie.h" + +using FSTMATCH = fst::SortedMatcher; + +DecoderState::DecoderState(const std::vector &vocabulary, + size_t beam_size, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + Scorer *ext_scorer) + : abs_time_step(0) + , beam_size(beam_size) + , cutoff_prob(cutoff_prob) + , cutoff_top_n(cutoff_top_n) + , blank_id(blank_id) + , log_input(log_input) + , vocabulary(vocabulary) + , ext_scorer(ext_scorer) +{ + // assign space id + auto it = std::find(vocabulary.begin(), vocabulary.end(), " "); + // if no space in vocabulary + if (it == vocabulary.end()) { + space_id = -2; + } else { + space_id = std::distance(vocabulary.begin(), it); + } + + // init prefixes' root + root.score = root.log_prob_b_prev = 0.0; + prefixes.push_back(&root); + + if (ext_scorer != nullptr && !ext_scorer->is_character_based()) { + auto fst_dict = static_cast(ext_scorer->dictionary); + fst::StdVectorFst *dict_ptr = fst_dict->Copy(true); + root.set_dictionary(dict_ptr); + auto matcher = std::make_shared(*dict_ptr, fst::MATCH_INPUT); + root.set_matcher(matcher); + } +} + + +void +DecoderState::next(const std::vector> &probs_seq) +{ + // dimension check + size_t num_time_steps = probs_seq.size(); + for (size_t i = 0; i < num_time_steps; ++i) { + VALID_CHECK_EQ(probs_seq[i].size(), + vocabulary.size(), + "The shape of probs_seq does not match with " + "the shape of the vocabulary"); + } + + // prefix search over time + for (size_t time_step = 0; time_step < num_time_steps; ++time_step, ++abs_time_step) { + auto &prob = probs_seq[time_step]; + + float min_cutoff = -NUM_FLT_INF; + bool full_beam = false; + if (ext_scorer != nullptr) { + size_t num_prefixes = std::min(prefixes.size(), beam_size); + std::sort( + prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare); + float blank_prob = log_input ? prob[blank_id] : std::log(prob[blank_id]); + min_cutoff = prefixes[num_prefixes - 1]->score + + blank_prob - std::max(0.0, ext_scorer->beta); + full_beam = (num_prefixes == beam_size); + } + + std::vector> log_prob_idx = + get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n, log_input); + // loop over chars + for (size_t index = 0; index < log_prob_idx.size(); index++) { + auto c = log_prob_idx[index].first; + auto log_prob_c = log_prob_idx[index].second; + + for (size_t i = 0; i < prefixes.size() && i < beam_size; ++i) { + auto prefix = prefixes[i]; + if (full_beam && log_prob_c + prefix->score < min_cutoff) { + break; + } + // blank + if (c == blank_id) { + prefix->log_prob_b_cur = + log_sum_exp(prefix->log_prob_b_cur, log_prob_c + prefix->score); + continue; + } + // repeated character + if (c == prefix->character) { + prefix->log_prob_nb_cur = log_sum_exp( + prefix->log_prob_nb_cur, log_prob_c + prefix->log_prob_nb_prev); + } + // get new prefix + auto prefix_new = prefix->get_path_trie(c, abs_time_step, log_prob_c); + + if (prefix_new != nullptr) { + float log_p = -NUM_FLT_INF; + + if (c == prefix->character && + prefix->log_prob_b_prev > -NUM_FLT_INF) { + log_p = log_prob_c + prefix->log_prob_b_prev; + } else if (c != prefix->character) { + log_p = log_prob_c + prefix->score; + } + + // language model scoring + if (ext_scorer != nullptr && + (c == space_id || ext_scorer->is_character_based())) { + PathTrie *prefix_to_score = nullptr; + // skip scoring the space + if (ext_scorer->is_character_based()) { + prefix_to_score = prefix_new; + } else { + prefix_to_score = prefix; + } + + float score = 0.0; + std::vector ngram; + ngram = ext_scorer->make_ngram(prefix_to_score); + score = ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha; + log_p += score; + log_p += ext_scorer->beta; + } + prefix_new->log_prob_nb_cur = + log_sum_exp(prefix_new->log_prob_nb_cur, log_p); + } + } // end of loop over prefix + } // end of loop over vocabulary + + + prefixes.clear(); + // update log probs + root.iterate_to_vec(prefixes); + + // only preserve top beam_size prefixes + if (prefixes.size() >= beam_size) { + std::nth_element(prefixes.begin(), + prefixes.begin() + beam_size, + prefixes.end(), + prefix_compare); + for (size_t i = beam_size; i < prefixes.size(); ++i) { + prefixes[i]->remove(); + } + + prefixes.resize(beam_size); + } + } // end of loop over time +} + +std::vector> +DecoderState::decode() +{ + std::vector prefixes_copy = prefixes; + std::unordered_map scores; + for (PathTrie* prefix : prefixes_copy) { + scores[prefix] = prefix->score; + } + + // score the last word of each prefix that doesn't end with space + if (ext_scorer != nullptr && !ext_scorer->is_character_based()) { + for (size_t i = 0; i < beam_size && i < prefixes_copy.size(); ++i) { + auto prefix = prefixes_copy[i]; + if (!prefix->is_empty() && prefix->character != space_id) { + float score = 0.0; + std::vector ngram = ext_scorer->make_ngram(prefix); + score = ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha; + score += ext_scorer->beta; + scores[prefix] += score; + } + } + } + + using namespace std::placeholders; + size_t num_prefixes = std::min(prefixes_copy.size(), beam_size); + std::sort(prefixes_copy.begin(), prefixes_copy.begin() + num_prefixes, + std::bind(prefix_compare_external_scores, _1, _2, scores)); + + // compute aproximate ctc score as the return score, without affecting the + // return order of decoding result. To delete when decoder gets stable. + for (size_t i = 0; i < beam_size && i < prefixes_copy.size(); ++i) { + double approx_ctc = scores[prefixes_copy[i]]; + if (ext_scorer != nullptr) { + std::vector output; + std::vector timesteps; + prefixes_copy[i]->get_path_vec(output, timesteps); + auto prefix_length = output.size(); + auto words = ext_scorer->split_labels(output); + // remove word insert + approx_ctc = approx_ctc - prefix_length * ext_scorer->beta; + // remove language model weight: + approx_ctc -= (ext_scorer->get_sent_log_prob(words)) * ext_scorer->alpha; + } + prefixes_copy[i]->approx_ctc = approx_ctc; + } + + return get_beam_search_result(prefixes_copy, beam_size); +} + +std::vector> ctc_beam_search_decoder( + const std::vector> &probs_seq, + const std::vector &vocabulary, + size_t beam_size, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + Scorer *ext_scorer) +{ + DecoderState state(vocabulary, beam_size, cutoff_prob, cutoff_top_n, blank_id, + log_input, ext_scorer); + state.next(probs_seq); + return state.decode(); +} + + +std::vector> ctc_beam_search_decoder_with_given_state( + const std::vector> &probs_seq, + DecoderState *state, + bool is_eos) + { + state->next(probs_seq); + if (is_eos) { + return state->decode(); + } + else { + return {}; + } + + } + +std::vector>> +ctc_beam_search_decoder_batch( + const std::vector>> &probs_split, + const std::vector &vocabulary, + size_t beam_size, + size_t num_processes, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + Scorer *ext_scorer) +{ + VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!"); + // thread pool + ThreadPool pool(num_processes); + // number of samples + size_t batch_size = probs_split.size(); + + // enqueue the tasks of decoding + std::vector>>> res; + for (size_t i = 0; i < batch_size; ++i) { + res.emplace_back(pool.enqueue(ctc_beam_search_decoder, + std::cref(probs_split[i]), + std::cref(vocabulary), + beam_size, + cutoff_prob, + cutoff_top_n, + blank_id, + log_input, + ext_scorer)); + } + + + + // get decoding results + std::vector>> batch_results; + for (size_t i = 0; i < batch_size; ++i) { + batch_results.emplace_back(res[i].get()); + } + return batch_results; +} + + +std::vector>> ctc_beam_search_decoder_batch_with_states +(const std::vector>> &probs_split, + size_t num_processes, + std::vector &states, + const std::vector &is_eos_s) + +{ + VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!"); + // thread pool + ThreadPool pool(num_processes); + // number of samples + size_t batch_size = probs_split.size(); + + + // enqueue the tasks of decoding + std::vector>>> res; + for (size_t i = 0; i < batch_size; ++i) { + res.emplace_back(pool.enqueue(ctc_beam_search_decoder_with_given_state, + std::cref(probs_split[i]), + static_cast(states[i]), + is_eos_s[i])); + } + + // get decoding results + std::vector>> batch_results; + for (size_t i = 0; i < batch_size; ++i) { + batch_results.emplace_back(res[i].get()); + } + return batch_results; +} diff --git a/ctcdecode-master/ctcdecode/src/ctc_beam_search_decoder.h b/ctcdecode-master/ctcdecode/src/ctc_beam_search_decoder.h new file mode 100644 index 0000000..09ff2ee --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/ctc_beam_search_decoder.h @@ -0,0 +1,134 @@ +#ifndef CTC_BEAM_SEARCH_DECODER_H_ +#define CTC_BEAM_SEARCH_DECODER_H_ + +#include +#include +#include + +#include "scorer.h" +#include "output.h" + +/* CTC Beam Search Decoder + + * Parameters: + * probs_seq: 2-D vector that each element is a vector of probabilities + * over vocabulary of one time step. + * vocabulary: A vector of vocabulary. + * beam_size: The width of beam search. + * cutoff_prob: Cutoff probability for pruning. + * cutoff_top_n: Cutoff number for pruning. + * ext_scorer: External scorer to evaluate a prefix, which consists of + * n-gram language model scoring and word insertion term. + * Default null, decoding the input sample without scorer. + * Return: + * A vector that each element is a pair of score and decoding result, + * in desending order. +*/ + +std::vector> ctc_beam_search_decoder( + const std::vector> &probs_seq, + const std::vector &vocabulary, + size_t beam_size, + double cutoff_prob = 1.0, + size_t cutoff_top_n = 40, + size_t blank_id = 0, + int log_input = 0, + Scorer *ext_scorer = nullptr); + + + +/* CTC Beam Search Decoder for batch data + + * Parameters: + * probs_seq: 3-D vector that each element is a 2-D vector that can be used + * by ctc_beam_search_decoder(). + * vocabulary: A vector of vocabulary. + * beam_size: The width of beam search. + * num_processes: Number of threads for beam search. + * cutoff_prob: Cutoff probability for pruning. + * cutoff_top_n: Cutoff number for pruning. + * ext_scorer: External scorer to evaluate a prefix, which consists of + * n-gram language model scoring and word insertion term. + * Default null, decoding the input sample without scorer. + * Return: + * A 2-D vector that each element is a vector of beam search decoding + * result for one audio sample. +*/ +std::vector>> +ctc_beam_search_decoder_batch( + const std::vector>> &probs_split, + const std::vector &vocabulary, + size_t beam_size, + size_t num_processes, + double cutoff_prob = 1.0, + size_t cutoff_top_n = 40, + size_t blank_id = 0, + int log_input = 0, + Scorer *ext_scorer = nullptr); + + + + + +class DecoderState +{ + int abs_time_step; + int space_id; + size_t beam_size; + double cutoff_prob; + size_t cutoff_top_n; + size_t blank_id; + int log_input; + std::vector vocabulary; + Scorer *ext_scorer; + + std::vector prefixes; + PathTrie root; + +public: + /* Initialize CTC beam search decoder for streaming + * + * Parameters: + * vocabulary: A vector of vocabulary. + * beam_size: The width of beam search. + * cutoff_prob: Cutoff probability for pruning. + * cutoff_top_n: Cutoff number for pruning. + * ext_scorer: External scorer to evaluate a prefix, which consists of + * n-gram language model scoring and word insertion term. + * Default null, decoding the input sample without scorer. + */ + DecoderState(const std::vector &vocabulary, + size_t beam_size, + double cutoff_prob, + size_t cutoff_top_n, + size_t blank_id, + int log_input, + Scorer *ext_scorer); + ~DecoderState() = default; + + /* Process logits in decoder stream + * + * Parameters: + * probs: 2-D vector where each element is a vector of probabilities + * over alphabet of one time step. + */ + void next(const std::vector> &probs_seq); + + /* Get current transcription from the decoder stream state + * + * Return: + * A vector where each element is a pair of score and decoding result, + * in descending order. + */ + std::vector> decode(); +}; + + +std::vector>> +ctc_beam_search_decoder_batch_with_states( + const std::vector>> &probs_split, + size_t num_processes, + std::vector &states, + const std::vector &is_eos_s); + +#endif // CTC_BEAM_SEARCH_DECODER_H_ diff --git a/ctcdecode-master/ctcdecode/src/decoder_utils.cpp b/ctcdecode-master/ctcdecode/src/decoder_utils.cpp new file mode 100644 index 0000000..2a35fe4 --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/decoder_utils.cpp @@ -0,0 +1,193 @@ +#include "decoder_utils.h" + +#include +#include +#include +#include +using namespace std; + + +std::vector> get_pruned_log_probs( + const std::vector &prob_step, + double cutoff_prob, + size_t cutoff_top_n, + int log_input) { + std::vector> prob_idx; + double log_cutoff_prob = log(cutoff_prob); + for (size_t i = 0; i < prob_step.size(); ++i) { + prob_idx.push_back(std::pair(i, prob_step[i])); + } + // pruning of vacobulary + size_t cutoff_len = prob_step.size(); + if (log_cutoff_prob < 0.0 || cutoff_top_n < cutoff_len) { + std::sort( + prob_idx.begin(), prob_idx.end(), pair_comp_second_rev); + if (log_cutoff_prob < 0.0) { + double cum_prob = 0.0; + cutoff_len = 0; + for (size_t i = 0; i < prob_idx.size(); ++i) { + cum_prob = log_sum_exp(cum_prob, log_input ? prob_idx[i].second : log(prob_idx[i].second) ); + cutoff_len += 1; + if (cum_prob >= cutoff_prob || cutoff_len >= cutoff_top_n) break; + } + }else{ + cutoff_len = cutoff_top_n; + } + prob_idx = std::vector>( + prob_idx.begin(), prob_idx.begin() + cutoff_len); + } + std::vector> log_prob_idx; + for (size_t i = 0; i < cutoff_len; ++i) { + log_prob_idx.push_back(std::pair( + prob_idx[i].first, log_input ? prob_idx[i].second : log(prob_idx[i].second + NUM_FLT_MIN))); + } + return log_prob_idx; +} + + +std::vector> get_beam_search_result( + const std::vector &prefixes, + size_t beam_size) { + // allow for the post processing + std::vector space_prefixes; + if (space_prefixes.empty()) { + for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) { + space_prefixes.push_back(prefixes[i]); + } + } + + std::sort(space_prefixes.begin(), space_prefixes.end(), prefix_compare); + std::vector> output_vecs; + for (size_t i = 0; i < beam_size && i < space_prefixes.size(); ++i) { + std::vector output; + std::vector timesteps; + space_prefixes[i]->get_path_vec(output, timesteps); + Output outputs; + outputs.tokens = output; + outputs.timesteps = timesteps; + std::pair output_pair(-space_prefixes[i]->approx_ctc, + outputs); + output_vecs.emplace_back(output_pair); + } + return output_vecs; +} + +size_t get_utf8_str_len(const std::string &str) { + size_t str_len = 0; + for (char c : str) { + str_len += ((c & 0xc0) != 0x80); + } + return str_len; +} + +std::vector split_utf8_str(const std::string &str) { + std::vector result; + std::string out_str; + + for (char c : str) { + if ((c & 0xc0) != 0x80) // new UTF-8 character + { + if (!out_str.empty()) { + result.push_back(out_str); + out_str.clear(); + } + } + + out_str.append(1, c); + } + result.push_back(out_str); + return result; +} + +std::vector split_str(const std::string &s, + const std::string &delim) { + std::vector result; + std::size_t start = 0, delim_len = delim.size(); + while (true) { + std::size_t end = s.find(delim, start); + if (end == std::string::npos) { + if (start < s.size()) { + result.push_back(s.substr(start)); + } + break; + } + if (end > start) { + result.push_back(s.substr(start, end - start)); + } + start = end + delim_len; + } + return result; +} + +bool prefix_compare(const PathTrie *x, const PathTrie *y) { + if (x->score == y->score) { + if (x->character == y->character) { + return false; + } else { + return (x->character < y->character); + } + } else { + return x->score > y->score; + } +} + +bool prefix_compare_external_scores(const PathTrie *x, const PathTrie *y, + const std::unordered_map& scores) { + if (scores.at(x) == scores.at(y)) { + if (x->character == y->character) { + return false; + } else { + return (x->character < y->character); + } + } else { + return scores.at(x) > scores.at(y); + } +} + +void add_word_to_fst(const std::vector &word, + fst::StdVectorFst *dictionary) { + if (dictionary->NumStates() == 0) { + fst::StdVectorFst::StateId start = dictionary->AddState(); + assert(start == 0); + dictionary->SetStart(start); + } + fst::StdVectorFst::StateId src = dictionary->Start(); + fst::StdVectorFst::StateId dst; + for (auto c : word) { + dst = dictionary->AddState(); + dictionary->AddArc(src, fst::StdArc(c, c, 0, dst)); + src = dst; + } + dictionary->SetFinal(dst, fst::StdArc::Weight::One()); +} + +bool add_word_to_dictionary( + const std::string &word, + const std::unordered_map &char_map, + bool add_space, + int SPACE_ID, + fst::StdVectorFst *dictionary) { + auto characters = split_utf8_str(word); + + std::vector int_word; + + for (auto &c : characters) { + if (c == " ") { + int_word.push_back(SPACE_ID); + } else { + auto int_c = char_map.find(c); + if (int_c != char_map.end()) { + int_word.push_back(int_c->second); + } else { + return false; // return without adding + } + } + } + + if (add_space) { + int_word.push_back(SPACE_ID); + } + + add_word_to_fst(int_word, dictionary); + return true; // return with successful adding +} diff --git a/ctcdecode-master/ctcdecode/src/decoder_utils.h b/ctcdecode-master/ctcdecode/src/decoder_utils.h new file mode 100644 index 0000000..2f3eea6 --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/decoder_utils.h @@ -0,0 +1,102 @@ +#ifndef DECODER_UTILS_H_ +#define DECODER_UTILS_H_ + +#include +#include +#include + +#include "fst/log.h" +#include "path_trie.h" +#include "output.h" + +const float NUM_FLT_INF = std::numeric_limits::max(); +const float NUM_FLT_MIN = std::numeric_limits::min(); +const float NUM_FLT_LOGE = 0.4342944819; + +// inline function for validation check +inline void check( + bool x, const char *expr, const char *file, int line, const char *err) { + if (!x) { + std::cout << "[" << file << ":" << line << "] "; + LOG(FATAL) << "\"" << expr << "\" check failed. " << err; + } +} + +#define VALID_CHECK(x, info) \ + check(static_cast(x), #x, __FILE__, __LINE__, info) +#define VALID_CHECK_EQ(x, y, info) VALID_CHECK((x) == (y), info) +#define VALID_CHECK_GT(x, y, info) VALID_CHECK((x) > (y), info) +#define VALID_CHECK_LT(x, y, info) VALID_CHECK((x) < (y), info) + + +// Function template for comparing two pairs +template +bool pair_comp_first_rev(const std::pair &a, + const std::pair &b) { + return a.first > b.first; +} + +// Function template for comparing two pairs +template +bool pair_comp_second_rev(const std::pair &a, + const std::pair &b) { + return a.second > b.second; +} + +// Return the sum of two probabilities in log scale +template +T log_sum_exp(const T &x, const T &y) { + static T num_min = -std::numeric_limits::max(); + if (x <= num_min) return y; + if (y <= num_min) return x; + T xmax = std::max(x, y); + return std::log(std::exp(x - xmax) + std::exp(y - xmax)) + xmax; +} + +// Get pruned probability vector for each time step's beam search +std::vector> get_pruned_log_probs( + const std::vector &prob_step, + double cutoff_prob, + size_t cutoff_top_n, + int log_input); + +// Get beam search result from prefixes in trie tree +std::vector> get_beam_search_result( + const std::vector &prefixes, + size_t beam_size); + +// Functor for prefix comparison +bool prefix_compare(const PathTrie *x, const PathTrie *y); + +bool prefix_compare_external_scores(const PathTrie *x, const PathTrie *y, + const std::unordered_map& scores); + +/* Get length of utf8 encoding string + * See: http://stackoverflow.com/a/4063229 + */ +size_t get_utf8_str_len(const std::string &str); + +/* Split a string into a list of strings on a given string + * delimiter. NB: delimiters on beginning / end of string are + * trimmed. Eg, "FooBarFoo" split on "Foo" returns ["Bar"]. + */ +std::vector split_str(const std::string &s, + const std::string &delim); + +/* Splits string into vector of strings representing + * UTF-8 characters (not same as chars) + */ +std::vector split_utf8_str(const std::string &str); + +// Add a word in index to the dicionary of fst +void add_word_to_fst(const std::vector &word, + fst::StdVectorFst *dictionary); + +// Add a word in string to dictionary +bool add_word_to_dictionary( + const std::string &word, + const std::unordered_map &char_map, + bool add_space, + int SPACE_ID, + fst::StdVectorFst *dictionary); +#endif // DECODER_UTILS_H diff --git a/ctcdecode-master/ctcdecode/src/output.h b/ctcdecode-master/ctcdecode/src/output.h new file mode 100644 index 0000000..a921ee2 --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/output.h @@ -0,0 +1,11 @@ +#ifndef OUTPUT_H_ +#define OUTPUT_H_ + +/* Struct for the beam search output, containing the tokens based on the vocabulary indices, and the timesteps + * for each token in the beam search output + */ +struct Output { + std::vector tokens, timesteps; +}; + +#endif // OUTPUT_H_ diff --git a/ctcdecode-master/ctcdecode/src/path_trie.cpp b/ctcdecode-master/ctcdecode/src/path_trie.cpp new file mode 100644 index 0000000..bdf2bab --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/path_trie.cpp @@ -0,0 +1,174 @@ +#include "path_trie.h" + +#include +#include +#include +#include +#include + +#include "decoder_utils.h" + +PathTrie::PathTrie() { + log_prob_b_prev = -NUM_FLT_INF; + log_prob_nb_prev = -NUM_FLT_INF; + log_prob_b_cur = -NUM_FLT_INF; + log_prob_nb_cur = -NUM_FLT_INF; + log_prob_c = -NUM_FLT_INF; + score = -NUM_FLT_INF; + + ROOT_ = -1; + character = ROOT_; + timestep = 0; + exists_ = true; + parent = nullptr; + + dictionary_ = nullptr; + dictionary_state_ = 0; + has_dictionary_ = false; + + matcher_ = nullptr; +} + +PathTrie::~PathTrie() { + for (auto child : children_) { + delete child.second; + } +} + +PathTrie* PathTrie::get_path_trie(int new_char, int new_timestep, float cur_log_prob_c, bool reset) { + auto child = children_.begin(); + for (child = children_.begin(); child != children_.end(); ++child) { + if (child->first == new_char) { + if (child->second->log_prob_c < cur_log_prob_c) { + child->second->log_prob_c = cur_log_prob_c; + child->second->timestep = new_timestep; + } + break; + } + } + if (child != children_.end()) { + if (!child->second->exists_) { + child->second->exists_ = true; + child->second->log_prob_b_prev = -NUM_FLT_INF; + child->second->log_prob_nb_prev = -NUM_FLT_INF; + child->second->log_prob_b_cur = -NUM_FLT_INF; + child->second->log_prob_nb_cur = -NUM_FLT_INF; + } + return (child->second); + } else { + if (has_dictionary_) { + matcher_->SetState(dictionary_state_); + bool found = matcher_->Find(new_char + 1); + if (!found) { + // Adding this character causes word outside dictionary + auto FSTZERO = fst::TropicalWeight::Zero(); + auto final_weight = dictionary_->Final(dictionary_state_); + bool is_final = (final_weight != FSTZERO); + if (is_final && reset) { + dictionary_state_ = dictionary_->Start(); + } + return nullptr; + } else { + PathTrie* new_path = new PathTrie; + new_path->character = new_char; + new_path->timestep = new_timestep; + new_path->parent = this; + new_path->dictionary_ = dictionary_; + new_path->has_dictionary_ = true; + new_path->matcher_ = matcher_; + new_path->log_prob_c = cur_log_prob_c; + + // set spell checker state + // check to see if next state is final + auto FSTZERO = fst::TropicalWeight::Zero(); + auto final_weight = dictionary_->Final(matcher_->Value().nextstate); + bool is_final = (final_weight != FSTZERO); + if (is_final && reset) { + // restart spell checker at the start state + new_path->dictionary_state_ = dictionary_->Start(); + } else { + // go to next state + new_path->dictionary_state_ = matcher_->Value().nextstate; + } + + children_.push_back(std::make_pair(new_char, new_path)); + return new_path; + } + } else { + PathTrie* new_path = new PathTrie; + new_path->character = new_char; + new_path->timestep = new_timestep; + new_path->parent = this; + new_path->log_prob_c = cur_log_prob_c; + children_.push_back(std::make_pair(new_char, new_path)); + return new_path; + } + } +} + +PathTrie* PathTrie::get_path_vec(std::vector& output, std::vector& timesteps) { + return get_path_vec(output, timesteps, ROOT_); +} + +PathTrie* PathTrie::get_path_vec(std::vector& output, + std::vector& timesteps, + int stop, + size_t max_steps) { + if (character == stop || character == ROOT_ || output.size() == max_steps) { + std::reverse(output.begin(), output.end()); + std::reverse(timesteps.begin(), timesteps.end()); + return this; + } else { + output.push_back(character); + timesteps.push_back(timestep); + return parent->get_path_vec(output, timesteps, stop, max_steps); + } +} + +void PathTrie::iterate_to_vec(std::vector& output) { + if (exists_) { + log_prob_b_prev = log_prob_b_cur; + log_prob_nb_prev = log_prob_nb_cur; + + log_prob_b_cur = -NUM_FLT_INF; + log_prob_nb_cur = -NUM_FLT_INF; + + score = log_sum_exp(log_prob_b_prev, log_prob_nb_prev); + output.push_back(this); + } + for (auto child : children_) { + child.second->iterate_to_vec(output); + } +} + +void PathTrie::remove() { + exists_ = false; + + if (children_.size() == 0) { + auto child = parent->children_.begin(); + for (child = parent->children_.begin(); child != parent->children_.end(); + ++child) { + if (child->first == character) { + parent->children_.erase(child); + break; + } + } + + if (parent->children_.size() == 0 && !parent->exists_) { + parent->remove(); + } + + delete this; + } +} + +void PathTrie::set_dictionary(fst::StdVectorFst* dictionary) { + dictionary_ = dictionary; + dictionary_state_ = dictionary->Start(); + has_dictionary_ = true; +} + +using FSTMATCH = fst::SortedMatcher; +void PathTrie::set_matcher(std::shared_ptr matcher) { + matcher_ = matcher; +} diff --git a/ctcdecode-master/ctcdecode/src/path_trie.h b/ctcdecode-master/ctcdecode/src/path_trie.h new file mode 100644 index 0000000..baa27db --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/path_trie.h @@ -0,0 +1,70 @@ +#ifndef PATH_TRIE_H +#define PATH_TRIE_H + +#include +#include +#include +#include +#include + +#include "fst/fstlib.h" + +/* Trie tree for prefix storing and manipulating, with a dictionary in + * finite-state transducer for spelling correction. + */ +class PathTrie { +public: + PathTrie(); + ~PathTrie(); + + // get new prefix after appending new char + PathTrie* get_path_trie(int new_char, int new_timestep, float log_prob_c, bool reset = true); + + // get the prefix in index from root to current node + PathTrie* get_path_vec(std::vector& output, std::vector& timesteps); + + // get the prefix in index from some stop node to current nodel + PathTrie* get_path_vec(std::vector& output, + std::vector& timesteps, + int stop, + size_t max_steps = std::numeric_limits::max()); + + // update log probs + void iterate_to_vec(std::vector& output); + + // set dictionary for FST + void set_dictionary(fst::StdVectorFst* dictionary); + + void set_matcher(std::shared_ptr>); + + bool is_empty() { return ROOT_ == character; } + + // remove current path from root + void remove(); + + float log_prob_b_prev; + float log_prob_nb_prev; + float log_prob_b_cur; + float log_prob_nb_cur; + float log_prob_c; + float score; + float approx_ctc; + int character; + int timestep; + PathTrie* parent; + +private: + int ROOT_; + bool exists_; + bool has_dictionary_; + + std::vector> children_; + + // pointer to dictionary of FST + fst::StdVectorFst* dictionary_; + fst::StdVectorFst::StateId dictionary_state_; + // true if finding ars in FST + std::shared_ptr> matcher_; +}; + +#endif // PATH_TRIE_H diff --git a/ctcdecode-master/ctcdecode/src/scorer.cpp b/ctcdecode-master/ctcdecode/src/scorer.cpp new file mode 100644 index 0000000..c3550b3 --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/scorer.cpp @@ -0,0 +1,230 @@ +#include "scorer.h" + +#include +#include + +#include "lm/config.hh" +#include "lm/model.hh" +#include "lm/state.hh" +#include "util/string_piece.hh" +#include "util/tokenize_piece.hh" + +#include "decoder_utils.h" + +using namespace lm::ngram; + +Scorer::Scorer(double alpha, + double beta, + const std::string& lm_path, + const std::vector& vocab_list) { + this->alpha = alpha; + this->beta = beta; + + dictionary = nullptr; + is_character_based_ = true; + language_model_ = nullptr; + + max_order_ = 0; + dict_size_ = 0; + SPACE_ID_ = -1; + + setup(lm_path, vocab_list); +} + +Scorer::~Scorer() { + if (language_model_ != nullptr) { + delete static_cast(language_model_); + } + if (dictionary != nullptr) { + delete static_cast(dictionary); + } +} + +void Scorer::setup(const std::string& lm_path, + const std::vector& vocab_list) { + // load language model + load_lm(lm_path); + // set char map for scorer + set_char_map(vocab_list); + // fill the dictionary for FST + if (!is_character_based()) { + fill_dictionary(true); + } +} + +void Scorer::load_lm(const std::string& lm_path) { + const char* filename = lm_path.c_str(); + VALID_CHECK_EQ(access(filename, F_OK), 0, "Invalid language model path"); + + RetriveStrEnumerateVocab enumerate; + lm::ngram::Config config; + config.enumerate_vocab = &enumerate; + language_model_ = lm::ngram::LoadVirtual(filename, config); + max_order_ = static_cast(language_model_)->Order(); + vocabulary_ = enumerate.vocabulary; + for (size_t i = 0; i < vocabulary_.size(); ++i) { + if (is_character_based_ && vocabulary_[i] != UNK_TOKEN && + vocabulary_[i] != START_TOKEN && vocabulary_[i] != END_TOKEN && + get_utf8_str_len(enumerate.vocabulary[i]) > 1) { + is_character_based_ = false; + } + } +} + +double Scorer::get_log_cond_prob(const std::vector& words) { + lm::base::Model* model = static_cast(language_model_); + double cond_prob; + lm::ngram::State state, tmp_state, out_state; + // avoid to inserting in begin + model->NullContextWrite(&state); + for (size_t i = 0; i < words.size(); ++i) { + lm::WordIndex word_index = model->BaseVocabulary().Index(words[i]); + // encounter OOV + if (word_index == 0) { + return OOV_SCORE; + } + cond_prob = model->BaseScore(&state, word_index, &out_state); + tmp_state = state; + state = out_state; + out_state = tmp_state; + } + // return loge prob + return cond_prob/NUM_FLT_LOGE; +} + +double Scorer::get_sent_log_prob(const std::vector& words) { + std::vector sentence; + if (words.size() == 0) { + for (size_t i = 0; i < max_order_; ++i) { + sentence.push_back(START_TOKEN); + } + } else { + for (size_t i = 0; i < max_order_ - 1; ++i) { + sentence.push_back(START_TOKEN); + } + sentence.insert(sentence.end(), words.begin(), words.end()); + } + sentence.push_back(END_TOKEN); + return get_log_prob(sentence); +} + +double Scorer::get_log_prob(const std::vector& words) { + assert(words.size() > max_order_); + double score = 0.0; + for (size_t i = 0; i < words.size() - max_order_ + 1; ++i) { + std::vector ngram(words.begin() + i, + words.begin() + i + max_order_); + score += get_log_cond_prob(ngram); + } + return score; +} + +void Scorer::reset_params(float alpha, float beta) { + this->alpha = alpha; + this->beta = beta; +} + +std::string Scorer::vec2str(const std::vector& input) { + std::string word; + for (auto ind : input) { + word += char_list_[ind]; + } + return word; +} + +std::vector Scorer::split_labels(const std::vector& labels) { + if (labels.empty()) return {}; + + std::string s = vec2str(labels); + std::vector words; + if (is_character_based_) { + words = split_utf8_str(s); + } else { + words = split_str(s, " "); + } + return words; +} + +void Scorer::set_char_map(const std::vector& char_list) { + char_list_ = char_list; + char_map_.clear(); + + for (size_t i = 0; i < char_list_.size(); i++) { + if (char_list_[i] == " ") { + SPACE_ID_ = i; + } + // The initial state of FST is state 0, hence the index of chars in + // the FST should start from 1 to avoid the conflict with the initial + // state, otherwise wrong decoding results would be given. + char_map_[char_list_[i]] = i + 1; + } +} + +std::vector Scorer::make_ngram(PathTrie* prefix) { + std::vector ngram; + PathTrie* current_node = prefix; + PathTrie* new_node = nullptr; + + for (int order = 0; order < max_order_; order++) { + std::vector prefix_vec; + std::vector prefix_steps; + + if (is_character_based_) { + new_node = current_node->get_path_vec(prefix_vec, prefix_steps, -1, 1); + current_node = new_node; + } else { + new_node = current_node->get_path_vec(prefix_vec, prefix_steps, SPACE_ID_); + current_node = new_node->parent; // Skipping spaces + } + + // reconstruct word + std::string word = vec2str(prefix_vec); + ngram.push_back(word); + + if (new_node->character == -1) { + // No more spaces, but still need order + for (int i = 0; i < max_order_ - order - 1; i++) { + ngram.push_back(START_TOKEN); + } + break; + } + } + std::reverse(ngram.begin(), ngram.end()); + return ngram; +} + +void Scorer::fill_dictionary(bool add_space) { + fst::StdVectorFst dictionary; + // For each unigram convert to ints and put in trie + int dict_size = 0; + for (const auto& word : vocabulary_) { + bool added = add_word_to_dictionary( + word, char_map_, add_space, SPACE_ID_ + 1, &dictionary); + dict_size += added ? 1 : 0; + } + + dict_size_ = dict_size; + + /* Simplify FST + + * This gets rid of "epsilon" transitions in the FST. + * These are transitions that don't require a string input to be taken. + * Getting rid of them is necessary to make the FST determinisitc, but + * can greatly increase the size of the FST + */ + fst::RmEpsilon(&dictionary); + fst::StdVectorFst* new_dict = new fst::StdVectorFst; + + /* This makes the FST deterministic, meaning for any string input there's + * only one possible state the FST could be in. It is assumed our + * dictionary is deterministic when using it. + * (lest we'd have to check for multiple transitions at each state) + */ + fst::Determinize(dictionary, new_dict); + + /* Finds the simplest equivalent fst. This is unnecessary but decreases + * memory usage of the dictionary + */ + fst::Minimize(new_dict); + this->dictionary = new_dict; +} diff --git a/ctcdecode-master/ctcdecode/src/scorer.h b/ctcdecode-master/ctcdecode/src/scorer.h new file mode 100644 index 0000000..5ebc719 --- /dev/null +++ b/ctcdecode-master/ctcdecode/src/scorer.h @@ -0,0 +1,112 @@ +#ifndef SCORER_H_ +#define SCORER_H_ + +#include +#include +#include +#include + +#include "lm/enumerate_vocab.hh" +#include "lm/virtual_interface.hh" +#include "lm/word_index.hh" +#include "util/string_piece.hh" + +#include "path_trie.h" + +const double OOV_SCORE = -1000.0; +const std::string START_TOKEN = ""; +const std::string UNK_TOKEN = ""; +const std::string END_TOKEN = ""; + +// Implement a callback to retrive the dictionary of language model. +class RetriveStrEnumerateVocab : public lm::EnumerateVocab { +public: + RetriveStrEnumerateVocab() {} + + void Add(lm::WordIndex index, const StringPiece &str) { + vocabulary.push_back(std::string(str.data(), str.length())); + } + + std::vector vocabulary; +}; + +/* External scorer to query score for n-gram or sentence, including language + * model scoring and word insertion. + * + * Example: + * Scorer scorer(alpha, beta, "path_of_language_model"); + * scorer.get_log_cond_prob({ "WORD1", "WORD2", "WORD3" }); + * scorer.get_sent_log_prob({ "WORD1", "WORD2", "WORD3" }); + */ +class Scorer { +public: + Scorer(double alpha, + double beta, + const std::string &lm_path, + const std::vector &vocabulary); + ~Scorer(); + + double get_log_cond_prob(const std::vector &words); + + double get_sent_log_prob(const std::vector &words); + + // return the max order + size_t get_max_order() const { return max_order_; } + + // return the dictionary size of language model + size_t get_dict_size() const { return dict_size_; } + + // retrun true if the language model is character based + bool is_character_based() const { return is_character_based_; } + + // reset params alpha & beta + void reset_params(float alpha, float beta); + + // make ngram for a given prefix + std::vector make_ngram(PathTrie *prefix); + + // trransform the labels in index to the vector of words (word based lm) or + // the vector of characters (character based lm) + std::vector split_labels(const std::vector &labels); + + // language model weight + double alpha; + // word insertion weight + double beta; + + // pointer to the dictionary of FST + void *dictionary; + +protected: + // necessary setup: load language model, set char map, fill FST's dictionary + void setup(const std::string &lm_path, + const std::vector &vocab_list); + + // load language model from given path + void load_lm(const std::string &lm_path); + + // fill dictionary for FST + void fill_dictionary(bool add_space); + + // set char map + void set_char_map(const std::vector &char_list); + + double get_log_prob(const std::vector &words); + + // translate the vector in index to string + std::string vec2str(const std::vector &input); + +private: + void *language_model_; + bool is_character_based_; + size_t max_order_; + size_t dict_size_; + + int SPACE_ID_; + std::vector char_list_; + std::unordered_map char_map_; + + std::vector vocabulary_; +}; + +#endif // SCORER_H_ diff --git a/ctcdecode-master/requirements.txt b/ctcdecode-master/requirements.txt new file mode 100644 index 0000000..08ed5ee --- /dev/null +++ b/ctcdecode-master/requirements.txt @@ -0,0 +1 @@ +torch \ No newline at end of file diff --git a/ctcdecode-master/setup.py b/ctcdecode-master/setup.py new file mode 100644 index 0000000..5358408 --- /dev/null +++ b/ctcdecode-master/setup.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python +import glob +import multiprocessing.pool +import os +import tarfile +import urllib.request +import warnings + +from setuptools import distutils, find_packages, setup +from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths + + +def download_extract(url, dl_path): + if not os.path.isfile(dl_path): + # Already downloaded + urllib.request.urlretrieve(url, dl_path) + if dl_path.endswith(".tar.gz") and os.path.isdir(dl_path[: -len(".tar.gz")]): + # Already extracted + return + tar = tarfile.open(dl_path) + tar.extractall("third_party/") + tar.close() + + +# Download/Extract openfst, boost +download_extract( + "https://github.com/parlance/ctcdecode/releases/download/v1.0/openfst-1.6.7.tar.gz", + "third_party/openfst-1.6.7.tar.gz", +) +download_extract( + "https://github.com/parlance/ctcdecode/releases/download/v1.0/boost_1_67_0.tar.gz", + "third_party/boost_1_67_0.tar.gz", +) + +for file in ["third_party/kenlm/setup.py", "third_party/ThreadPool/ThreadPool.h"]: + if not os.path.exists(file): + warnings.warn("File `{}` does not appear to be present. Did you forget `git submodule update`?".format(file)) + + +# Does gcc compile with this header and library? +def compile_test(header, library): + dummy_path = os.path.join(os.path.dirname(__file__), "dummy") + command = ( + 'bash -c "g++ -include ' + + header + + " -l" + + library + + " -x c++ - <<<'int main() {}' -o " + + dummy_path + + " >/dev/null 2>/dev/null && rm " + + dummy_path + + ' 2>/dev/null"' + ) + return os.system(command) == 0 + + +compile_args = ["-O3", "-DKENLM_MAX_ORDER=6", "-std=c++17", "-fPIC"] +ext_libs = [] +if compile_test("zlib.h", "z"): + compile_args.append("-DHAVE_ZLIB") + ext_libs.append("z") + +if compile_test("bzlib.h", "bz2"): + compile_args.append("-DHAVE_BZLIB") + ext_libs.append("bz2") + +if compile_test("lzma.h", "lzma"): + compile_args.append("-DHAVE_XZLIB") + ext_libs.append("lzma") + +third_party_libs = ["kenlm", "openfst-1.6.7/src/include", "ThreadPool", "boost_1_67_0", "utf8"] +compile_args.extend(["-DINCLUDE_KENLM", "-DKENLM_MAX_ORDER=6"]) +lib_sources = ( + glob.glob("third_party/kenlm/util/*.cc") + + glob.glob("third_party/kenlm/lm/*.cc") + + glob.glob("third_party/kenlm/util/double-conversion/*.cc") + + glob.glob("third_party/openfst-1.6.7/src/lib/*.cc") +) +lib_sources = [fn for fn in lib_sources if not (fn.endswith("main.cc") or fn.endswith("test.cc"))] + +third_party_includes = [os.path.realpath(os.path.join("third_party", lib)) for lib in third_party_libs] +ctc_sources = glob.glob("ctcdecode/src/*.cpp") + +extension = CppExtension( + name="ctcdecode._ext.ctc_decode", + package=True, + with_cuda=False, + sources=ctc_sources + lib_sources, + include_dirs=third_party_includes + include_paths(), + libraries=ext_libs, + extra_compile_args=compile_args, + language="c++", +) + + +# monkey-patch for parallel compilation +# See: https://stackoverflow.com/a/13176803 +def parallelCCompile( + self, + sources, + output_dir=None, + macros=None, + include_dirs=None, + debug=0, + extra_preargs=None, + extra_postargs=None, + depends=None, +): + # those lines are copied from distutils.ccompiler.CCompiler directly + macros, objects, extra_postargs, pp_opts, build = self._setup_compile( + output_dir, macros, include_dirs, sources, depends, extra_postargs + ) + cc_args = self._get_cc_args(pp_opts, debug, extra_preargs) + + # parallel code + def _single_compile(obj): + try: + src, ext = build[obj] + except KeyError: + return + self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts) + + # convert to list, imap is evaluated on-demand + thread_pool = multiprocessing.pool.ThreadPool(os.cpu_count()) + list(thread_pool.imap(_single_compile, objects)) + return objects + + +# hack compile to support parallel compiling +distutils.ccompiler.CCompiler.compile = parallelCCompile + +setup( + name="ctcdecode", + version="1.0.3", + description="CTC Decoder for PyTorch based on Paddle Paddle's implementation", + url="https://github.com/parlance/ctcdecode", + author="Ryan Leary", + author_email="ryanleary@gmail.com", + # Exclude the build files. + packages=find_packages(exclude=["build"]), + ext_modules=[extension], + cmdclass={"build_ext": BuildExtension}, +) diff --git a/ctcdecode-master/tests/test.arpa b/ctcdecode-master/tests/test.arpa new file mode 100644 index 0000000..c4d2e6d --- /dev/null +++ b/ctcdecode-master/tests/test.arpa @@ -0,0 +1,124 @@ + +\data\ +ngram 1=37 +ngram 2=47 +ngram 3=11 +ngram 4=6 +ngram 5=4 + +\1-grams: +-1.383514 , -0.30103 +-1.139057 . -0.845098 +-1.029493 +-99 -0.4149733 +-1.995635 -20 +-1.285941 a -0.69897 +-1.687872 also -0.30103 +-1.687872 beyond -0.30103 +-1.687872 biarritz -0.30103 +-1.687872 call -0.30103 +-1.687872 concerns -0.30103 +-1.687872 consider -0.30103 +-1.687872 considering -0.30103 +-1.687872 for -0.30103 +-1.509559 higher -0.30103 +-1.687872 however -0.30103 +-1.687872 i -0.30103 +-1.687872 immediate -0.30103 +-1.687872 in -0.30103 +-1.687872 is -0.30103 +-1.285941 little -0.69897 +-1.383514 loin -0.30103 +-1.687872 look -0.30103 +-1.285941 looking -0.4771212 +-1.206319 more -0.544068 +-1.509559 on -0.4771212 +-1.509559 screening -0.4771212 +-1.687872 small -0.30103 +-1.687872 the -0.30103 +-1.687872 to -0.30103 +-1.687872 watch -0.30103 +-1.687872 watching -0.30103 +-1.687872 what -0.30103 +-1.687872 would -0.30103 +-3.141592 foo +-2.718281 bar 3.0 +-6.535897 baz -0.0 + +\2-grams: +-0.6925742 , . +-0.7522095 , however +-0.7522095 , is +-0.0602359 . +-0.4846522 looking -0.4771214 +-1.051485 screening +-1.07153 the +-1.07153 watching +-1.07153 what +-0.09132547 a little -0.69897 +-0.2922095 also call +-0.2922095 beyond immediate +-0.2705918 biarritz . +-0.2922095 call for +-0.2922095 concerns in +-0.2922095 consider watch +-0.2922095 considering consider +-0.2834328 for , +-0.5511513 higher more +-0.5845945 higher small +-0.2834328 however , +-0.2922095 i would +-0.2922095 immediate concerns +-0.2922095 in biarritz +-0.2922095 is to +-0.09021038 little more -0.1998621 +-0.7273645 loin , +-0.6925742 loin . +-0.6708385 loin +-0.2922095 look beyond +-0.4638903 looking higher +-0.4638903 looking on -0.4771212 +-0.5136299 more . -0.4771212 +-0.3561665 more loin +-0.1649931 on a -0.4771213 +-0.1649931 screening a -0.4771213 +-0.2705918 small . +-0.287799 the screening +-0.2922095 to look +-0.2622373 watch +-0.2922095 watching considering +-0.2922095 what i +-0.2922095 would also +-2 also would -6 +-15 -2 +-4 however -1 +-6 foo bar + +\3-grams: +-0.01916512 more . +-0.0283603 on a little -0.4771212 +-0.0283603 screening a little -0.4771212 +-0.01660496 a little more -0.09409451 +-0.3488368 looking higher +-0.3488368 looking on -0.4771212 +-0.1892331 little more loin +-0.04835128 looking on a -0.4771212 +-3 also would consider -7 +-6 however -12 +-7 to look a + +\4-grams: +-0.009249173 looking on a little -0.4771212 +-0.005464747 on a little more -0.4771212 +-0.005464747 screening a little more +-0.1453306 a little more loin +-0.01552657 looking on a -0.4771212 +-4 also would consider higher -8 + +\5-grams: +-0.003061223 looking on a little +-0.001813953 looking on a little more +-0.0432557 on a little more loin +-5 also would consider higher looking + +\end\ diff --git a/ctcdecode-master/tests/test_decode.py b/ctcdecode-master/tests/test_decode.py new file mode 100644 index 0000000..2dcb83f --- /dev/null +++ b/ctcdecode-master/tests/test_decode.py @@ -0,0 +1,215 @@ +"""Test decoders.""" +from __future__ import absolute_import, division, print_function + +import os +import unittest + +import ctcdecode +import torch + + +class TestDecoders(unittest.TestCase): + def setUp(self): + self.vocab_list = ["'", " ", "a", "b", "c", "d", "_"] + self.beam_size = 20 + self.probs_seq1 = [ + [0.06390443, 0.21124858, 0.27323887, 0.06870235, 0.0361254, 0.18184413, 0.16493624], + [0.03309247, 0.22866108, 0.24390638, 0.09699597, 0.31895462, 0.0094893, 0.06890021], + [0.218104, 0.19992557, 0.18245131, 0.08503348, 0.14903535, 0.08424043, 0.08120984], + [0.12094152, 0.19162472, 0.01473646, 0.28045061, 0.24246305, 0.05206269, 0.09772094], + [0.1333387, 0.00550838, 0.00301669, 0.21745861, 0.20803985, 0.41317442, 0.01946335], + [0.16468227, 0.1980699, 0.1906545, 0.18963251, 0.19860937, 0.04377724, 0.01457421], + ] + self.probs_seq2 = [ + [0.08034842, 0.22671944, 0.05799633, 0.36814645, 0.11307441, 0.04468023, 0.10903471], + [0.09742457, 0.12959763, 0.09435383, 0.21889204, 0.15113123, 0.10219457, 0.20640612], + [0.45033529, 0.09091417, 0.15333208, 0.07939558, 0.08649316, 0.12298585, 0.01654384], + [0.02512238, 0.22079203, 0.19664364, 0.11906379, 0.07816055, 0.22538587, 0.13483174], + [0.17928453, 0.06065261, 0.41153005, 0.1172041, 0.11880313, 0.07113197, 0.04139363], + [0.15882358, 0.1235788, 0.23376776, 0.20510435, 0.00279306, 0.05294827, 0.22298418], + ] + self.greedy_result = ["ac'bdc", "b'da"] + self.beam_search_result = ["acdc", "b'a", "a a"] + + def convert_to_string(self, tokens, vocab, seq_len): + return "".join([vocab[x] for x in tokens[0:seq_len]]) + + def test_beam_search_decoder_1(self): + probs_seq = torch.FloatTensor([self.probs_seq1]) + decoder = ctcdecode.CTCBeamDecoder( + self.vocab_list, beam_width=self.beam_size, blank_id=self.vocab_list.index("_") + ) + beam_result, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq) + output_str = self.convert_to_string(beam_result[0][0], self.vocab_list, out_seq_len[0][0]) + self.assertEqual(output_str, self.beam_search_result[0]) + + def test_beam_search_decoder_2(self): + probs_seq = torch.FloatTensor([self.probs_seq2]) + decoder = ctcdecode.CTCBeamDecoder( + self.vocab_list, beam_width=self.beam_size, blank_id=self.vocab_list.index("_") + ) + beam_result, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq) + output_str = self.convert_to_string(beam_result[0][0], self.vocab_list, out_seq_len[0][0]) + self.assertEqual(output_str, self.beam_search_result[1]) + + def test_beam_search_decoder_3(self): + lm_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test.arpa") + probs_seq = torch.FloatTensor([self.probs_seq2]) + + decoder = ctcdecode.CTCBeamDecoder( + self.vocab_list, beam_width=self.beam_size, blank_id=self.vocab_list.index("_"), model_path=lm_path + ) + beam_result, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq) + output_str = self.convert_to_string(beam_result[0][0], self.vocab_list, out_seq_len[0][0]) + self.assertEqual(output_str, self.beam_search_result[2]) + + def test_beam_search_decoder_batch(self): + probs_seq = torch.FloatTensor([self.probs_seq1, self.probs_seq2]) + decoder = ctcdecode.CTCBeamDecoder( + self.vocab_list, beam_width=self.beam_size, blank_id=self.vocab_list.index("_"), num_processes=24 + ) + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq) + output_str1 = self.convert_to_string(beam_results[0][0], self.vocab_list, out_seq_len[0][0]) + output_str2 = self.convert_to_string(beam_results[1][0], self.vocab_list, out_seq_len[1][0]) + self.assertEqual(output_str1, self.beam_search_result[0]) + self.assertEqual(output_str2, self.beam_search_result[1]) + del decoder + + def test_beam_search_decoder_batch_log(self): + probs_seq = torch.FloatTensor([self.probs_seq1, self.probs_seq2]).log() + decoder = ctcdecode.CTCBeamDecoder( + self.vocab_list, + beam_width=self.beam_size, + blank_id=self.vocab_list.index("_"), + log_probs_input=True, + num_processes=24, + ) + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq) + output_str1 = self.convert_to_string(beam_results[0][0], self.vocab_list, out_seq_len[0][0]) + output_str2 = self.convert_to_string(beam_results[1][0], self.vocab_list, out_seq_len[1][0]) + self.assertEqual(output_str1, self.beam_search_result[0]) + self.assertEqual(output_str2, self.beam_search_result[1]) + + def test_online_decoder_decoding(self): + lm_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test.arpa") + decoder = ctcdecode.OnlineCTCBeamDecoder( + self.vocab_list, + beam_width=self.beam_size, + blank_id=self.vocab_list.index("_"), + log_probs_input=True, + num_processes=24, + model_path=lm_path, + ) + state1 = ctcdecode.DecoderState(decoder) + state2 = ctcdecode.DecoderState(decoder) + + probs_seq = torch.FloatTensor([self.probs_seq2, self.probs_seq2]).log() + + is_eos_s = [True for _ in range(len(probs_seq))] + + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq, [state1, state2], is_eos_s) + output_str1 = self.convert_to_string(beam_results[0][0], self.vocab_list, out_seq_len[0][0]) + output_str2 = self.convert_to_string(beam_results[1][0], self.vocab_list, out_seq_len[1][0]) + + self.assertEqual(output_str1, self.beam_search_result[2]) + self.assertEqual(output_str2, self.beam_search_result[2]) + + def test_online_decoder_decoding_no_lm(self): + decoder = ctcdecode.OnlineCTCBeamDecoder( + self.vocab_list, + beam_width=self.beam_size, + blank_id=self.vocab_list.index("_"), + log_probs_input=True, + num_processes=24, + ) + state1 = ctcdecode.DecoderState(decoder) + state2 = ctcdecode.DecoderState(decoder) + + probs_seq = torch.FloatTensor([self.probs_seq1, self.probs_seq2]).log() + + is_eos_s = [True for _ in range(len(probs_seq))] + + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode( + probs_seq, [state1, state2], is_eos_s + ) + output_str1 = self.convert_to_string(beam_results[0][0], self.vocab_list, out_seq_len[0][0]) + output_str2 = self.convert_to_string(beam_results[1][0], self.vocab_list, out_seq_len[1][0]) + + self.assertEqual(output_str1, self.beam_search_result[0]) + self.assertEqual(output_str2, self.beam_search_result[1]) + + def test_online_decoder_decoding_with_two_calls(self): + lm_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test.arpa") + decoder = ctcdecode.OnlineCTCBeamDecoder( + self.vocab_list, + beam_width=self.beam_size, + blank_id=self.vocab_list.index("_"), + log_probs_input=True, + num_processes=24, + model_path=lm_path, + ) + state1 = ctcdecode.DecoderState(decoder) + + probs_seq = torch.FloatTensor([self.probs_seq2]).log() + + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq[:, :2], [state1], [False]) + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(probs_seq[:, 2:], [state1], [True]) + + output_str1 = self.convert_to_string(beam_results[0][0], self.vocab_list, out_seq_len[0][0]) + self.assertEqual(output_str1, self.beam_search_result[2]) + + def test_online_decoder_decoding_with_two_calls_no_lm(self): + decoder = ctcdecode.OnlineCTCBeamDecoder( + self.vocab_list, + beam_width=self.beam_size, + blank_id=self.vocab_list.index("_"), + log_probs_input=True, + num_processes=24, + ) + state1 = ctcdecode.DecoderState(decoder) + state2 = ctcdecode.DecoderState(decoder) + + probs_seq = torch.FloatTensor([self.probs_seq1, self.probs_seq2]).log() + + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode( + probs_seq[:, :2], [state1, state2], [False, False] + ) + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode( + probs_seq[:, 2:], [state1, state2], [True, True] + ) + + del state1, state2 + size = beam_results.shape + output_str1 = self.convert_to_string(beam_results[0][0], self.vocab_list, out_seq_len[0][0]) + output_str2 = self.convert_to_string(beam_results[1][0], self.vocab_list, out_seq_len[1][0]) + + self.assertEqual(output_str1, self.beam_search_result[0]) + self.assertEqual(output_str2, self.beam_search_result[1]) + + def test_online_decoder_decoding_with_a_lot_calls_no_lm_check_size(self): + decoder = ctcdecode.OnlineCTCBeamDecoder( + self.vocab_list, + beam_width=self.beam_size, + blank_id=self.vocab_list.index("_"), + log_probs_input=True, + num_processes=24, + ) + state1 = ctcdecode.DecoderState(decoder) + + probs_seq = torch.FloatTensor([self.probs_seq1]).log() + + for i in range(1000): + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode( + probs_seq, [state1], [False, False] + ) + + beam_results, beam_scores, timesteps, out_seq_len = decoder.decode( + probs_seq, [state1], [True, True] + ) + + del state1 + self.assertGreaterEqual(beam_results.shape[2], out_seq_len.max()) + + +if __name__ == "__main__": + unittest.main() diff --git a/ctcdecode-master/third_party/ThreadPool/COPYING b/ctcdecode-master/third_party/ThreadPool/COPYING new file mode 100644 index 0000000..7d69178 --- /dev/null +++ b/ctcdecode-master/third_party/ThreadPool/COPYING @@ -0,0 +1,20 @@ +Copyright (c) 2012 Jakob Progsch, Václav Zeman + +This software is provided 'as-is', without any express or implied +warranty. In no event will the authors be held liable for any damages +arising from the use of this software. + +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it +freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + + 3. This notice may not be removed or altered from any source + distribution. diff --git a/ctcdecode-master/third_party/ThreadPool/README.md b/ctcdecode-master/third_party/ThreadPool/README.md new file mode 100644 index 0000000..be3d25a --- /dev/null +++ b/ctcdecode-master/third_party/ThreadPool/README.md @@ -0,0 +1,17 @@ +ThreadPool +========== + +A simple C++11 Thread Pool implementation. + +Basic usage: +```c++ +// create thread pool with 4 worker threads +ThreadPool pool(4); + +// enqueue and store future +auto result = pool.enqueue([](int answer) { return answer; }, 42); + +// get result from future +std::cout << result.get() << std::endl; + +``` diff --git a/ctcdecode-master/third_party/ThreadPool/ThreadPool.h b/ctcdecode-master/third_party/ThreadPool/ThreadPool.h new file mode 100644 index 0000000..4183203 --- /dev/null +++ b/ctcdecode-master/third_party/ThreadPool/ThreadPool.h @@ -0,0 +1,98 @@ +#ifndef THREAD_POOL_H +#define THREAD_POOL_H + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class ThreadPool { +public: + ThreadPool(size_t); + template + auto enqueue(F&& f, Args&&... args) + -> std::future::type>; + ~ThreadPool(); +private: + // need to keep track of threads so we can join them + std::vector< std::thread > workers; + // the task queue + std::queue< std::function > tasks; + + // synchronization + std::mutex queue_mutex; + std::condition_variable condition; + bool stop; +}; + +// the constructor just launches some amount of workers +inline ThreadPool::ThreadPool(size_t threads) + : stop(false) +{ + for(size_t i = 0;i task; + + { + std::unique_lock lock(this->queue_mutex); + this->condition.wait(lock, + [this]{ return this->stop || !this->tasks.empty(); }); + if(this->stop && this->tasks.empty()) + return; + task = std::move(this->tasks.front()); + this->tasks.pop(); + } + + task(); + } + } + ); +} + +// add new work item to the pool +template +auto ThreadPool::enqueue(F&& f, Args&&... args) + -> std::future::type> +{ + using return_type = typename std::result_of::type; + + auto task = std::make_shared< std::packaged_task >( + std::bind(std::forward(f), std::forward(args)...) + ); + + std::future res = task->get_future(); + { + std::unique_lock lock(queue_mutex); + + // don't allow enqueueing after stopping the pool + if(stop) + throw std::runtime_error("enqueue on stopped ThreadPool"); + + tasks.emplace([task](){ (*task)(); }); + } + condition.notify_one(); + return res; +} + +// the destructor joins all threads +inline ThreadPool::~ThreadPool() +{ + { + std::unique_lock lock(queue_mutex); + stop = true; + } + condition.notify_all(); + for(std::thread &worker: workers) + worker.join(); +} + +#endif diff --git a/ctcdecode-master/third_party/ThreadPool/example.cpp b/ctcdecode-master/third_party/ThreadPool/example.cpp new file mode 100644 index 0000000..837277b --- /dev/null +++ b/ctcdecode-master/third_party/ThreadPool/example.cpp @@ -0,0 +1,29 @@ +#include +#include +#include + +#include "ThreadPool.h" + +int main() +{ + + ThreadPool pool(4); + std::vector< std::future > results; + + for(int i = 0; i < 8; ++i) { + results.emplace_back( + pool.enqueue([i] { + std::cout << "hello " << i << std::endl; + std::this_thread::sleep_for(std::chrono::seconds(1)); + std::cout << "world " << i << std::endl; + return i*i; + }) + ); + } + + for(auto && result: results) + std::cout << result.get() << ' '; + std::cout << std::endl; + + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/.gitignore b/ctcdecode-master/third_party/kenlm/.gitignore new file mode 100644 index 0000000..2e28eaf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/.gitignore @@ -0,0 +1,18 @@ +util/file_piece.cc.gz +*.swp +*.o +doc/ +build/ +._* +windows/Win32 +windows/x64 +windows/*.user +windows/*.sdf +windows/*.opensdf +windows/*.suo +CMakeFiles +cmake_install.cmake +CMakeCache.txt +CTestTestfile.cmake +DartConfiguration.tcl +Makefile diff --git a/ctcdecode-master/third_party/kenlm/BUILDING b/ctcdecode-master/third_party/kenlm/BUILDING new file mode 100644 index 0000000..f6a1081 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/BUILDING @@ -0,0 +1,14 @@ +KenLM has switched to cmake + cmake . + make -j 4 +But they recommend building out of tree + mkdir -p build && cd build + cmake .. + make -j 4 + +If you only want the query code and do not care about compression (.gz, .bz2, and .xz): + ./compile_query_only.sh + +Windows: + The windows directory has visual studio files. Note that you need to compile + the kenlm project before build_binary and ngram_query projects. diff --git a/ctcdecode-master/third_party/kenlm/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/CMakeLists.txt new file mode 100644 index 0000000..2b6a232 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/CMakeLists.txt @@ -0,0 +1,82 @@ +cmake_minimum_required(VERSION 2.6) + +# Define a single cmake project +project(kenlm) + +option(FORCE_STATIC "Build static executables" OFF) +if (FORCE_STATIC) + #presumably overkill, is there a better way? + #http://cmake.3232098.n2.nabble.com/Howto-compile-static-executable-td5580269.html + set(Boost_USE_STATIC_LIBS ON) + set_property(GLOBAL PROPERTY LINK_SEARCH_START_STATIC ON) + set_property(GLOBAL PROPERTY LINK_SEARCH_END_STATIC ON) + set(BUILD_SHARED_LIBRARIES OFF) + if (MSVC) + set(flag_vars + CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE + CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO + CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE + CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO) + foreach(flag_var ${flag_vars}) + if(${flag_var} MATCHES "/MD") + string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}") + endif(${flag_var} MATCHES "/MD") + endforeach(flag_var) + else (MSVC) + if (NOT CMAKE_C_COMPILER_ID MATCHES ".*Clang") + set(CMAKE_EXE_LINKER_FLAGS "-static-libgcc -static-libstdc++ -static") + endif () + set(CMAKE_FIND_LIBRARY_SUFFIXES ".a") + endif () + #Annoyingly the exectuables say "File not found" unless these are set + set(CMAKE_EXE_LINK_DYNAMIC_C_FLAGS) + set(CMAKE_EXE_LINK_DYNAMIC_CXX_FLAGS) + set(CMAKE_SHARED_LIBRARY_C_FLAGS) + set(CMAKE_SHARED_LIBRARY_CXX_FLAGS) + set(CMAKE_SHARED_LIBRARY_LINK_C_FLAGS) + set(CMAKE_SHARED_LIBRARY_LINK_CXX_FLAGS) +endif () + +# Compile all executables into bin/ +set(EXECUTABLE_OUTPUT_PATH ${PROJECT_BINARY_DIR}/bin) + +# Compile all libraries into lib/ +set(LIBRARY_OUTPUT_PATH ${PROJECT_BINARY_DIR}/lib) + +if (NOT CMAKE_BUILD_TYPE) + set(CMAKE_BUILD_TYPE Release) +endif() + +# Tell cmake that we want unit tests to be compiled +include(CTest) +enable_testing() + +# Add our CMake helper functions +include(cmake/KenLMFunctions.cmake) + +if(MSVC) + set(CMAKE_C_FLAGS "${CMAKE_CXX_FLAGS} /w34716") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /w34716") +endif() + +# And our helper modules +list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/modules) + +# We need boost +find_package(Boost 1.41.0 REQUIRED COMPONENTS + program_options + system + thread + unit_test_framework +) + +# Define where include files live +include_directories( + ${PROJECT_SOURCE_DIR} + ${Boost_INCLUDE_DIRS} +) + +# Process subdirectories +add_subdirectory(util) +add_subdirectory(lm) + diff --git a/ctcdecode-master/third_party/kenlm/COPYING b/ctcdecode-master/third_party/kenlm/COPYING new file mode 100644 index 0000000..4362b49 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/COPYING @@ -0,0 +1,502 @@ + GNU LESSER GENERAL PUBLIC LICENSE + Version 2.1, February 1999 + + Copyright (C) 1991, 1999 Free Software Foundation, Inc. + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + +[This is the first released version of the Lesser GPL. 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The default is UTF-8 which is also the encoding used for all +# text before the first occurrence of this tag. Doxygen uses libiconv (or the +# iconv built into libc) for the transcoding. See +# http://www.gnu.org/software/libiconv for the list of possible encodings. + +DOXYFILE_ENCODING = UTF-8 + +# The PROJECT_NAME tag is a single word (or a sequence of words surrounded +# by quotes) that should identify the project. + +PROJECT_NAME = KenLM + +# The PROJECT_NUMBER tag can be used to enter a project or revision number. +# This could be handy for archiving the generated documentation or +# if some version control system is used. + +PROJECT_NUMBER = + +# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) +# base path where the generated documentation will be put. +# If a relative path is entered, it will be relative to the location +# where doxygen was started. If left blank the current directory will be used. + +OUTPUT_DIRECTORY = doc + +# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create +# 4096 sub-directories (in 2 levels) under the output directory of each output +# format and will distribute the generated files over these directories. +# Enabling this option can be useful when feeding doxygen a huge amount of +# source files, where putting all generated files in the same directory would +# otherwise cause performance problems for the file system. + +CREATE_SUBDIRS = NO + +# The OUTPUT_LANGUAGE tag is used to specify the language in which all +# documentation generated by doxygen is written. Doxygen will use this +# information to generate all constant output in the proper language. +# The default language is English, other supported languages are: +# Afrikaans, Arabic, Brazilian, Catalan, Chinese, Chinese-Traditional, +# Croatian, Czech, Danish, Dutch, Esperanto, Farsi, Finnish, French, German, +# Greek, Hungarian, Italian, Japanese, Japanese-en (Japanese with English +# messages), Korean, Korean-en, Lithuanian, Norwegian, Macedonian, Persian, +# Polish, Portuguese, Romanian, Russian, Serbian, Serbian-Cyrilic, Slovak, +# Slovene, Spanish, Swedish, Ukrainian, and Vietnamese. + +OUTPUT_LANGUAGE = English + +# If the BRIEF_MEMBER_DESC tag is set to YES (the default) Doxygen will +# include brief member descriptions after the members that are listed in +# the file and class documentation (similar to JavaDoc). +# Set to NO to disable this. + +BRIEF_MEMBER_DESC = YES + +# If the REPEAT_BRIEF tag is set to YES (the default) Doxygen will prepend +# the brief description of a member or function before the detailed description. +# Note: if both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the +# brief descriptions will be completely suppressed. + +REPEAT_BRIEF = YES + +# This tag implements a quasi-intelligent brief description abbreviator +# that is used to form the text in various listings. Each string +# in this list, if found as the leading text of the brief description, will be +# stripped from the text and the result after processing the whole list, is +# used as the annotated text. Otherwise, the brief description is used as-is. +# If left blank, the following values are used ("$name" is automatically +# replaced with the name of the entity): "The $name class" "The $name widget" +# "The $name file" "is" "provides" "specifies" "contains" +# "represents" "a" "an" "the" + +ABBREVIATE_BRIEF = + +# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then +# Doxygen will generate a detailed section even if there is only a brief +# description. + +ALWAYS_DETAILED_SEC = YES + +# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all +# inherited members of a class in the documentation of that class as if those +# members were ordinary class members. Constructors, destructors and assignment +# operators of the base classes will not be shown. + +INLINE_INHERITED_MEMB = NO + +# If the FULL_PATH_NAMES tag is set to YES then Doxygen will prepend the full +# path before files name in the file list and in the header files. If set +# to NO the shortest path that makes the file name unique will be used. + +FULL_PATH_NAMES = YES + +# If the FULL_PATH_NAMES tag is set to YES then the STRIP_FROM_PATH tag +# can be used to strip a user-defined part of the path. Stripping is +# only done if one of the specified strings matches the left-hand part of +# the path. The tag can be used to show relative paths in the file list. +# If left blank the directory from which doxygen is run is used as the +# path to strip. + +STRIP_FROM_PATH = + +# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of +# the path mentioned in the documentation of a class, which tells +# the reader which header file to include in order to use a class. +# If left blank only the name of the header file containing the class +# definition is used. Otherwise one should specify the include paths that +# are normally passed to the compiler using the -I flag. + +STRIP_FROM_INC_PATH = + +# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter +# (but less readable) file names. This can be useful is your file systems +# doesn't support long names like on DOS, Mac, or CD-ROM. + +SHORT_NAMES = NO + +# If the JAVADOC_AUTOBRIEF tag is set to YES then Doxygen +# will interpret the first line (until the first dot) of a JavaDoc-style +# comment as the brief description. If set to NO, the JavaDoc +# comments will behave just like regular Qt-style comments +# (thus requiring an explicit @brief command for a brief description.) + +JAVADOC_AUTOBRIEF = YES + +# If the QT_AUTOBRIEF tag is set to YES then Doxygen will +# interpret the first line (until the first dot) of a Qt-style +# comment as the brief description. If set to NO, the comments +# will behave just like regular Qt-style comments (thus requiring +# an explicit \brief command for a brief description.) + +QT_AUTOBRIEF = NO + +# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen +# treat a multi-line C++ special comment block (i.e. a block of //! or /// +# comments) as a brief description. This used to be the default behaviour. +# The new default is to treat a multi-line C++ comment block as a detailed +# description. Set this tag to YES if you prefer the old behaviour instead. + +MULTILINE_CPP_IS_BRIEF = NO + +# If the INHERIT_DOCS tag is set to YES (the default) then an undocumented +# member inherits the documentation from any documented member that it +# re-implements. + +INHERIT_DOCS = YES + +# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce +# a new page for each member. If set to NO, the documentation of a member will +# be part of the file/class/namespace that contains it. + +SEPARATE_MEMBER_PAGES = NO + +# The TAB_SIZE tag can be used to set the number of spaces in a tab. +# Doxygen uses this value to replace tabs by spaces in code fragments. + +TAB_SIZE = 8 + +# This tag can be used to specify a number of aliases that acts +# as commands in the documentation. An alias has the form "name=value". +# For example adding "sideeffect=\par Side Effects:\n" will allow you to +# put the command \sideeffect (or @sideeffect) in the documentation, which +# will result in a user-defined paragraph with heading "Side Effects:". +# You can put \n's in the value part of an alias to insert newlines. + +ALIASES = + +# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C +# sources only. Doxygen will then generate output that is more tailored for C. +# For instance, some of the names that are used will be different. The list +# of all members will be omitted, etc. + +OPTIMIZE_OUTPUT_FOR_C = NO + +# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java +# sources only. Doxygen will then generate output that is more tailored for +# Java. For instance, namespaces will be presented as packages, qualified +# scopes will look different, etc. + +OPTIMIZE_OUTPUT_JAVA = NO + +# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran +# sources only. Doxygen will then generate output that is more tailored for +# Fortran. + +OPTIMIZE_FOR_FORTRAN = NO + +# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL +# sources. Doxygen will then generate output that is tailored for +# VHDL. + +OPTIMIZE_OUTPUT_VHDL = NO + +# Doxygen selects the parser to use depending on the extension of the files it parses. +# With this tag you can assign which parser to use for a given extension. +# Doxygen has a built-in mapping, but you can override or extend it using this tag. +# The format is ext=language, where ext is a file extension, and language is one of +# the parsers supported by doxygen: IDL, Java, Javascript, C#, C, C++, D, PHP, +# Objective-C, Python, Fortran, VHDL, C, C++. For instance to make doxygen treat +# .inc files as Fortran files (default is PHP), and .f files as C (default is Fortran), +# use: inc=Fortran f=C. Note that for custom extensions you also need to set FILE_PATTERNS otherwise the files are not read by doxygen. + +EXTENSION_MAPPING = + +# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want +# to include (a tag file for) the STL sources as input, then you should +# set this tag to YES in order to let doxygen match functions declarations and +# definitions whose arguments contain STL classes (e.g. func(std::string); v.s. +# func(std::string) {}). This also make the inheritance and collaboration +# diagrams that involve STL classes more complete and accurate. + +BUILTIN_STL_SUPPORT = NO + +# If you use Microsoft's C++/CLI language, you should set this option to YES to +# enable parsing support. + +CPP_CLI_SUPPORT = NO + +# Set the SIP_SUPPORT tag to YES if your project consists of sip sources only. +# Doxygen will parse them like normal C++ but will assume all classes use public +# instead of private inheritance when no explicit protection keyword is present. + +SIP_SUPPORT = NO + +# For Microsoft's IDL there are propget and propput attributes to indicate getter +# and setter methods for a property. Setting this option to YES (the default) +# will make doxygen to replace the get and set methods by a property in the +# documentation. This will only work if the methods are indeed getting or +# setting a simple type. If this is not the case, or you want to show the +# methods anyway, you should set this option to NO. + +IDL_PROPERTY_SUPPORT = YES + +# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC +# tag is set to YES, then doxygen will reuse the documentation of the first +# member in the group (if any) for the other members of the group. By default +# all members of a group must be documented explicitly. + +DISTRIBUTE_GROUP_DOC = NO + +# Set the SUBGROUPING tag to YES (the default) to allow class member groups of +# the same type (for instance a group of public functions) to be put as a +# subgroup of that type (e.g. under the Public Functions section). Set it to +# NO to prevent subgrouping. Alternatively, this can be done per class using +# the \nosubgrouping command. + +SUBGROUPING = YES + +# When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum +# is documented as struct, union, or enum with the name of the typedef. So +# typedef struct TypeS {} TypeT, will appear in the documentation as a struct +# with name TypeT. When disabled the typedef will appear as a member of a file, +# namespace, or class. And the struct will be named TypeS. This can typically +# be useful for C code in case the coding convention dictates that all compound +# types are typedef'ed and only the typedef is referenced, never the tag name. + +TYPEDEF_HIDES_STRUCT = NO + +# The SYMBOL_CACHE_SIZE determines the size of the internal cache use to +# determine which symbols to keep in memory and which to flush to disk. +# When the cache is full, less often used symbols will be written to disk. +# For small to medium size projects (<1000 input files) the default value is +# probably good enough. For larger projects a too small cache size can cause +# doxygen to be busy swapping symbols to and from disk most of the time +# causing a significant performance penality. +# If the system has enough physical memory increasing the cache will improve the +# performance by keeping more symbols in memory. Note that the value works on +# a logarithmic scale so increasing the size by one will rougly double the +# memory usage. The cache size is given by this formula: +# 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0, +# corresponding to a cache size of 2^16 = 65536 symbols + +SYMBOL_CACHE_SIZE = 0 + +#--------------------------------------------------------------------------- +# Build related configuration options +#--------------------------------------------------------------------------- + +# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in +# documentation are documented, even if no documentation was available. +# Private class members and static file members will be hidden unless +# the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES + +EXTRACT_ALL = NO + +# If the EXTRACT_PRIVATE tag is set to YES all private members of a class +# will be included in the documentation. + +EXTRACT_PRIVATE = YES + +# If the EXTRACT_STATIC tag is set to YES all static members of a file +# will be included in the documentation. + +EXTRACT_STATIC = NO + +# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) +# defined locally in source files will be included in the documentation. +# If set to NO only classes defined in header files are included. + +EXTRACT_LOCAL_CLASSES = YES + +# This flag is only useful for Objective-C code. When set to YES local +# methods, which are defined in the implementation section but not in +# the interface are included in the documentation. +# If set to NO (the default) only methods in the interface are included. + +EXTRACT_LOCAL_METHODS = NO + +# If this flag is set to YES, the members of anonymous namespaces will be +# extracted and appear in the documentation as a namespace called +# 'anonymous_namespace{file}', where file will be replaced with the base +# name of the file that contains the anonymous namespace. By default +# anonymous namespace are hidden. + +EXTRACT_ANON_NSPACES = NO + +# If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all +# undocumented members of documented classes, files or namespaces. +# If set to NO (the default) these members will be included in the +# various overviews, but no documentation section is generated. +# This option has no effect if EXTRACT_ALL is enabled. + +HIDE_UNDOC_MEMBERS = YES + +# If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all +# undocumented classes that are normally visible in the class hierarchy. +# If set to NO (the default) these classes will be included in the various +# overviews. This option has no effect if EXTRACT_ALL is enabled. + +HIDE_UNDOC_CLASSES = YES + +# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all +# friend (class|struct|union) declarations. +# If set to NO (the default) these declarations will be included in the +# documentation. + +HIDE_FRIEND_COMPOUNDS = NO + +# If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any +# documentation blocks found inside the body of a function. +# If set to NO (the default) these blocks will be appended to the +# function's detailed documentation block. + +HIDE_IN_BODY_DOCS = NO + +# The INTERNAL_DOCS tag determines if documentation +# that is typed after a \internal command is included. If the tag is set +# to NO (the default) then the documentation will be excluded. +# Set it to YES to include the internal documentation. + +INTERNAL_DOCS = NO + +# If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate +# file names in lower-case letters. If set to YES upper-case letters are also +# allowed. This is useful if you have classes or files whose names only differ +# in case and if your file system supports case sensitive file names. Windows +# and Mac users are advised to set this option to NO. + +CASE_SENSE_NAMES = YES + +# If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen +# will show members with their full class and namespace scopes in the +# documentation. If set to YES the scope will be hidden. + +HIDE_SCOPE_NAMES = NO + +# If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen +# will put a list of the files that are included by a file in the documentation +# of that file. + +SHOW_INCLUDE_FILES = YES + +# If the INLINE_INFO tag is set to YES (the default) then a tag [inline] +# is inserted in the documentation for inline members. + +INLINE_INFO = YES + +# If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen +# will sort the (detailed) documentation of file and class members +# alphabetically by member name. If set to NO the members will appear in +# declaration order. + +SORT_MEMBER_DOCS = YES + +# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the +# brief documentation of file, namespace and class members alphabetically +# by member name. If set to NO (the default) the members will appear in +# declaration order. + +SORT_BRIEF_DOCS = NO + +# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the (brief and detailed) documentation of class members so that constructors and destructors are listed first. If set to NO (the default) the constructors will appear in the respective orders defined by SORT_MEMBER_DOCS and SORT_BRIEF_DOCS. This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO. + +SORT_MEMBERS_CTORS_1ST = YES + +# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the +# hierarchy of group names into alphabetical order. If set to NO (the default) +# the group names will appear in their defined order. + +SORT_GROUP_NAMES = NO + +# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be +# sorted by fully-qualified names, including namespaces. If set to +# NO (the default), the class list will be sorted only by class name, +# not including the namespace part. +# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. +# Note: This option applies only to the class list, not to the +# alphabetical list. + +SORT_BY_SCOPE_NAME = YES + +# The GENERATE_TODOLIST tag can be used to enable (YES) or +# disable (NO) the todo list. This list is created by putting \todo +# commands in the documentation. + +GENERATE_TODOLIST = YES + +# The GENERATE_TESTLIST tag can be used to enable (YES) or +# disable (NO) the test list. This list is created by putting \test +# commands in the documentation. + +GENERATE_TESTLIST = YES + +# The GENERATE_BUGLIST tag can be used to enable (YES) or +# disable (NO) the bug list. This list is created by putting \bug +# commands in the documentation. + +GENERATE_BUGLIST = YES + +# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or +# disable (NO) the deprecated list. This list is created by putting +# \deprecated commands in the documentation. + +GENERATE_DEPRECATEDLIST= YES + +# The ENABLED_SECTIONS tag can be used to enable conditional +# documentation sections, marked by \if sectionname ... \endif. + +ENABLED_SECTIONS = + +# The MAX_INITIALIZER_LINES tag determines the maximum number of lines +# the initial value of a variable or define consists of for it to appear in +# the documentation. If the initializer consists of more lines than specified +# here it will be hidden. Use a value of 0 to hide initializers completely. +# The appearance of the initializer of individual variables and defines in the +# documentation can be controlled using \showinitializer or \hideinitializer +# command in the documentation regardless of this setting. + +MAX_INITIALIZER_LINES = 30 + +# Set the SHOW_USED_FILES tag to NO to disable the list of files generated +# at the bottom of the documentation of classes and structs. If set to YES the +# list will mention the files that were used to generate the documentation. + +SHOW_USED_FILES = YES + +# If the sources in your project are distributed over multiple directories +# then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy +# in the documentation. The default is NO. + +SHOW_DIRECTORIES = NO + +# Set the SHOW_FILES tag to NO to disable the generation of the Files page. +# This will remove the Files entry from the Quick Index and from the +# Folder Tree View (if specified). The default is YES. + +SHOW_FILES = YES + +# Set the SHOW_NAMESPACES tag to NO to disable the generation of the +# Namespaces page. +# This will remove the Namespaces entry from the Quick Index +# and from the Folder Tree View (if specified). The default is YES. + +SHOW_NAMESPACES = YES + +# The FILE_VERSION_FILTER tag can be used to specify a program or script that +# doxygen should invoke to get the current version for each file (typically from +# the version control system). Doxygen will invoke the program by executing (via +# popen()) the command , where is the value of +# the FILE_VERSION_FILTER tag, and is the name of an input file +# provided by doxygen. Whatever the program writes to standard output +# is used as the file version. See the manual for examples. + +FILE_VERSION_FILTER = + +# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed by +# doxygen. The layout file controls the global structure of the generated output files +# in an output format independent way. The create the layout file that represents +# doxygen's defaults, run doxygen with the -l option. You can optionally specify a +# file name after the option, if omitted DoxygenLayout.xml will be used as the name +# of the layout file. + +LAYOUT_FILE = + +#--------------------------------------------------------------------------- +# configuration options related to warning and progress messages +#--------------------------------------------------------------------------- + +# The QUIET tag can be used to turn on/off the messages that are generated +# by doxygen. Possible values are YES and NO. If left blank NO is used. + +QUIET = NO + +# The WARNINGS tag can be used to turn on/off the warning messages that are +# generated by doxygen. Possible values are YES and NO. If left blank +# NO is used. + +WARNINGS = YES + +# If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings +# for undocumented members. If EXTRACT_ALL is set to YES then this flag will +# automatically be disabled. + +WARN_IF_UNDOCUMENTED = YES + +# If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for +# potential errors in the documentation, such as not documenting some +# parameters in a documented function, or documenting parameters that +# don't exist or using markup commands wrongly. + +WARN_IF_DOC_ERROR = YES + +# This WARN_NO_PARAMDOC option can be abled to get warnings for +# functions that are documented, but have no documentation for their parameters +# or return value. If set to NO (the default) doxygen will only warn about +# wrong or incomplete parameter documentation, but not about the absence of +# documentation. + +WARN_NO_PARAMDOC = NO + +# The WARN_FORMAT tag determines the format of the warning messages that +# doxygen can produce. The string should contain the $file, $line, and $text +# tags, which will be replaced by the file and line number from which the +# warning originated and the warning text. Optionally the format may contain +# $version, which will be replaced by the version of the file (if it could +# be obtained via FILE_VERSION_FILTER) + +WARN_FORMAT = "$file:$line: $text" + +# The WARN_LOGFILE tag can be used to specify a file to which warning +# and error messages should be written. If left blank the output is written +# to stderr. + +WARN_LOGFILE = + +#--------------------------------------------------------------------------- +# configuration options related to the input files +#--------------------------------------------------------------------------- + +# The INPUT tag can be used to specify the files and/or directories that contain +# documented source files. You may enter file names like "myfile.cpp" or +# directories like "/usr/src/myproject". Separate the files or directories +# with spaces. + +INPUT = lm lm/builder lm/filter lm/interpolate lm/wrappers util util/double-conversion util/stream + +# This tag can be used to specify the character encoding of the source files +# that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is +# also the default input encoding. Doxygen uses libiconv (or the iconv built +# into libc) for the transcoding. See http://www.gnu.org/software/libiconv for +# the list of possible encodings. + +INPUT_ENCODING = UTF-8 + +# If the value of the INPUT tag contains directories, you can use the +# FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp +# and *.h) to filter out the source-files in the directories. If left +# blank the following patterns are tested: +# *.c *.cc *.cxx *.cpp *.c++ *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh *.hxx +# *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.py *.f90 + +FILE_PATTERNS = + +# The RECURSIVE tag can be used to turn specify whether or not subdirectories +# should be searched for input files as well. Possible values are YES and NO. +# If left blank NO is used. + +RECURSIVE = NO + +# The EXCLUDE tag can be used to specify files and/or directories that should +# excluded from the INPUT source files. This way you can easily exclude a +# subdirectory from a directory tree whose root is specified with the INPUT tag. + +EXCLUDE = + +# The EXCLUDE_SYMLINKS tag can be used select whether or not files or +# directories that are symbolic links (a Unix filesystem feature) are excluded +# from the input. + +EXCLUDE_SYMLINKS = NO + +# If the value of the INPUT tag contains directories, you can use the +# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude +# certain files from those directories. Note that the wildcards are matched +# against the file with absolute path, so to exclude all test directories +# for example use the pattern */test/* + +EXCLUDE_PATTERNS = + +# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names +# (namespaces, classes, functions, etc.) that should be excluded from the +# output. The symbol name can be a fully qualified name, a word, or if the +# wildcard * is used, a substring. Examples: ANamespace, AClass, +# AClass::ANamespace, ANamespace::*Test + +EXCLUDE_SYMBOLS = + +# The EXAMPLE_PATH tag can be used to specify one or more files or +# directories that contain example code fragments that are included (see +# the \include command). + +EXAMPLE_PATH = + +# If the value of the EXAMPLE_PATH tag contains directories, you can use the +# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp +# and *.h) to filter out the source-files in the directories. If left +# blank all files are included. + +EXAMPLE_PATTERNS = + +# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be +# searched for input files to be used with the \include or \dontinclude +# commands irrespective of the value of the RECURSIVE tag. +# Possible values are YES and NO. If left blank NO is used. + +EXAMPLE_RECURSIVE = NO + +# The IMAGE_PATH tag can be used to specify one or more files or +# directories that contain image that are included in the documentation (see +# the \image command). + +IMAGE_PATH = + +# The INPUT_FILTER tag can be used to specify a program that doxygen should +# invoke to filter for each input file. Doxygen will invoke the filter program +# by executing (via popen()) the command , where +# is the value of the INPUT_FILTER tag, and is the name of an +# input file. Doxygen will then use the output that the filter program writes +# to standard output. +# If FILTER_PATTERNS is specified, this tag will be +# ignored. + +INPUT_FILTER = + +# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern +# basis. +# Doxygen will compare the file name with each pattern and apply the +# filter if there is a match. +# The filters are a list of the form: +# pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further +# info on how filters are used. If FILTER_PATTERNS is empty, INPUT_FILTER +# is applied to all files. + +FILTER_PATTERNS = + +# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using +# INPUT_FILTER) will be used to filter the input files when producing source +# files to browse (i.e. when SOURCE_BROWSER is set to YES). + +FILTER_SOURCE_FILES = NO + +#--------------------------------------------------------------------------- +# configuration options related to source browsing +#--------------------------------------------------------------------------- + +# If the SOURCE_BROWSER tag is set to YES then a list of source files will +# be generated. Documented entities will be cross-referenced with these sources. +# Note: To get rid of all source code in the generated output, make sure also +# VERBATIM_HEADERS is set to NO. + +SOURCE_BROWSER = NO + +# Setting the INLINE_SOURCES tag to YES will include the body +# of functions and classes directly in the documentation. + +INLINE_SOURCES = NO + +# Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct +# doxygen to hide any special comment blocks from generated source code +# fragments. Normal C and C++ comments will always remain visible. + +STRIP_CODE_COMMENTS = YES + +# If the REFERENCED_BY_RELATION tag is set to YES +# then for each documented function all documented +# functions referencing it will be listed. + +REFERENCED_BY_RELATION = NO + +# If the REFERENCES_RELATION tag is set to YES +# then for each documented function all documented entities +# called/used by that function will be listed. + +REFERENCES_RELATION = NO + +# If the REFERENCES_LINK_SOURCE tag is set to YES (the default) +# and SOURCE_BROWSER tag is set to YES, then the hyperlinks from +# functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will +# link to the source code. +# Otherwise they will link to the documentation. + +REFERENCES_LINK_SOURCE = YES + +# If the USE_HTAGS tag is set to YES then the references to source code +# will point to the HTML generated by the htags(1) tool instead of doxygen +# built-in source browser. The htags tool is part of GNU's global source +# tagging system (see http://www.gnu.org/software/global/global.html). You +# will need version 4.8.6 or higher. + +USE_HTAGS = NO + +# If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen +# will generate a verbatim copy of the header file for each class for +# which an include is specified. Set to NO to disable this. + +VERBATIM_HEADERS = YES + +#--------------------------------------------------------------------------- +# configuration options related to the alphabetical class index +#--------------------------------------------------------------------------- + +# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index +# of all compounds will be generated. Enable this if the project +# contains a lot of classes, structs, unions or interfaces. + +ALPHABETICAL_INDEX = NO + +# If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then +# the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns +# in which this list will be split (can be a number in the range [1..20]) + +COLS_IN_ALPHA_INDEX = 5 + +# In case all classes in a project start with a common prefix, all +# classes will be put under the same header in the alphabetical index. +# The IGNORE_PREFIX tag can be used to specify one or more prefixes that +# should be ignored while generating the index headers. + +IGNORE_PREFIX = + +#--------------------------------------------------------------------------- +# configuration options related to the HTML output +#--------------------------------------------------------------------------- + +# If the GENERATE_HTML tag is set to YES (the default) Doxygen will +# generate HTML output. + +GENERATE_HTML = YES + +# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. +# If a relative path is entered the value of OUTPUT_DIRECTORY will be +# put in front of it. If left blank `html' will be used as the default path. + +HTML_OUTPUT = html + +# The HTML_FILE_EXTENSION tag can be used to specify the file extension for +# each generated HTML page (for example: .htm,.php,.asp). If it is left blank +# doxygen will generate files with .html extension. + +HTML_FILE_EXTENSION = .html + +# The HTML_HEADER tag can be used to specify a personal HTML header for +# each generated HTML page. If it is left blank doxygen will generate a +# standard header. + +HTML_HEADER = + +# The HTML_FOOTER tag can be used to specify a personal HTML footer for +# each generated HTML page. If it is left blank doxygen will generate a +# standard footer. + +HTML_FOOTER = + +# If the HTML_TIMESTAMP tag is set to YES then the generated HTML +# documentation will contain the timesstamp. + +HTML_TIMESTAMP = NO + +# The HTML_STYLESHEET tag can be used to specify a user-defined cascading +# style sheet that is used by each HTML page. It can be used to +# fine-tune the look of the HTML output. If the tag is left blank doxygen +# will generate a default style sheet. Note that doxygen will try to copy +# the style sheet file to the HTML output directory, so don't put your own +# stylesheet in the HTML output directory as well, or it will be erased! + +HTML_STYLESHEET = + +# If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes, +# files or namespaces will be aligned in HTML using tables. If set to +# NO a bullet list will be used. + +HTML_ALIGN_MEMBERS = YES + +# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML +# documentation will contain sections that can be hidden and shown after the +# page has loaded. For this to work a browser that supports +# JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox +# Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari). + +HTML_DYNAMIC_SECTIONS = NO + +# If the GENERATE_DOCSET tag is set to YES, additional index files +# will be generated that can be used as input for Apple's Xcode 3 +# integrated development environment, introduced with OSX 10.5 (Leopard). +# To create a documentation set, doxygen will generate a Makefile in the +# HTML output directory. Running make will produce the docset in that +# directory and running "make install" will install the docset in +# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find +# it at startup. +# See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html for more information. + +GENERATE_DOCSET = NO + +# When GENERATE_DOCSET tag is set to YES, this tag determines the name of the +# feed. A documentation feed provides an umbrella under which multiple +# documentation sets from a single provider (such as a company or product suite) +# can be grouped. + +DOCSET_FEEDNAME = "Doxygen generated docs" + +# When GENERATE_DOCSET tag is set to YES, this tag specifies a string that +# should uniquely identify the documentation set bundle. This should be a +# reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen +# will append .docset to the name. + +DOCSET_BUNDLE_ID = org.doxygen.Project + +# If the GENERATE_HTMLHELP tag is set to YES, additional index files +# will be generated that can be used as input for tools like the +# Microsoft HTML help workshop to generate a compiled HTML help file (.chm) +# of the generated HTML documentation. + +GENERATE_HTMLHELP = NO + +# If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can +# be used to specify the file name of the resulting .chm file. You +# can add a path in front of the file if the result should not be +# written to the html output directory. + +CHM_FILE = + +# If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can +# be used to specify the location (absolute path including file name) of +# the HTML help compiler (hhc.exe). If non-empty doxygen will try to run +# the HTML help compiler on the generated index.hhp. + +HHC_LOCATION = + +# If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag +# controls if a separate .chi index file is generated (YES) or that +# it should be included in the master .chm file (NO). + +GENERATE_CHI = NO + +# If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING +# is used to encode HtmlHelp index (hhk), content (hhc) and project file +# content. + +CHM_INDEX_ENCODING = + +# If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag +# controls whether a binary table of contents is generated (YES) or a +# normal table of contents (NO) in the .chm file. + +BINARY_TOC = NO + +# The TOC_EXPAND flag can be set to YES to add extra items for group members +# to the contents of the HTML help documentation and to the tree view. + +TOC_EXPAND = NO + +# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and QHP_VIRTUAL_FOLDER +# are set, an additional index file will be generated that can be used as input for +# Qt's qhelpgenerator to generate a Qt Compressed Help (.qch) of the generated +# HTML documentation. + +GENERATE_QHP = NO + +# If the QHG_LOCATION tag is specified, the QCH_FILE tag can +# be used to specify the file name of the resulting .qch file. +# The path specified is relative to the HTML output folder. + +QCH_FILE = + +# The QHP_NAMESPACE tag specifies the namespace to use when generating +# Qt Help Project output. For more information please see +# http://doc.trolltech.com/qthelpproject.html#namespace + +QHP_NAMESPACE = + +# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating +# Qt Help Project output. For more information please see +# http://doc.trolltech.com/qthelpproject.html#virtual-folders + +QHP_VIRTUAL_FOLDER = doc + +# If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to add. +# For more information please see +# http://doc.trolltech.com/qthelpproject.html#custom-filters + +QHP_CUST_FILTER_NAME = + +# The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the custom filter to add.For more information please see +# Qt Help Project / Custom Filters. + +QHP_CUST_FILTER_ATTRS = + +# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this project's +# filter section matches. +# Qt Help Project / Filter Attributes. + +QHP_SECT_FILTER_ATTRS = + +# If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can +# be used to specify the location of Qt's qhelpgenerator. +# If non-empty doxygen will try to run qhelpgenerator on the generated +# .qhp file. + +QHG_LOCATION = + +# The DISABLE_INDEX tag can be used to turn on/off the condensed index at +# top of each HTML page. The value NO (the default) enables the index and +# the value YES disables it. + +DISABLE_INDEX = NO + +# This tag can be used to set the number of enum values (range [1..20]) +# that doxygen will group on one line in the generated HTML documentation. + +ENUM_VALUES_PER_LINE = 4 + +# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index +# structure should be generated to display hierarchical information. +# If the tag value is set to YES, a side panel will be generated +# containing a tree-like index structure (just like the one that +# is generated for HTML Help). For this to work a browser that supports +# JavaScript, DHTML, CSS and frames is required (i.e. any modern browser). +# Windows users are probably better off using the HTML help feature. + +GENERATE_TREEVIEW = NO + +# By enabling USE_INLINE_TREES, doxygen will generate the Groups, Directories, +# and Class Hierarchy pages using a tree view instead of an ordered list. + +USE_INLINE_TREES = NO + +# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be +# used to set the initial width (in pixels) of the frame in which the tree +# is shown. + +TREEVIEW_WIDTH = 250 + +# Use this tag to change the font size of Latex formulas included +# as images in the HTML documentation. The default is 10. Note that +# when you change the font size after a successful doxygen run you need +# to manually remove any form_*.png images from the HTML output directory +# to force them to be regenerated. + +FORMULA_FONTSIZE = 10 + +# When the SEARCHENGINE tag is enable doxygen will generate a search box for the HTML output. The underlying search engine uses javascript +# and DHTML and should work on any modern browser. Note that when using HTML help (GENERATE_HTMLHELP) or Qt help (GENERATE_QHP) +# there is already a search function so this one should typically +# be disabled. + +SEARCHENGINE = YES + +#--------------------------------------------------------------------------- +# configuration options related to the LaTeX output +#--------------------------------------------------------------------------- + +# If the GENERATE_LATEX tag is set to YES (the default) Doxygen will +# generate Latex output. + +GENERATE_LATEX = YES + +# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. +# If a relative path is entered the value of OUTPUT_DIRECTORY will be +# put in front of it. If left blank `latex' will be used as the default path. + +LATEX_OUTPUT = latex + +# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be +# invoked. If left blank `latex' will be used as the default command name. + +LATEX_CMD_NAME = latex + +# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to +# generate index for LaTeX. If left blank `makeindex' will be used as the +# default command name. + +MAKEINDEX_CMD_NAME = makeindex + +# If the COMPACT_LATEX tag is set to YES Doxygen generates more compact +# LaTeX documents. This may be useful for small projects and may help to +# save some trees in general. + +COMPACT_LATEX = NO + +# The PAPER_TYPE tag can be used to set the paper type that is used +# by the printer. Possible values are: a4, a4wide, letter, legal and +# executive. If left blank a4wide will be used. + +PAPER_TYPE = a4wide + +# The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX +# packages that should be included in the LaTeX output. + +EXTRA_PACKAGES = + +# The LATEX_HEADER tag can be used to specify a personal LaTeX header for +# the generated latex document. The header should contain everything until +# the first chapter. If it is left blank doxygen will generate a +# standard header. Notice: only use this tag if you know what you are doing! + +LATEX_HEADER = + +# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated +# is prepared for conversion to pdf (using ps2pdf). The pdf file will +# contain links (just like the HTML output) instead of page references +# This makes the output suitable for online browsing using a pdf viewer. + +PDF_HYPERLINKS = YES + +# If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of +# plain latex in the generated Makefile. Set this option to YES to get a +# higher quality PDF documentation. + +USE_PDFLATEX = YES + +# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode. +# command to the generated LaTeX files. This will instruct LaTeX to keep +# running if errors occur, instead of asking the user for help. +# This option is also used when generating formulas in HTML. + +LATEX_BATCHMODE = NO + +# If LATEX_HIDE_INDICES is set to YES then doxygen will not +# include the index chapters (such as File Index, Compound Index, etc.) +# in the output. + +LATEX_HIDE_INDICES = NO + +# If LATEX_SOURCE_CODE is set to YES then doxygen will include source code with syntax highlighting in the LaTeX output. Note that which sources are shown also depends on other settings such as SOURCE_BROWSER. + +LATEX_SOURCE_CODE = NO + +#--------------------------------------------------------------------------- +# configuration options related to the RTF output +#--------------------------------------------------------------------------- + +# If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output +# The RTF output is optimized for Word 97 and may not look very pretty with +# other RTF readers or editors. + +GENERATE_RTF = NO + +# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. +# If a relative path is entered the value of OUTPUT_DIRECTORY will be +# put in front of it. If left blank `rtf' will be used as the default path. + +RTF_OUTPUT = rtf + +# If the COMPACT_RTF tag is set to YES Doxygen generates more compact +# RTF documents. This may be useful for small projects and may help to +# save some trees in general. + +COMPACT_RTF = NO + +# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated +# will contain hyperlink fields. The RTF file will +# contain links (just like the HTML output) instead of page references. +# This makes the output suitable for online browsing using WORD or other +# programs which support those fields. +# Note: wordpad (write) and others do not support links. + +RTF_HYPERLINKS = NO + +# Load stylesheet definitions from file. Syntax is similar to doxygen's +# config file, i.e. a series of assignments. You only have to provide +# replacements, missing definitions are set to their default value. + +RTF_STYLESHEET_FILE = + +# Set optional variables used in the generation of an rtf document. +# Syntax is similar to doxygen's config file. + +RTF_EXTENSIONS_FILE = + +#--------------------------------------------------------------------------- +# configuration options related to the man page output +#--------------------------------------------------------------------------- + +# If the GENERATE_MAN tag is set to YES (the default) Doxygen will +# generate man pages + +GENERATE_MAN = NO + +# The MAN_OUTPUT tag is used to specify where the man pages will be put. +# If a relative path is entered the value of OUTPUT_DIRECTORY will be +# put in front of it. If left blank `man' will be used as the default path. + +MAN_OUTPUT = man + +# The MAN_EXTENSION tag determines the extension that is added to +# the generated man pages (default is the subroutine's section .3) + +MAN_EXTENSION = .3 + +# If the MAN_LINKS tag is set to YES and Doxygen generates man output, +# then it will generate one additional man file for each entity +# documented in the real man page(s). These additional files +# only source the real man page, but without them the man command +# would be unable to find the correct page. The default is NO. + +MAN_LINKS = NO + +#--------------------------------------------------------------------------- +# configuration options related to the XML output +#--------------------------------------------------------------------------- + +# If the GENERATE_XML tag is set to YES Doxygen will +# generate an XML file that captures the structure of +# the code including all documentation. + +GENERATE_XML = NO + +# The XML_OUTPUT tag is used to specify where the XML pages will be put. +# If a relative path is entered the value of OUTPUT_DIRECTORY will be +# put in front of it. If left blank `xml' will be used as the default path. + +XML_OUTPUT = xml + +# The XML_SCHEMA tag can be used to specify an XML schema, +# which can be used by a validating XML parser to check the +# syntax of the XML files. + +XML_SCHEMA = + +# The XML_DTD tag can be used to specify an XML DTD, +# which can be used by a validating XML parser to check the +# syntax of the XML files. + +XML_DTD = + +# If the XML_PROGRAMLISTING tag is set to YES Doxygen will +# dump the program listings (including syntax highlighting +# and cross-referencing information) to the XML output. Note that +# enabling this will significantly increase the size of the XML output. + +XML_PROGRAMLISTING = YES + +#--------------------------------------------------------------------------- +# configuration options for the AutoGen Definitions output +#--------------------------------------------------------------------------- + +# If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will +# generate an AutoGen Definitions (see autogen.sf.net) file +# that captures the structure of the code including all +# documentation. Note that this feature is still experimental +# and incomplete at the moment. + +GENERATE_AUTOGEN_DEF = NO + +#--------------------------------------------------------------------------- +# configuration options related to the Perl module output +#--------------------------------------------------------------------------- + +# If the GENERATE_PERLMOD tag is set to YES Doxygen will +# generate a Perl module file that captures the structure of +# the code including all documentation. Note that this +# feature is still experimental and incomplete at the +# moment. + +GENERATE_PERLMOD = NO + +# If the PERLMOD_LATEX tag is set to YES Doxygen will generate +# the necessary Makefile rules, Perl scripts and LaTeX code to be able +# to generate PDF and DVI output from the Perl module output. + +PERLMOD_LATEX = NO + +# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be +# nicely formatted so it can be parsed by a human reader. +# This is useful +# if you want to understand what is going on. +# On the other hand, if this +# tag is set to NO the size of the Perl module output will be much smaller +# and Perl will parse it just the same. + +PERLMOD_PRETTY = YES + +# The names of the make variables in the generated doxyrules.make file +# are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. +# This is useful so different doxyrules.make files included by the same +# Makefile don't overwrite each other's variables. + +PERLMOD_MAKEVAR_PREFIX = + +#--------------------------------------------------------------------------- +# Configuration options related to the preprocessor +#--------------------------------------------------------------------------- + +# If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will +# evaluate all C-preprocessor directives found in the sources and include +# files. + +ENABLE_PREPROCESSING = YES + +# If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro +# names in the source code. If set to NO (the default) only conditional +# compilation will be performed. Macro expansion can be done in a controlled +# way by setting EXPAND_ONLY_PREDEF to YES. + +MACRO_EXPANSION = NO + +# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES +# then the macro expansion is limited to the macros specified with the +# PREDEFINED and EXPAND_AS_DEFINED tags. + +EXPAND_ONLY_PREDEF = NO + +# If the SEARCH_INCLUDES tag is set to YES (the default) the includes files +# in the INCLUDE_PATH (see below) will be search if a #include is found. + +SEARCH_INCLUDES = YES + +# The INCLUDE_PATH tag can be used to specify one or more directories that +# contain include files that are not input files but should be processed by +# the preprocessor. + +INCLUDE_PATH = + +# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard +# patterns (like *.h and *.hpp) to filter out the header-files in the +# directories. If left blank, the patterns specified with FILE_PATTERNS will +# be used. + +INCLUDE_FILE_PATTERNS = + +# The PREDEFINED tag can be used to specify one or more macro names that +# are defined before the preprocessor is started (similar to the -D option of +# gcc). The argument of the tag is a list of macros of the form: name +# or name=definition (no spaces). If the definition and the = are +# omitted =1 is assumed. To prevent a macro definition from being +# undefined via #undef or recursively expanded use the := operator +# instead of the = operator. + +PREDEFINED = + +# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then +# this tag can be used to specify a list of macro names that should be expanded. +# The macro definition that is found in the sources will be used. +# Use the PREDEFINED tag if you want to use a different macro definition. + +EXPAND_AS_DEFINED = + +# If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then +# doxygen's preprocessor will remove all function-like macros that are alone +# on a line, have an all uppercase name, and do not end with a semicolon. Such +# function macros are typically used for boiler-plate code, and will confuse +# the parser if not removed. + +SKIP_FUNCTION_MACROS = YES + +#--------------------------------------------------------------------------- +# Configuration::additions related to external references +#--------------------------------------------------------------------------- + +# The TAGFILES option can be used to specify one or more tagfiles. +# Optionally an initial location of the external documentation +# can be added for each tagfile. The format of a tag file without +# this location is as follows: +# +# TAGFILES = file1 file2 ... +# Adding location for the tag files is done as follows: +# +# TAGFILES = file1=loc1 "file2 = loc2" ... +# where "loc1" and "loc2" can be relative or absolute paths or +# URLs. If a location is present for each tag, the installdox tool +# does not have to be run to correct the links. +# Note that each tag file must have a unique name +# (where the name does NOT include the path) +# If a tag file is not located in the directory in which doxygen +# is run, you must also specify the path to the tagfile here. + +TAGFILES = + +# When a file name is specified after GENERATE_TAGFILE, doxygen will create +# a tag file that is based on the input files it reads. + +GENERATE_TAGFILE = + +# If the ALLEXTERNALS tag is set to YES all external classes will be listed +# in the class index. If set to NO only the inherited external classes +# will be listed. + +ALLEXTERNALS = NO + +# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed +# in the modules index. If set to NO, only the current project's groups will +# be listed. + +EXTERNAL_GROUPS = YES + +# The PERL_PATH should be the absolute path and name of the perl script +# interpreter (i.e. the result of `which perl'). + +PERL_PATH = /usr/bin/perl + +#--------------------------------------------------------------------------- +# Configuration options related to the dot tool +#--------------------------------------------------------------------------- + +# If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will +# generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base +# or super classes. Setting the tag to NO turns the diagrams off. Note that +# this option is superseded by the HAVE_DOT option below. This is only a +# fallback. It is recommended to install and use dot, since it yields more +# powerful graphs. + +CLASS_DIAGRAMS = YES + +# You can define message sequence charts within doxygen comments using the \msc +# command. Doxygen will then run the mscgen tool (see +# http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the +# documentation. The MSCGEN_PATH tag allows you to specify the directory where +# the mscgen tool resides. If left empty the tool is assumed to be found in the +# default search path. + +MSCGEN_PATH = + +# If set to YES, the inheritance and collaboration graphs will hide +# inheritance and usage relations if the target is undocumented +# or is not a class. + +HIDE_UNDOC_RELATIONS = YES + +# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is +# available from the path. This tool is part of Graphviz, a graph visualization +# toolkit from AT&T and Lucent Bell Labs. The other options in this section +# have no effect if this option is set to NO (the default) + +HAVE_DOT = NO + +# By default doxygen will write a font called FreeSans.ttf to the output +# directory and reference it in all dot files that doxygen generates. This +# font does not include all possible unicode characters however, so when you need +# these (or just want a differently looking font) you can specify the font name +# using DOT_FONTNAME. You need need to make sure dot is able to find the font, +# which can be done by putting it in a standard location or by setting the +# DOTFONTPATH environment variable or by setting DOT_FONTPATH to the directory +# containing the font. + +DOT_FONTNAME = FreeSans + +# The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs. +# The default size is 10pt. + +DOT_FONTSIZE = 10 + +# By default doxygen will tell dot to use the output directory to look for the +# FreeSans.ttf font (which doxygen will put there itself). If you specify a +# different font using DOT_FONTNAME you can set the path where dot +# can find it using this tag. + +DOT_FONTPATH = + +# If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen +# will generate a graph for each documented class showing the direct and +# indirect inheritance relations. Setting this tag to YES will force the +# the CLASS_DIAGRAMS tag to NO. + +CLASS_GRAPH = YES + +# If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen +# will generate a graph for each documented class showing the direct and +# indirect implementation dependencies (inheritance, containment, and +# class references variables) of the class with other documented classes. + +COLLABORATION_GRAPH = YES + +# If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen +# will generate a graph for groups, showing the direct groups dependencies + +GROUP_GRAPHS = YES + +# If the UML_LOOK tag is set to YES doxygen will generate inheritance and +# collaboration diagrams in a style similar to the OMG's Unified Modeling +# Language. + +UML_LOOK = NO + +# If set to YES, the inheritance and collaboration graphs will show the +# relations between templates and their instances. + +TEMPLATE_RELATIONS = NO + +# If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT +# tags are set to YES then doxygen will generate a graph for each documented +# file showing the direct and indirect include dependencies of the file with +# other documented files. + +INCLUDE_GRAPH = YES + +# If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and +# HAVE_DOT tags are set to YES then doxygen will generate a graph for each +# documented header file showing the documented files that directly or +# indirectly include this file. + +INCLUDED_BY_GRAPH = YES + +# If the CALL_GRAPH and HAVE_DOT options are set to YES then +# doxygen will generate a call dependency graph for every global function +# or class method. Note that enabling this option will significantly increase +# the time of a run. So in most cases it will be better to enable call graphs +# for selected functions only using the \callgraph command. + +CALL_GRAPH = NO + +# If the CALLER_GRAPH and HAVE_DOT tags are set to YES then +# doxygen will generate a caller dependency graph for every global function +# or class method. Note that enabling this option will significantly increase +# the time of a run. So in most cases it will be better to enable caller +# graphs for selected functions only using the \callergraph command. + +CALLER_GRAPH = NO + +# If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen +# will graphical hierarchy of all classes instead of a textual one. + +GRAPHICAL_HIERARCHY = YES + +# If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES +# then doxygen will show the dependencies a directory has on other directories +# in a graphical way. The dependency relations are determined by the #include +# relations between the files in the directories. + +DIRECTORY_GRAPH = YES + +# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images +# generated by dot. Possible values are png, jpg, or gif +# If left blank png will be used. + +DOT_IMAGE_FORMAT = png + +# The tag DOT_PATH can be used to specify the path where the dot tool can be +# found. If left blank, it is assumed the dot tool can be found in the path. + +DOT_PATH = + +# The DOTFILE_DIRS tag can be used to specify one or more directories that +# contain dot files that are included in the documentation (see the +# \dotfile command). + +DOTFILE_DIRS = + +# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of +# nodes that will be shown in the graph. If the number of nodes in a graph +# becomes larger than this value, doxygen will truncate the graph, which is +# visualized by representing a node as a red box. Note that doxygen if the +# number of direct children of the root node in a graph is already larger than +# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note +# that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH. + +DOT_GRAPH_MAX_NODES = 50 + +# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the +# graphs generated by dot. A depth value of 3 means that only nodes reachable +# from the root by following a path via at most 3 edges will be shown. Nodes +# that lay further from the root node will be omitted. Note that setting this +# option to 1 or 2 may greatly reduce the computation time needed for large +# code bases. Also note that the size of a graph can be further restricted by +# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction. + +MAX_DOT_GRAPH_DEPTH = 0 + +# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent +# background. This is disabled by default, because dot on Windows does not +# seem to support this out of the box. Warning: Depending on the platform used, +# enabling this option may lead to badly anti-aliased labels on the edges of +# a graph (i.e. they become hard to read). + +DOT_TRANSPARENT = NO + +# Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output +# files in one run (i.e. multiple -o and -T options on the command line). This +# makes dot run faster, but since only newer versions of dot (>1.8.10) +# support this, this feature is disabled by default. + +DOT_MULTI_TARGETS = NO + +# If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will +# generate a legend page explaining the meaning of the various boxes and +# arrows in the dot generated graphs. + +GENERATE_LEGEND = YES + +# If the DOT_CLEANUP tag is set to YES (the default) Doxygen will +# remove the intermediate dot files that are used to generate +# the various graphs. + +DOT_CLEANUP = YES diff --git a/ctcdecode-master/third_party/kenlm/LICENSE b/ctcdecode-master/third_party/kenlm/LICENSE new file mode 100644 index 0000000..47d547b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/LICENSE @@ -0,0 +1,25 @@ +Most of the code here is licensed under the LGPL. There are exceptions that +have their own licenses, listed below. See comments in those files for more +details. + +util/getopt.* is getopt for Windows +util/murmur_hash.cc +util/string_piece.hh and util/string_piece.cc +util/double-conversion/LICENSE covers util/double-conversion except the build files +util/file.cc contains a modified implementation of mkstemp under the LGPL +util/integer_to_string.* is BSD + +For the rest: + + KenLM is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published + by the Free Software Foundation, either version 2.1 of the License, or + (at your option) any later version. + + KenLM is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License 2.1 + along with KenLM code. If not, see . diff --git a/ctcdecode-master/third_party/kenlm/MANIFEST.in b/ctcdecode-master/third_party/kenlm/MANIFEST.in new file mode 100644 index 0000000..bec8496 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/MANIFEST.in @@ -0,0 +1,9 @@ +# file GENERATED by distutils, do NOT edit +include setup.py +include lm/*.cc +include lm/*.hh +include python/*.cpp +include util/*.cc +include util/*.hh +include util/double-conversion/*.cc +include util/double-conversion/*.h diff --git a/ctcdecode-master/third_party/kenlm/README.md b/ctcdecode-master/third_party/kenlm/README.md new file mode 100644 index 0000000..2cef658 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/README.md @@ -0,0 +1,104 @@ +# kenlm + +Language model inference code by Kenneth Heafield (kenlm at kheafield.com) + +I do development in master on https://github.com/kpu/kenlm/. Normally, it works, but I do not guarantee it will compile, give correct answers, or generate non-broken binary files. For a more stable release, get http://kheafield.com/code/kenlm.tar.gz . + +The website http://kheafield.com/code/kenlm/ has more documentation. If you're a decoder developer, please download the latest version from there instead of copying from another decoder. + +## Compiling +Use cmake, see [BUILDING](BUILDING) for more detail. +```bash +mkdir -p build +cd build +cmake .. +make -j 4 +``` + +## Compiling with your own build system +If you want to compile with your own build system (Makefile etc) or to use as a library, there are a number of macros you can set on the g++ command line or in util/have.hh . + +* `KENLM_MAX_ORDER` is the maximum order that can be loaded. This is done to make state an efficient POD rather than a vector. +* `HAVE_ICU` If your code links against ICU, define this to disable the internal StringPiece and replace it with ICU's copy of StringPiece, avoiding naming conflicts. + +ARPA files can be read in compressed format with these options: +* `HAVE_ZLIB` Supports gzip. Link with -lz. +* `HAVE_BZLIB` Supports bzip2. Link with -lbz2. +* `HAVE_XZLIB` Supports xz. Link with -llzma. + +Note that these macros impact only `read_compressed.cc` and `read_compressed_test.cc`. The bjam build system will auto-detect bzip2 and xz support. + +## Estimation +lmplz estimates unpruned language models with modified Kneser-Ney smoothing. After compiling with bjam, run +```bash +bin/lmplz -o 5 text.arpa +``` +The algorithm is on-disk, using an amount of memory that you specify. See http://kheafield.com/code/kenlm/estimation/ for more. + +MT Marathon 2012 team members Ivan Pouzyrevsky and Mohammed Mediani contributed to the computation design and early implementation. Jon Clark contributed to the design, clarified points about smoothing, and added logging. + +## Filtering + +filter takes an ARPA or count file and removes entries that will never be queried. The filter criterion can be corpus-level vocabulary, sentence-level vocabulary, or sentence-level phrases. Run +```bash +bin/filter +``` +and see http://kheafield.com/code/kenlm/filter/ for more documentation. + +## Querying + +Two data structures are supported: probing and trie. Probing is a probing hash table with keys that are 64-bit hashes of n-grams and floats as values. Trie is a fairly standard trie but with bit-level packing so it uses the minimum number of bits to store word indices and pointers. The trie node entries are sorted by word index. Probing is the fastest and uses the most memory. Trie uses the least memory and a bit slower. + +As is the custom in language modeling, all probabilities are log base 10. + +With trie, resident memory is 58% of IRST's smallest version and 21% of SRI's compact version. Simultaneously, trie CPU's use is 81% of IRST's fastest version and 84% of SRI's fast version. KenLM's probing hash table implementation goes even faster at the expense of using more memory. See http://kheafield.com/code/kenlm/benchmark/. + +Binary format via mmap is supported. Run `./build_binary` to make one then pass the binary file name to the appropriate Model constructor. + +## Platforms +`murmur_hash.cc` and `bit_packing.hh` perform unaligned reads and writes that make the code architecture-dependent. +It has been sucessfully tested on x86\_64, x86, and PPC64. +ARM support is reportedly working, at least on the iphone. + +Runs on Linux, OS X, Cygwin, and MinGW. + +Hideo Okuma and Tomoyuki Yoshimura from NICT contributed ports to ARM and MinGW. + +## Decoder developers +- I recommend copying the code and distributing it with your decoder. However, please send improvements upstream. + +- It's possible to compile the query-only code without Boost, but useful things like estimating models require Boost. + +- Select the macros you want, listed in the previous section. + +- There are two build systems: compile.sh and Jamroot+Jamfile. They're pretty simple and are intended to be reimplemented in your build system. + +- Use either the interface in `lm/model.hh` or `lm/virtual_interface.hh`. Interface documentation is in comments of `lm/virtual_interface.hh` and `lm/model.hh`. + +- There are several possible data structures in `model.hh`. Use `RecognizeBinary` in `binary_format.hh` to determine which one a user has provided. You probably already implement feature functions as an abstract virtual base class with several children. I suggest you co-opt this existing virtual dispatch by templatizing the language model feature implementation on the KenLM model identified by `RecognizeBinary`. This is the strategy used in Moses and cdec. + +- See `lm/config.hh` for run-time tuning options. + +## Contributors +Contributions to KenLM are welcome. Please base your contributions on https://github.com/kpu/kenlm and send pull requests (or I might give you commit access). Downstream copies in Moses and cdec are maintained by overwriting them so do not make changes there. + +## Python module +Contributed by Victor Chahuneau. + +### Installation + +```bash +pip install https://github.com/kpu/kenlm/archive/master.zip +``` + +### Basic Usage +```python +import kenlm +model = kenlm.Model('lm/test.arpa') +print(model.score('this is a sentence .', bos = True, eos = True)) +``` +See [python/example.py](python/example.py) and [python/kenlm.pyx](python/kenlm.pyx) for more, including stateful APIs. + +--- + +The name was Hieu Hoang's idea, not mine. diff --git a/ctcdecode-master/third_party/kenlm/clean_query_only.sh b/ctcdecode-master/third_party/kenlm/clean_query_only.sh new file mode 100644 index 0000000..2636265 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/clean_query_only.sh @@ -0,0 +1,2 @@ +#!/bin/bash +rm -rf {lm,util,util/double-conversion}/*.o bin/{query,build_binary} diff --git a/ctcdecode-master/third_party/kenlm/cmake/KenLMFunctions.cmake b/ctcdecode-master/third_party/kenlm/cmake/KenLMFunctions.cmake new file mode 100644 index 0000000..b374db4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/cmake/KenLMFunctions.cmake @@ -0,0 +1,85 @@ +# Helper functions used across the CMake build system + +include(CMakeParseArguments) + +# Adds a bunch of executables to the build, each depending on the specified +# dependent object files and linking against the specified libraries +function(AddExes) + set(multiValueArgs EXES DEPENDS LIBRARIES) + cmake_parse_arguments(AddExes "" "" "${multiValueArgs}" ${ARGN}) + + # Iterate through the executable list + foreach(exe ${AddExes_EXES}) + + # Compile the executable, linking against the requisite dependent object files + add_executable(${exe} ${exe}_main.cc ${AddExes_DEPENDS}) + + # Link the executable against the supplied libraries + target_link_libraries(${exe} ${AddExes_LIBRARIES}) + + # Group executables together + set_target_properties(${exe} PROPERTIES FOLDER executables) + + # End for loop + endforeach(exe) + + # Install the executable files + install(TARGETS ${AddExes_EXES} DESTINATION bin) +endfunction() + +# Adds a single test to the build, depending on the specified dependent +# object files, linking against the specified libraries, and with the +# specified command line arguments +function(KenLMAddTest) + cmake_parse_arguments(KenLMAddTest "" "TEST" + "DEPENDS;LIBRARIES;TEST_ARGS" ${ARGN}) + + # Compile the executable, linking against the requisite dependent object files + add_executable(${KenLMAddTest_TEST} + ${KenLMAddTest_TEST}.cc + ${KenLMAddTest_DEPENDS}) + + if (Boost_USE_STATIC_LIBS) + set(DYNLINK_FLAGS) + else() + set(DYNLINK_FLAGS COMPILE_FLAGS -DBOOST_TEST_DYN_LINK) + endif() + + # Require the following compile flag + set_target_properties(${KenLMAddTest_TEST} PROPERTIES + ${DYNLINK_FLAGS} + RUNTIME_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/tests) + + target_link_libraries(${KenLMAddTest_TEST} ${KenLMAddTest_LIBRARIES} ${TIMER_LINK}) + + set(test_params "") + if(KenLMAddTest_TEST_ARGS) + set(test_params ${KenLMAddTest_TEST_ARGS}) + endif() + + # Specify command arguments for how to run each unit test + # + # Assuming that foo was defined via add_executable(foo ...), + # the syntax $ gives the full path to the executable. + # + add_test(NAME ${KenLMAddTest_TEST} + COMMAND $ ${test_params}) + + # Group unit tests together + set_target_properties(${KenLMAddTest_TEST} PROPERTIES FOLDER "unit_tests") +endfunction() + +# Adds a bunch of tests to the build, each depending on the specified +# dependent object files and linking against the specified libraries +function(AddTests) + set(multiValueArgs TESTS DEPENDS LIBRARIES TEST_ARGS) + cmake_parse_arguments(AddTests "" "" "${multiValueArgs}" ${ARGN}) + + # Iterate through the Boost tests list + foreach(test ${AddTests_TESTS}) + KenLMAddTest(TEST ${test} + DEPENDS ${AddTests_DEPENDS} + LIBRARIES ${AddTests_LIBRARIES} + TEST_ARGS ${AddTests_TEST_ARGS}) + endforeach(test) +endfunction() diff --git a/ctcdecode-master/third_party/kenlm/cmake/modules/FindEigen3.cmake b/ctcdecode-master/third_party/kenlm/cmake/modules/FindEigen3.cmake new file mode 100644 index 0000000..cea1afe --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/cmake/modules/FindEigen3.cmake @@ -0,0 +1,90 @@ +# - Try to find Eigen3 lib +# +# This module supports requiring a minimum version, e.g. you can do +# find_package(Eigen3 3.1.2) +# to require version 3.1.2 or newer of Eigen3. +# +# Once done this will define +# +# EIGEN3_FOUND - system has eigen lib with correct version +# EIGEN3_INCLUDE_DIR - the eigen include directory +# EIGEN3_VERSION - eigen version +# +# This module reads hints about search locations from +# the following enviroment variables: +# +# EIGEN3_ROOT +# EIGEN3_ROOT_DIR + +# Copyright (c) 2006, 2007 Montel Laurent, +# Copyright (c) 2008, 2009 Gael Guennebaud, +# Copyright (c) 2009 Benoit Jacob +# Redistribution and use is allowed according to the terms of the 2-clause BSD license. + +if(NOT Eigen3_FIND_VERSION) + if(NOT Eigen3_FIND_VERSION_MAJOR) + set(Eigen3_FIND_VERSION_MAJOR 2) + endif(NOT Eigen3_FIND_VERSION_MAJOR) + if(NOT Eigen3_FIND_VERSION_MINOR) + set(Eigen3_FIND_VERSION_MINOR 91) + endif(NOT Eigen3_FIND_VERSION_MINOR) + if(NOT Eigen3_FIND_VERSION_PATCH) + set(Eigen3_FIND_VERSION_PATCH 0) + endif(NOT Eigen3_FIND_VERSION_PATCH) + + set(Eigen3_FIND_VERSION "${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}") +endif(NOT Eigen3_FIND_VERSION) + +macro(_eigen3_check_version) + file(READ "${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h" _eigen3_version_header) + + string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen3_world_version_match "${_eigen3_version_header}") + set(EIGEN3_WORLD_VERSION "${CMAKE_MATCH_1}") + string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen3_major_version_match "${_eigen3_version_header}") + set(EIGEN3_MAJOR_VERSION "${CMAKE_MATCH_1}") + string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen3_minor_version_match "${_eigen3_version_header}") + set(EIGEN3_MINOR_VERSION "${CMAKE_MATCH_1}") + + set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION}) + if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION}) + set(EIGEN3_VERSION_OK FALSE) + else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION}) + set(EIGEN3_VERSION_OK TRUE) + endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION}) + + if(NOT EIGEN3_VERSION_OK) + + message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, " + "but at least version ${Eigen3_FIND_VERSION} is required") + endif(NOT EIGEN3_VERSION_OK) +endmacro(_eigen3_check_version) + +if (EIGEN3_INCLUDE_DIR) + + # in cache already + _eigen3_check_version() + set(EIGEN3_FOUND ${EIGEN3_VERSION_OK}) + +else (EIGEN3_INCLUDE_DIR) + + find_path(EIGEN3_INCLUDE_DIR NAMES signature_of_eigen3_matrix_library + HINTS + ENV EIGEN3_ROOT + ENV EIGEN3_ROOT_DIR + PATHS + ${CMAKE_INSTALL_PREFIX}/include + ${KDE4_INCLUDE_DIR} + PATH_SUFFIXES eigen3 eigen + ) + + if(EIGEN3_INCLUDE_DIR) + _eigen3_check_version() + endif(EIGEN3_INCLUDE_DIR) + + include(FindPackageHandleStandardArgs) + find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK) + + mark_as_advanced(EIGEN3_INCLUDE_DIR) + +endif(EIGEN3_INCLUDE_DIR) + diff --git a/ctcdecode-master/third_party/kenlm/compile_query_only.sh b/ctcdecode-master/third_party/kenlm/compile_query_only.sh new file mode 100644 index 0000000..7a82f49 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/compile_query_only.sh @@ -0,0 +1,34 @@ +#!/bin/bash +#This is just an example compilation. You should integrate these files into your build system. Boost jam is provided and preferred. + +echo You must use ./bjam if you want language model estimation, filtering, or support for compressed files \(.gz, .bz2, .xz\) 1>&2 + +rm {lm,util}/*.o 2>/dev/null +set -e + +CXX=${CXX:-g++} + +CXXFLAGS+=" -I. -O3 -DNDEBUG -DKENLM_MAX_ORDER=6" + +#If this fails for you, consider using bjam. +if [ ${#NPLM} != 0 ]; then + CXXFLAGS+=" -DHAVE_NPLM -lneuralLM -L$NPLM/src -I$NPLM/src -lboost_thread-mt -fopenmp" + ADDED_PATHS="lm/wrappers/*.cc" +fi +echo 'Compiling with '$CXX $CXXFLAGS + +#Grab all cc files in these directories except those ending in test.cc or main.cc +objects="" +for i in util/double-conversion/*.cc util/*.cc lm/*.cc $ADDED_PATHS; do + if [ "${i%test.cc}" == "$i" ] && [ "${i%main.cc}" == "$i" ]; then + $CXX $CXXFLAGS -c $i -o ${i%.cc}.o + objects="$objects ${i%.cc}.o" + fi +done + +mkdir -p bin +if [ "$(uname)" != Darwin ]; then + CXXFLAGS="$CXXFLAGS -lrt" +fi +$CXX lm/build_binary_main.cc $objects -o bin/build_binary $CXXFLAGS $LDFLAGS +$CXX lm/query_main.cc $objects -o bin/query $CXXFLAGS $LDFLAGS diff --git a/ctcdecode-master/third_party/kenlm/lm/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/lm/CMakeLists.txt new file mode 100644 index 0000000..cb76e57 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/CMakeLists.txt @@ -0,0 +1,73 @@ +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +set(KENLM_LM_SOURCE + bhiksha.cc + binary_format.cc + config.cc + lm_exception.cc + model.cc + quantize.cc + read_arpa.cc + search_hashed.cc + search_trie.cc + sizes.cc + trie.cc + trie_sort.cc + value_build.cc + virtual_interface.cc + vocab.cc +) + + +# Group these objects together for later use. +# +# Given add_library(foo OBJECT ${my_foo_sources}), +# refer to these objects as $ +# +add_subdirectory(common) + +if (NOT MSVC) + set(THREADS pthread) +endif() + +add_library(kenlm ${KENLM_LM_SOURCE} ${KENLM_LM_COMMON_SOURCE}) +target_link_libraries(kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) + +set(KENLM_MAX_ORDER 6 CACHE STRING "Maximum supported ngram order") +target_compile_definitions(kenlm PUBLIC -DKENLM_MAX_ORDER=${KENLM_MAX_ORDER}) + +# This directory has children that need to be processed +add_subdirectory(builder) +add_subdirectory(filter) +add_subdirectory(interpolate) + +# Explicitly list the executable files to be compiled +set(EXE_LIST + query + fragment + build_binary + kenlm_benchmark +) + +set(LM_LIBS kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) + +AddExes(EXES ${EXE_LIST} + LIBRARIES ${LM_LIBS}) + +if(BUILD_TESTING) + + set(KENLM_BOOST_TESTS_LIST left_test partial_test) + AddTests(TESTS ${KENLM_BOOST_TESTS_LIST} + LIBRARIES ${LM_LIBS} + TEST_ARGS ${CMAKE_CURRENT_SOURCE_DIR}/test.arpa) + + # model_test requires an extra command line parameter + KenLMAddTest(TEST model_test + LIBRARIES ${LM_LIBS} + TEST_ARGS ${CMAKE_CURRENT_SOURCE_DIR}/test.arpa + ${CMAKE_CURRENT_SOURCE_DIR}/test_nounk.arpa) +endif() diff --git a/ctcdecode-master/third_party/kenlm/lm/bhiksha.cc b/ctcdecode-master/third_party/kenlm/lm/bhiksha.cc new file mode 100644 index 0000000..4262b61 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/bhiksha.cc @@ -0,0 +1,94 @@ +#include "lm/bhiksha.hh" + +#include "lm/binary_format.hh" +#include "lm/config.hh" +#include "util/file.hh" +#include "util/exception.hh" + +#include + +namespace lm { +namespace ngram { +namespace trie { + +DontBhiksha::DontBhiksha(const void * /*base*/, uint64_t /*max_offset*/, uint64_t max_next, const Config &/*config*/) : + next_(util::BitsMask::ByMax(max_next)) {} + +const uint8_t kArrayBhikshaVersion = 0; + +// TODO: put this in binary file header instead when I change the binary file format again. +void ArrayBhiksha::UpdateConfigFromBinary(const BinaryFormat &file, uint64_t offset, Config &config) { + uint8_t buffer[2]; + file.ReadForConfig(buffer, 2, offset); + uint8_t version = buffer[0]; + uint8_t configured_bits = buffer[1]; + if (version != kArrayBhikshaVersion) UTIL_THROW(FormatLoadException, "This file has sorted array compression version " << (unsigned) version << " but the code expects version " << (unsigned)kArrayBhikshaVersion); + config.pointer_bhiksha_bits = configured_bits; +} + +namespace { + +// Find argmin_{chopped \in [0, RequiredBits(max_next)]} ChoppedDelta(max_offset) +uint8_t ChopBits(uint64_t max_offset, uint64_t max_next, const Config &config) { + uint8_t required = util::RequiredBits(max_next); + uint8_t best_chop = 0; + int64_t lowest_change = std::numeric_limits::max(); + // There are probably faster ways but I don't care because this is only done once per order at construction time. + for (uint8_t chop = 0; chop <= std::min(required, config.pointer_bhiksha_bits); ++chop) { + int64_t change = (max_next >> (required - chop)) * 64 /* table cost in bits */ + - max_offset * static_cast(chop); /* savings in bits*/ + if (change < lowest_change) { + lowest_change = change; + best_chop = chop; + } + } + return best_chop; +} + +std::size_t ArrayCount(uint64_t max_offset, uint64_t max_next, const Config &config) { + uint8_t required = util::RequiredBits(max_next); + uint8_t chopping = ChopBits(max_offset, max_next, config); + return (max_next >> (required - chopping)) + 1 /* we store 0 too */; +} +} // namespace + +uint64_t ArrayBhiksha::Size(uint64_t max_offset, uint64_t max_next, const Config &config) { + return sizeof(uint64_t) * (1 /* header */ + ArrayCount(max_offset, max_next, config)) + 7 /* 8-byte alignment */; +} + +uint8_t ArrayBhiksha::InlineBits(uint64_t max_offset, uint64_t max_next, const Config &config) { + return util::RequiredBits(max_next) - ChopBits(max_offset, max_next, config); +} + +namespace { + +void *AlignTo8(void *from) { + uint8_t *val = reinterpret_cast(from); + std::size_t remainder = reinterpret_cast(val) & 7; + if (!remainder) return val; + return val + 8 - remainder; +} + +} // namespace + +ArrayBhiksha::ArrayBhiksha(void *base, uint64_t max_offset, uint64_t max_next, const Config &config) + : next_inline_(util::BitsMask::ByBits(InlineBits(max_offset, max_next, config))), + offset_begin_(reinterpret_cast(AlignTo8(base)) + 1 /* 8-byte header */), + offset_end_(offset_begin_ + ArrayCount(max_offset, max_next, config)), + write_to_(reinterpret_cast(AlignTo8(base)) + 1 /* 8-byte header */ + 1 /* first entry is 0 */), + original_base_(base) {} + +void ArrayBhiksha::FinishedLoading(const Config &config) { + // *offset_begin_ = 0 but without a const_cast. + *(write_to_ - (write_to_ - offset_begin_)) = 0; + + if (write_to_ != offset_end_) UTIL_THROW(util::Exception, "Did not get all the array entries that were expected."); + + uint8_t *head_write = reinterpret_cast(original_base_); + *(head_write++) = kArrayBhikshaVersion; + *(head_write++) = config.pointer_bhiksha_bits; +} + +} // namespace trie +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/bhiksha.hh b/ctcdecode-master/third_party/kenlm/lm/bhiksha.hh new file mode 100644 index 0000000..36438f1 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/bhiksha.hh @@ -0,0 +1,122 @@ +/* Simple implementation of + * @inproceedings{bhikshacompression, + * author={Bhiksha Raj and Ed Whittaker}, + * year={2003}, + * title={Lossless Compression of Language Model Structure and Word Identifiers}, + * booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing}, + * pages={388--391}, + * } + * + * Currently only used for next pointers. + */ + +#ifndef LM_BHIKSHA_H +#define LM_BHIKSHA_H + +#include "lm/model_type.hh" +#include "lm/trie.hh" +#include "util/bit_packing.hh" +#include "util/sorted_uniform.hh" + +#include +#include +#include + +namespace lm { +namespace ngram { +struct Config; +class BinaryFormat; + +namespace trie { + +class DontBhiksha { + public: + static const ModelType kModelTypeAdd = static_cast(0); + + static void UpdateConfigFromBinary(const BinaryFormat &, uint64_t, Config &/*config*/) {} + + static uint64_t Size(uint64_t /*max_offset*/, uint64_t /*max_next*/, const Config &/*config*/) { return 0; } + + static uint8_t InlineBits(uint64_t /*max_offset*/, uint64_t max_next, const Config &/*config*/) { + return util::RequiredBits(max_next); + } + + DontBhiksha(const void *base, uint64_t max_offset, uint64_t max_next, const Config &config); + + void ReadNext(const void *base, uint64_t bit_offset, uint64_t /*index*/, uint8_t total_bits, NodeRange &out) const { + out.begin = util::ReadInt57(base, bit_offset, next_.bits, next_.mask); + out.end = util::ReadInt57(base, bit_offset + total_bits, next_.bits, next_.mask); + //assert(out.end >= out.begin); + } + + void WriteNext(void *base, uint64_t bit_offset, uint64_t /*index*/, uint64_t value) { + util::WriteInt57(base, bit_offset, next_.bits, value); + } + + void FinishedLoading(const Config &/*config*/) {} + + uint8_t InlineBits() const { return next_.bits; } + + private: + util::BitsMask next_; +}; + +class ArrayBhiksha { + public: + static const ModelType kModelTypeAdd = kArrayAdd; + + static void UpdateConfigFromBinary(const BinaryFormat &file, uint64_t offset, Config &config); + + static uint64_t Size(uint64_t max_offset, uint64_t max_next, const Config &config); + + static uint8_t InlineBits(uint64_t max_offset, uint64_t max_next, const Config &config); + + ArrayBhiksha(void *base, uint64_t max_offset, uint64_t max_value, const Config &config); + + void ReadNext(const void *base, uint64_t bit_offset, uint64_t index, uint8_t total_bits, NodeRange &out) const { + // Some assertions are commented out because they are expensive. + // assert(*offset_begin_ == 0); + // std::upper_bound returns the first element that is greater. Want the + // last element that is <= to the index. + const uint64_t *begin_it = std::upper_bound(offset_begin_, offset_end_, index) - 1; + // Since *offset_begin_ == 0, the position should be in range. + // assert(begin_it >= offset_begin_); + const uint64_t *end_it; + for (end_it = begin_it + 1; (end_it < offset_end_) && (*end_it <= index + 1); ++end_it) {} + // assert(end_it == std::upper_bound(offset_begin_, offset_end_, index + 1)); + --end_it; + // assert(end_it >= begin_it); + out.begin = ((begin_it - offset_begin_) << next_inline_.bits) | + util::ReadInt57(base, bit_offset, next_inline_.bits, next_inline_.mask); + out.end = ((end_it - offset_begin_) << next_inline_.bits) | + util::ReadInt57(base, bit_offset + total_bits, next_inline_.bits, next_inline_.mask); + // If this fails, consider rebuilding your model using KenLM after 1e333d786b748555e8f368d2bbba29a016c98052 + assert(out.end >= out.begin); + } + + void WriteNext(void *base, uint64_t bit_offset, uint64_t index, uint64_t value) { + uint64_t encode = value >> next_inline_.bits; + for (; write_to_ <= offset_begin_ + encode; ++write_to_) *write_to_ = index; + util::WriteInt57(base, bit_offset, next_inline_.bits, value & next_inline_.mask); + } + + void FinishedLoading(const Config &config); + + uint8_t InlineBits() const { return next_inline_.bits; } + + private: + const util::BitsMask next_inline_; + + const uint64_t *const offset_begin_; + const uint64_t *const offset_end_; + + uint64_t *write_to_; + + void *original_base_; +}; + +} // namespace trie +} // namespace ngram +} // namespace lm + +#endif // LM_BHIKSHA_H diff --git a/ctcdecode-master/third_party/kenlm/lm/binary_format.cc b/ctcdecode-master/third_party/kenlm/lm/binary_format.cc new file mode 100644 index 0000000..802943f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/binary_format.cc @@ -0,0 +1,302 @@ +#include "lm/binary_format.hh" + +#include "lm/lm_exception.hh" +#include "util/file.hh" +#include "util/file_piece.hh" + +#include +#include +#include +#include +#include + +#include + +namespace lm { +namespace ngram { + +const char *kModelNames[6] = {"probing hash tables", "probing hash tables with rest costs", "trie", "trie with quantization", "trie with array-compressed pointers", "trie with quantization and array-compressed pointers"}; + +namespace { +const char kMagicBeforeVersion[] = "mmap lm http://kheafield.com/code format version"; +const char kMagicBytes[] = "mmap lm http://kheafield.com/code format version 5\n\0"; +// This must be shorter than kMagicBytes and indicates an incomplete binary file (i.e. build failed). +const char kMagicIncomplete[] = "mmap lm http://kheafield.com/code incomplete\n"; +const long int kMagicVersion = 5; + +// Old binary files built on 32-bit machines have this header. +// TODO: eliminate with next binary release. +struct OldSanity { + char magic[sizeof(kMagicBytes)]; + float zero_f, one_f, minus_half_f; + WordIndex one_word_index, max_word_index; + uint64_t one_uint64; + + void SetToReference() { + std::memset(this, 0, sizeof(OldSanity)); + std::memcpy(magic, kMagicBytes, sizeof(magic)); + zero_f = 0.0; one_f = 1.0; minus_half_f = -0.5; + one_word_index = 1; + max_word_index = std::numeric_limits::max(); + one_uint64 = 1; + } +}; + + +// Test values aligned to 8 bytes. +struct Sanity { + char magic[ALIGN8(sizeof(kMagicBytes))]; + float zero_f, one_f, minus_half_f; + WordIndex one_word_index, max_word_index, padding_to_8; + uint64_t one_uint64; + + void SetToReference() { + std::memset(this, 0, sizeof(Sanity)); + std::memcpy(magic, kMagicBytes, sizeof(kMagicBytes)); + zero_f = 0.0; one_f = 1.0; minus_half_f = -0.5; + one_word_index = 1; + max_word_index = std::numeric_limits::max(); + padding_to_8 = 0; + one_uint64 = 1; + } +}; + +std::size_t TotalHeaderSize(unsigned char order) { + return ALIGN8(sizeof(Sanity) + sizeof(FixedWidthParameters) + sizeof(uint64_t) * order); +} + +void WriteHeader(void *to, const Parameters ¶ms) { + Sanity header = Sanity(); + header.SetToReference(); + std::memcpy(to, &header, sizeof(Sanity)); + char *out = reinterpret_cast(to) + sizeof(Sanity); + + *reinterpret_cast(out) = params.fixed; + out += sizeof(FixedWidthParameters); + + uint64_t *counts = reinterpret_cast(out); + for (std::size_t i = 0; i < params.counts.size(); ++i) { + counts[i] = params.counts[i]; + } +} + +} // namespace + +bool IsBinaryFormat(int fd) { + const uint64_t size = util::SizeFile(fd); + if (size == util::kBadSize || (size <= static_cast(sizeof(Sanity)))) return false; + // Try reading the header. + util::scoped_memory memory; + try { + util::MapRead(util::LAZY, fd, 0, sizeof(Sanity), memory); + } catch (const util::Exception &e) { + return false; + } + Sanity reference_header = Sanity(); + reference_header.SetToReference(); + if (!std::memcmp(memory.get(), &reference_header, sizeof(Sanity))) return true; + if (!std::memcmp(memory.get(), kMagicIncomplete, strlen(kMagicIncomplete))) { + UTIL_THROW(FormatLoadException, "This binary file did not finish building"); + } + if (!std::memcmp(memory.get(), kMagicBeforeVersion, strlen(kMagicBeforeVersion))) { + char *end_ptr; + const char *begin_version = static_cast(memory.get()) + strlen(kMagicBeforeVersion); + long int version = std::strtol(begin_version, &end_ptr, 10); + if ((end_ptr != begin_version) && version != kMagicVersion) { + UTIL_THROW(FormatLoadException, "Binary file has version " << version << " but this implementation expects version " << kMagicVersion << " so you'll have to use the ARPA to rebuild your binary"); + } + + OldSanity old_sanity = OldSanity(); + old_sanity.SetToReference(); + UTIL_THROW_IF(!std::memcmp(memory.get(), &old_sanity, sizeof(OldSanity)), FormatLoadException, "Looks like this is an old 32-bit format. The old 32-bit format has been removed so that 64-bit and 32-bit files are exchangeable."); + UTIL_THROW(FormatLoadException, "File looks like it should be loaded with mmap, but the test values don't match. Try rebuilding the binary format LM using the same code revision, compiler, and architecture"); + } + return false; +} + +void ReadHeader(int fd, Parameters &out) { + util::SeekOrThrow(fd, sizeof(Sanity)); + util::ReadOrThrow(fd, &out.fixed, sizeof(out.fixed)); + if (out.fixed.probing_multiplier < 1.0) + UTIL_THROW(FormatLoadException, "Binary format claims to have a probing multiplier of " << out.fixed.probing_multiplier << " which is < 1.0."); + + out.counts.resize(static_cast(out.fixed.order)); + if (out.fixed.order) util::ReadOrThrow(fd, &*out.counts.begin(), sizeof(uint64_t) * out.fixed.order); +} + +void MatchCheck(ModelType model_type, unsigned int search_version, const Parameters ¶ms) { + if (params.fixed.model_type != model_type) { + if (static_cast(params.fixed.model_type) >= (sizeof(kModelNames) / sizeof(const char *))) + UTIL_THROW(FormatLoadException, "The binary file claims to be model type " << static_cast(params.fixed.model_type) << " but this is not implemented for in this inference code."); + UTIL_THROW(FormatLoadException, "The binary file was built for " << kModelNames[params.fixed.model_type] << " but the inference code is trying to load " << kModelNames[model_type]); + } + UTIL_THROW_IF(search_version != params.fixed.search_version, FormatLoadException, "The binary file has " << kModelNames[params.fixed.model_type] << " version " << params.fixed.search_version << " but this code expects " << kModelNames[params.fixed.model_type] << " version " << search_version); +} + +const std::size_t kInvalidSize = static_cast(-1); + +BinaryFormat::BinaryFormat(const Config &config) + : write_method_(config.write_method), write_mmap_(config.write_mmap), load_method_(config.load_method), + header_size_(kInvalidSize), vocab_size_(kInvalidSize), vocab_string_offset_(kInvalidOffset) {} + +void BinaryFormat::InitializeBinary(int fd, ModelType model_type, unsigned int search_version, Parameters ¶ms) { + file_.reset(fd); + write_mmap_ = NULL; // Ignore write requests; this is already in binary format. + ReadHeader(fd, params); + MatchCheck(model_type, search_version, params); + header_size_ = TotalHeaderSize(params.counts.size()); +} + +void BinaryFormat::ReadForConfig(void *to, std::size_t amount, uint64_t offset_excluding_header) const { + assert(header_size_ != kInvalidSize); + util::ErsatzPRead(file_.get(), to, amount, offset_excluding_header + header_size_); +} + +void *BinaryFormat::LoadBinary(std::size_t size) { + assert(header_size_ != kInvalidSize); + const uint64_t file_size = util::SizeFile(file_.get()); + // The header is smaller than a page, so we have to map the whole header as well. + uint64_t total_map = static_cast(header_size_) + static_cast(size); + UTIL_THROW_IF(file_size != util::kBadSize && file_size < total_map, FormatLoadException, "Binary file has size " << file_size << " but the headers say it should be at least " << total_map); + + util::MapRead(load_method_, file_.get(), 0, util::CheckOverflow(total_map), mapping_); + + vocab_string_offset_ = total_map; + return reinterpret_cast(mapping_.get()) + header_size_; +} + +void *BinaryFormat::SetupJustVocab(std::size_t memory_size, uint8_t order) { + vocab_size_ = memory_size; + if (!write_mmap_) { + header_size_ = 0; + util::HugeMalloc(memory_size, true, memory_vocab_); + return reinterpret_cast(memory_vocab_.get()); + } + header_size_ = TotalHeaderSize(order); + std::size_t total = util::CheckOverflow(static_cast(header_size_) + static_cast(memory_size)); + file_.reset(util::CreateOrThrow(write_mmap_)); + // some gccs complain about uninitialized variables even though all enum values are covered. + void *vocab_base = NULL; + switch (write_method_) { + case Config::WRITE_MMAP: + mapping_.reset(util::MapZeroedWrite(file_.get(), total), total, util::scoped_memory::MMAP_ALLOCATED); + util::AdviseHugePages(vocab_base, total); + vocab_base = mapping_.get(); + break; + case Config::WRITE_AFTER: + util::ResizeOrThrow(file_.get(), 0); + util::HugeMalloc(total, true, memory_vocab_); + vocab_base = memory_vocab_.get(); + break; + } + strncpy(reinterpret_cast(vocab_base), kMagicIncomplete, header_size_); + return reinterpret_cast(vocab_base) + header_size_; +} + +void *BinaryFormat::GrowForSearch(std::size_t memory_size, std::size_t vocab_pad, void *&vocab_base) { + assert(vocab_size_ != kInvalidSize); + vocab_pad_ = vocab_pad; + std::size_t new_size = header_size_ + vocab_size_ + vocab_pad_ + memory_size; + vocab_string_offset_ = new_size; + if (!write_mmap_ || write_method_ == Config::WRITE_AFTER) { + util::HugeMalloc(memory_size, true, memory_search_); + assert(header_size_ == 0 || write_mmap_); + vocab_base = reinterpret_cast(memory_vocab_.get()) + header_size_; + util::AdviseHugePages(memory_search_.get(), memory_size); + return reinterpret_cast(memory_search_.get()); + } + + assert(write_method_ == Config::WRITE_MMAP); + // Also known as total size without vocab words. + // Grow the file to accomodate the search, using zeros. + // According to man mmap, behavior is undefined when the file is resized + // underneath a mmap that is not a multiple of the page size. So to be + // safe, we'll unmap it and map it again. + mapping_.reset(); + util::ResizeOrThrow(file_.get(), new_size); + void *ret; + MapFile(vocab_base, ret); + util::AdviseHugePages(ret, new_size); + return ret; +} + +void BinaryFormat::WriteVocabWords(const std::string &buffer, void *&vocab_base, void *&search_base) { + // Checking Config's include_vocab is the responsibility of the caller. + assert(header_size_ != kInvalidSize && vocab_size_ != kInvalidSize); + if (!write_mmap_) { + // Unchanged base. + vocab_base = reinterpret_cast(memory_vocab_.get()); + search_base = reinterpret_cast(memory_search_.get()); + return; + } + if (write_method_ == Config::WRITE_MMAP) { + mapping_.reset(); + } + util::SeekOrThrow(file_.get(), VocabStringReadingOffset()); + util::WriteOrThrow(file_.get(), &buffer[0], buffer.size()); + if (write_method_ == Config::WRITE_MMAP) { + MapFile(vocab_base, search_base); + } else { + vocab_base = reinterpret_cast(memory_vocab_.get()) + header_size_; + search_base = reinterpret_cast(memory_search_.get()); + } +} + +void BinaryFormat::FinishFile(const Config &config, ModelType model_type, unsigned int search_version, const std::vector &counts) { + if (!write_mmap_) return; + switch (write_method_) { + case Config::WRITE_MMAP: + util::SyncOrThrow(mapping_.get(), mapping_.size()); + break; + case Config::WRITE_AFTER: + util::SeekOrThrow(file_.get(), 0); + util::WriteOrThrow(file_.get(), memory_vocab_.get(), memory_vocab_.size()); + util::SeekOrThrow(file_.get(), header_size_ + vocab_size_ + vocab_pad_); + util::WriteOrThrow(file_.get(), memory_search_.get(), memory_search_.size()); + util::FSyncOrThrow(file_.get()); + break; + } + // header and vocab share the same mmap. + Parameters params = Parameters(); + memset(¶ms, 0, sizeof(Parameters)); + params.counts = counts; + params.fixed.order = counts.size(); + params.fixed.probing_multiplier = config.probing_multiplier; + params.fixed.model_type = model_type; + params.fixed.has_vocabulary = config.include_vocab; + params.fixed.search_version = search_version; + switch (write_method_) { + case Config::WRITE_MMAP: + WriteHeader(mapping_.get(), params); + util::SyncOrThrow(mapping_.get(), mapping_.size()); + break; + case Config::WRITE_AFTER: + { + std::vector buffer(TotalHeaderSize(counts.size())); + WriteHeader(&buffer[0], params); + util::SeekOrThrow(file_.get(), 0); + util::WriteOrThrow(file_.get(), &buffer[0], buffer.size()); + } + break; + } +} + +void BinaryFormat::MapFile(void *&vocab_base, void *&search_base) { + mapping_.reset(util::MapOrThrow(vocab_string_offset_, true, util::kFileFlags, false, file_.get()), vocab_string_offset_, util::scoped_memory::MMAP_ALLOCATED); + vocab_base = reinterpret_cast(mapping_.get()) + header_size_; + search_base = reinterpret_cast(mapping_.get()) + header_size_ + vocab_size_ + vocab_pad_; +} + +bool RecognizeBinary(const char *file, ModelType &recognized) { + util::scoped_fd fd(util::OpenReadOrThrow(file)); + if (!IsBinaryFormat(fd.get())) { + return false; + } + Parameters params; + ReadHeader(fd.get(), params); + recognized = params.fixed.model_type; + return true; +} + +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/binary_format.hh b/ctcdecode-master/third_party/kenlm/lm/binary_format.hh new file mode 100644 index 0000000..ff99b95 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/binary_format.hh @@ -0,0 +1,106 @@ +#ifndef LM_BINARY_FORMAT_H +#define LM_BINARY_FORMAT_H + +#include "lm/config.hh" +#include "lm/model_type.hh" +#include "lm/read_arpa.hh" + +#include "util/file_piece.hh" +#include "util/mmap.hh" +#include "util/scoped.hh" + +#include +#include + +#include + +namespace lm { +namespace ngram { + +extern const char *kModelNames[6]; + +/*Inspect a file to determine if it is a binary lm. If not, return false. + * If so, return true and set recognized to the type. This is the only API in + * this header designed for use by decoder authors. + */ +bool RecognizeBinary(const char *file, ModelType &recognized); + +struct FixedWidthParameters { + unsigned char order; + float probing_multiplier; + // What type of model is this? + ModelType model_type; + // Does the end of the file have the actual strings in the vocabulary? + bool has_vocabulary; + unsigned int search_version; +}; + +// This is a macro instead of an inline function so constants can be assigned using it. +#define ALIGN8(a) ((std::ptrdiff_t(((a)-1)/8)+1)*8) + +// Parameters stored in the header of a binary file. +struct Parameters { + FixedWidthParameters fixed; + std::vector counts; +}; + +class BinaryFormat { + public: + explicit BinaryFormat(const Config &config); + + // Reading a binary file: + // Takes ownership of fd + void InitializeBinary(int fd, ModelType model_type, unsigned int search_version, Parameters ¶ms); + // Used to read parts of the file to update the config object before figuring out full size. + void ReadForConfig(void *to, std::size_t amount, uint64_t offset_excluding_header) const; + // Actually load the binary file and return a pointer to the beginning of the search area. + void *LoadBinary(std::size_t size); + + uint64_t VocabStringReadingOffset() const { + assert(vocab_string_offset_ != kInvalidOffset); + return vocab_string_offset_; + } + + // Writing a binary file or initializing in RAM from ARPA: + // Size for vocabulary. + void *SetupJustVocab(std::size_t memory_size, uint8_t order); + // Warning: can change the vocaulary base pointer. + void *GrowForSearch(std::size_t memory_size, std::size_t vocab_pad, void *&vocab_base); + // Warning: can change vocabulary and search base addresses. + void WriteVocabWords(const std::string &buffer, void *&vocab_base, void *&search_base); + // Write the header at the beginning of the file. + void FinishFile(const Config &config, ModelType model_type, unsigned int search_version, const std::vector &counts); + + private: + void MapFile(void *&vocab_base, void *&search_base); + + // Copied from configuration. + const Config::WriteMethod write_method_; + const char *write_mmap_; + util::LoadMethod load_method_; + + // File behind memory, if any. + util::scoped_fd file_; + + // If there is a file involved, a single mapping. + util::scoped_memory mapping_; + + // If the data is only in memory, separately allocate each because the trie + // knows vocab's size before it knows search's size (because SRILM might + // have pruned). + util::scoped_memory memory_vocab_, memory_search_; + + // Memory ranges. Note that these may not be contiguous and may not all + // exist. + std::size_t header_size_, vocab_size_, vocab_pad_; + // aka end of search. + uint64_t vocab_string_offset_; + + static const uint64_t kInvalidOffset = (uint64_t)-1; +}; + +bool IsBinaryFormat(int fd); + +} // namespace ngram +} // namespace lm +#endif // LM_BINARY_FORMAT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/blank.hh b/ctcdecode-master/third_party/kenlm/lm/blank.hh new file mode 100644 index 0000000..e09054c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/blank.hh @@ -0,0 +1,42 @@ +#ifndef LM_BLANK_H +#define LM_BLANK_H + +#include +#include +#include + +namespace lm { +namespace ngram { + +/* Suppose "foo bar" appears with zero backoff but there is no trigram + * beginning with these words. Then, when scoring "foo bar", the model could + * return out_state containing "bar" or even null context if "bar" also has no + * backoff and is never followed by another word. Then the backoff is set to + * kNoExtensionBackoff. If the n-gram might be extended, then out_state must + * contain the full n-gram, in which case kExtensionBackoff is set. In any + * case, if an n-gram has non-zero backoff, the full state is returned so + * backoff can be properly charged. + * These differ only in sign bit because the backoff is in fact zero in either + * case. + */ +const float kNoExtensionBackoff = -0.0; +const float kExtensionBackoff = 0.0; +const uint64_t kNoExtensionQuant = 0; +const uint64_t kExtensionQuant = 1; + +inline void SetExtension(float &backoff) { + if (backoff == kNoExtensionBackoff) backoff = kExtensionBackoff; +} + +// This compiles down nicely. +inline bool HasExtension(const float &backoff) { + typedef union { float f; uint32_t i; } UnionValue; + UnionValue compare, interpret; + compare.f = kNoExtensionBackoff; + interpret.f = backoff; + return compare.i != interpret.i; +} + +} // namespace ngram +} // namespace lm +#endif // LM_BLANK_H diff --git a/ctcdecode-master/third_party/kenlm/lm/build_binary_main.cc b/ctcdecode-master/third_party/kenlm/lm/build_binary_main.cc new file mode 100644 index 0000000..35206e6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/build_binary_main.cc @@ -0,0 +1,234 @@ +#include "lm/model.hh" +#include "lm/sizes.hh" +#include "util/file_piece.hh" +#include "util/usage.hh" + +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef WIN32 +#include "util/getopt.hh" +#else +#include +#endif + +namespace lm { +namespace ngram { +namespace { + +void Usage(const char *name, const char *default_mem) { + std::cerr << "Usage: " << name << " [-u log10_unknown_probability] [-s] [-i] [-w mmap|after] [-p probing_multiplier] [-T trie_temporary] [-S trie_building_mem] [-q bits] [-b bits] [-a bits] [type] input.arpa [output.mmap]\n\n" +"-u sets the log10 probability for if the ARPA file does not have one.\n" +" Default is -100. The ARPA file will always take precedence.\n" +"-s allows models to be built even if they do not have and .\n" +"-i allows buggy models from IRSTLM by mapping positive log probability to 0.\n" +"-w mmap|after determines how writing is done.\n" +" mmap maps the binary file and writes to it. Default for trie.\n" +" after allocates anonymous memory, builds, and writes. Default for probing.\n" +"-r \"order1.arpa order2 order3 order4\" adds lower-order rest costs from these\n" +" model files. order1.arpa must be an ARPA file. All others may be ARPA or\n" +" the same data structure as being built. All files must have the same\n" +" vocabulary. For probing, the unigrams must be in the same order.\n\n" +"type is either probing or trie. Default is probing.\n\n" +"probing uses a probing hash table. It is the fastest but uses the most memory.\n" +"-p sets the space multiplier and must be >1.0. The default is 1.5.\n\n" +"trie is a straightforward trie with bit-level packing. It uses the least\n" +"memory and is still faster than SRI or IRST. Building the trie format uses an\n" +"on-disk sort to save memory.\n" +"-T is the temporary directory prefix. Default is the output file name.\n" +"-S determines memory use for sorting. Default is " << default_mem << ". This is compatible\n" +" with GNU sort. The number is followed by a unit: \% for percent of physical\n" +" memory, b for bytes, K for Kilobytes, M for megabytes, then G,T,P,E,Z,Y. \n" +" Default unit is K for Kilobytes.\n" +"-q turns quantization on and sets the number of bits (e.g. -q 8).\n" +"-b sets backoff quantization bits. Requires -q and defaults to that value.\n" +"-a compresses pointers using an array of offsets. The parameter is the\n" +" maximum number of bits encoded by the array. Memory is minimized subject\n" +" to the maximum, so pick 255 to minimize memory.\n\n" +"-h print this help message.\n\n" +"Get a memory estimate by passing an ARPA file without an output file name.\n"; + exit(1); +} + +// I could really use boost::lexical_cast right about now. +float ParseFloat(const char *from) { + char *end; + float ret = strtod(from, &end); + if (*end) throw util::ParseNumberException(from); + return ret; +} +unsigned long int ParseUInt(const char *from) { + char *end; + unsigned long int ret = strtoul(from, &end, 10); + if (*end) throw util::ParseNumberException(from); + return ret; +} + +uint8_t ParseBitCount(const char *from) { + unsigned long val = ParseUInt(from); + if (val > 25) { + util::ParseNumberException e(from); + e << " bit counts are limited to 25."; + } + return val; +} + +void ParseFileList(const char *from, std::vector &to) { + to.clear(); + while (true) { + const char *i; + for (i = from; *i && *i != ' '; ++i) {} + to.push_back(std::string(from, i - from)); + if (!*i) break; + from = i + 1; + } +} + +void ProbingQuantizationUnsupported() { + std::cerr << "Quantization is only implemented in the trie data structure." << std::endl; + exit(1); +} + +} // namespace ngram +} // namespace lm +} // namespace + +int main(int argc, char *argv[]) { + using namespace lm::ngram; + + const char *default_mem = util::GuessPhysicalMemory() ? "80%" : "1G"; + + if (argc == 2 && !strcmp(argv[1], "--help")) + Usage(argv[0], default_mem); + + try { + bool quantize = false, set_backoff_bits = false, bhiksha = false, set_write_method = false, rest = false; + lm::ngram::Config config; + config.building_memory = util::ParseSize(default_mem); + int opt; + while ((opt = getopt(argc, argv, "q:b:a:u:p:t:T:m:S:w:sir:h")) != -1) { + switch(opt) { + case 'q': + config.prob_bits = ParseBitCount(optarg); + if (!set_backoff_bits) config.backoff_bits = config.prob_bits; + quantize = true; + break; + case 'b': + config.backoff_bits = ParseBitCount(optarg); + set_backoff_bits = true; + break; + case 'a': + config.pointer_bhiksha_bits = ParseBitCount(optarg); + bhiksha = true; + break; + case 'u': + config.unknown_missing_logprob = ParseFloat(optarg); + break; + case 'p': + config.probing_multiplier = ParseFloat(optarg); + break; + case 't': // legacy + case 'T': + config.temporary_directory_prefix = optarg; + util::NormalizeTempPrefix(config.temporary_directory_prefix); + break; + case 'm': // legacy + config.building_memory = ParseUInt(optarg) * 1048576; + break; + case 'S': + config.building_memory = std::min(static_cast(std::numeric_limits::max()), util::ParseSize(optarg)); + break; + case 'w': + set_write_method = true; + if (!strcmp(optarg, "mmap")) { + config.write_method = Config::WRITE_MMAP; + } else if (!strcmp(optarg, "after")) { + config.write_method = Config::WRITE_AFTER; + } else { + Usage(argv[0], default_mem); + } + break; + case 's': + config.sentence_marker_missing = lm::SILENT; + break; + case 'i': + config.positive_log_probability = lm::SILENT; + break; + case 'r': + rest = true; + ParseFileList(optarg, config.rest_lower_files); + config.rest_function = Config::REST_LOWER; + break; + case 'h': // help + default: + Usage(argv[0], default_mem); + } + } + if (!quantize && set_backoff_bits) { + std::cerr << "You specified backoff quantization (-b) but not probability quantization (-q)" << std::endl; + abort(); + } + if (optind + 1 == argc) { + ShowSizes(argv[optind], config); + return 0; + } + const char *model_type; + const char *from_file; + + if (optind + 2 == argc) { + model_type = "probing"; + from_file = argv[optind]; + config.write_mmap = argv[optind + 1]; + } else if (optind + 3 == argc) { + model_type = argv[optind]; + from_file = argv[optind + 1]; + config.write_mmap = argv[optind + 2]; + } else { + Usage(argv[0], default_mem); + return 1; + } + if (!strcmp(model_type, "probing")) { + if (!set_write_method) config.write_method = Config::WRITE_AFTER; + if (quantize || set_backoff_bits) ProbingQuantizationUnsupported(); + if (rest) { + RestProbingModel(from_file, config); + } else { + ProbingModel(from_file, config); + } + } else if (!strcmp(model_type, "trie")) { + if (rest) { + std::cerr << "Rest + trie is not supported yet." << std::endl; + return 1; + } + if (!set_write_method) config.write_method = Config::WRITE_MMAP; + if (quantize) { + if (bhiksha) { + QuantArrayTrieModel(from_file, config); + } else { + QuantTrieModel(from_file, config); + } + } else { + if (bhiksha) { + ArrayTrieModel(from_file, config); + } else { + TrieModel(from_file, config); + } + } + } else { + Usage(argv[0], default_mem); + } + } + catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + std::cerr << "ERROR" << std::endl; + return 1; + } + std::cerr << "SUCCESS" << std::endl; + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/lm/builder/CMakeLists.txt new file mode 100644 index 0000000..35000b1 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/CMakeLists.txt @@ -0,0 +1,52 @@ +# This CMake file was created by Lane Schwartz + +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +# +# In order to set correct paths to these files +# in case this variable is referenced by CMake files in the parent directory, +# we prefix all files with ${CMAKE_CURRENT_SOURCE_DIR}. +# +set(KENLM_BUILDER_SOURCE + ${CMAKE_CURRENT_SOURCE_DIR}/adjust_counts.cc + ${CMAKE_CURRENT_SOURCE_DIR}/corpus_count.cc + ${CMAKE_CURRENT_SOURCE_DIR}/initial_probabilities.cc + ${CMAKE_CURRENT_SOURCE_DIR}/interpolate.cc + ${CMAKE_CURRENT_SOURCE_DIR}/output.cc + ${CMAKE_CURRENT_SOURCE_DIR}/pipeline.cc + ) + + +# Group these objects together for later use. +# +# Given add_library(foo OBJECT ${my_foo_sources}), +# refer to these objects as $ +# +add_library(kenlm_builder ${KENLM_BUILDER_SOURCE}) + +target_link_libraries(kenlm_builder kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) + +if (NOT MSVC) + set(THREADS pthread) +endif() + +AddExes(EXES lmplz + LIBRARIES kenlm_builder kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) +AddExes(EXES count_ngrams + LIBRARIES kenlm_builder kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) + +if(BUILD_TESTING) + + # Explicitly list the Boost test files to be compiled + set(KENLM_BOOST_TESTS_LIST + adjust_counts_test + corpus_count_test + ) + + AddTests(TESTS ${KENLM_BOOST_TESTS_LIST} + LIBRARIES kenlm_builder kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) +endif() diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/README.md b/ctcdecode-master/third_party/kenlm/lm/builder/README.md new file mode 100644 index 0000000..be0d35e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/README.md @@ -0,0 +1,47 @@ +Dependencies +============ + +Boost >= 1.42.0 is required. + +For Ubuntu, +```bash +sudo apt-get install libboost1.48-all-dev +``` + +Alternatively, you can download, compile, and install it yourself: + +```bash +wget http://sourceforge.net/projects/boost/files/boost/1.52.0/boost_1_52_0.tar.gz/download -O boost_1_52_0.tar.gz +tar -xvzf boost_1_52_0.tar.gz +cd boost_1_52_0 +./bootstrap.sh +./b2 +sudo ./b2 install +``` + +Local install options (in a user-space prefix directory) are also possible. See http://www.boost.org/doc/libs/1_52_0/doc/html/bbv2/installation.html. + + +Building +======== + +```bash +bjam +``` +Your distribution might package bjam and boost-build separately from Boost. Both are required. + +Usage +===== + +Run +```bash +$ bin/lmplz +``` +to see command line arguments + +Running +======= + +```bash +bin/lmplz -o 5 text.arpa +``` diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/TODO b/ctcdecode-master/third_party/kenlm/lm/builder/TODO new file mode 100644 index 0000000..cb5aef3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/TODO @@ -0,0 +1,5 @@ +More tests! +Sharding. +Some way to manage all the crazy config options. +Option to build the binary file directly. +Interpolation of different orders. diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts.cc b/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts.cc new file mode 100644 index 0000000..dc1e1ff --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts.cc @@ -0,0 +1,350 @@ +#include "lm/builder/adjust_counts.hh" +#include "lm/common/ngram_stream.hh" +#include "lm/builder/payload.hh" + +#include +#include +#include + +namespace lm { namespace builder { + +BadDiscountException::BadDiscountException() throw() {} +BadDiscountException::~BadDiscountException() throw() {} + +namespace { +// Return last word in full that is different. +const WordIndex* FindDifference(const NGram &full, const NGram &lower_last) { + const WordIndex *cur_word = full.end() - 1; + const WordIndex *pre_word = lower_last.end() - 1; + // Find last difference. + for (; pre_word >= lower_last.begin() && *pre_word == *cur_word; --cur_word, --pre_word) {} + return cur_word; +} + +class StatCollector { + public: + StatCollector(std::size_t order, std::vector &counts, std::vector &counts_pruned, std::vector &discounts) + : orders_(order), full_(orders_.back()), counts_(counts), counts_pruned_(counts_pruned), discounts_(discounts) { + memset(&orders_[0], 0, sizeof(OrderStat) * order); + } + + ~StatCollector() {} + + void CalculateDiscounts(const DiscountConfig &config) { + counts_.resize(orders_.size()); + counts_pruned_.resize(orders_.size()); + for (std::size_t i = 0; i < orders_.size(); ++i) { + const OrderStat &s = orders_[i]; + counts_[i] = s.count; + counts_pruned_[i] = s.count_pruned; + } + + discounts_ = config.overwrite; + discounts_.resize(orders_.size()); + for (std::size_t i = config.overwrite.size(); i < orders_.size(); ++i) { + const OrderStat &s = orders_[i]; + try { + for (unsigned j = 1; j < 4; ++j) { + // TODO: Specialize error message for j == 3, meaning 3+ + UTIL_THROW_IF(s.n[j] == 0, BadDiscountException, "Could not calculate Kneser-Ney discounts for " + << (i+1) << "-grams with adjusted count " << (j+1) << " because we didn't observe any " + << (i+1) << "-grams with adjusted count " << j << "; Is this small or artificial data?\n" + << "Try deduplicating the input. To override this error for e.g. a class-based model, rerun with --discount_fallback\n"); + } + + // See equation (26) in Chen and Goodman. + discounts_[i].amount[0] = 0.0; + float y = static_cast(s.n[1]) / static_cast(s.n[1] + 2.0 * s.n[2]); + for (unsigned j = 1; j < 4; ++j) { + discounts_[i].amount[j] = static_cast(j) - static_cast(j + 1) * y * static_cast(s.n[j+1]) / static_cast(s.n[j]); + UTIL_THROW_IF(discounts_[i].amount[j] < 0.0 || discounts_[i].amount[j] > j, BadDiscountException, "ERROR: " << (i+1) << "-gram discount out of range for adjusted count " << j << ": " << discounts_[i].amount[j]); + } + } catch (const BadDiscountException &e) { + switch (config.bad_action) { + case THROW_UP: + throw; + case COMPLAIN: + std::cerr << "Substituting fallback discounts for order " << i << ": D1=" << config.fallback.amount[1] << " D2=" << config.fallback.amount[2] << " D3+=" << config.fallback.amount[3] << std::endl; + case SILENT: + break; + } + discounts_[i] = config.fallback; + } + } + } + + void Add(std::size_t order_minus_1, uint64_t count, bool pruned = false) { + OrderStat &stat = orders_[order_minus_1]; + ++stat.count; + if (!pruned) + ++stat.count_pruned; + if (count < 5) ++stat.n[count]; + } + + void AddFull(uint64_t count, bool pruned = false) { + ++full_.count; + if (!pruned) + ++full_.count_pruned; + if (count < 5) ++full_.n[count]; + } + + private: + struct OrderStat { + // n_1 in equation 26 of Chen and Goodman etc + uint64_t n[5]; + uint64_t count; + uint64_t count_pruned; + }; + + std::vector orders_; + OrderStat &full_; + + std::vector &counts_; + std::vector &counts_pruned_; + std::vector &discounts_; +}; + +// Reads all entries in order like NGramStream does. +// But deletes any entries that have in the 1st (not 0th) position on the +// way out by putting other entries in their place. This disrupts the sort +// order but we don't care because the data is going to be sorted again. +class CollapseStream { + public: + CollapseStream(const util::stream::ChainPosition &position, uint64_t prune_threshold, const std::vector& prune_words) : + current_(NULL, NGram::OrderFromSize(position.GetChain().EntrySize())), + prune_threshold_(prune_threshold), + prune_words_(prune_words), + block_(position) { + StartBlock(); + } + + const NGram &operator*() const { return current_; } + const NGram *operator->() const { return ¤t_; } + + operator bool() const { return block_; } + + CollapseStream &operator++() { + assert(block_); + + if (current_.begin()[1] == kBOS && current_.Base() < copy_from_) { + memcpy(current_.Base(), copy_from_, current_.TotalSize()); + UpdateCopyFrom(); + + // Mark highest order n-grams for later pruning + if(current_.Value().count <= prune_threshold_) { + current_.Value().Mark(); + } + + if(!prune_words_.empty()) { + for(WordIndex* i = current_.begin(); i != current_.end(); i++) { + if(prune_words_[*i]) { + current_.Value().Mark(); + break; + } + } + } + + } + + current_.NextInMemory(); + uint8_t *block_base = static_cast(block_->Get()); + if (current_.Base() == block_base + block_->ValidSize()) { + block_->SetValidSize(copy_from_ + current_.TotalSize() - block_base); + ++block_; + StartBlock(); + } + + // Mark highest order n-grams for later pruning + if(current_.Value().count <= prune_threshold_) { + current_.Value().Mark(); + } + + if(!prune_words_.empty()) { + for(WordIndex* i = current_.begin(); i != current_.end(); i++) { + if(prune_words_[*i]) { + current_.Value().Mark(); + break; + } + } + } + + return *this; + } + + private: + void StartBlock() { + for (; ; ++block_) { + if (!block_) return; + if (block_->ValidSize()) break; + } + current_.ReBase(block_->Get()); + copy_from_ = static_cast(block_->Get()) + block_->ValidSize(); + UpdateCopyFrom(); + + // Mark highest order n-grams for later pruning + if(current_.Value().count <= prune_threshold_) { + current_.Value().Mark(); + } + + if(!prune_words_.empty()) { + for(WordIndex* i = current_.begin(); i != current_.end(); i++) { + if(prune_words_[*i]) { + current_.Value().Mark(); + break; + } + } + } + + } + + // Find last without bos. + void UpdateCopyFrom() { + for (copy_from_ -= current_.TotalSize(); copy_from_ >= current_.Base(); copy_from_ -= current_.TotalSize()) { + if (NGram(copy_from_, current_.Order()).begin()[1] != kBOS) break; + } + } + + NGram current_; + + // Goes backwards in the block + uint8_t *copy_from_; + uint64_t prune_threshold_; + const std::vector& prune_words_; + util::stream::Link block_; +}; + +} // namespace + +void AdjustCounts::Run(const util::stream::ChainPositions &positions) { + const std::size_t order = positions.size(); + StatCollector stats(order, counts_, counts_pruned_, discounts_); + if (order == 1) { + + // Only unigrams. Just collect stats. + for (NGramStream full(positions[0]); full; ++full) { + + // Do not prune + if(*full->begin() > 2) { + if(full->Value().count <= prune_thresholds_[0]) + full->Value().Mark(); + + if(!prune_words_.empty() && prune_words_[*full->begin()]) + full->Value().Mark(); + } + + stats.AddFull(full->Value().UnmarkedCount(), full->Value().IsMarked()); + } + + stats.CalculateDiscounts(discount_config_); + return; + } + + NGramStreams streams; + streams.Init(positions, positions.size() - 1); + + CollapseStream full(positions[positions.size() - 1], prune_thresholds_.back(), prune_words_); + + // Initialization: has count 0 and so does . + NGramStream *lower_valid = streams.begin(); + const NGramStream *const streams_begin = streams.begin(); + streams[0]->Value().count = 0; + *streams[0]->begin() = kUNK; + stats.Add(0, 0); + (++streams[0])->Value().count = 0; + *streams[0]->begin() = kBOS; + // is not in stats yet because it will get put in later. + + // This keeps track of actual counts for lower orders. It is not output + // (only adjusted counts are), but used to determine pruning. + std::vector actual_counts(positions.size(), 0); + // Something of a hack: don't prune . + actual_counts[0] = std::numeric_limits::max(); + + // Iterate over full (the stream of the highest order ngrams) + for (; full; ++full) { + const WordIndex *different = FindDifference(*full, **lower_valid); + std::size_t same = full->end() - 1 - different; + + // STEP 1: Output all the n-grams that changed. + for (; lower_valid >= streams.begin() + same; --lower_valid) { + uint64_t order_minus_1 = lower_valid - streams_begin; + if(actual_counts[order_minus_1] <= prune_thresholds_[order_minus_1]) + (*lower_valid)->Value().Mark(); + + if(!prune_words_.empty()) { + for(WordIndex* i = (*lower_valid)->begin(); i != (*lower_valid)->end(); i++) { + if(prune_words_[*i]) { + (*lower_valid)->Value().Mark(); + break; + } + } + } + + stats.Add(order_minus_1, (*lower_valid)->Value().UnmarkedCount(), (*lower_valid)->Value().IsMarked()); + ++*lower_valid; + } + + // STEP 2: Update n-grams that still match. + // n-grams that match get count from the full entry. + for (std::size_t i = 0; i < same; ++i) { + actual_counts[i] += full->Value().UnmarkedCount(); + } + // Increment the number of unique extensions for the longest match. + if (same) ++streams[same - 1]->Value().count; + + // STEP 3: Initialize new n-grams. + // This is here because bos is also const WordIndex *, so copy gets + // consistent argument types. + const WordIndex *full_end = full->end(); + // Initialize and mark as valid up to bos. + const WordIndex *bos; + for (bos = different; (bos > full->begin()) && (*bos != kBOS); --bos) { + NGramStream &to = *++lower_valid; + std::copy(bos, full_end, to->begin()); + to->Value().count = 1; + actual_counts[lower_valid - streams_begin] = full->Value().UnmarkedCount(); + } + // Now bos indicates where is or is the 0th word of full. + if (bos != full->begin()) { + // There is an beyond the 0th word. + NGramStream &to = *++lower_valid; + std::copy(bos, full_end, to->begin()); + + // Anything that begins with has full non adjusted count. + to->Value().count = full->Value().UnmarkedCount(); + actual_counts[lower_valid - streams_begin] = full->Value().UnmarkedCount(); + } else { + stats.AddFull(full->Value().UnmarkedCount(), full->Value().IsMarked()); + } + assert(lower_valid >= &streams[0]); + } + + // The above loop outputs n-grams when it observes changes. This outputs + // the last n-grams. + for (NGramStream *s = streams.begin(); s <= lower_valid; ++s) { + uint64_t lower_count = actual_counts[(*s)->Order() - 1]; + if(lower_count <= prune_thresholds_[(*s)->Order() - 1]) + (*s)->Value().Mark(); + + if(!prune_words_.empty()) { + for(WordIndex* i = (*s)->begin(); i != (*s)->end(); i++) { + if(prune_words_[*i]) { + (*s)->Value().Mark(); + break; + } + } + } + + stats.Add(s - streams.begin(), lower_count, (*s)->Value().IsMarked()); + ++*s; + } + // Poison everyone! Except the N-grams which were already poisoned by the input. + for (NGramStream *s = streams.begin(); s != streams.end(); ++s) + s->Poison(); + + stats.CalculateDiscounts(discount_config_); + + // NOTE: See special early-return case for unigrams near the top of this function +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts.hh b/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts.hh new file mode 100644 index 0000000..29319ba --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts.hh @@ -0,0 +1,72 @@ +#ifndef LM_BUILDER_ADJUST_COUNTS_H +#define LM_BUILDER_ADJUST_COUNTS_H + +#include "lm/builder/discount.hh" +#include "lm/lm_exception.hh" +#include "util/exception.hh" + +#include + +#include + +namespace util { namespace stream { class ChainPositions; } } + +namespace lm { +namespace builder { + +class BadDiscountException : public util::Exception { + public: + BadDiscountException() throw(); + ~BadDiscountException() throw(); +}; + +struct DiscountConfig { + // Overrides discounts for orders [1,discount_override.size()]. + std::vector overwrite; + // If discounting fails for an order, copy them from here. + Discount fallback; + // What to do when discounts are out of range or would trigger divison by + // zero. It it does something other than THROW_UP, use fallback_discount. + WarningAction bad_action; +}; + +/* Compute adjusted counts. + * Input: unique suffix sorted N-grams (and just the N-grams) with raw counts. + * Output: [1,N]-grams with adjusted counts. + * [1,N)-grams are in suffix order + * N-grams are in undefined order (they're going to be sorted anyway). + */ +class AdjustCounts { + public: + // counts: output + // counts_pruned: output + // discounts: mostly output. If the input already has entries, they will be kept. + // prune_thresholds: input. n-grams with normal (not adjusted) count below this will be pruned. + AdjustCounts( + const std::vector &prune_thresholds, + std::vector &counts, + std::vector &counts_pruned, + const std::vector &prune_words, + const DiscountConfig &discount_config, + std::vector &discounts) + : prune_thresholds_(prune_thresholds), counts_(counts), counts_pruned_(counts_pruned), + prune_words_(prune_words), discount_config_(discount_config), discounts_(discounts) + {} + + void Run(const util::stream::ChainPositions &positions); + + private: + const std::vector &prune_thresholds_; + std::vector &counts_; + std::vector &counts_pruned_; + const std::vector &prune_words_; + + DiscountConfig discount_config_; + std::vector &discounts_; +}; + +} // namespace builder +} // namespace lm + +#endif // LM_BUILDER_ADJUST_COUNTS_H + diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts_test.cc b/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts_test.cc new file mode 100644 index 0000000..fff551f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/adjust_counts_test.cc @@ -0,0 +1,112 @@ +#include "lm/builder/adjust_counts.hh" + +#include "lm/common/ngram_stream.hh" +#include "lm/builder/payload.hh" +#include "util/scoped.hh" + +#include +#define BOOST_TEST_MODULE AdjustCounts +#include + +namespace lm { namespace builder { namespace { + +class KeepCopy { + public: + KeepCopy() : size_(0) {} + + void Run(const util::stream::ChainPosition &position) { + for (util::stream::Link link(position); link; ++link) { + mem_.call_realloc(size_ + link->ValidSize()); + memcpy(static_cast(mem_.get()) + size_, link->Get(), link->ValidSize()); + size_ += link->ValidSize(); + } + } + + uint8_t *Get() { return static_cast(mem_.get()); } + std::size_t Size() const { return size_; } + + private: + util::scoped_malloc mem_; + std::size_t size_; +}; + +struct Gram4 { + WordIndex ids[4]; + uint64_t count; +}; + +class WriteInput { + public: + void Run(const util::stream::ChainPosition &position) { + NGramStream input(position); + Gram4 grams[] = { + {{0,0,0,0},10}, + {{0,0,3,0},3}, + // bos + {{1,1,1,2},5}, + {{0,0,3,2},5}, + }; + for (size_t i = 0; i < sizeof(grams) / sizeof(Gram4); ++i, ++input) { + memcpy(input->begin(), grams[i].ids, sizeof(WordIndex) * 4); + input->Value().count = grams[i].count; + } + input.Poison(); + } +}; + +BOOST_AUTO_TEST_CASE(Simple) { + KeepCopy outputs[4]; + std::vector counts; + std::vector discount; + { + util::stream::ChainConfig config; + config.total_memory = 100; + config.block_count = 1; + util::stream::Chains chains(4); + for (unsigned i = 0; i < 4; ++i) { + config.entry_size = NGram::TotalSize(i + 1); + chains.push_back(config); + } + + chains[3] >> WriteInput(); + util::stream::ChainPositions for_adjust(chains); + for (unsigned i = 0; i < 4; ++i) { + chains[i] >> boost::ref(outputs[i]); + } + chains >> util::stream::kRecycle; + std::vector counts_pruned(4); + std::vector prune_thresholds(4); + DiscountConfig discount_config; + discount_config.fallback = Discount(); + discount_config.bad_action = THROW_UP; + BOOST_CHECK_THROW(AdjustCounts(prune_thresholds, counts, counts_pruned, std::vector(), discount_config, discount).Run(for_adjust), BadDiscountException); + } + BOOST_REQUIRE_EQUAL(4UL, counts.size()); + BOOST_CHECK_EQUAL(4UL, counts[0]); + // These are no longer set because the discounts are bad. +/* BOOST_CHECK_EQUAL(4UL, counts[1]); + BOOST_CHECK_EQUAL(3UL, counts[2]); + BOOST_CHECK_EQUAL(3UL, counts[3]);*/ + BOOST_REQUIRE_EQUAL(NGram::TotalSize(1) * 4, outputs[0].Size()); + NGram uni(outputs[0].Get(), 1); + BOOST_CHECK_EQUAL(kUNK, *uni.begin()); + BOOST_CHECK_EQUAL(0ULL, uni.Value().count); + uni.NextInMemory(); + BOOST_CHECK_EQUAL(kBOS, *uni.begin()); + BOOST_CHECK_EQUAL(0ULL, uni.Value().count); + uni.NextInMemory(); + BOOST_CHECK_EQUAL(0UL, *uni.begin()); + BOOST_CHECK_EQUAL(2ULL, uni.Value().count); + uni.NextInMemory(); + BOOST_CHECK_EQUAL(2ULL, uni.Value().count); + BOOST_CHECK_EQUAL(2UL, *uni.begin()); + + BOOST_REQUIRE_EQUAL(NGram::TotalSize(2) * 4, outputs[1].Size()); + NGram bi(outputs[1].Get(), 2); + BOOST_CHECK_EQUAL(0UL, *bi.begin()); + BOOST_CHECK_EQUAL(0UL, *(bi.begin() + 1)); + BOOST_CHECK_EQUAL(1ULL, bi.Value().count); + bi.NextInMemory(); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/combine_counts.hh b/ctcdecode-master/third_party/kenlm/lm/builder/combine_counts.hh new file mode 100644 index 0000000..2eda517 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/combine_counts.hh @@ -0,0 +1,31 @@ +#ifndef LM_BUILDER_COMBINE_COUNTS_H +#define LM_BUILDER_COMBINE_COUNTS_H + +#include "lm/builder/payload.hh" +#include "lm/common/ngram.hh" +#include "lm/common/compare.hh" +#include "lm/word_index.hh" +#include "util/stream/sort.hh" + +#include +#include + +namespace lm { +namespace builder { + +// Sum counts for the same n-gram. +struct CombineCounts { + bool operator()(void *first_void, const void *second_void, const SuffixOrder &compare) const { + NGram first(first_void, compare.Order()); + // There isn't a const version of NGram. + NGram second(const_cast(second_void), compare.Order()); + if (memcmp(first.begin(), second.begin(), sizeof(WordIndex) * compare.Order())) return false; + first.Value().count += second.Value().count; + return true; + } +}; + +} // namespace builder +} // namespace lm + +#endif // LM_BUILDER_COMBINE_COUNTS_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count.cc b/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count.cc new file mode 100644 index 0000000..0761cdf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count.cc @@ -0,0 +1,287 @@ +#include "lm/builder/corpus_count.hh" + +#include "lm/builder/payload.hh" +#include "lm/common/ngram.hh" +#include "lm/lm_exception.hh" +#include "lm/vocab.hh" +#include "lm/word_index.hh" +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/file_piece.hh" +#include "util/murmur_hash.hh" +#include "util/probing_hash_table.hh" +#include "util/scoped.hh" +#include "util/stream/chain.hh" +#include "util/tokenize_piece.hh" + +#include + +#include + +namespace lm { +namespace builder { +namespace { + +class DedupeHash : public std::unary_function { + public: + explicit DedupeHash(std::size_t order) : size_(order * sizeof(WordIndex)) {} + + std::size_t operator()(const WordIndex *start) const { + return util::MurmurHashNative(start, size_); + } + + private: + const std::size_t size_; +}; + +class DedupeEquals : public std::binary_function { + public: + explicit DedupeEquals(std::size_t order) : size_(order * sizeof(WordIndex)) {} + + bool operator()(const WordIndex *first, const WordIndex *second) const { + return !memcmp(first, second, size_); + } + + private: + const std::size_t size_; +}; + +struct DedupeEntry { + typedef WordIndex *Key; + Key GetKey() const { return key; } + void SetKey(WordIndex *to) { key = to; } + Key key; + static DedupeEntry Construct(WordIndex *at) { + DedupeEntry ret; + ret.key = at; + return ret; + } +}; + + +// TODO: don't have this here, should be with probing hash table defaults? +const float kProbingMultiplier = 1.5; + +typedef util::ProbingHashTable Dedupe; + +class Writer { + public: + Writer(std::size_t order, const util::stream::ChainPosition &position, void *dedupe_mem, std::size_t dedupe_mem_size) + : block_(position), gram_(block_->Get(), order), + dedupe_invalid_(order, std::numeric_limits::max()), + dedupe_(dedupe_mem, dedupe_mem_size, &dedupe_invalid_[0], DedupeHash(order), DedupeEquals(order)), + buffer_(new WordIndex[order - 1]), + block_size_(position.GetChain().BlockSize()) { + dedupe_.Clear(); + assert(Dedupe::Size(position.GetChain().BlockSize() / position.GetChain().EntrySize(), kProbingMultiplier) == dedupe_mem_size); + if (order == 1) { + // Add special words. AdjustCounts is responsible if order != 1. + AddUnigramWord(kUNK); + AddUnigramWord(kBOS); + } + } + + ~Writer() { + block_->SetValidSize(reinterpret_cast(gram_.begin()) - static_cast(block_->Get())); + (++block_).Poison(); + } + + // Write context with a bunch of + void StartSentence() { + for (WordIndex *i = gram_.begin(); i != gram_.end() - 1; ++i) { + *i = kBOS; + } + } + + void Append(WordIndex word) { + *(gram_.end() - 1) = word; + Dedupe::MutableIterator at; + bool found = dedupe_.FindOrInsert(DedupeEntry::Construct(gram_.begin()), at); + if (found) { + // Already present. + NGram already(at->key, gram_.Order()); + ++(already.Value().count); + // Shift left by one. + memmove(gram_.begin(), gram_.begin() + 1, sizeof(WordIndex) * (gram_.Order() - 1)); + return; + } + // Complete the write. + gram_.Value().count = 1; + // Prepare the next n-gram. + if (reinterpret_cast(gram_.begin()) + gram_.TotalSize() != static_cast(block_->Get()) + block_size_) { + NGram last(gram_); + gram_.NextInMemory(); + std::copy(last.begin() + 1, last.end(), gram_.begin()); + return; + } + // Block end. Need to store the context in a temporary buffer. + std::copy(gram_.begin() + 1, gram_.end(), buffer_.get()); + dedupe_.Clear(); + block_->SetValidSize(block_size_); + gram_.ReBase((++block_)->Get()); + std::copy(buffer_.get(), buffer_.get() + gram_.Order() - 1, gram_.begin()); + } + + private: + void AddUnigramWord(WordIndex index) { + *gram_.begin() = index; + gram_.Value().count = 0; + gram_.NextInMemory(); + if (gram_.Base() == static_cast(block_->Get()) + block_size_) { + block_->SetValidSize(block_size_); + gram_.ReBase((++block_)->Get()); + } + } + + util::stream::Link block_; + + NGram gram_; + + // This is the memory behind the invalid value in dedupe_. + std::vector dedupe_invalid_; + // Hash table combiner implementation. + Dedupe dedupe_; + + // Small buffer to hold existing ngrams when shifting across a block boundary. + boost::scoped_array buffer_; + + const std::size_t block_size_; +}; + +} // namespace + +float CorpusCount::DedupeMultiplier(std::size_t order) { + return kProbingMultiplier * static_cast(sizeof(DedupeEntry)) / static_cast(NGram::TotalSize(order)); +} + +std::size_t CorpusCount::VocabUsage(std::size_t vocab_estimate) { + return ngram::GrowableVocab::MemUsage(vocab_estimate); +} + +CorpusCount::CorpusCount(util::FilePiece &from, int vocab_write, bool dynamic_vocab, uint64_t &token_count, WordIndex &type_count, std::vector &prune_words, const std::string& prune_vocab_filename, std::size_t entries_per_block, WarningAction disallowed_symbol) + : from_(from), vocab_write_(vocab_write), dynamic_vocab_(dynamic_vocab), token_count_(token_count), type_count_(type_count), + prune_words_(prune_words), prune_vocab_filename_(prune_vocab_filename), + dedupe_mem_size_(Dedupe::Size(entries_per_block, kProbingMultiplier)), + dedupe_mem_(util::MallocOrThrow(dedupe_mem_size_)), + disallowed_symbol_action_(disallowed_symbol) { +} + +namespace { +void ComplainDisallowed(StringPiece word, WarningAction &action) { + switch (action) { + case SILENT: + return; + case COMPLAIN: + std::cerr << "Warning: " << word << " appears in the input. All instances of , , and will be interpreted as whitespace." << std::endl; + action = SILENT; + return; + case THROW_UP: + UTIL_THROW(FormatLoadException, "Special word " << word << " is not allowed in the corpus. I plan to support models containing in the future. Pass --skip_symbols to convert these symbols to whitespace."); + } +} + +// Vocab ids are given in a precompiled hash table. +class VocabGiven { + public: + explicit VocabGiven(int fd) { + util::MapRead(util::POPULATE_OR_READ, fd, 0, util::CheckOverflow(util::SizeOrThrow(fd)), table_backing_); + // Leave space for header with size. + table_ = Table(static_cast(table_backing_.get()) + sizeof(uint64_t), table_backing_.size() - sizeof(uint64_t)); + bos_ = FindOrInsert(""); + eos_ = FindOrInsert(""); + } + + WordIndex FindOrInsert(const StringPiece &word) const { + Table::ConstIterator it; + if (table_.Find(util::MurmurHash64A(word.data(), word.size()), it)) { + return it->value; + } else { + return 0; // . + } + } + + WordIndex Index(const StringPiece &word) const { + return FindOrInsert(word); + } + + WordIndex Size() const { + return *static_cast(table_backing_.get()); + } + + bool IsSpecial(WordIndex word) const { + return word == 0 || word == bos_ || word == eos_; + } + + private: + util::scoped_memory table_backing_; + + typedef util::ProbingHashTable Table; + Table table_; + + WordIndex bos_, eos_; +}; +} // namespace + +void CorpusCount::Run(const util::stream::ChainPosition &position) { + if (dynamic_vocab_) { + ngram::GrowableVocab vocab(type_count_, vocab_write_); + RunWithVocab(position, vocab); + } else { + VocabGiven vocab(vocab_write_); + RunWithVocab(position, vocab); + } +} + +template void CorpusCount::RunWithVocab(const util::stream::ChainPosition &position, Vocab &vocab) { + token_count_ = 0; + type_count_ = 0; + const WordIndex end_sentence = vocab.FindOrInsert(""); + Writer writer(NGram::OrderFromSize(position.GetChain().EntrySize()), position, dedupe_mem_.get(), dedupe_mem_size_); + uint64_t count = 0; + bool delimiters[256]; + util::BoolCharacter::Build("\0\t\n\r ", delimiters); + StringPiece w; + while(true) { + writer.StartSentence(); + while (from_.ReadWordSameLine(w, delimiters)) { + WordIndex word = vocab.FindOrInsert(w); + if (UTIL_UNLIKELY(vocab.IsSpecial(word))) { + ComplainDisallowed(w, disallowed_symbol_action_); + continue; + } + writer.Append(word); + ++count; + } + if (!from_.ReadLineOrEOF(w)) break; + writer.Append(end_sentence); + } + token_count_ = count; + type_count_ = vocab.Size(); + + // Create list of unigrams that are supposed to be pruned + if (!prune_vocab_filename_.empty()) { + try { + util::FilePiece prune_vocab_file(prune_vocab_filename_.c_str()); + + prune_words_.resize(vocab.Size(), true); + try { + while (true) { + StringPiece word(prune_vocab_file.ReadDelimited(delimiters)); + prune_words_[vocab.Index(word)] = false; + } + } catch (const util::EndOfFileException &e) {} + + // Never prune , , + prune_words_[kUNK] = false; + prune_words_[kBOS] = false; + prune_words_[kEOS] = false; + + } catch (const util::Exception &e) { + std::cerr << e.what() << std::endl; + abort(); + } + } +} + +} // namespace builder +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count.hh b/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count.hh new file mode 100644 index 0000000..bf1cfc8 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count.hh @@ -0,0 +1,56 @@ +#ifndef LM_BUILDER_CORPUS_COUNT_H +#define LM_BUILDER_CORPUS_COUNT_H + +#include "lm/lm_exception.hh" +#include "lm/word_index.hh" +#include "util/scoped.hh" + +#include +#include +#include +#include + +namespace util { +class FilePiece; +namespace stream { +class ChainPosition; +} // namespace stream +} // namespace util + +namespace lm { +namespace builder { + +class CorpusCount { + public: + // Memory usage will be DedupeMultipler(order) * block_size + total_chain_size + unknown vocab_hash_size + static float DedupeMultiplier(std::size_t order); + + // How much memory vocabulary will use based on estimated size of the vocab. + static std::size_t VocabUsage(std::size_t vocab_estimate); + + // token_count: out. + // type_count aka vocabulary size. Initialize to an estimate. It is set to the exact value. + CorpusCount(util::FilePiece &from, int vocab_write, bool dynamic_vocab, uint64_t &token_count, WordIndex &type_count, std::vector &prune_words, const std::string& prune_vocab_filename, std::size_t entries_per_block, WarningAction disallowed_symbol); + + void Run(const util::stream::ChainPosition &position); + + private: + template void RunWithVocab(const util::stream::ChainPosition &position, Vocab &vocab); + + util::FilePiece &from_; + int vocab_write_; + bool dynamic_vocab_; + uint64_t &token_count_; + WordIndex &type_count_; + std::vector& prune_words_; + const std::string& prune_vocab_filename_; + + std::size_t dedupe_mem_size_; + util::scoped_malloc dedupe_mem_; + + WarningAction disallowed_symbol_action_; +}; + +} // namespace builder +} // namespace lm +#endif // LM_BUILDER_CORPUS_COUNT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count_test.cc b/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count_test.cc new file mode 100644 index 0000000..fdd1a70 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/corpus_count_test.cc @@ -0,0 +1,80 @@ +#include "lm/builder/corpus_count.hh" + +#include "lm/builder/payload.hh" +#include "lm/common/ngram_stream.hh" +#include "lm/common/ngram.hh" + +#include "util/file.hh" +#include "util/file_piece.hh" +#include "util/tokenize_piece.hh" +#include "util/stream/chain.hh" +#include "util/stream/stream.hh" + +#define BOOST_TEST_MODULE CorpusCountTest +#include + +namespace lm { namespace builder { namespace { + +#define Check(str, cnt) { \ + BOOST_REQUIRE(stream); \ + w = stream->begin(); \ + for (util::TokenIter t(str, " "); t; ++t, ++w) { \ + BOOST_CHECK_EQUAL(*t, v[*w]); \ + } \ + BOOST_CHECK_EQUAL((uint64_t)cnt, stream->Value().count); \ + ++stream; \ +} + +BOOST_AUTO_TEST_CASE(Short) { + util::scoped_fd input_file(util::MakeTemp("corpus_count_test_temp")); + const char input[] = "looking on a little more loin\non a little more loin\non foo little more loin\nbar\n\n"; + // Blocks of 10 are + // looking on a little more loin on a little[duplicate] more[duplicate] loin[duplicate] [duplicate] on[duplicate] foo + // little more loin bar + + util::WriteOrThrow(input_file.get(), input, sizeof(input) - 1); + util::SeekOrThrow(input_file.get(), 0); + util::FilePiece input_piece(input_file.release(), "temp file"); + + util::stream::ChainConfig config; + config.entry_size = NGram::TotalSize(3); + config.total_memory = config.entry_size * 20; + config.block_count = 2; + + util::scoped_fd vocab(util::MakeTemp("corpus_count_test_vocab")); + + util::stream::Chain chain(config); + uint64_t token_count; + WordIndex type_count = 10; + std::vector prune_words; + CorpusCount counter(input_piece, vocab.get(), true, token_count, type_count, prune_words, "", chain.BlockSize() / chain.EntrySize(), SILENT); + chain >> boost::ref(counter); + NGramStream stream(chain.Add()); + chain >> util::stream::kRecycle; + + const char *v[] = {"", "", "", "looking", "on", "a", "little", "more", "loin", "foo", "bar"}; + + WordIndex *w; + + Check(" looking", 1); + Check(" looking on", 1); + Check("looking on a", 1); + Check("on a little", 2); + Check("a little more", 2); + Check("little more loin", 2); + Check("more loin ", 2); + Check(" on", 2); + Check(" on a", 1); + Check(" on foo", 1); + Check("on foo little", 1); + Check("foo little more", 1); + Check("little more loin", 1); + Check("more loin ", 1); + Check(" bar", 1); + Check(" bar ", 1); + Check(" ", 1); + BOOST_CHECK(!stream); + BOOST_CHECK_EQUAL(sizeof(v) / sizeof(const char*), type_count); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/count_ngrams_main.cc b/ctcdecode-master/third_party/kenlm/lm/builder/count_ngrams_main.cc new file mode 100644 index 0000000..066be77 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/count_ngrams_main.cc @@ -0,0 +1,99 @@ +#include "lm/builder/combine_counts.hh" +#include "lm/builder/corpus_count.hh" +#include "lm/common/compare.hh" +#include "util/stream/chain.hh" +#include "util/stream/io.hh" +#include "util/stream/sort.hh" +#include "util/file.hh" +#include "util/file_piece.hh" +#include "util/usage.hh" + +#include + +#include + +namespace { +class SizeNotify { + public: + SizeNotify(std::size_t &out) : behind_(out) {} + + void operator()(const std::string &from) { + behind_ = util::ParseSize(from); + } + + private: + std::size_t &behind_; +}; + +boost::program_options::typed_value *SizeOption(std::size_t &to, const char *default_value) { + return boost::program_options::value()->notifier(SizeNotify(to))->default_value(default_value); +} + +} // namespace + +int main(int argc, char *argv[]) { + namespace po = boost::program_options; + unsigned order; + std::size_t ram; + std::string temp_prefix, vocab_table, vocab_list; + po::options_description options("corpus count"); + options.add_options() + ("help,h", po::bool_switch(), "Show this help message") + ("order,o", po::value(&order)->required(), "Order") + ("temp_prefix,T", po::value(&temp_prefix)->default_value(util::DefaultTempDirectory()), "Temporary file prefix") + ("memory,S", SizeOption(ram, "80%"), "RAM") + ("read_vocab_table", po::value(&vocab_table), "Vocabulary hash table to read. This should be a probing hash table with size at the beginning.") + ("write_vocab_list", po::value(&vocab_list), "Vocabulary list to write as null-delimited strings."); + + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, options), vm); + if (argc == 1 || vm["help"].as()) { + std::cerr << "Counts n-grams from standard input.\n" << options << std::endl; + return 1; + } + po::notify(vm); + + if (!(vocab_table.empty() ^ vocab_list.empty())) { + std::cerr << "Specify one of --read_vocab_table or --write_vocab_list for vocabulary handling." << std::endl; + return 1; + } + + util::NormalizeTempPrefix(temp_prefix); + + util::scoped_fd vocab_file(vocab_table.empty() ? util::CreateOrThrow(vocab_list.c_str()) : util::OpenReadOrThrow(vocab_table.c_str())); + + std::size_t blocks = 2; + std::size_t remaining_size = ram - util::SizeOrThrow(vocab_file.get()); + + std::size_t memory_for_chain = + // This much memory to work with after vocab hash table. + static_cast(remaining_size) / + // Solve for block size including the dedupe multiplier for one block. + (static_cast(blocks) + lm::builder::CorpusCount::DedupeMultiplier(order)) * + // Chain likes memory expressed in terms of total memory. + static_cast(blocks); + std::cerr << "Using " << memory_for_chain << " for chains." << std::endl; + + util::stream::Chain chain(util::stream::ChainConfig(lm::NGram::TotalSize(order), blocks, memory_for_chain)); + util::FilePiece f(0, NULL, &std::cerr); + uint64_t token_count = 0; + lm::WordIndex type_count = 0; + std::vector empty_prune; + std::string empty_string; + lm::builder::CorpusCount counter(f, vocab_file.get(), vocab_table.empty(), token_count, type_count, empty_prune, empty_string, chain.BlockSize() / chain.EntrySize(), lm::THROW_UP); + chain >> boost::ref(counter); + + util::stream::SortConfig sort_config; + sort_config.temp_prefix = temp_prefix; + sort_config.buffer_size = 64 * 1024 * 1024; + // Intended to run in parallel. + sort_config.total_memory = remaining_size; + util::stream::Sort sorted(chain, sort_config, lm::SuffixOrder(order), lm::builder::CombineCounts()); + chain.Wait(true); + util::stream::Chain chain2(util::stream::ChainConfig(lm::NGram::TotalSize(order), blocks, sort_config.buffer_size)); + sorted.Output(chain2); + // Inefficiently copies if there's only one block. + chain2 >> util::stream::WriteAndRecycle(1); + chain2.Wait(true); + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/debug_print.hh b/ctcdecode-master/third_party/kenlm/lm/builder/debug_print.hh new file mode 100644 index 0000000..4b9f306 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/debug_print.hh @@ -0,0 +1,70 @@ +#ifndef LM_BUILDER_DEBUG_PRINT_H +#define LM_BUILDER_DEBUG_PRINT_H + +#include "lm/builder/payload.hh" +#include "lm/common/print.hh" +#include "lm/common/ngram_stream.hh" +#include "util/file_stream.hh" +#include "util/file.hh" + +#include + +namespace lm { namespace builder { +// Not defined, only specialized. +template void PrintPayload(util::FileStream &to, const BuildingPayload &payload); +template <> inline void PrintPayload(util::FileStream &to, const BuildingPayload &payload) { + to << payload.count; +} +template <> inline void PrintPayload(util::FileStream &to, const BuildingPayload &payload) { + to << log10(payload.uninterp.prob) << ' ' << log10(payload.uninterp.gamma); +} +template <> inline void PrintPayload(util::FileStream &to, const BuildingPayload &payload) { + to << payload.complete.prob << ' ' << payload.complete.backoff; +} + +// template parameter is the type stored. +template class Print { + public: + static void DumpSeparateFiles(const VocabReconstitute &vocab, const std::string &file_base, util::stream::Chains &chains) { + for (unsigned int i = 0; i < chains.size(); ++i) { + std::string file(file_base + boost::lexical_cast(i)); + chains[i] >> Print(vocab, util::CreateOrThrow(file.c_str())); + } + } + + explicit Print(const VocabReconstitute &vocab, int fd) : vocab_(vocab), to_(fd) {} + + void Run(const util::stream::ChainPositions &chains) { + util::scoped_fd fd(to_); + util::FileStream out(to_); + NGramStreams streams(chains); + for (NGramStream *s = streams.begin(); s != streams.end(); ++s) { + DumpStream(*s, out); + } + } + + void Run(const util::stream::ChainPosition &position) { + util::scoped_fd fd(to_); + util::FileStream out(to_); + NGramStream stream(position); + DumpStream(stream, out); + } + + private: + void DumpStream(NGramStream &stream, util::FileStream &to) { + for (; stream; ++stream) { + PrintPayload(to, stream->Value()); + for (const WordIndex *w = stream->begin(); w != stream->end(); ++w) { + to << ' ' << vocab_.Lookup(*w) << '=' << *w; + } + to << '\n'; + } + } + + const VocabReconstitute &vocab_; + int to_; +}; + +}} // namespaces + +#endif // LM_BUILDER_DEBUG_PRINT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/discount.hh b/ctcdecode-master/third_party/kenlm/lm/builder/discount.hh new file mode 100644 index 0000000..e2f4084 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/discount.hh @@ -0,0 +1,26 @@ +#ifndef LM_BUILDER_DISCOUNT_H +#define LM_BUILDER_DISCOUNT_H + +#include + +#include + +namespace lm { +namespace builder { + +struct Discount { + float amount[4]; + + float Get(uint64_t count) const { + return amount[std::min(count, 3)]; + } + + float Apply(uint64_t count) const { + return static_cast(count) - Get(count); + } +}; + +} // namespace builder +} // namespace lm + +#endif // LM_BUILDER_DISCOUNT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/dump_counts_main.cc b/ctcdecode-master/third_party/kenlm/lm/builder/dump_counts_main.cc new file mode 100644 index 0000000..26078d0 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/dump_counts_main.cc @@ -0,0 +1,36 @@ +#include "lm/common/print.hh" +#include "lm/word_index.hh" +#include "util/file.hh" +#include "util/read_compressed.hh" + +#include + +#include +#include + +int main(int argc, char *argv[]) { + if (argc != 4) { + std::cerr << "Usage: " << argv[0] << " counts vocabulary order\n" + "The counts file contains records with 4-byte vocabulary ids followed by 8-byte\n" + "counts. Each record has order many vocabulary ids.\n" + "The vocabulary file contains the words delimited by NULL in order of id.\n" + "The vocabulary file may not be compressed because it is mmapped but the counts\n" + "file can be compressed.\n"; + return 1; + } + util::ReadCompressed counts(util::OpenReadOrThrow(argv[1])); + util::scoped_fd vocab_file(util::OpenReadOrThrow(argv[2])); + lm::VocabReconstitute vocab(vocab_file.get()); + unsigned int order = boost::lexical_cast(argv[3]); + std::vector record(sizeof(uint32_t) * order + sizeof(uint64_t)); + while (std::size_t got = counts.ReadOrEOF(&*record.begin(), record.size())) { + UTIL_THROW_IF(got != record.size(), util::Exception, "Read " << got << " bytes at the end of file, which is not a complete record of length " << record.size()); + const lm::WordIndex *words = reinterpret_cast(&*record.begin()); + for (const lm::WordIndex *i = words; i != words + order; ++i) { + UTIL_THROW_IF(*i >= vocab.Size(), util::Exception, "Vocab ID " << *i << " is larger than the vocab file's maximum of " << vocab.Size() << ". Are you sure you have the right order and vocab file for these counts?"); + std::cout << vocab.Lookup(*i) << ' '; + } + // TODO don't use std::cout because it is slow. Add fast uint64_t printing support to FileStream. + std::cout << *reinterpret_cast(words + order) << '\n'; + } +} diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/hash_gamma.hh b/ctcdecode-master/third_party/kenlm/lm/builder/hash_gamma.hh new file mode 100644 index 0000000..4bef47e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/hash_gamma.hh @@ -0,0 +1,19 @@ +#ifndef LM_BUILDER_HASH_GAMMA__ +#define LM_BUILDER_HASH_GAMMA__ + +#include + +namespace lm { namespace builder { + +#pragma pack(push) +#pragma pack(4) + +struct HashGamma { + uint64_t hash_value; + float gamma; +}; + +#pragma pack(pop) + +}} // namespaces +#endif // LM_BUILDER_HASH_GAMMA__ diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/header_info.hh b/ctcdecode-master/third_party/kenlm/lm/builder/header_info.hh new file mode 100644 index 0000000..d01d049 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/header_info.hh @@ -0,0 +1,28 @@ +#ifndef LM_BUILDER_HEADER_INFO_H +#define LM_BUILDER_HEADER_INFO_H + +#include +#include +#include + +namespace lm { namespace builder { + +// Some configuration info that is used to add +// comments to the beginning of an ARPA file +struct HeaderInfo { + std::string input_file; + uint64_t token_count; + std::vector counts_pruned; + + HeaderInfo() {} + + HeaderInfo(const std::string& input_file_in, uint64_t token_count_in, const std::vector &counts_pruned_in) + : input_file(input_file_in), token_count(token_count_in), counts_pruned(counts_pruned_in) {} + + // TODO: Add smoothing type + // TODO: More info if multiple models were interpolated +}; + +}} // namespaces + +#endif diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/initial_probabilities.cc b/ctcdecode-master/third_party/kenlm/lm/builder/initial_probabilities.cc new file mode 100644 index 0000000..5b8d86d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/initial_probabilities.cc @@ -0,0 +1,306 @@ +#include "lm/builder/initial_probabilities.hh" + +#include "lm/builder/discount.hh" +#include "lm/builder/hash_gamma.hh" +#include "lm/builder/payload.hh" +#include "lm/common/special.hh" +#include "lm/common/ngram_stream.hh" +#include "util/murmur_hash.hh" +#include "util/file.hh" +#include "util/stream/chain.hh" +#include "util/stream/io.hh" +#include "util/stream/stream.hh" + +#include + +namespace lm { namespace builder { + +namespace { +struct BufferEntry { + // Gamma from page 20 of Chen and Goodman. + float gamma; + // \sum_w a(c w) for all w. + float denominator; +}; + +struct HashBufferEntry : public BufferEntry { + // Hash value of ngram. Used to join contexts with backoffs. + uint64_t hash_value; +}; + +// Reads all entries in order like NGramStream does. +// But deletes any entries that have CutoffCount below or equal to pruning +// threshold. +class PruneNGramStream { + public: + PruneNGramStream(const util::stream::ChainPosition &position, const SpecialVocab &specials) : + current_(NULL, NGram::OrderFromSize(position.GetChain().EntrySize())), + dest_(NULL, NGram::OrderFromSize(position.GetChain().EntrySize())), + currentCount_(0), + block_(position), + specials_(specials) + { + StartBlock(); + } + + NGram &operator*() { return current_; } + NGram *operator->() { return ¤t_; } + + operator bool() const { + return block_; + } + + PruneNGramStream &operator++() { + assert(block_); + if(UTIL_UNLIKELY(current_.Order() == 1 && specials_.IsSpecial(*current_.begin()))) + dest_.NextInMemory(); + else if(currentCount_ > 0) { + if(dest_.Base() < current_.Base()) { + memcpy(dest_.Base(), current_.Base(), current_.TotalSize()); + } + dest_.NextInMemory(); + } + + current_.NextInMemory(); + + uint8_t *block_base = static_cast(block_->Get()); + if (current_.Base() == block_base + block_->ValidSize()) { + block_->SetValidSize(dest_.Base() - block_base); + ++block_; + StartBlock(); + if (block_) { + currentCount_ = current_.Value().CutoffCount(); + } + } else { + currentCount_ = current_.Value().CutoffCount(); + } + + return *this; + } + + private: + void StartBlock() { + for (; ; ++block_) { + if (!block_) return; + if (block_->ValidSize()) break; + } + current_.ReBase(block_->Get()); + currentCount_ = current_.Value().CutoffCount(); + + dest_.ReBase(block_->Get()); + } + + NGram current_; // input iterator + NGram dest_; // output iterator + + uint64_t currentCount_; + + util::stream::Link block_; + + const SpecialVocab specials_; +}; + +// Extract an array of HashedGamma from an array of BufferEntry. +class OnlyGamma { + public: + explicit OnlyGamma(bool pruning) : pruning_(pruning) {} + + void Run(const util::stream::ChainPosition &position) { + for (util::stream::Link block_it(position); block_it; ++block_it) { + if(pruning_) { + const HashBufferEntry *in = static_cast(block_it->Get()); + const HashBufferEntry *end = static_cast(block_it->ValidEnd()); + + // Just make it point to the beginning of the stream so it can be overwritten + // With HashGamma values. Do not attempt to interpret the values until set below. + HashGamma *out = static_cast(block_it->Get()); + for (; in < end; out += 1, in += 1) { + // buffering, otherwise might overwrite values too early + float gamma_buf = in->gamma; + uint64_t hash_buf = in->hash_value; + + out->gamma = gamma_buf; + out->hash_value = hash_buf; + } + block_it->SetValidSize((block_it->ValidSize() * sizeof(HashGamma)) / sizeof(HashBufferEntry)); + } + else { + float *out = static_cast(block_it->Get()); + const float *in = out; + const float *end = static_cast(block_it->ValidEnd()); + for (out += 1, in += 2; in < end; out += 1, in += 2) { + *out = *in; + } + block_it->SetValidSize(block_it->ValidSize() / 2); + } + } + } + + private: + bool pruning_; +}; + +class AddRight { + public: + AddRight(const Discount &discount, const util::stream::ChainPosition &input, bool pruning) + : discount_(discount), input_(input), pruning_(pruning) {} + + void Run(const util::stream::ChainPosition &output) { + NGramStream in(input_); + util::stream::Stream out(output); + + std::vector previous(in->Order() - 1); + // Silly windows requires this workaround to just get an invalid pointer when empty. + void *const previous_raw = previous.empty() ? NULL : static_cast(&previous[0]); + const std::size_t size = sizeof(WordIndex) * previous.size(); + + for(; in; ++out) { + memcpy(previous_raw, in->begin(), size); + uint64_t denominator = 0; + uint64_t normalizer = 0; + + uint64_t counts[4]; + memset(counts, 0, sizeof(counts)); + do { + denominator += in->Value().UnmarkedCount(); + + // Collect unused probability mass from pruning. + // Becomes 0 for unpruned ngrams. + normalizer += in->Value().UnmarkedCount() - in->Value().CutoffCount(); + + // Chen&Goodman do not mention counting based on cutoffs, but + // backoff becomes larger than 1 otherwise, so probably needs + // to count cutoffs. Counts normally without pruning. + if(in->Value().CutoffCount() > 0) + ++counts[std::min(in->Value().CutoffCount(), static_cast(3))]; + + } while (++in && !memcmp(previous_raw, in->begin(), size)); + + BufferEntry &entry = *reinterpret_cast(out.Get()); + entry.denominator = static_cast(denominator); + entry.gamma = 0.0; + for (unsigned i = 1; i <= 3; ++i) { + entry.gamma += discount_.Get(i) * static_cast(counts[i]); + } + + // Makes model sum to 1 with pruning (I hope). + entry.gamma += normalizer; + + entry.gamma /= entry.denominator; + + if(pruning_) { + // If pruning is enabled the stream actually contains HashBufferEntry, see InitialProbabilities(...), + // so add a hash value that identifies the current ngram. + static_cast(&entry)->hash_value = util::MurmurHashNative(previous_raw, size); + } + } + out.Poison(); + } + + private: + const Discount &discount_; + const util::stream::ChainPosition input_; + bool pruning_; +}; + +class MergeRight { + public: + MergeRight(bool interpolate_unigrams, const util::stream::ChainPosition &from_adder, const Discount &discount, const SpecialVocab &specials) + : interpolate_unigrams_(interpolate_unigrams), from_adder_(from_adder), discount_(discount), specials_(specials) {} + + // calculate the initial probability of each n-gram (before order-interpolation) + // Run() gets invoked once for each order + void Run(const util::stream::ChainPosition &primary) { + util::stream::Stream summed(from_adder_); + + PruneNGramStream grams(primary, specials_); + + // Without interpolation, the interpolation weight goes to . + if (grams->Order() == 1) { + BufferEntry sums(*static_cast(summed.Get())); + // Special case for + assert(*grams->begin() == kUNK); + float gamma_assign; + if (interpolate_unigrams_) { + // Default: treat like a zeroton. + gamma_assign = sums.gamma; + grams->Value().uninterp.prob = 0.0; + } else { + // SRI: give all the interpolation mass to + gamma_assign = 0.0; + grams->Value().uninterp.prob = sums.gamma; + } + grams->Value().uninterp.gamma = gamma_assign; + + for (++grams; *grams->begin() != specials_.BOS(); ++grams) { + grams->Value().uninterp.prob = discount_.Apply(grams->Value().count) / sums.denominator; + grams->Value().uninterp.gamma = gamma_assign; + } + + // Special case for : probability 1.0. This allows to be + // explicitly scored as part of the sentence without impacting + // probability and computes q correctly as b(). + assert(*grams->begin() == specials_.BOS()); + grams->Value().uninterp.prob = 1.0; + grams->Value().uninterp.gamma = 0.0; + + while (++grams) { + grams->Value().uninterp.prob = discount_.Apply(grams->Value().count) / sums.denominator; + grams->Value().uninterp.gamma = gamma_assign; + } + ++summed; + return; + } + + std::vector previous(grams->Order() - 1); + const std::size_t size = sizeof(WordIndex) * previous.size(); + for (; grams; ++summed) { + memcpy(&previous[0], grams->begin(), size); + const BufferEntry &sums = *static_cast(summed.Get()); + + do { + BuildingPayload &pay = grams->Value(); + pay.uninterp.prob = discount_.Apply(grams->Value().UnmarkedCount()) / sums.denominator; + pay.uninterp.gamma = sums.gamma; + } while (++grams && !memcmp(&previous[0], grams->begin(), size)); + } + } + + private: + bool interpolate_unigrams_; + util::stream::ChainPosition from_adder_; + Discount discount_; + const SpecialVocab specials_; +}; + +} // namespace + +void InitialProbabilities( + const InitialProbabilitiesConfig &config, + const std::vector &discounts, + util::stream::Chains &primary, + util::stream::Chains &second_in, + util::stream::Chains &gamma_out, + const std::vector &prune_thresholds, + bool prune_vocab, + const SpecialVocab &specials) { + for (size_t i = 0; i < primary.size(); ++i) { + util::stream::ChainConfig gamma_config = config.adder_out; + if(prune_vocab || prune_thresholds[i] > 0) + gamma_config.entry_size = sizeof(HashBufferEntry); + else + gamma_config.entry_size = sizeof(BufferEntry); + + util::stream::ChainPosition second(second_in[i].Add()); + second_in[i] >> util::stream::kRecycle; + gamma_out.push_back(gamma_config); + gamma_out[i] >> AddRight(discounts[i], second, prune_vocab || prune_thresholds[i] > 0); + + primary[i] >> MergeRight(config.interpolate_unigrams, gamma_out[i].Add(), discounts[i], specials); + + // Don't bother with the OnlyGamma thread for something to discard. + if (i) gamma_out[i] >> OnlyGamma(prune_vocab || prune_thresholds[i] > 0); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/initial_probabilities.hh b/ctcdecode-master/third_party/kenlm/lm/builder/initial_probabilities.hh new file mode 100644 index 0000000..caeea58 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/initial_probabilities.hh @@ -0,0 +1,45 @@ +#ifndef LM_BUILDER_INITIAL_PROBABILITIES_H +#define LM_BUILDER_INITIAL_PROBABILITIES_H + +#include "lm/builder/discount.hh" +#include "lm/word_index.hh" +#include "util/stream/config.hh" + +#include + +namespace util { namespace stream { class Chains; } } + +namespace lm { +class SpecialVocab; +namespace builder { + +struct InitialProbabilitiesConfig { + // These should be small buffers to keep the adder from getting too far ahead + util::stream::ChainConfig adder_in; + util::stream::ChainConfig adder_out; + // SRILM doesn't normally interpolate unigrams. + bool interpolate_unigrams; +}; + +/* Compute initial (uninterpolated) probabilities + * primary: the normal chain of n-grams. Incoming is context sorted adjusted + * counts. Outgoing has uninterpolated probabilities for use by Interpolate. + * second_in: a second copy of the primary input. Discard the output. + * gamma_out: Computed gamma values are output on these chains in suffix order. + * The values are bare floats and should be buffered for interpolation to + * use. + */ +void InitialProbabilities( + const InitialProbabilitiesConfig &config, + const std::vector &discounts, + util::stream::Chains &primary, + util::stream::Chains &second_in, + util::stream::Chains &gamma_out, + const std::vector &prune_thresholds, + bool prune_vocab, + const SpecialVocab &vocab); + +} // namespace builder +} // namespace lm + +#endif // LM_BUILDER_INITIAL_PROBABILITIES_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/interpolate.cc b/ctcdecode-master/third_party/kenlm/lm/builder/interpolate.cc new file mode 100644 index 0000000..a62ef43 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/interpolate.cc @@ -0,0 +1,166 @@ +#include "lm/builder/interpolate.hh" + +#include "lm/builder/hash_gamma.hh" +#include "lm/builder/payload.hh" +#include "lm/common/compare.hh" +#include "lm/common/joint_order.hh" +#include "lm/common/ngram_stream.hh" +#include "lm/lm_exception.hh" +#include "util/fixed_array.hh" +#include "util/murmur_hash.hh" + +#include +#include +#include + +namespace lm { namespace builder { +namespace { + +/* Calculate q, the collapsed probability and backoff, as defined in + * @inproceedings{Heafield-rest, + * author = {Kenneth Heafield and Philipp Koehn and Alon Lavie}, + * title = {Language Model Rest Costs and Space-Efficient Storage}, + * year = {2012}, + * month = {July}, + * booktitle = {Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning}, + * address = {Jeju Island, Korea}, + * pages = {1169--1178}, + * url = {http://kheafield.com/professional/edinburgh/rest\_paper.pdf}, + * } + * This is particularly convenient to calculate during interpolation because + * the needed backoff terms are already accessed at the same time. + */ +class OutputQ { + public: + explicit OutputQ(std::size_t order) : q_delta_(order) {} + + void Gram(unsigned order_minus_1, float full_backoff, ProbBackoff &out) { + float &q_del = q_delta_[order_minus_1]; + if (order_minus_1) { + // Divide by context's backoff (which comes in as out.backoff) + q_del = q_delta_[order_minus_1 - 1] / out.backoff * full_backoff; + } else { + q_del = full_backoff; + } + out.prob = log10f(out.prob * q_del); + // TODO: stop wastefully outputting this! + out.backoff = 0.0; + } + + private: + // Product of backoffs in the numerator divided by backoffs in the + // denominator. Does not include + std::vector q_delta_; +}; + +/* Default: output probability and backoff */ +class OutputProbBackoff { + public: + explicit OutputProbBackoff(std::size_t /*order*/) {} + + void Gram(unsigned /*order_minus_1*/, float full_backoff, ProbBackoff &out) const { + // Correcting for numerical precision issues. Take that IRST. + out.prob = std::min(0.0f, log10f(out.prob)); + out.backoff = log10f(full_backoff); + } +}; + +template class Callback { + public: + Callback(float uniform_prob, const util::stream::ChainPositions &backoffs, const std::vector &prune_thresholds, bool prune_vocab, const SpecialVocab &specials) + : backoffs_(backoffs.size()), probs_(backoffs.size() + 2), + prune_thresholds_(prune_thresholds), + prune_vocab_(prune_vocab), + output_(backoffs.size() + 1 /* order */), + specials_(specials) { + probs_[0] = uniform_prob; + for (std::size_t i = 0; i < backoffs.size(); ++i) { + backoffs_.push_back(backoffs[i]); + } + } + + ~Callback() { + for (std::size_t i = 0; i < backoffs_.size(); ++i) { + if(prune_vocab_ || prune_thresholds_[i + 1] > 0) + while(backoffs_[i]) + ++backoffs_[i]; + + if (backoffs_[i]) { + std::cerr << "Backoffs do not match for order " << (i + 1) << std::endl; + abort(); + } + } + } + + void Enter(unsigned order_minus_1, void *data) { + NGram gram(data, order_minus_1 + 1); + BuildingPayload &pay = gram.Value(); + pay.complete.prob = pay.uninterp.prob + pay.uninterp.gamma * probs_[order_minus_1]; + probs_[order_minus_1 + 1] = pay.complete.prob; + + float out_backoff; + if (order_minus_1 < backoffs_.size() && *(gram.end() - 1) != specials_.UNK() && *(gram.end() - 1) != specials_.EOS() && backoffs_[order_minus_1]) { + if(prune_vocab_ || prune_thresholds_[order_minus_1 + 1] > 0) { + //Compute hash value for current context + uint64_t current_hash = util::MurmurHashNative(gram.begin(), gram.Order() * sizeof(WordIndex)); + + const HashGamma *hashed_backoff = static_cast(backoffs_[order_minus_1].Get()); + while(current_hash != hashed_backoff->hash_value && ++backoffs_[order_minus_1]) + hashed_backoff = static_cast(backoffs_[order_minus_1].Get()); + + if(current_hash == hashed_backoff->hash_value) { + out_backoff = hashed_backoff->gamma; + ++backoffs_[order_minus_1]; + } else { + // Has been pruned away so it is not a context anymore + out_backoff = 1.0; + } + } else { + out_backoff = *static_cast(backoffs_[order_minus_1].Get()); + ++backoffs_[order_minus_1]; + } + } else { + // Not a context. + out_backoff = 1.0; + } + + output_.Gram(order_minus_1, out_backoff, pay.complete); + } + + void Exit(unsigned, void *) const {} + + private: + util::FixedArray backoffs_; + + std::vector probs_; + const std::vector& prune_thresholds_; + bool prune_vocab_; + + Output output_; + const SpecialVocab specials_; +}; +} // namespace + +Interpolate::Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector& prune_thresholds, bool prune_vocab, bool output_q, const SpecialVocab &specials) + : uniform_prob_(1.0 / static_cast(vocab_size)), // Includes but excludes . + backoffs_(backoffs), + prune_thresholds_(prune_thresholds), + prune_vocab_(prune_vocab), + output_q_(output_q), + specials_(specials) {} + +// perform order-wise interpolation +void Interpolate::Run(const util::stream::ChainPositions &positions) { + assert(positions.size() == backoffs_.size() + 1); + if (output_q_) { + typedef Callback C; + C callback(uniform_prob_, backoffs_, prune_thresholds_, prune_vocab_, specials_); + JointOrder(positions, callback); + } else { + typedef Callback C; + C callback(uniform_prob_, backoffs_, prune_thresholds_, prune_vocab_, specials_); + JointOrder(positions, callback); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/interpolate.hh b/ctcdecode-master/third_party/kenlm/lm/builder/interpolate.hh new file mode 100644 index 0000000..d20cd54 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/interpolate.hh @@ -0,0 +1,37 @@ +#ifndef LM_BUILDER_INTERPOLATE_H +#define LM_BUILDER_INTERPOLATE_H + +#include "lm/common/special.hh" +#include "lm/word_index.hh" +#include "util/stream/multi_stream.hh" + +#include + +#include + +namespace lm { namespace builder { + +/* Interpolate step. + * Input: suffix sorted n-grams with (p_uninterpolated, gamma) from + * InitialProbabilities. + * Output: suffix sorted n-grams with complete probability + */ +class Interpolate { + public: + // Normally vocab_size is the unigram count-1 (since p() = 0) but might + // be larger when the user specifies a consistent vocabulary size. + explicit Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector &prune_thresholds, bool prune_vocab, bool output_q, const SpecialVocab &specials); + + void Run(const util::stream::ChainPositions &positions); + + private: + float uniform_prob_; + util::stream::ChainPositions backoffs_; + const std::vector prune_thresholds_; + bool prune_vocab_; + bool output_q_; + const SpecialVocab specials_; +}; + +}} // namespaces +#endif // LM_BUILDER_INTERPOLATE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/lmplz_main.cc b/ctcdecode-master/third_party/kenlm/lm/builder/lmplz_main.cc new file mode 100644 index 0000000..7c1bffb --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/lmplz_main.cc @@ -0,0 +1,220 @@ +#include "lm/builder/output.hh" +#include "lm/builder/pipeline.hh" +#include "lm/common/size_option.hh" +#include "lm/lm_exception.hh" +#include "util/file.hh" +#include "util/file_piece.hh" +#include "util/usage.hh" + +#include + +#include +#include +#include + +namespace { + +// Parse and validate pruning thresholds then return vector of threshold counts +// for each n-grams order. +std::vector ParsePruning(const std::vector ¶m, std::size_t order) { + // convert to vector of integers + std::vector prune_thresholds; + prune_thresholds.reserve(order); + for (std::vector::const_iterator it(param.begin()); it != param.end(); ++it) { + try { + prune_thresholds.push_back(boost::lexical_cast(*it)); + } catch(const boost::bad_lexical_cast &) { + UTIL_THROW(util::Exception, "Bad pruning threshold " << *it); + } + } + + // Fill with zeros by default. + if (prune_thresholds.empty()) { + prune_thresholds.resize(order, 0); + return prune_thresholds; + } + + // validate pruning threshold if specified + // throw if each n-gram order has not threshold specified + UTIL_THROW_IF(prune_thresholds.size() > order, util::Exception, "You specified pruning thresholds for orders 1 through " << prune_thresholds.size() << " but the model only has order " << order); + // threshold for unigram can only be 0 (no pruning) + + // check if threshold are not in decreasing order + uint64_t lower_threshold = 0; + for (std::vector::iterator it = prune_thresholds.begin(); it != prune_thresholds.end(); ++it) { + UTIL_THROW_IF(lower_threshold > *it, util::Exception, "Pruning thresholds should be in non-decreasing order. Otherwise substrings would be removed, which is bad for query-time data structures."); + lower_threshold = *it; + } + + // Pad to all orders using the last value. + prune_thresholds.resize(order, prune_thresholds.back()); + return prune_thresholds; +} + +lm::builder::Discount ParseDiscountFallback(const std::vector ¶m) { + lm::builder::Discount ret; + UTIL_THROW_IF(param.size() > 3, util::Exception, "Specify at most three fallback discounts: 1, 2, and 3+"); + UTIL_THROW_IF(param.empty(), util::Exception, "Fallback discounting enabled, but no discount specified"); + ret.amount[0] = 0.0; + for (unsigned i = 0; i < 3; ++i) { + float discount = boost::lexical_cast(param[i < param.size() ? i : (param.size() - 1)]); + UTIL_THROW_IF(discount < 0.0 || discount > static_cast(i+1), util::Exception, "The discount for count " << (i+1) << " was parsed as " << discount << " which is not in the range [0, " << (i+1) << "]."); + ret.amount[i + 1] = discount; + } + return ret; +} + +} // namespace + +int main(int argc, char *argv[]) { + try { + namespace po = boost::program_options; + po::options_description options("Language model building options"); + lm::builder::PipelineConfig pipeline; + + std::string text, intermediate, arpa; + std::vector pruning; + std::vector discount_fallback; + std::vector discount_fallback_default; + discount_fallback_default.push_back("0.5"); + discount_fallback_default.push_back("1"); + discount_fallback_default.push_back("1.5"); + bool verbose_header; + + options.add_options() + ("help,h", po::bool_switch(), "Show this help message") + ("order,o", po::value(&pipeline.order) +#if BOOST_VERSION >= 104200 + ->required() +#endif + , "Order of the model") + ("interpolate_unigrams", po::value(&pipeline.initial_probs.interpolate_unigrams)->default_value(true)->implicit_value(true), "Interpolate the unigrams (default) as opposed to giving lots of mass to like SRI. If you want SRI's behavior with a large and the old lmplz default, use --interpolate_unigrams 0.") + ("skip_symbols", po::bool_switch(), "Treat , , and as whitespace instead of throwing an exception") + ("temp_prefix,T", po::value(&pipeline.sort.temp_prefix)->default_value(util::DefaultTempDirectory()), "Temporary file prefix") + ("memory,S", lm:: SizeOption(pipeline.sort.total_memory, util::GuessPhysicalMemory() ? "80%" : "1G"), "Sorting memory") + ("minimum_block", lm::SizeOption(pipeline.minimum_block, "8K"), "Minimum block size to allow") + ("sort_block", lm::SizeOption(pipeline.sort.buffer_size, "64M"), "Size of IO operations for sort (determines arity)") + ("block_count", po::value(&pipeline.block_count)->default_value(2), "Block count (per order)") + ("vocab_estimate", po::value(&pipeline.vocab_estimate)->default_value(1000000), "Assume this vocabulary size for purposes of calculating memory in step 1 (corpus count) and pre-sizing the hash table") + ("vocab_pad", po::value(&pipeline.vocab_size_for_unk)->default_value(0), "If the vocabulary is smaller than this value, pad with to reach this size. Requires --interpolate_unigrams") + ("verbose_header", po::bool_switch(&verbose_header), "Add a verbose header to the ARPA file that includes information such as token count, smoothing type, etc.") + ("text", po::value(&text), "Read text from a file instead of stdin") + ("arpa", po::value(&arpa), "Write ARPA to a file instead of stdout") + ("intermediate", po::value(&intermediate), "Write ngrams to intermediate files. Turns off ARPA output (which can be reactivated by --arpa file). Forces --renumber on.") + ("renumber", po::bool_switch(&pipeline.renumber_vocabulary), "Rrenumber the vocabulary identifiers so that they are monotone with the hash of each string. This is consistent with the ordering used by the trie data structure.") + ("collapse_values", po::bool_switch(&pipeline.output_q), "Collapse probability and backoff into a single value, q that yields the same sentence-level probabilities. See http://kheafield.com/professional/edinburgh/rest_paper.pdf for more details, including a proof.") + ("prune", po::value >(&pruning)->multitoken(), "Prune n-grams with count less than or equal to the given threshold. Specify one value for each order i.e. 0 0 1 to prune singleton trigrams and above. The sequence of values must be non-decreasing and the last value applies to any remaining orders. Default is to not prune, which is equivalent to --prune 0.") + ("limit_vocab_file", po::value(&pipeline.prune_vocab_file)->default_value(""), "Read allowed vocabulary separated by whitespace. N-grams that contain vocabulary items not in this list will be pruned. Can be combined with --prune arg") + ("discount_fallback", po::value >(&discount_fallback)->multitoken()->implicit_value(discount_fallback_default, "0.5 1 1.5"), "The closed-form estimate for Kneser-Ney discounts does not work without singletons or doubletons. It can also fail if these values are out of range. This option falls back to user-specified discounts when the closed-form estimate fails. Note that this option is generally a bad idea: you should deduplicate your corpus instead. However, class-based models need custom discounts because they lack singleton unigrams. Provide up to three discounts (for adjusted counts 1, 2, and 3+), which will be applied to all orders where the closed-form estimates fail."); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, options), vm); + + if (argc == 1 || vm["help"].as()) { + std::cerr << + "Builds unpruned language models with modified Kneser-Ney smoothing.\n\n" + "Please cite:\n" + "@inproceedings{Heafield-estimate,\n" + " author = {Kenneth Heafield and Ivan Pouzyrevsky and Jonathan H. Clark and Philipp Koehn},\n" + " title = {Scalable Modified {Kneser-Ney} Language Model Estimation},\n" + " year = {2013},\n" + " month = {8},\n" + " booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics},\n" + " address = {Sofia, Bulgaria},\n" + " url = {http://kheafield.com/professional/edinburgh/estimate\\_paper.pdf},\n" + "}\n\n" + "Provide the corpus on stdin. The ARPA file will be written to stdout. Order of\n" + "the model (-o) is the only mandatory option. As this is an on-disk program,\n" + "setting the temporary file location (-T) and sorting memory (-S) is recommended.\n\n" + "Memory sizes are specified like GNU sort: a number followed by a unit character.\n" + "Valid units are \% for percentage of memory (supported platforms only) and (in\n" + "increasing powers of 1024): b, K, M, G, T, P, E, Z, Y. Default is K (*1024).\n"; + uint64_t mem = util::GuessPhysicalMemory(); + if (mem) { + std::cerr << "This machine has " << mem << " bytes of memory.\n\n"; + } else { + std::cerr << "Unable to determine the amount of memory on this machine.\n\n"; + } + std::cerr << options << std::endl; + return 1; + } + + po::notify(vm); + + // required() appeared in Boost 1.42.0. +#if BOOST_VERSION < 104200 + if (!vm.count("order")) { + std::cerr << "the option '--order' is required but missing" << std::endl; + return 1; + } +#endif + + if (pipeline.vocab_size_for_unk && !pipeline.initial_probs.interpolate_unigrams) { + std::cerr << "--vocab_pad requires --interpolate_unigrams be on" << std::endl; + return 1; + } + + if (vm["skip_symbols"].as()) { + pipeline.disallowed_symbol_action = lm::COMPLAIN; + } else { + pipeline.disallowed_symbol_action = lm::THROW_UP; + } + + if (vm.count("discount_fallback")) { + pipeline.discount.fallback = ParseDiscountFallback(discount_fallback); + pipeline.discount.bad_action = lm::COMPLAIN; + } else { + // Unused, just here to prevent the compiler from complaining about uninitialized. + pipeline.discount.fallback = lm::builder::Discount(); + pipeline.discount.bad_action = lm::THROW_UP; + } + + // parse pruning thresholds. These depend on order, so it is not done as a notifier. + pipeline.prune_thresholds = ParsePruning(pruning, pipeline.order); + + if (!vm["limit_vocab_file"].as().empty()) { + pipeline.prune_vocab = true; + } + else { + pipeline.prune_vocab = false; + } + + util::NormalizeTempPrefix(pipeline.sort.temp_prefix); + + lm::builder::InitialProbabilitiesConfig &initial = pipeline.initial_probs; + // TODO: evaluate options for these. + initial.adder_in.total_memory = 32768; + initial.adder_in.block_count = 2; + initial.adder_out.total_memory = 32768; + initial.adder_out.block_count = 2; + pipeline.read_backoffs = initial.adder_out; + + // Read from stdin, write to stdout by default + util::scoped_fd in(0), out(1); + if (vm.count("text")) { + in.reset(util::OpenReadOrThrow(text.c_str())); + } + if (vm.count("arpa")) { + out.reset(util::CreateOrThrow(arpa.c_str())); + } + + try { + bool writing_intermediate = vm.count("intermediate"); + if (writing_intermediate) { + pipeline.renumber_vocabulary = true; + } + lm::builder::Output output(writing_intermediate ? intermediate : pipeline.sort.temp_prefix, writing_intermediate, pipeline.output_q); + if (!writing_intermediate || vm.count("arpa")) { + output.Add(new lm::builder::PrintHook(out.release(), verbose_header)); + } + lm::builder::Pipeline(pipeline, in.release(), output); + } catch (const util::MallocException &e) { + std::cerr << e.what() << std::endl; + std::cerr << "Try rerunning with a more conservative -S setting than " << vm["memory"].as() << std::endl; + return 1; + } + util::PrintUsage(std::cerr); + } catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + return 1; + } +} diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/output.cc b/ctcdecode-master/third_party/kenlm/lm/builder/output.cc new file mode 100644 index 0000000..604fa22 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/output.cc @@ -0,0 +1,52 @@ +#include "lm/builder/output.hh" + +#include "lm/common/model_buffer.hh" +#include "lm/common/print.hh" +#include "util/file_stream.hh" +#include "util/stream/multi_stream.hh" + +#include + +namespace lm { namespace builder { + +OutputHook::~OutputHook() {} + +Output::Output(StringPiece file_base, bool keep_buffer, bool output_q) + : buffer_(file_base, keep_buffer, output_q) {} + +void Output::SinkProbs(util::stream::Chains &chains) { + Apply(PROB_PARALLEL_HOOK, chains); + if (!buffer_.Keep() && !Have(PROB_SEQUENTIAL_HOOK)) { + chains >> util::stream::kRecycle; + chains.Wait(true); + return; + } + buffer_.Sink(chains, header_.counts_pruned); + chains >> util::stream::kRecycle; + chains.Wait(false); + if (Have(PROB_SEQUENTIAL_HOOK)) { + std::cerr << "=== 5/5 Writing ARPA model ===" << std::endl; + buffer_.Source(chains); + Apply(PROB_SEQUENTIAL_HOOK, chains); + chains >> util::stream::kRecycle; + chains.Wait(true); + } +} + +void Output::Apply(HookType hook_type, util::stream::Chains &chains) { + for (boost::ptr_vector::iterator entry = outputs_[hook_type].begin(); entry != outputs_[hook_type].end(); ++entry) { + entry->Sink(header_, VocabFile(), chains); + } +} + +void PrintHook::Sink(const HeaderInfo &info, int vocab_file, util::stream::Chains &chains) { + if (verbose_header_) { + util::FileStream out(file_.get(), 50); + out << "# Input file: " << info.input_file << '\n'; + out << "# Token count: " << info.token_count << '\n'; + out << "# Smoothing: Modified Kneser-Ney" << '\n'; + } + chains >> PrintARPA(vocab_file, file_.get(), info.counts_pruned); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/output.hh b/ctcdecode-master/third_party/kenlm/lm/builder/output.hh new file mode 100644 index 0000000..69d6c6d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/output.hh @@ -0,0 +1,85 @@ +#ifndef LM_BUILDER_OUTPUT_H +#define LM_BUILDER_OUTPUT_H + +#include "lm/builder/header_info.hh" +#include "lm/common/model_buffer.hh" +#include "util/file.hh" + +#include +#include + +namespace util { namespace stream { class Chains; class ChainPositions; } } + +/* Outputs from lmplz: ARPA, sharded files, etc */ +namespace lm { namespace builder { + +// These are different types of hooks. Values should be consecutive to enable a vector lookup. +enum HookType { + // TODO: counts. + PROB_PARALLEL_HOOK, // Probability and backoff (or just q). Output must process the orders in parallel or there will be a deadlock. + PROB_SEQUENTIAL_HOOK, // Probability and backoff (or just q). Output can process orders any way it likes. This requires writing the data to disk then reading. Useful for ARPA files, which put unigrams first etc. + NUMBER_OF_HOOKS // Keep this last so we know how many values there are. +}; + +class OutputHook { + public: + explicit OutputHook(HookType hook_type) : type_(hook_type) {} + + virtual ~OutputHook(); + + virtual void Sink(const HeaderInfo &info, int vocab_file, util::stream::Chains &chains) = 0; + + HookType Type() const { return type_; } + + private: + HookType type_; +}; + +class Output : boost::noncopyable { + public: + Output(StringPiece file_base, bool keep_buffer, bool output_q); + + // Takes ownership. + void Add(OutputHook *hook) { + outputs_[hook->Type()].push_back(hook); + } + + bool Have(HookType hook_type) const { + return !outputs_[hook_type].empty(); + } + + int VocabFile() const { return buffer_.VocabFile(); } + + void SetHeader(const HeaderInfo &header) { header_ = header; } + const HeaderInfo &GetHeader() const { return header_; } + + // This is called by the pipeline. + void SinkProbs(util::stream::Chains &chains); + + unsigned int Steps() const { return Have(PROB_SEQUENTIAL_HOOK); } + + private: + void Apply(HookType hook_type, util::stream::Chains &chains); + + ModelBuffer buffer_; + + boost::ptr_vector outputs_[NUMBER_OF_HOOKS]; + HeaderInfo header_; +}; + +class PrintHook : public OutputHook { + public: + // Takes ownership + PrintHook(int write_fd, bool verbose_header) + : OutputHook(PROB_SEQUENTIAL_HOOK), file_(write_fd), verbose_header_(verbose_header) {} + + void Sink(const HeaderInfo &info, int vocab_file, util::stream::Chains &chains); + + private: + util::scoped_fd file_; + bool verbose_header_; +}; + +}} // namespaces + +#endif // LM_BUILDER_OUTPUT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/payload.hh b/ctcdecode-master/third_party/kenlm/lm/builder/payload.hh new file mode 100644 index 0000000..1f1fa88 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/payload.hh @@ -0,0 +1,48 @@ +#ifndef LM_BUILDER_PAYLOAD_H +#define LM_BUILDER_PAYLOAD_H + +#include "lm/weights.hh" +#include "lm/word_index.hh" +#include + +namespace lm { namespace builder { + +struct Uninterpolated { + float prob; // Uninterpolated probability. + float gamma; // Interpolation weight for lower order. +}; + +union BuildingPayload { + uint64_t count; + Uninterpolated uninterp; + ProbBackoff complete; + + /*mjd**********************************************************************/ + bool IsMarked() const { + return count >> (sizeof(count) * 8 - 1); + } + + void Mark() { + count |= (1ULL << (sizeof(count) * 8 - 1)); + } + + void Unmark() { + count &= ~(1ULL << (sizeof(count) * 8 - 1)); + } + + uint64_t UnmarkedCount() const { + return count & ~(1ULL << (sizeof(count) * 8 - 1)); + } + + uint64_t CutoffCount() const { + return IsMarked() ? 0 : UnmarkedCount(); + } + /*mjd**********************************************************************/ +}; + +const WordIndex kBOS = 1; +const WordIndex kEOS = 2; + +}} // namespaces + +#endif // LM_BUILDER_PAYLOAD_H diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/pipeline.cc b/ctcdecode-master/third_party/kenlm/lm/builder/pipeline.cc new file mode 100644 index 0000000..d131636 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/pipeline.cc @@ -0,0 +1,385 @@ +#include "lm/builder/pipeline.hh" + +#include "lm/builder/adjust_counts.hh" +#include "lm/builder/combine_counts.hh" +#include "lm/builder/corpus_count.hh" +#include "lm/builder/hash_gamma.hh" +#include "lm/builder/initial_probabilities.hh" +#include "lm/builder/interpolate.hh" +#include "lm/builder/output.hh" +#include "lm/common/compare.hh" +#include "lm/common/renumber.hh" + +#include "lm/sizes.hh" +#include "lm/vocab.hh" + +#include "util/exception.hh" +#include "util/file.hh" +#include "util/stream/io.hh" + +#include +#include +#include +#include + +namespace lm { namespace builder { + +using util::stream::Sorts; + +namespace { + +void PrintStatistics(const std::vector &counts, const std::vector &counts_pruned, const std::vector &discounts) { + std::cerr << "Statistics:\n"; + for (size_t i = 0; i < counts.size(); ++i) { + std::cerr << (i + 1) << ' ' << counts_pruned[i]; + if(counts[i] != counts_pruned[i]) + std::cerr << "/" << counts[i]; + + for (size_t d = 1; d <= 3; ++d) + std::cerr << " D" << d << (d == 3 ? "+=" : "=") << discounts[i].amount[d]; + std::cerr << '\n'; + } +} + +class Master { + public: + explicit Master(PipelineConfig &config, unsigned output_steps) + : config_(config), chains_(config.order), unigrams_(util::MakeTemp(config_.TempPrefix())), steps_(output_steps + 4) { + config_.minimum_block = std::max(NGram::TotalSize(config_.order), config_.minimum_block); + } + + const PipelineConfig &Config() const { return config_; } + + util::stream::Chains &MutableChains() { return chains_; } + + template Master &operator>>(const T &worker) { + chains_ >> worker; + return *this; + } + + // This takes the (partially) sorted ngrams and sets up for adjusted counts. + void InitForAdjust(util::stream::Sort &ngrams, WordIndex types, std::size_t subtract_for_numbering) { + const std::size_t each_order_min = config_.minimum_block * config_.block_count; + // We know how many unigrams there are. Don't allocate more than needed to them. + const std::size_t min_chains = (config_.order - 1) * each_order_min + + std::min(types * NGram::TotalSize(1), each_order_min); + // Prevent overflow in subtracting. + const std::size_t total = std::max(config_.TotalMemory(), min_chains + subtract_for_numbering + config_.minimum_block); + // Do merge sort with calculated laziness. + const std::size_t merge_using = ngrams.Merge(std::min(total - min_chains - subtract_for_numbering, ngrams.DefaultLazy())); + + std::vector count_bounds(1, types); + CreateChains(total - merge_using - subtract_for_numbering, count_bounds); + ngrams.Output(chains_.back(), merge_using); + } + + // For initial probabilities, but this is generic. + void SortAndReadTwice(const std::vector &counts, Sorts &sorts, util::stream::Chains &second, util::stream::ChainConfig second_config) { + bool unigrams_are_sorted = !config_.renumber_vocabulary; + // Do merge first before allocating chain memory. + for (std::size_t i = 0; i < config_.order - unigrams_are_sorted; ++i) { + sorts[i].Merge(0); + } + // There's no lazy merge, so just divide memory amongst the chains. + CreateChains(config_.TotalMemory(), counts); + chains_.back().ActivateProgress(); + if (unigrams_are_sorted) { + chains_[0] >> unigrams_.Source(); + second_config.entry_size = NGram::TotalSize(1); + second.push_back(second_config); + second.back() >> unigrams_.Source(); + } + for (std::size_t i = unigrams_are_sorted; i < config_.order; ++i) { + util::scoped_fd fd(sorts[i - unigrams_are_sorted].StealCompleted()); + chains_[i].SetProgressTarget(util::SizeOrThrow(fd.get())); + chains_[i] >> util::stream::PRead(util::DupOrThrow(fd.get()), true); + second_config.entry_size = NGram::TotalSize(i + 1); + second.push_back(second_config); + second.back() >> util::stream::PRead(fd.release(), true); + } + } + + // There is no sort after this, so go for broke on lazy merging. + template void MaximumLazyInput(const std::vector &counts, Sorts &sorts) { + // Determine the minimum we can use for all the chains. + std::size_t min_chains = 0; + for (std::size_t i = 0; i < config_.order; ++i) { + min_chains += std::min(counts[i] * NGram::TotalSize(i + 1), static_cast(config_.minimum_block)); + } + std::size_t for_merge = min_chains > config_.TotalMemory() ? 0 : (config_.TotalMemory() - min_chains); + std::vector laziness; + // Prioritize longer n-grams. + for (util::stream::Sort *i = sorts.end() - 1; i >= sorts.begin(); --i) { + laziness.push_back(i->Merge(for_merge)); + assert(for_merge >= laziness.back()); + for_merge -= laziness.back(); + } + std::reverse(laziness.begin(), laziness.end()); + + CreateChains(for_merge + min_chains, counts); + chains_.back().ActivateProgress(); + chains_[0] >> unigrams_.Source(); + for (std::size_t i = 1; i < config_.order; ++i) { + sorts[i - 1].Output(chains_[i], laziness[i - 1]); + } + } + + template void SetupSorts(Sorts &sorts, bool exclude_unigrams) { + sorts.Init(config_.order - exclude_unigrams); + // Unigrams don't get sorted because their order is always the same. + if (exclude_unigrams) chains_[0] >> unigrams_.Sink() >> util::stream::kRecycle; + for (std::size_t i = exclude_unigrams; i < config_.order; ++i) { + sorts.push_back(chains_[i], config_.sort, Compare(i + 1)); + } + chains_.Wait(true); + } + + unsigned int Steps() const { return steps_; } + + private: + // Create chains, allocating memory to them. Totally heuristic. Count + // bounds are upper bounds on the counts or not present. + void CreateChains(std::size_t remaining_mem, const std::vector &count_bounds) { + std::vector assignments; + assignments.reserve(config_.order); + // Start by assigning maximum memory usage (to be refined later). + for (std::size_t i = 0; i < count_bounds.size(); ++i) { + assignments.push_back(static_cast(std::min( + static_cast(remaining_mem), + count_bounds[i] * static_cast(NGram::TotalSize(i + 1))))); + } + assignments.resize(config_.order, remaining_mem); + + // Now we know how much memory everybody wants. How much will they get? + // Proportional to this. + std::vector portions; + // Indices of orders that have yet to be assigned. + std::vector unassigned; + for (std::size_t i = 0; i < config_.order; ++i) { + portions.push_back(static_cast((i+1) * NGram::TotalSize(i+1))); + unassigned.push_back(i); + } + /*If somebody doesn't eat their full dinner, give it to the rest of the + * family. Then somebody else might not eat their full dinner etc. Ends + * when everybody unassigned is hungry. + */ + float sum; + bool found_more; + std::vector block_count(config_.order); + do { + sum = 0.0; + for (std::size_t i = 0; i < unassigned.size(); ++i) { + sum += portions[unassigned[i]]; + } + found_more = false; + // If the proportional assignment is more than needed, give it just what it needs. + for (std::vector::iterator i = unassigned.begin(); i != unassigned.end();) { + if (assignments[*i] <= remaining_mem * (portions[*i] / sum)) { + remaining_mem -= assignments[*i]; + block_count[*i] = 1; + i = unassigned.erase(i); + found_more = true; + } else { + ++i; + } + } + } while (found_more); + for (std::vector::iterator i = unassigned.begin(); i != unassigned.end(); ++i) { + assignments[*i] = remaining_mem * (portions[*i] / sum); + block_count[*i] = config_.block_count; + } + chains_.clear(); + std::cerr << "Chain sizes:"; + for (std::size_t i = 0; i < config_.order; ++i) { + // Always have enough for at least one record. + // This was crashing if e.g. there was no 5-gram. + assignments[i] = std::max(assignments[i], block_count[i] * NGram::TotalSize(i + 1)); + std::cerr << ' ' << (i+1) << ":" << assignments[i]; + chains_.push_back(util::stream::ChainConfig(NGram::TotalSize(i + 1), block_count[i], assignments[i])); + } + std::cerr << std::endl; + } + + PipelineConfig &config_; + + util::stream::Chains chains_; + + util::stream::FileBuffer unigrams_; + + const unsigned int steps_; +}; + +util::stream::Sort *CountText(int text_file /* input */, int vocab_file /* output */, Master &master, uint64_t &token_count, WordIndex &type_count, std::string &text_file_name, std::vector &prune_words) { + const PipelineConfig &config = master.Config(); + std::cerr << "=== 1/" << master.Steps() << " Counting and sorting n-grams ===" << std::endl; + + const std::size_t vocab_usage = CorpusCount::VocabUsage(config.vocab_estimate); + UTIL_THROW_IF(config.TotalMemory() < vocab_usage, util::Exception, "Vocab hash size estimate " << vocab_usage << " exceeds total memory " << config.TotalMemory()); + std::size_t memory_for_chain = + // This much memory to work with after vocab hash table. + static_cast(config.TotalMemory() - vocab_usage) / + // Solve for block size including the dedupe multiplier for one block. + (static_cast(config.block_count) + CorpusCount::DedupeMultiplier(config.order)) * + // Chain likes memory expressed in terms of total memory. + static_cast(config.block_count); + util::stream::Chain chain(util::stream::ChainConfig(NGram::TotalSize(config.order), config.block_count, memory_for_chain)); + + type_count = config.vocab_estimate; + util::FilePiece text(text_file, NULL, &std::cerr); + text_file_name = text.FileName(); + CorpusCount counter(text, vocab_file, true, token_count, type_count, prune_words, config.prune_vocab_file, chain.BlockSize() / chain.EntrySize(), config.disallowed_symbol_action); + chain >> boost::ref(counter); + + util::scoped_ptr > sorter(new util::stream::Sort(chain, config.sort, SuffixOrder(config.order), CombineCounts())); + chain.Wait(true); + return sorter.release(); +} + +void InitialProbabilities(const std::vector &counts, const std::vector &counts_pruned, const std::vector &discounts, Master &master, Sorts &primary, util::FixedArray &gammas, const std::vector &prune_thresholds, bool prune_vocab, const SpecialVocab &specials) { + const PipelineConfig &config = master.Config(); + util::stream::Chains second(config.order); + + { + Sorts sorts; + master.SetupSorts(sorts, !config.renumber_vocabulary); + PrintStatistics(counts, counts_pruned, discounts); + lm::ngram::ShowSizes(counts_pruned); + std::cerr << "=== 3/" << master.Steps() << " Calculating and sorting initial probabilities ===" << std::endl; + master.SortAndReadTwice(counts_pruned, sorts, second, config.initial_probs.adder_in); + } + + util::stream::Chains gamma_chains(config.order); + InitialProbabilities(config.initial_probs, discounts, master.MutableChains(), second, gamma_chains, prune_thresholds, prune_vocab, specials); + // Don't care about gamma for 0. + gamma_chains[0] >> util::stream::kRecycle; + gammas.Init(config.order - 1); + for (std::size_t i = 1; i < config.order; ++i) { + gammas.push_back(util::MakeTemp(config.TempPrefix())); + gamma_chains[i] >> gammas[i - 1].Sink() >> util::stream::kRecycle; + } + // Has to be done here due to gamma_chains scope. + master.SetupSorts(primary, true); +} + +void InterpolateProbabilities(const std::vector &counts, Master &master, Sorts &primary, util::FixedArray &gammas, Output &output, const SpecialVocab &specials) { + std::cerr << "=== 4/" << master.Steps() << " Calculating and writing order-interpolated probabilities ===" << std::endl; + const PipelineConfig &config = master.Config(); + master.MaximumLazyInput(counts, primary); + + util::stream::Chains gamma_chains(config.order - 1); + for (std::size_t i = 0; i < config.order - 1; ++i) { + util::stream::ChainConfig read_backoffs(config.read_backoffs); + + if(config.prune_vocab || config.prune_thresholds[i + 1] > 0) + read_backoffs.entry_size = sizeof(HashGamma); + else + read_backoffs.entry_size = sizeof(float); + + gamma_chains.push_back(read_backoffs); + gamma_chains.back() >> gammas[i].Source(true); + } + master >> Interpolate(std::max(master.Config().vocab_size_for_unk, counts[0] - 1 /* is not included */), util::stream::ChainPositions(gamma_chains), config.prune_thresholds, config.prune_vocab, config.output_q, specials); + gamma_chains >> util::stream::kRecycle; + output.SinkProbs(master.MutableChains()); +} + +class VocabNumbering { + public: + VocabNumbering(int final_vocab, StringPiece temp_prefix, bool renumber) + : final_vocab_(final_vocab), + renumber_(renumber), + specials_(kBOS, kEOS) { + if (renumber) { + temporary_.reset(util::MakeTemp(temp_prefix)); + } + } + + int WriteOnTheFly() const { return renumber_ ? temporary_.get() : final_vocab_; } + + // Compute the vocabulary mapping and return the memory used. + std::size_t ComputeMapping(WordIndex type_count) { + if (!renumber_) return 0; + ngram::SortedVocabulary::ComputeRenumbering(type_count, temporary_.get(), final_vocab_, vocab_mapping_); + temporary_.reset(); + return sizeof(WordIndex) * vocab_mapping_.size(); + } + + void ApplyRenumber(util::stream::Chains &chains) { + if (!renumber_) return; + for (std::size_t i = 0; i < chains.size(); ++i) { + chains[i] >> Renumber(&*vocab_mapping_.begin(), i + 1); + } + specials_ = SpecialVocab(vocab_mapping_[specials_.BOS()], vocab_mapping_[specials_.EOS()]); + } + + const SpecialVocab &Specials() const { return specials_; } + + private: + int final_vocab_; + // Out of order vocab file created on the fly. + util::scoped_fd temporary_; + + bool renumber_; + + std::vector vocab_mapping_; + + SpecialVocab specials_; +}; + +} // namespace + +void Pipeline(PipelineConfig &config, int text_file, Output &output) { + // Some fail-fast sanity checks. + if (config.sort.buffer_size * 4 > config.TotalMemory()) { + config.sort.buffer_size = config.TotalMemory() / 4; + std::cerr << "Warning: changing sort block size to " << config.sort.buffer_size << " bytes due to low total memory." << std::endl; + } + if (config.minimum_block < NGram::TotalSize(config.order)) { + config.minimum_block = NGram::TotalSize(config.order); + std::cerr << "Warning: raising minimum block to " << config.minimum_block << " to fit an ngram in every block." << std::endl; + } + UTIL_THROW_IF(config.sort.buffer_size < config.minimum_block, util::Exception, "Sort block size " << config.sort.buffer_size << " is below the minimum block size " << config.minimum_block << "."); + UTIL_THROW_IF(config.TotalMemory() < config.minimum_block * config.order * config.block_count, util::Exception, + "Not enough memory to fit " << (config.order * config.block_count) << " blocks with minimum size " << config.minimum_block << ". Increase memory to " << (config.minimum_block * config.order * config.block_count) << " bytes or decrease the minimum block size."); + + Master master(config, output.Steps()); + // master's destructor will wait for chains. But they might be deadlocked if + // this thread dies because e.g. it ran out of memory. + try { + VocabNumbering numbering(output.VocabFile(), config.TempPrefix(), config.renumber_vocabulary); + uint64_t token_count; + WordIndex type_count; + std::string text_file_name; + std::vector prune_words; + util::scoped_ptr > sorted_counts( + CountText(text_file, numbering.WriteOnTheFly(), master, token_count, type_count, text_file_name, prune_words)); + std::cerr << "Unigram tokens " << token_count << " types " << type_count << std::endl; + + // Create vocab mapping, which uses temporary memory, while nothing else is happening. + std::size_t subtract_for_numbering = numbering.ComputeMapping(type_count); + + std::cerr << "=== 2/" << master.Steps() << " Calculating and sorting adjusted counts ===" << std::endl; + master.InitForAdjust(*sorted_counts, type_count, subtract_for_numbering); + sorted_counts.reset(); + + std::vector counts; + std::vector counts_pruned; + std::vector discounts; + master >> AdjustCounts(config.prune_thresholds, counts, counts_pruned, prune_words, config.discount, discounts); + numbering.ApplyRenumber(master.MutableChains()); + + { + util::FixedArray gammas; + Sorts primary; + InitialProbabilities(counts, counts_pruned, discounts, master, primary, gammas, config.prune_thresholds, config.prune_vocab, numbering.Specials()); + output.SetHeader(HeaderInfo(text_file_name, token_count, counts_pruned)); + // Also does output. + InterpolateProbabilities(counts_pruned, master, primary, gammas, output, numbering.Specials()); + } + } catch (const util::Exception &e) { + std::cerr << e.what() << std::endl; + abort(); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/builder/pipeline.hh b/ctcdecode-master/third_party/kenlm/lm/builder/pipeline.hh new file mode 100644 index 0000000..66f1fd9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/builder/pipeline.hh @@ -0,0 +1,76 @@ +#ifndef LM_BUILDER_PIPELINE_H +#define LM_BUILDER_PIPELINE_H + +#include "lm/builder/adjust_counts.hh" +#include "lm/builder/initial_probabilities.hh" +#include "lm/builder/header_info.hh" +#include "lm/lm_exception.hh" +#include "lm/word_index.hh" +#include "util/stream/config.hh" +#include "util/file_piece.hh" + +#include +#include + +namespace lm { namespace builder { + +class Output; + +struct PipelineConfig { + std::size_t order; + util::stream::SortConfig sort; + InitialProbabilitiesConfig initial_probs; + util::stream::ChainConfig read_backoffs; + + // Estimated vocabulary size. Used for sizing CorpusCount memory and + // initial probing hash table sizing, also in CorpusCount. + lm::WordIndex vocab_estimate; + + // Minimum block size to tolerate. + std::size_t minimum_block; + + // Number of blocks to use. This will be overridden to 1 if everything fits. + std::size_t block_count; + + // n-gram count thresholds for pruning. 0 values means no pruning for + // corresponding n-gram order + std::vector prune_thresholds; //mjd + bool prune_vocab; + std::string prune_vocab_file; + + /* Renumber the vocabulary the way the trie likes it? */ + bool renumber_vocabulary; + + // What to do with discount failures. + DiscountConfig discount; + + // Compute collapsed q values instead of probability and backoff + bool output_q; + + /* Computing the perplexity of LMs with different vocabularies is hard. For + * example, the lowest perplexity is attained by a unigram model that + * predicts p() = 1 and has no other vocabulary. Also, linearly + * interpolated models will sum to more than 1 because is duplicated + * (SRI just pretends p() = 0 for these purposes, which makes it sum to + * 1 but comes with its own problems). This option will make the vocabulary + * a particular size by replicating multiple times for purposes of + * computing vocabulary size. It has no effect if the actual vocabulary is + * larger. This parameter serves the same purpose as IRSTLM's "dub". + */ + uint64_t vocab_size_for_unk; + + /* What to do the first time , , or appears in the input. If + * this is anything but THROW_UP, then the symbol will always be treated as + * whitespace. + */ + WarningAction disallowed_symbol_action; + + const std::string &TempPrefix() const { return sort.temp_prefix; } + std::size_t TotalMemory() const { return sort.total_memory; } +}; + +// Takes ownership of text_file and out_arpa. +void Pipeline(PipelineConfig &config, int text_file, Output &output); + +}} // namespaces +#endif // LM_BUILDER_PIPELINE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/lm/common/CMakeLists.txt new file mode 100644 index 0000000..31a0e46 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/CMakeLists.txt @@ -0,0 +1,25 @@ +# This CMake file was created by Lane Schwartz + +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +# +# In order to set correct paths to these files +# in case this variable is referenced by CMake files in the parent directory, +# we prefix all files with ${CMAKE_CURRENT_SOURCE_DIR}. +# +set(KENLM_LM_COMMON_SOURCE + ${CMAKE_CURRENT_SOURCE_DIR}/model_buffer.cc + ${CMAKE_CURRENT_SOURCE_DIR}/print.cc + ${CMAKE_CURRENT_SOURCE_DIR}/renumber.cc + ${CMAKE_CURRENT_SOURCE_DIR}/size_option.cc + PARENT_SCOPE) + +if(BUILD_TESTING) + KenLMAddTest(TEST model_buffer_test + LIBRARIES kenlm + TEST_ARGS ${CMAKE_CURRENT_SOURCE_DIR}/test_data) +endif() diff --git a/ctcdecode-master/third_party/kenlm/lm/common/compare.hh b/ctcdecode-master/third_party/kenlm/lm/common/compare.hh new file mode 100644 index 0000000..2adaba4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/compare.hh @@ -0,0 +1,185 @@ +#ifndef LM_COMMON_COMPARE_H +#define LM_COMMON_COMPARE_H + +#include "lm/common/ngram.hh" +#include "lm/word_index.hh" + +#include +#include + +namespace lm { + +/** + * Abstract parent class for defining custom n-gram comparators. + */ +template class Comparator : public std::binary_function { + public: + + /** + * Constructs a comparator capable of comparing two n-grams. + * + * @param order Number of words in each n-gram + */ + explicit Comparator(std::size_t order) : order_(order) {} + + /** + * Applies the comparator using the Compare method that must be defined in any class that inherits from this class. + * + * @param lhs A pointer to the n-gram on the left-hand side of the comparison + * @param rhs A pointer to the n-gram on the right-hand side of the comparison + * + * @see ContextOrder::Compare + * @see PrefixOrder::Compare + * @see SuffixOrder::Compare + */ + inline bool operator()(const void *lhs, const void *rhs) const { + return static_cast(this)->Compare(static_cast(lhs), static_cast(rhs)); + } + + /** Gets the n-gram order defined for this comparator. */ + std::size_t Order() const { return order_; } + + protected: + std::size_t order_; +}; + +/** + * N-gram comparator that compares n-grams according to their reverse (suffix) order. + * + * This comparator compares n-grams lexicographically, one word at a time, + * beginning with the last word of each n-gram and ending with the first word of each n-gram. + * + * Some examples of n-gram comparisons as defined by this comparator: + * - a b c == a b c + * - a b c < a b d + * - a b c > a d b + * - a b c > a b b + * - a b c > x a c + * - a b c < x y z + */ +class SuffixOrder : public Comparator { + public: + + /** + * Constructs a comparator capable of comparing two n-grams. + * + * @param order Number of words in each n-gram + */ + explicit SuffixOrder(std::size_t order) : Comparator(order) {} + + /** + * Compares two n-grams lexicographically, one word at a time, + * beginning with the last word of each n-gram and ending with the first word of each n-gram. + * + * @param lhs A pointer to the n-gram on the left-hand side of the comparison + * @param rhs A pointer to the n-gram on the right-hand side of the comparison + */ + inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const { + for (std::size_t i = order_ - 1; i != 0; --i) { + if (lhs[i] != rhs[i]) + return lhs[i] < rhs[i]; + } + return lhs[0] < rhs[0]; + } + + static const unsigned kMatchOffset = 1; +}; + + +/** + * N-gram comparator that compares n-grams according to the reverse (suffix) order of the n-gram context. + * + * This comparator compares n-grams lexicographically, one word at a time, + * beginning with the penultimate word of each n-gram and ending with the first word of each n-gram; + * finally, this comparator compares the last word of each n-gram. + * + * Some examples of n-gram comparisons as defined by this comparator: + * - a b c == a b c + * - a b c < a b d + * - a b c < a d b + * - a b c > a b b + * - a b c > x a c + * - a b c < x y z + */ +class ContextOrder : public Comparator { + public: + + /** + * Constructs a comparator capable of comparing two n-grams. + * + * @param order Number of words in each n-gram + */ + explicit ContextOrder(std::size_t order) : Comparator(order) {} + + /** + * Compares two n-grams lexicographically, one word at a time, + * beginning with the penultimate word of each n-gram and ending with the first word of each n-gram; + * finally, this comparator compares the last word of each n-gram. + * + * @param lhs A pointer to the n-gram on the left-hand side of the comparison + * @param rhs A pointer to the n-gram on the right-hand side of the comparison + */ + inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const { + for (int i = order_ - 2; i >= 0; --i) { + if (lhs[i] != rhs[i]) + return lhs[i] < rhs[i]; + } + return lhs[order_ - 1] < rhs[order_ - 1]; + } +}; + +/** + * N-gram comparator that compares n-grams according to their natural (prefix) order. + * + * This comparator compares n-grams lexicographically, one word at a time, + * beginning with the first word of each n-gram and ending with the last word of each n-gram. + * + * Some examples of n-gram comparisons as defined by this comparator: + * - a b c == a b c + * - a b c < a b d + * - a b c < a d b + * - a b c > a b b + * - a b c < x a c + * - a b c < x y z + */ +class PrefixOrder : public Comparator { + public: + + /** + * Constructs a comparator capable of comparing two n-grams. + * + * @param order Number of words in each n-gram + */ + explicit PrefixOrder(std::size_t order) : Comparator(order) {} + + /** + * Compares two n-grams lexicographically, one word at a time, + * beginning with the first word of each n-gram and ending with the last word of each n-gram. + * + * @param lhs A pointer to the n-gram on the left-hand side of the comparison + * @param rhs A pointer to the n-gram on the right-hand side of the comparison + */ + inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const { + for (std::size_t i = 0; i < order_; ++i) { + if (lhs[i] != rhs[i]) + return lhs[i] < rhs[i]; + } + return false; + } + + static const unsigned kMatchOffset = 0; +}; + +template struct SuffixLexicographicLess : public std::binary_function { + bool operator()(const Range first, const Range second) const { + for (const WordIndex *f = first.end() - 1, *s = second.end() - 1; f >= first.begin() && s >= second.begin(); --f, --s) { + if (*f < *s) return true; + if (*f > *s) return false; + } + return first.size() < second.size(); + } +}; + +} // namespace lm + +#endif // LM_COMMON_COMPARE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/joint_order.hh b/ctcdecode-master/third_party/kenlm/lm/common/joint_order.hh new file mode 100644 index 0000000..6113bb8 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/joint_order.hh @@ -0,0 +1,71 @@ +#ifndef LM_COMMON_JOINT_ORDER_H +#define LM_COMMON_JOINT_ORDER_H + +#include "lm/common/ngram_stream.hh" +#include "lm/lm_exception.hh" + +#ifdef DEBUG +#include "util/fixed_array.hh" +#include +#endif + +#include + +namespace lm { + +template void JointOrder(const util::stream::ChainPositions &positions, Callback &callback) { + // Allow matching to reference streams[-1]. + util::FixedArray > streams_with_dummy(positions.size() + 1); + // A bogus stream for [-1]. + streams_with_dummy.push_back(); + for (std::size_t i = 0; i < positions.size(); ++i) { + streams_with_dummy.push_back(positions[i], NGramHeader(NULL, i + 1)); + } + ProxyStream *streams = streams_with_dummy.begin() + 1; + + std::size_t order; + for (order = 0; order < positions.size() && streams[order]; ++order) {} + assert(order); // should always have . + + // Debugging only: call comparison function to sanity check order. +#ifdef DEBUG + util::FixedArray less_compare(order); + for (unsigned i = 0; i < order; ++i) + less_compare.push_back(i + 1); +#endif // DEBUG + + std::size_t current = 0; + while (true) { + // Does the context match the lower one? + if (!memcmp(streams[static_cast(current) - 1]->begin(), streams[current]->begin() + Compare::kMatchOffset, sizeof(WordIndex) * current)) { + callback.Enter(current, streams[current].Get()); + // Transition to looking for extensions. + if (++current < order) continue; + } +#ifdef DEBUG + // match_check[current - 1] matches current-grams + // The lower-order stream (which skips fewer current-grams) should always be <= the higher order-stream (which can skip current-grams). + else if (!less_compare[current - 1](streams[static_cast(current) - 1]->begin(), streams[current]->begin() + Compare::kMatchOffset)) { + std::cerr << "Stream out of order detected" << std::endl; + abort(); + } +#endif // DEBUG + // No extension left. + while(true) { + assert(current > 0); + --current; + callback.Exit(current, streams[current].Get()); + + if (++streams[current]) break; + + UTIL_THROW_IF(order != current + 1, FormatLoadException, "Detected n-gram without matching suffix"); + + order = current; + if (!order) return; + } + } +} + +} // namespaces + +#endif // LM_COMMON_JOINT_ORDER_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/model_buffer.cc b/ctcdecode-master/third_party/kenlm/lm/common/model_buffer.cc new file mode 100644 index 0000000..82d28af --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/model_buffer.cc @@ -0,0 +1,144 @@ +#include "lm/common/model_buffer.hh" + +#include "lm/common/compare.hh" +#include "lm/state.hh" +#include "lm/weights.hh" +#include "util/exception.hh" +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/file_piece.hh" +#include "util/stream/io.hh" +#include "util/stream/multi_stream.hh" + +#include + +#include + +namespace lm { + +namespace { +const char kMetadataHeader[] = "KenLM intermediate binary file"; +} // namespace + +ModelBuffer::ModelBuffer(StringPiece file_base, bool keep_buffer, bool output_q) + : file_base_(file_base.data(), file_base.size()), keep_buffer_(keep_buffer), output_q_(output_q), + vocab_file_(keep_buffer ? util::CreateOrThrow((file_base_ + ".vocab").c_str()) : util::MakeTemp(file_base_)) {} + +ModelBuffer::ModelBuffer(StringPiece file_base) + : file_base_(file_base.data(), file_base.size()), keep_buffer_(false) { + const std::string full_name = file_base_ + ".kenlm_intermediate"; + util::FilePiece in(full_name.c_str()); + StringPiece token = in.ReadLine(); + UTIL_THROW_IF2(token != kMetadataHeader, "File " << full_name << " begins with \"" << token << "\" not " << kMetadataHeader); + + token = in.ReadDelimited(); + UTIL_THROW_IF2(token != "Counts", "Expected Counts, got \"" << token << "\" in " << full_name); + char got; + while ((got = in.get()) == ' ') { + counts_.push_back(in.ReadULong()); + } + UTIL_THROW_IF2(got != '\n', "Expected newline at end of counts."); + + token = in.ReadDelimited(); + UTIL_THROW_IF2(token != "Payload", "Expected Payload, got \"" << token << "\" in " << full_name); + token = in.ReadDelimited(); + if (token == "q") { + output_q_ = true; + } else if (token == "pb") { + output_q_ = false; + } else { + UTIL_THROW(util::Exception, "Unknown payload " << token); + } + + vocab_file_.reset(util::OpenReadOrThrow((file_base_ + ".vocab").c_str())); + + files_.Init(counts_.size()); + for (unsigned long i = 0; i < counts_.size(); ++i) { + files_.push_back(util::OpenReadOrThrow((file_base_ + '.' + boost::lexical_cast(i + 1)).c_str())); + } +} + +void ModelBuffer::Sink(util::stream::Chains &chains, const std::vector &counts) { + counts_ = counts; + // Open files. + files_.Init(chains.size()); + for (std::size_t i = 0; i < chains.size(); ++i) { + if (keep_buffer_) { + files_.push_back(util::CreateOrThrow( + (file_base_ + '.' + boost::lexical_cast(i + 1)).c_str() + )); + } else { + files_.push_back(util::MakeTemp(file_base_)); + } + chains[i] >> util::stream::Write(files_.back().get()); + } + if (keep_buffer_) { + util::scoped_fd metadata(util::CreateOrThrow((file_base_ + ".kenlm_intermediate").c_str())); + util::FileStream meta(metadata.get(), 200); + meta << kMetadataHeader << "\nCounts"; + for (std::vector::const_iterator i = counts_.begin(); i != counts_.end(); ++i) { + meta << ' ' << *i; + } + meta << "\nPayload " << (output_q_ ? "q" : "pb") << '\n'; + } +} + +void ModelBuffer::Source(util::stream::Chains &chains) { + assert(chains.size() <= files_.size()); + for (unsigned int i = 0; i < chains.size(); ++i) { + chains[i].SetProgressTarget(util::SizeOrThrow(files_[i].get())); + chains[i] >> util::stream::PRead(files_[i].get()); + } +} + +void ModelBuffer::Source(std::size_t order_minus_1, util::stream::Chain &chain) { + chain >> util::stream::PRead(files_[order_minus_1].get()); +} + +float ModelBuffer::SlowQuery(const ngram::State &context, WordIndex word, ngram::State &out) const { + // Lookup unigram. + ProbBackoff value; + util::ErsatzPRead(RawFile(0), &value, sizeof(value), word * (sizeof(WordIndex) + sizeof(value)) + sizeof(WordIndex)); + out.backoff[0] = value.backoff; + out.words[0] = word; + out.length = 1; + + std::vector buffer(context.length + 1), query(context.length + 1); + std::reverse_copy(context.words, context.words + context.length, query.begin()); + query[context.length] = word; + + for (std::size_t order = 2; order <= query.size() && order <= context.length + 1; ++order) { + SuffixOrder less(order); + const WordIndex *key = &*query.end() - order; + int file = RawFile(order - 1); + std::size_t length = order * sizeof(WordIndex) + sizeof(ProbBackoff); + // TODO: cache file size? + uint64_t begin = 0, end = util::SizeOrThrow(file) / length; + while (true) { + if (end <= begin) { + // Did not find for order. + return std::accumulate(context.backoff + out.length - 1, context.backoff + context.length, value.prob); + } + uint64_t test = begin + (end - begin) / 2; + util::ErsatzPRead(file, &*buffer.begin(), sizeof(WordIndex) * order, test * length); + + if (less(&*buffer.begin(), key)) { + begin = test + 1; + } else if (less(key, &*buffer.begin())) { + end = test; + } else { + // Found it. + util::ErsatzPRead(file, &value, sizeof(value), test * length + sizeof(WordIndex) * order); + if (order != Order()) { + out.length = order; + out.backoff[order - 1] = value.backoff; + out.words[order - 1] = *key; + } + break; + } + } + } + return value.prob; +} + +} // namespace diff --git a/ctcdecode-master/third_party/kenlm/lm/common/model_buffer.hh b/ctcdecode-master/third_party/kenlm/lm/common/model_buffer.hh new file mode 100644 index 0000000..f7674af --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/model_buffer.hh @@ -0,0 +1,74 @@ +#ifndef LM_COMMON_MODEL_BUFFER_H +#define LM_COMMON_MODEL_BUFFER_H + +/* Format with separate files in suffix order. Each file contains + * n-grams of the same order. + */ +#include "lm/word_index.hh" +#include "util/file.hh" +#include "util/fixed_array.hh" +#include "util/string_piece.hh" + +#include +#include + +namespace util { namespace stream { +class Chains; +class Chain; +}} // namespaces + +namespace lm { + +namespace ngram { class State; } + +class ModelBuffer { + public: + // Construct for writing. Must call VocabFile() and fill it with null-delimited vocab words. + ModelBuffer(StringPiece file_base, bool keep_buffer, bool output_q); + + // Load from file. + explicit ModelBuffer(StringPiece file_base); + + // Must call VocabFile and populate before calling this function. + void Sink(util::stream::Chains &chains, const std::vector &counts); + + // Read files and write to the given chains. If fewer chains are provided, + // only do the lower orders. + void Source(util::stream::Chains &chains); + + void Source(std::size_t order_minus_1, util::stream::Chain &chain); + + // The order of the n-gram model that is associated with the model buffer. + std::size_t Order() const { return counts_.size(); } + // Requires Sink or load from file. + const std::vector &Counts() const { + assert(!counts_.empty()); + return counts_; + } + + int VocabFile() const { return vocab_file_.get(); } + + int RawFile(std::size_t order_minus_1) const { + return files_[order_minus_1].get(); + } + + bool Keep() const { return keep_buffer_; } + + // Slowly execute a language model query with binary search. + // This is used by interpolation to gather tuning probabilities rather than + // scanning the files. + float SlowQuery(const ngram::State &context, WordIndex word, ngram::State &out) const; + + private: + const std::string file_base_; + const bool keep_buffer_; + bool output_q_; + std::vector counts_; + + util::scoped_fd vocab_file_; + util::FixedArray files_; +}; + +} // namespace lm + +#endif // LM_COMMON_MODEL_BUFFER_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/model_buffer_test.cc b/ctcdecode-master/third_party/kenlm/lm/common/model_buffer_test.cc new file mode 100644 index 0000000..7a9f263 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/model_buffer_test.cc @@ -0,0 +1,44 @@ +#include "lm/common/model_buffer.hh" +#include "lm/model.hh" +#include "lm/state.hh" + +#define BOOST_TEST_MODULE ModelBufferTest +#include + +namespace lm { namespace { + +BOOST_AUTO_TEST_CASE(Query) { + std::string dir("test_data/"); + if (boost::unit_test::framework::master_test_suite().argc == 2) { + dir = boost::unit_test::framework::master_test_suite().argv[1]; + } + ngram::Model ref((dir + "/toy0.arpa").c_str()); + ModelBuffer test(dir + "/toy0"); + ngram::State ref_state, test_state; + WordIndex a = ref.GetVocabulary().Index("a"); + BOOST_CHECK_CLOSE( + ref.FullScore(ref.BeginSentenceState(), a, ref_state).prob, + test.SlowQuery(ref.BeginSentenceState(), a, test_state), + 0.001); + BOOST_CHECK_EQUAL((unsigned)ref_state.length, (unsigned)test_state.length); + BOOST_CHECK_EQUAL(ref_state.words[0], test_state.words[0]); + BOOST_CHECK_EQUAL(ref_state.backoff[0], test_state.backoff[0]); + BOOST_CHECK(ref_state == test_state); + + ngram::State ref_state2, test_state2; + WordIndex b = ref.GetVocabulary().Index("b"); + BOOST_CHECK_CLOSE( + ref.FullScore(ref_state, b, ref_state2).prob, + test.SlowQuery(test_state, b, test_state2), + 0.001); + BOOST_CHECK(ref_state2 == test_state2); + BOOST_CHECK_EQUAL(ref_state2.backoff[0], test_state2.backoff[0]); + + BOOST_CHECK_CLOSE( + ref.FullScore(ref_state2, 0, ref_state).prob, + test.SlowQuery(test_state2, 0, test_state), + 0.001); + // The reference does state minimization but this doesn't. +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/common/ngram.hh b/ctcdecode-master/third_party/kenlm/lm/common/ngram.hh new file mode 100644 index 0000000..7a6d1c3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/ngram.hh @@ -0,0 +1,77 @@ +#ifndef LM_COMMON_NGRAM_H +#define LM_COMMON_NGRAM_H + +#include "lm/weights.hh" +#include "lm/word_index.hh" + +#include +#include +#include +#include + +namespace lm { + +class NGramHeader { + public: + NGramHeader(void *begin, std::size_t order) + : begin_(static_cast(begin)), end_(begin_ + order) {} + + NGramHeader() : begin_(NULL), end_(NULL) {} + + const uint8_t *Base() const { return reinterpret_cast(begin_); } + uint8_t *Base() { return reinterpret_cast(begin_); } + + void ReBase(void *to) { + std::size_t difference = end_ - begin_; + begin_ = reinterpret_cast(to); + end_ = begin_ + difference; + } + + // These are for the vocab index. + // Lower-case in deference to STL. + const WordIndex *begin() const { return begin_; } + WordIndex *begin() { return begin_; } + const WordIndex *end() const { return end_; } + WordIndex *end() { return end_; } + + std::size_t size() const { return end_ - begin_; } + std::size_t Order() const { return end_ - begin_; } + + private: + WordIndex *begin_, *end_; +}; + +template class NGram : public NGramHeader { + public: + typedef PayloadT Payload; + + NGram() : NGramHeader(NULL, 0) {} + + NGram(void *begin, std::size_t order) : NGramHeader(begin, order) {} + + // Would do operator++ but that can get confusing for a stream. + void NextInMemory() { + ReBase(&Value() + 1); + } + + static std::size_t TotalSize(std::size_t order) { + return order * sizeof(WordIndex) + sizeof(Payload); + } + std::size_t TotalSize() const { + // Compiler should optimize this. + return TotalSize(Order()); + } + + static std::size_t OrderFromSize(std::size_t size) { + std::size_t ret = (size - sizeof(Payload)) / sizeof(WordIndex); + assert(size == TotalSize(ret)); + return ret; + } + + const Payload &Value() const { return *reinterpret_cast(end()); } + Payload &Value() { return *reinterpret_cast(end()); } +}; + +} // namespace lm + +#endif // LM_COMMON_NGRAM_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/ngram_stream.hh b/ctcdecode-master/third_party/kenlm/lm/common/ngram_stream.hh new file mode 100644 index 0000000..8bdf36e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/ngram_stream.hh @@ -0,0 +1,65 @@ +#ifndef LM_BUILDER_NGRAM_STREAM_H +#define LM_BUILDER_NGRAM_STREAM_H + +#include "lm/common/ngram.hh" +#include "util/stream/chain.hh" +#include "util/stream/multi_stream.hh" +#include "util/stream/stream.hh" + +#include + +namespace lm { + +template class ProxyStream { + public: + // Make an invalid stream. + ProxyStream() {} + + explicit ProxyStream(const util::stream::ChainPosition &position, const Proxy &proxy = Proxy()) + : proxy_(proxy), stream_(position) { + proxy_.ReBase(stream_.Get()); + } + + Proxy &operator*() { return proxy_; } + const Proxy &operator*() const { return proxy_; } + + Proxy *operator->() { return &proxy_; } + const Proxy *operator->() const { return &proxy_; } + + void *Get() { return stream_.Get(); } + const void *Get() const { return stream_.Get(); } + + operator bool() const { return stream_; } + bool operator!() const { return !stream_; } + void Poison() { stream_.Poison(); } + + ProxyStream &operator++() { + ++stream_; + proxy_.ReBase(stream_.Get()); + return *this; + } + + private: + Proxy proxy_; + util::stream::Stream stream_; +}; + +template class NGramStream : public ProxyStream > { + public: + // Make an invalid stream. + NGramStream() {} + + explicit NGramStream(const util::stream::ChainPosition &position) : + ProxyStream >(position, NGram(NULL, NGram::OrderFromSize(position.GetChain().EntrySize()))) {} +}; + +template class NGramStreams : public util::stream::GenericStreams > { + private: + typedef util::stream::GenericStreams > P; + public: + NGramStreams() : P() {} + NGramStreams(const util::stream::ChainPositions &positions) : P(positions) {} +}; + +} // namespace +#endif // LM_BUILDER_NGRAM_STREAM_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/print.cc b/ctcdecode-master/third_party/kenlm/lm/common/print.cc new file mode 100644 index 0000000..518b62f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/print.cc @@ -0,0 +1,62 @@ +#include "lm/common/print.hh" + +#include "lm/common/ngram_stream.hh" +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/mmap.hh" +#include "util/scoped.hh" + +#include +#include + +namespace lm { + +VocabReconstitute::VocabReconstitute(int fd) { + uint64_t size = util::SizeOrThrow(fd); + util::MapRead(util::POPULATE_OR_READ, fd, 0, size, memory_); + const char *const start = static_cast(memory_.get()); + const char *i; + for (i = start; i != start + size; i += strlen(i) + 1) { + map_.push_back(i); + } + // Last one for LookupPiece. + map_.push_back(i); +} + +namespace { +template void PrintLead(const VocabReconstitute &vocab, ProxyStream &stream, util::FileStream &out) { + out << stream->Value().prob << '\t' << vocab.Lookup(*stream->begin()); + for (const WordIndex *i = stream->begin() + 1; i != stream->end(); ++i) { + out << ' ' << vocab.Lookup(*i); + } +} +} // namespace + +void PrintARPA::Run(const util::stream::ChainPositions &positions) { + VocabReconstitute vocab(vocab_fd_); + util::FileStream out(out_fd_); + out << "\\data\\\n"; + for (size_t i = 0; i < positions.size(); ++i) { + out << "ngram " << (i+1) << '=' << counts_[i] << '\n'; + } + out << '\n'; + + for (unsigned order = 1; order < positions.size(); ++order) { + out << "\\" << order << "-grams:" << '\n'; + for (ProxyStream > stream(positions[order - 1], NGram(NULL, order)); stream; ++stream) { + PrintLead(vocab, stream, out); + out << '\t' << stream->Value().backoff << '\n'; + } + out << '\n'; + } + + out << "\\" << positions.size() << "-grams:" << '\n'; + for (ProxyStream > stream(positions.back(), NGram(NULL, positions.size())); stream; ++stream) { + PrintLead(vocab, stream, out); + out << '\n'; + } + out << '\n'; + out << "\\end\\\n"; +} + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/common/print.hh b/ctcdecode-master/third_party/kenlm/lm/common/print.hh new file mode 100644 index 0000000..6aa08b3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/print.hh @@ -0,0 +1,58 @@ +#ifndef LM_COMMON_PRINT_H +#define LM_COMMON_PRINT_H + +#include "lm/word_index.hh" +#include "util/mmap.hh" +#include "util/string_piece.hh" + +#include +#include + +namespace util { namespace stream { class ChainPositions; }} + +// Warning: PrintARPA routines read all unigrams before all bigrams before all +// trigrams etc. So if other parts of the chain move jointly, you'll have to +// buffer. + +namespace lm { + +class VocabReconstitute { + public: + // fd must be alive for life of this object; does not take ownership. + explicit VocabReconstitute(int fd); + + const char *Lookup(WordIndex index) const { + assert(index < map_.size() - 1); + return map_[index]; + } + + StringPiece LookupPiece(WordIndex index) const { + return StringPiece(map_[index], map_[index + 1] - 1 - map_[index]); + } + + std::size_t Size() const { + // There's an extra entry to support StringPiece lengths. + return map_.size() - 1; + } + + private: + util::scoped_memory memory_; + std::vector map_; +}; + +class PrintARPA { + public: + // Does not take ownership of vocab_fd or out_fd. + explicit PrintARPA(int vocab_fd, int out_fd, const std::vector &counts) + : vocab_fd_(vocab_fd), out_fd_(out_fd), counts_(counts) {} + + void Run(const util::stream::ChainPositions &positions); + + private: + int vocab_fd_; + int out_fd_; + std::vector counts_; +}; + +} // namespace lm +#endif // LM_COMMON_PRINT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/renumber.cc b/ctcdecode-master/third_party/kenlm/lm/common/renumber.cc new file mode 100644 index 0000000..0632a14 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/renumber.cc @@ -0,0 +1,17 @@ +#include "lm/common/renumber.hh" +#include "lm/common/ngram.hh" + +#include "util/stream/stream.hh" + +namespace lm { + +void Renumber::Run(const util::stream::ChainPosition &position) { + for (util::stream::Stream stream(position); stream; ++stream) { + NGramHeader gram(stream.Get(), order_); + for (WordIndex *w = gram.begin(); w != gram.end(); ++w) { + *w = new_numbers_[*w]; + } + } +} + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/common/renumber.hh b/ctcdecode-master/third_party/kenlm/lm/common/renumber.hh new file mode 100644 index 0000000..ca25c4d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/renumber.hh @@ -0,0 +1,30 @@ +/* Map vocab ids. This is useful to merge independently collected counts or + * change the vocab ids to the order used by the trie. + */ +#ifndef LM_COMMON_RENUMBER_H +#define LM_COMMON_RENUMBER_H + +#include "lm/word_index.hh" + +#include + +namespace util { namespace stream { class ChainPosition; }} + +namespace lm { + +class Renumber { + public: + // Assumes the array is large enough to map all words and stays alive while + // the thread is active. + Renumber(const WordIndex *new_numbers, std::size_t order) + : new_numbers_(new_numbers), order_(order) {} + + void Run(const util::stream::ChainPosition &position); + + private: + const WordIndex *new_numbers_; + std::size_t order_; +}; + +} // namespace lm +#endif // LM_COMMON_RENUMBER_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/size_option.cc b/ctcdecode-master/third_party/kenlm/lm/common/size_option.cc new file mode 100644 index 0000000..46a920e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/size_option.cc @@ -0,0 +1,24 @@ +#include +#include "util/usage.hh" + +namespace lm { + +namespace { +class SizeNotify { + public: + explicit SizeNotify(std::size_t &out) : behind_(out) {} + + void operator()(const std::string &from) { + behind_ = util::ParseSize(from); + } + + private: + std::size_t &behind_; +}; +} + +boost::program_options::typed_value *SizeOption(std::size_t &to, const char *default_value) { + return boost::program_options::value()->notifier(SizeNotify(to))->default_value(default_value); +} + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/common/size_option.hh b/ctcdecode-master/third_party/kenlm/lm/common/size_option.hh new file mode 100644 index 0000000..d3b8e33 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/size_option.hh @@ -0,0 +1,11 @@ +#include + +#include +#include + +namespace lm { + +// Create a boost program option for data sizes. This parses sizes like 1T and 10k. +boost::program_options::typed_value *SizeOption(std::size_t &to, const char *default_value); + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/common/special.hh b/ctcdecode-master/third_party/kenlm/lm/common/special.hh new file mode 100644 index 0000000..0677cd7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/special.hh @@ -0,0 +1,27 @@ +#ifndef LM_COMMON_SPECIAL_H +#define LM_COMMON_SPECIAL_H + +#include "lm/word_index.hh" + +namespace lm { + +class SpecialVocab { + public: + SpecialVocab(WordIndex bos, WordIndex eos) : bos_(bos), eos_(eos) {} + + bool IsSpecial(WordIndex word) const { + return word == kUNK || word == bos_ || word == eos_; + } + + WordIndex UNK() const { return kUNK; } + WordIndex BOS() const { return bos_; } + WordIndex EOS() const { return eos_; } + + private: + WordIndex bos_; + WordIndex eos_; +}; + +} // namespace lm + +#endif // LM_COMMON_SPECIAL_H diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/generate.sh b/ctcdecode-master/third_party/kenlm/lm/common/test_data/generate.sh new file mode 100644 index 0000000..d725572 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/test_data/generate.sh @@ -0,0 +1,9 @@ +#!/bin/bash +../../../bin/lmplz --discount_fallback -o 3 -S 100M --intermediate toy0 --arpa toy0.arpa <OlHnY75I}qUdD?%DpPP4i 0 +0 -0.30103 +-0.46943438 a -0.30103 +-0.5720968 0 +-0.5720968 b -0.30103 + +\2-grams: +-0.37712017 a -0.30103 +-0.37712017 a a -0.30103 +-0.2984526 b a -0.30103 +-0.58682007 a 0 +-0.52201796 b 0 +-0.41574955 b -0.30103 +-0.58682007 a b -0.30103 + +\3-grams: +-0.14885087 a a +-0.33741078 b a a +-0.124077894 b a +-0.2997394 a b a +-0.42082912 b a +-0.397617 a b +-0.20102891 a a b + +\end\ diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy0.kenlm_intermediate b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy0.kenlm_intermediate new file mode 100644 index 0000000..8513475 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy0.kenlm_intermediate @@ -0,0 +1,3 @@ +KenLM intermediate binary file +Counts 5 7 7 +Payload pb diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy0.vocab b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy0.vocab new file mode 100644 index 0000000000000000000000000000000000000000..520c0f95676ae50596a7f23ff729403d0f339e6a GIT binary patch literal 19 YcmcC1&C9l9uqn1h($ literal 0 HcmV?d00001 diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.1 b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.1 new file mode 100644 index 0000000000000000000000000000000000000000..a50cec66785a9cb38b49cfedf918c223d313908c GIT binary patch literal 72 wcmZQz0D*@6Ad(S80Ksg9S^JoP^wLP#{UA0olns(&0b&JV7qB=hR2-xR0M-f+UjP6A literal 0 HcmV?d00001 diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.2 b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.2 new file mode 100644 index 0000000000000000000000000000000000000000..58d28a0166601e58667da6a4f7f8cfd1625486be GIT binary patch literal 112 zcmZQ%U|?VZVn53}`(`W5+6Q7I^I3p=W+1ko7`zWiF|b1UH=?=rgZPX<3{oSu# UNDa(95F3Qwn62Mu 0 +0 -0.30103 +-0.6146491 a -0.30103 +-0.6146491 0 +-0.7659168 c -0.30103 +-0.6146491 b -0.30103 + +\2-grams: +-0.4301247 a -0.30103 +-0.4301247 a a -0.30103 +-0.20660876 c 0 +-0.5404639 b 0 +-0.4740302 c -0.30103 +-0.4301247 a b -0.30103 +-0.3422159 b b -0.47712123 + +\3-grams: +-0.1638568 a a +-0.09113217 c +-0.7462621 b b +-0.1638568 a a b +-0.13823806 a b b +-0.13375957 b b b + +\end\ diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.kenlm_intermediate b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.kenlm_intermediate new file mode 100644 index 0000000..fe82667 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.kenlm_intermediate @@ -0,0 +1,3 @@ +KenLM intermediate binary file +Counts 6 7 6 +Payload pb diff --git a/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.vocab b/ctcdecode-master/third_party/kenlm/lm/common/test_data/toy1.vocab new file mode 100644 index 0000000000000000000000000000000000000000..763b2af9ca03faf7773505a784d4216714660989 GIT binary patch literal 21 acmcC1&C9l9uqn1 + +namespace lm { +namespace ngram { + +Config::Config() : + show_progress(true), + messages(&std::cerr), + enumerate_vocab(NULL), + unknown_missing(COMPLAIN), + sentence_marker_missing(THROW_UP), + positive_log_probability(THROW_UP), + unknown_missing_logprob(-100.0), + probing_multiplier(1.5), + building_memory(1073741824ULL), // 1 GB + temporary_directory_prefix(""), + arpa_complain(ALL), + write_mmap(NULL), + write_method(WRITE_AFTER), + include_vocab(true), + rest_function(REST_MAX), + prob_bits(8), + backoff_bits(8), + pointer_bhiksha_bits(22), + load_method(util::POPULATE_OR_READ) {} + +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/config.hh b/ctcdecode-master/third_party/kenlm/lm/config.hh new file mode 100644 index 0000000..21b9e7e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/config.hh @@ -0,0 +1,124 @@ +#ifndef LM_CONFIG_H +#define LM_CONFIG_H + +#include "lm/lm_exception.hh" +#include "util/mmap.hh" + +#include +#include +#include + +/* Configuration for ngram model. Separate header to reduce pollution. */ + +namespace lm { + +class EnumerateVocab; + +namespace ngram { + +struct Config { + // EFFECTIVE FOR BOTH ARPA AND BINARY READS + + // (default true) print progress bar to messages + bool show_progress; + + // Where to log messages including the progress bar. Set to NULL for + // silence. + std::ostream *messages; + + std::ostream *ProgressMessages() const { + return show_progress ? messages : 0; + } + + // This will be called with every string in the vocabulary by the + // constructor; it need only exist for the lifetime of the constructor. + // See enumerate_vocab.hh for more detail. Config does not take ownership; + // just delete/let it go out of scope after the constructor exits. + EnumerateVocab *enumerate_vocab; + + + // ONLY EFFECTIVE WHEN READING ARPA + + // What to do when isn't in the provided model. + WarningAction unknown_missing; + // What to do when or is missing from the model. + // If THROW_UP, the exception will be of type util::SpecialWordMissingException. + WarningAction sentence_marker_missing; + + // What to do with a positive log probability. For COMPLAIN and SILENT, map + // to 0. + WarningAction positive_log_probability; + + // The probability to substitute for if it's missing from the model. + // No effect if the model has or unknown_missing == THROW_UP. + float unknown_missing_logprob; + + // Size multiplier for probing hash table. Must be > 1. Space is linear in + // this. Time is probing_multiplier / (probing_multiplier - 1). No effect + // for sorted variant. + // If you find yourself setting this to a low number, consider using the + // TrieModel which has lower memory consumption. + float probing_multiplier; + + // Amount of memory to use for building. The actual memory usage will be + // higher since this just sets sort buffer size. Only applies to trie + // models. + std::size_t building_memory; + + // Template for temporary directory appropriate for passing to mkdtemp. + // The characters XXXXXX are appended before passing to mkdtemp. Only + // applies to trie. If empty, defaults to write_mmap. If that's NULL, + // defaults to input file name. + std::string temporary_directory_prefix; + + // Level of complaining to do when loading from ARPA instead of binary format. + enum ARPALoadComplain {ALL, EXPENSIVE, NONE}; + ARPALoadComplain arpa_complain; + + // While loading an ARPA file, also write out this binary format file. Set + // to NULL to disable. + const char *write_mmap; + + enum WriteMethod { + WRITE_MMAP, // Map the file directly. + WRITE_AFTER // Write after we're done. + }; + WriteMethod write_method; + + // Include the vocab in the binary file? Only effective if write_mmap != NULL. + bool include_vocab; + + + // Left rest options. Only used when the model includes rest costs. + enum RestFunction { + REST_MAX, // Maximum of any score to the left + REST_LOWER, // Use lower-order files given below. + }; + RestFunction rest_function; + // Only used for REST_LOWER. + std::vector rest_lower_files; + + + // Quantization options. Only effective for QuantTrieModel. One value is + // reserved for each of prob and backoff, so 2^bits - 1 buckets will be used + // to quantize (and one of the remaining backoffs will be 0). + uint8_t prob_bits, backoff_bits; + + // Bhiksha compression (simple form). Only works with trie. + uint8_t pointer_bhiksha_bits; + + + // ONLY EFFECTIVE WHEN READING BINARY + + // How to get the giant array into memory: lazy mmap, populate, read etc. + // See util/mmap.hh for details of MapMethod. + util::LoadMethod load_method; + + + // Set defaults. + Config(); +}; + +} /* namespace ngram */ } /* namespace lm */ + +#endif // LM_CONFIG_H diff --git a/ctcdecode-master/third_party/kenlm/lm/enumerate_vocab.hh b/ctcdecode-master/third_party/kenlm/lm/enumerate_vocab.hh new file mode 100644 index 0000000..f4c94cd --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/enumerate_vocab.hh @@ -0,0 +1,28 @@ +#ifndef LM_ENUMERATE_VOCAB_H +#define LM_ENUMERATE_VOCAB_H + +#include "lm/word_index.hh" +#include "util/string_piece.hh" + +namespace lm { + +/* If you need the actual strings in the vocabulary, inherit from this class + * and implement Add. Then put a pointer in Config.enumerate_vocab; it does + * not take ownership. Add is called once per vocab word. index starts at 0 + * and increases by 1 each time. This is only used by the Model constructor; + * the pointer is not retained by the class. + */ +class EnumerateVocab { + public: + virtual ~EnumerateVocab() {} + + virtual void Add(WordIndex index, const StringPiece &str) = 0; + + protected: + EnumerateVocab() {} +}; + +} // namespace lm + +#endif // LM_ENUMERATE_VOCAB_H + diff --git a/ctcdecode-master/third_party/kenlm/lm/facade.hh b/ctcdecode-master/third_party/kenlm/lm/facade.hh new file mode 100644 index 0000000..325ef15 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/facade.hh @@ -0,0 +1,73 @@ +#ifndef LM_FACADE_H +#define LM_FACADE_H + +#include "lm/virtual_interface.hh" +#include "util/string_piece.hh" + +#include + +namespace lm { +namespace base { + +// Common model interface that depends on knowing the specific classes. +// Curiously recurring template pattern. +template class ModelFacade : public Model { + public: + typedef StateT State; + typedef VocabularyT Vocabulary; + + /* Translate from void* to State */ + FullScoreReturn BaseFullScore(const void *in_state, const WordIndex new_word, void *out_state) const { + return static_cast(this)->FullScore( + *reinterpret_cast(in_state), + new_word, + *reinterpret_cast(out_state)); + } + + FullScoreReturn BaseFullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, void *out_state) const { + return static_cast(this)->FullScoreForgotState( + context_rbegin, + context_rend, + new_word, + *reinterpret_cast(out_state)); + } + + // Default Score function calls FullScore. Model can override this. + float Score(const State &in_state, const WordIndex new_word, State &out_state) const { + return static_cast(this)->FullScore(in_state, new_word, out_state).prob; + } + + float BaseScore(const void *in_state, const WordIndex new_word, void *out_state) const { + return static_cast(this)->Score( + *reinterpret_cast(in_state), + new_word, + *reinterpret_cast(out_state)); + } + + const State &BeginSentenceState() const { return begin_sentence_; } + const State &NullContextState() const { return null_context_; } + const Vocabulary &GetVocabulary() const { return *static_cast(&BaseVocabulary()); } + + protected: + ModelFacade() : Model(sizeof(State)) {} + + virtual ~ModelFacade() {} + + // begin_sentence and null_context can disappear after. vocab should stay. + void Init(const State &begin_sentence, const State &null_context, const Vocabulary &vocab, unsigned char order) { + begin_sentence_ = begin_sentence; + null_context_ = null_context; + begin_sentence_memory_ = &begin_sentence_; + null_context_memory_ = &null_context_; + base_vocab_ = &vocab; + order_ = order; + } + + private: + State begin_sentence_, null_context_; +}; + +} // mamespace base +} // namespace lm + +#endif // LM_FACADE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/lm/filter/CMakeLists.txt new file mode 100644 index 0000000..7e5f2d1 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/CMakeLists.txt @@ -0,0 +1,33 @@ +# This CMake file was created by Lane Schwartz + +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +# +# In order to set correct paths to these files +# in case this variable is referenced by CMake files in the parent directory, +# we prefix all files with ${CMAKE_CURRENT_SOURCE_DIR}. +# +set(KENLM_FILTER_SOURCE + ${CMAKE_CURRENT_SOURCE_DIR}/arpa_io.cc + ${CMAKE_CURRENT_SOURCE_DIR}/phrase.cc + ${CMAKE_CURRENT_SOURCE_DIR}/vocab.cc + ) + +# Group these objects together for later use. +# +# Given add_library(foo OBJECT ${my_foo_sources}), +# refer to these objects as $ +# +add_library(kenlm_filter ${KENLM_FILTER_SOURCE}) + +if (NOT MSVC) + set(THREADS pthread) +endif() + +AddExes(EXES filter phrase_table_vocab + LIBRARIES kenlm_filter kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) + diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/arpa_io.cc b/ctcdecode-master/third_party/kenlm/lm/filter/arpa_io.cc new file mode 100644 index 0000000..54c1fcc --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/arpa_io.cc @@ -0,0 +1,77 @@ +#include "lm/filter/arpa_io.hh" +#include "util/file_piece.hh" +#include "util/string_stream.hh" + +#include +#include +#include +#include + +#include +#include +#include + +namespace lm { + +ARPAInputException::ARPAInputException(const StringPiece &message) throw() { + *this << message; +} + +ARPAInputException::ARPAInputException(const StringPiece &message, const StringPiece &line) throw() { + *this << message << " in line " << line; +} + +ARPAInputException::~ARPAInputException() throw() {} + +// Seeking is the responsibility of the caller. +template void WriteCounts(Stream &out, const std::vector &number) { + out << "\n\\data\\\n"; + for (unsigned int i = 0; i < number.size(); ++i) { + out << "ngram " << i+1 << "=" << number[i] << '\n'; + } + out << '\n'; +} + +size_t SizeNeededForCounts(const std::vector &number) { + util::StringStream stream; + WriteCounts(stream, number); + return stream.str().size(); +} + +bool IsEntirelyWhiteSpace(const StringPiece &line) { + for (size_t i = 0; i < static_cast(line.size()); ++i) { + if (!isspace(line.data()[i])) return false; + } + return true; +} + +ARPAOutput::ARPAOutput(const char *name, size_t buffer_size) + : file_backing_(util::CreateOrThrow(name)), file_(file_backing_.get(), buffer_size) {} + +void ARPAOutput::ReserveForCounts(std::streampos reserve) { + for (std::streampos i = 0; i < reserve; i += std::streampos(1)) { + file_ << '\n'; + } +} + +void ARPAOutput::BeginLength(unsigned int length) { + file_ << '\\' << length << "-grams:" << '\n'; + fast_counter_ = 0; +} + +void ARPAOutput::EndLength(unsigned int length) { + file_ << '\n'; + if (length > counts_.size()) { + counts_.resize(length); + } + counts_[length - 1] = fast_counter_; +} + +void ARPAOutput::Finish() { + file_ << "\\end\\\n"; + file_.seekp(0); + WriteCounts(file_, counts_); + file_.flush(); +} + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/arpa_io.hh b/ctcdecode-master/third_party/kenlm/lm/filter/arpa_io.hh new file mode 100644 index 0000000..4756af5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/arpa_io.hh @@ -0,0 +1,99 @@ +#ifndef LM_FILTER_ARPA_IO_H +#define LM_FILTER_ARPA_IO_H +/* Input and output for ARPA format language model files. + */ +#include "lm/read_arpa.hh" +#include "util/exception.hh" +#include "util/file_stream.hh" +#include "util/string_piece.hh" +#include "util/tokenize_piece.hh" + +#include +#include + +#include +#include +#include + +#include +#include + +namespace util { class FilePiece; } + +namespace lm { + +class ARPAInputException : public util::Exception { + public: + explicit ARPAInputException(const StringPiece &message) throw(); + explicit ARPAInputException(const StringPiece &message, const StringPiece &line) throw(); + virtual ~ARPAInputException() throw(); +}; + +// Handling for the counts of n-grams at the beginning of ARPA files. +size_t SizeNeededForCounts(const std::vector &number); + +/* Writes an ARPA file. This has to be seekable so the counts can be written + * at the end. Hence, I just have it own a std::fstream instead of accepting + * a separately held std::ostream. TODO: use the fast one from estimation. + */ +class ARPAOutput : boost::noncopyable { + public: + explicit ARPAOutput(const char *name, size_t buffer_size = 65536); + + void ReserveForCounts(std::streampos reserve); + + void BeginLength(unsigned int length); + + void AddNGram(const StringPiece &line) { + file_ << line << '\n'; + ++fast_counter_; + } + + void AddNGram(const StringPiece &ngram, const StringPiece &line) { + AddNGram(line); + } + + template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line) { + AddNGram(line); + } + + void EndLength(unsigned int length); + + void Finish(); + + private: + util::scoped_fd file_backing_; + util::FileStream file_; + uint64_t fast_counter_; + std::vector counts_; +}; + + +template void ReadNGrams(util::FilePiece &in, unsigned int length, uint64_t number, Output &out) { + ReadNGramHeader(in, length); + out.BeginLength(length); + for (uint64_t i = 0; i < number; ++i) { + StringPiece line = in.ReadLine(); + util::TokenIter tabber(line, '\t'); + if (!tabber) throw ARPAInputException("blank line", line); + if (!++tabber) throw ARPAInputException("no tab", line); + + out.AddNGram(*tabber, line); + } + out.EndLength(length); +} + +template void ReadARPA(util::FilePiece &in_lm, Output &out) { + std::vector number; + ReadARPACounts(in_lm, number); + out.ReserveForCounts(SizeNeededForCounts(number)); + for (unsigned int i = 0; i < number.size(); ++i) { + ReadNGrams(in_lm, i + 1, number[i], out); + } + ReadEnd(in_lm); + out.Finish(); +} + +} // namespace lm + +#endif // LM_FILTER_ARPA_IO_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/count_io.hh b/ctcdecode-master/third_party/kenlm/lm/filter/count_io.hh new file mode 100644 index 0000000..1af6676 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/count_io.hh @@ -0,0 +1,89 @@ +#ifndef LM_FILTER_COUNT_IO_H +#define LM_FILTER_COUNT_IO_H + +#include +#include +#include + +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/file_piece.hh" + +namespace lm { + +class CountOutput : boost::noncopyable { + public: + explicit CountOutput(const char *name) : file_(util::CreateOrThrow(name)) {} + + void AddNGram(const StringPiece &line) { + file_ << line << '\n'; + } + + template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line) { + AddNGram(line); + } + + void AddNGram(const StringPiece &ngram, const StringPiece &line) { + AddNGram(line); + } + + private: + util::FileStream file_; +}; + +class CountBatch { + public: + explicit CountBatch(std::streamsize initial_read) + : initial_read_(initial_read) { + buffer_.reserve(initial_read); + } + + void Read(std::istream &in) { + buffer_.resize(initial_read_); + in.read(&*buffer_.begin(), initial_read_); + buffer_.resize(in.gcount()); + char got; + while (in.get(got) && got != '\n') + buffer_.push_back(got); + } + + template void Send(Output &out) { + for (util::TokenIter line(StringPiece(&*buffer_.begin(), buffer_.size()), '\n'); line; ++line) { + util::TokenIter tabber(*line, '\t'); + if (!tabber) { + std::cerr << "Warning: empty n-gram count line being removed\n"; + continue; + } + util::TokenIter words(*tabber, ' '); + if (!words) { + std::cerr << "Line has a tab but no words.\n"; + continue; + } + out.AddNGram(words, util::TokenIter::end(), *line); + } + } + + private: + std::streamsize initial_read_; + + // This could have been a std::string but that's less happy with raw writes. + std::vector buffer_; +}; + +template void ReadCount(util::FilePiece &in_file, Output &out) { + try { + while (true) { + StringPiece line = in_file.ReadLine(); + util::TokenIter tabber(line, '\t'); + if (!tabber) { + std::cerr << "Warning: empty n-gram count line being removed\n"; + continue; + } + out.AddNGram(*tabber, line); + } + } catch (const util::EndOfFileException &e) {} +} + +} // namespace lm + +#endif // LM_FILTER_COUNT_IO_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/filter_main.cc b/ctcdecode-master/third_party/kenlm/lm/filter/filter_main.cc new file mode 100644 index 0000000..6e89d1f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/filter_main.cc @@ -0,0 +1,253 @@ +#include "lm/filter/arpa_io.hh" +#include "lm/filter/format.hh" +#include "lm/filter/phrase.hh" +#ifndef NTHREAD +#include "lm/filter/thread.hh" +#endif +#include "lm/filter/vocab.hh" +#include "lm/filter/wrapper.hh" +#include "util/exception.hh" +#include "util/file_piece.hh" + +#include + +#include +#include +#include +#include + +namespace lm { +namespace { + +void DisplayHelp(const char *name) { + std::cerr + << "Usage: " << name << " mode [context] [phrase] [raw|arpa] [threads:m] [batch_size:m] (vocab|model):input_file output_file\n\n" + "copy mode just copies, but makes the format nicer for e.g. irstlm's broken\n" + " parser.\n" + "single mode treats the entire input as a single sentence.\n" + "multiple mode filters to multiple sentences in parallel. Each sentence is on\n" + " a separate line. A separate file is created for each sentence by appending\n" + " the 0-indexed line number to the output file name.\n" + "union mode produces one filtered model that is the union of models created by\n" + " multiple mode.\n\n" + "context means only the context (all but last word) has to pass the filter, but\n" + " the entire n-gram is output.\n\n" + "phrase means that the vocabulary is actually tab-delimited phrases and that the\n" + " phrases can generate the n-gram when assembled in arbitrary order and\n" + " clipped. Currently works with multiple or union mode.\n\n" + "The file format is set by [raw|arpa] with default arpa:\n" + "raw means space-separated tokens, optionally followed by a tab and arbitrary\n" + " text. This is useful for ngram count files.\n" + "arpa means the ARPA file format for n-gram language models.\n\n" +#ifndef NTHREAD + "threads:m sets m threads (default: conccurrency detected by boost)\n" + "batch_size:m sets the batch size for threading. Expect memory usage from this\n" + " of 2*threads*batch_size n-grams.\n\n" +#else + "This binary was compiled with -DNTHREAD, disabling threading. If you wanted\n" + " threading, compile without this flag against Boost >=1.42.0.\n\n" +#endif + "There are two inputs: vocabulary and model. Either may be given as a file\n" + " while the other is on stdin. Specify the type given as a file using\n" + " vocab: or model: before the file name. \n\n" + "For ARPA format, the output must be seekable. For raw format, it can be a\n" + " stream i.e. /dev/stdout\n"; +} + +typedef enum {MODE_COPY, MODE_SINGLE, MODE_MULTIPLE, MODE_UNION, MODE_UNSET} FilterMode; +typedef enum {FORMAT_ARPA, FORMAT_COUNT} Format; + +struct Config { + Config() : +#ifndef NTHREAD + batch_size(25000), + threads(boost::thread::hardware_concurrency()), +#endif + phrase(false), + context(false), + format(FORMAT_ARPA) + { +#ifndef NTHREAD + if (!threads) threads = 1; +#endif + } + +#ifndef NTHREAD + size_t batch_size; + size_t threads; +#endif + bool phrase; + bool context; + FilterMode mode; + Format format; +}; + +template void RunThreadedFilter(const Config &config, util::FilePiece &in_lm, Filter &filter, Output &output) { +#ifndef NTHREAD + if (config.threads == 1) { +#endif + Format::RunFilter(in_lm, filter, output); +#ifndef NTHREAD + } else { + typedef Controller Threaded; + Threaded threading(config.batch_size, config.threads * 2, config.threads, filter, output); + Format::RunFilter(in_lm, threading, output); + } +#endif +} + +template void RunContextFilter(const Config &config, util::FilePiece &in_lm, Filter filter, Output &output) { + if (config.context) { + ContextFilter context_filter(filter); + RunThreadedFilter, OutputBuffer, Output>(config, in_lm, context_filter, output); + } else { + RunThreadedFilter(config, in_lm, filter, output); + } +} + +template void DispatchBinaryFilter(const Config &config, util::FilePiece &in_lm, const Binary &binary, typename Format::Output &out) { + typedef BinaryFilter Filter; + RunContextFilter(config, in_lm, Filter(binary), out); +} + +template void DispatchFilterModes(const Config &config, std::istream &in_vocab, util::FilePiece &in_lm, const char *out_name) { + if (config.mode == MODE_MULTIPLE) { + if (config.phrase) { + typedef phrase::Multiple Filter; + phrase::Substrings substrings; + typename Format::Multiple out(out_name, phrase::ReadMultiple(in_vocab, substrings)); + RunContextFilter(config, in_lm, Filter(substrings), out); + } else { + typedef vocab::Multiple Filter; + boost::unordered_map > words; + typename Format::Multiple out(out_name, vocab::ReadMultiple(in_vocab, words)); + RunContextFilter(config, in_lm, Filter(words), out); + } + return; + } + + typename Format::Output out(out_name); + + if (config.mode == MODE_COPY) { + Format::Copy(in_lm, out); + return; + } + + if (config.mode == MODE_SINGLE) { + vocab::Single::Words words; + vocab::ReadSingle(in_vocab, words); + DispatchBinaryFilter(config, in_lm, vocab::Single(words), out); + return; + } + + if (config.mode == MODE_UNION) { + if (config.phrase) { + phrase::Substrings substrings; + phrase::ReadMultiple(in_vocab, substrings); + DispatchBinaryFilter(config, in_lm, phrase::Union(substrings), out); + } else { + vocab::Union::Words words; + vocab::ReadMultiple(in_vocab, words); + DispatchBinaryFilter(config, in_lm, vocab::Union(words), out); + } + return; + } +} + +} // namespace +} // namespace lm + +int main(int argc, char *argv[]) { + try { + if (argc < 4) { + lm::DisplayHelp(argv[0]); + return 1; + } + + // I used to have boost::program_options, but some users didn't want to compile boost. + lm::Config config; + config.mode = lm::MODE_UNSET; + for (int i = 1; i < argc - 2; ++i) { + const char *str = argv[i]; + if (!std::strcmp(str, "copy")) { + config.mode = lm::MODE_COPY; + } else if (!std::strcmp(str, "single")) { + config.mode = lm::MODE_SINGLE; + } else if (!std::strcmp(str, "multiple")) { + config.mode = lm::MODE_MULTIPLE; + } else if (!std::strcmp(str, "union")) { + config.mode = lm::MODE_UNION; + } else if (!std::strcmp(str, "phrase")) { + config.phrase = true; + } else if (!std::strcmp(str, "context")) { + config.context = true; + } else if (!std::strcmp(str, "arpa")) { + config.format = lm::FORMAT_ARPA; + } else if (!std::strcmp(str, "raw")) { + config.format = lm::FORMAT_COUNT; +#ifndef NTHREAD + } else if (!std::strncmp(str, "threads:", 8)) { + config.threads = boost::lexical_cast(str + 8); + if (!config.threads) { + std::cerr << "Specify at least one thread." << std::endl; + return 1; + } + } else if (!std::strncmp(str, "batch_size:", 11)) { + config.batch_size = boost::lexical_cast(str + 11); + if (config.batch_size < 5000) { + std::cerr << "Batch size must be at least one and should probably be >= 5000" << std::endl; + if (!config.batch_size) return 1; + } +#endif + } else { + lm::DisplayHelp(argv[0]); + return 1; + } + } + + if (config.mode == lm::MODE_UNSET) { + lm::DisplayHelp(argv[0]); + return 1; + } + + if (config.phrase && config.mode != lm::MODE_UNION && config.mode != lm::MODE_MULTIPLE) { + std::cerr << "Phrase constraint currently only works in multiple or union mode. If you really need it for single, put everything on one line and use union." << std::endl; + return 1; + } + + bool cmd_is_model = true; + const char *cmd_input = argv[argc - 2]; + if (!strncmp(cmd_input, "vocab:", 6)) { + cmd_is_model = false; + cmd_input += 6; + } else if (!strncmp(cmd_input, "model:", 6)) { + cmd_input += 6; + } else if (strchr(cmd_input, ':')) { + std::cerr << "Specify vocab: or model: before the input file name, not " << cmd_input << std::endl; + return 1; + } else { + std::cerr << "Assuming that " << cmd_input << " is a model file" << std::endl; + } + std::ifstream cmd_file; + std::istream *vocab; + if (cmd_is_model) { + vocab = &std::cin; + } else { + cmd_file.open(cmd_input, std::ios::in); + UTIL_THROW_IF(!cmd_file, util::ErrnoException, "Failed to open " << cmd_input); + vocab = &cmd_file; + } + + util::FilePiece model(cmd_is_model ? util::OpenReadOrThrow(cmd_input) : 0, cmd_is_model ? cmd_input : NULL, &std::cerr); + + if (config.format == lm::FORMAT_ARPA) { + lm::DispatchFilterModes(config, *vocab, model, argv[argc - 1]); + } else if (config.format == lm::FORMAT_COUNT) { + lm::DispatchFilterModes(config, *vocab, model, argv[argc - 1]); + } + return 0; + } catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + return 1; + } +} diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/format.hh b/ctcdecode-master/third_party/kenlm/lm/filter/format.hh new file mode 100644 index 0000000..d453f05 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/format.hh @@ -0,0 +1,250 @@ +#ifndef LM_FILTER_FORMAT_H +#define LM_FILTER_FORMAT_H + +#include "lm/filter/arpa_io.hh" +#include "lm/filter/count_io.hh" + +#include +#include + +#include + +namespace lm { + +template class MultipleOutput { + private: + typedef boost::ptr_vector Singles; + typedef typename Singles::iterator SinglesIterator; + + public: + MultipleOutput(const char *prefix, size_t number) { + files_.reserve(number); + std::string tmp; + for (unsigned int i = 0; i < number; ++i) { + tmp = prefix; + tmp += boost::lexical_cast(i); + files_.push_back(new Single(tmp.c_str())); + } + } + + void AddNGram(const StringPiece &line) { + for (SinglesIterator i = files_.begin(); i != files_.end(); ++i) + i->AddNGram(line); + } + + template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line) { + for (SinglesIterator i = files_.begin(); i != files_.end(); ++i) + i->AddNGram(begin, end, line); + } + + void SingleAddNGram(size_t offset, const StringPiece &line) { + files_[offset].AddNGram(line); + } + + template void SingleAddNGram(size_t offset, const Iterator &begin, const Iterator &end, const StringPiece &line) { + files_[offset].AddNGram(begin, end, line); + } + + protected: + Singles files_; +}; + +class MultipleARPAOutput : public MultipleOutput { + public: + MultipleARPAOutput(const char *prefix, size_t number) : MultipleOutput(prefix, number) {} + + void ReserveForCounts(std::streampos reserve) { + for (boost::ptr_vector::iterator i = files_.begin(); i != files_.end(); ++i) + i->ReserveForCounts(reserve); + } + + void BeginLength(unsigned int length) { + for (boost::ptr_vector::iterator i = files_.begin(); i != files_.end(); ++i) + i->BeginLength(length); + } + + void EndLength(unsigned int length) { + for (boost::ptr_vector::iterator i = files_.begin(); i != files_.end(); ++i) + i->EndLength(length); + } + + void Finish() { + for (boost::ptr_vector::iterator i = files_.begin(); i != files_.end(); ++i) + i->Finish(); + } +}; + +template class DispatchInput { + public: + DispatchInput(Filter &filter, Output &output) : filter_(filter), output_(output) {} + +/* template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line) { + filter_.AddNGram(begin, end, line, output_); + }*/ + + void AddNGram(const StringPiece &ngram, const StringPiece &line) { + filter_.AddNGram(ngram, line, output_); + } + + protected: + Filter &filter_; + Output &output_; +}; + +template class DispatchARPAInput : public DispatchInput { + private: + typedef DispatchInput B; + + public: + DispatchARPAInput(Filter &filter, Output &output) : B(filter, output) {} + + void ReserveForCounts(std::streampos reserve) { B::output_.ReserveForCounts(reserve); } + void BeginLength(unsigned int length) { B::output_.BeginLength(length); } + + void EndLength(unsigned int length) { + B::filter_.Flush(); + B::output_.EndLength(length); + } + void Finish() { B::output_.Finish(); } +}; + +struct ARPAFormat { + typedef ARPAOutput Output; + typedef MultipleARPAOutput Multiple; + static void Copy(util::FilePiece &in, Output &out) { + ReadARPA(in, out); + } + template static void RunFilter(util::FilePiece &in, Filter &filter, Out &output) { + DispatchARPAInput dispatcher(filter, output); + ReadARPA(in, dispatcher); + } +}; + +struct CountFormat { + typedef CountOutput Output; + typedef MultipleOutput Multiple; + static void Copy(util::FilePiece &in, Output &out) { + ReadCount(in, out); + } + template static void RunFilter(util::FilePiece &in, Filter &filter, Out &output) { + DispatchInput dispatcher(filter, output); + ReadCount(in, dispatcher); + } +}; + +/* For multithreading, the buffer classes hold batches of filter inputs and + * outputs in memory. The strings get reused a lot, so keep them around + * instead of clearing each time. + */ +class InputBuffer { + public: + InputBuffer() : actual_(0) {} + + void Reserve(size_t size) { lines_.reserve(size); } + + template void AddNGram(const StringPiece &ngram, const StringPiece &line, Output &output) { + if (lines_.size() == actual_) lines_.resize(lines_.size() + 1); + // TODO avoid this copy. + std::string &copied = lines_[actual_].line; + copied.assign(line.data(), line.size()); + lines_[actual_].ngram.set(copied.data() + (ngram.data() - line.data()), ngram.size()); + ++actual_; + } + + template void CallFilter(Filter &filter, Output &output) const { + for (std::vector::const_iterator i = lines_.begin(); i != lines_.begin() + actual_; ++i) { + filter.AddNGram(i->ngram, i->line, output); + } + } + + void Clear() { actual_ = 0; } + bool Empty() { return actual_ == 0; } + size_t Size() { return actual_; } + + private: + struct Line { + std::string line; + StringPiece ngram; + }; + + size_t actual_; + + std::vector lines_; +}; + +class BinaryOutputBuffer { + public: + BinaryOutputBuffer() {} + + void Reserve(size_t size) { + lines_.reserve(size); + } + + void AddNGram(const StringPiece &line) { + lines_.push_back(line); + } + + template void Flush(Output &output) { + for (std::vector::const_iterator i = lines_.begin(); i != lines_.end(); ++i) { + output.AddNGram(*i); + } + lines_.clear(); + } + + private: + std::vector lines_; +}; + +class MultipleOutputBuffer { + public: + MultipleOutputBuffer() : last_(NULL) {} + + void Reserve(size_t size) { + annotated_.reserve(size); + } + + void AddNGram(const StringPiece &line) { + annotated_.resize(annotated_.size() + 1); + annotated_.back().line = line; + } + + void SingleAddNGram(size_t offset, const StringPiece &line) { + if ((line.data() == last_.data()) && (line.length() == last_.length())) { + annotated_.back().systems.push_back(offset); + } else { + annotated_.resize(annotated_.size() + 1); + annotated_.back().systems.push_back(offset); + annotated_.back().line = line; + last_ = line; + } + } + + template void Flush(Output &output) { + for (std::vector::const_iterator i = annotated_.begin(); i != annotated_.end(); ++i) { + if (i->systems.empty()) { + output.AddNGram(i->line); + } else { + for (std::vector::const_iterator j = i->systems.begin(); j != i->systems.end(); ++j) { + output.SingleAddNGram(*j, i->line); + } + } + } + annotated_.clear(); + } + + private: + struct Annotated { + // If this is empty, send to all systems. + // A filter should never send to all systems and send to a single one. + std::vector systems; + StringPiece line; + }; + + StringPiece last_; + + std::vector annotated_; +}; + +} // namespace lm + +#endif // LM_FILTER_FORMAT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/phrase.cc b/ctcdecode-master/third_party/kenlm/lm/filter/phrase.cc new file mode 100644 index 0000000..d8260d5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/phrase.cc @@ -0,0 +1,292 @@ +#include "lm/filter/phrase.hh" + +#include "lm/filter/format.hh" + +#include +#include +#include +#include +#include +#include + +#include + +namespace lm { +namespace phrase { + +unsigned int ReadMultiple(std::istream &in, Substrings &out) { + bool sentence_content = false; + unsigned int sentence_id = 0; + std::vector phrase; + std::string word; + while (in) { + char c; + // Gather a word. + while (!isspace(c = in.get()) && in) word += c; + // Treat EOF like a newline. + if (!in) c = '\n'; + // Add the word to the phrase. + if (!word.empty()) { + phrase.push_back(util::MurmurHashNative(word.data(), word.size())); + word.clear(); + } + if (c == ' ') continue; + // It's more than just a space. Close out the phrase. + if (!phrase.empty()) { + sentence_content = true; + out.AddPhrase(sentence_id, phrase.begin(), phrase.end()); + phrase.clear(); + } + if (c == '\t' || c == '\v') continue; + // It's more than a space or tab: a newline. + if (sentence_content) { + ++sentence_id; + sentence_content = false; + } + } + if (!in.eof()) in.exceptions(std::istream::failbit | std::istream::badbit); + return sentence_id + sentence_content; +} + +namespace { +typedef unsigned int Sentence; +typedef std::vector Sentences; +} // namespace + +namespace detail { + +const StringPiece kEndSentence(""); + +class Arc { + public: + Arc() {} + + // For arcs from one vertex to another. + void SetPhrase(detail::Vertex &from, detail::Vertex &to, const Sentences &intersect) { + Set(to, intersect); + from_ = &from; + } + + /* For arcs from before the n-gram begins to somewhere in the n-gram (right + * aligned). These have no from_ vertex; it implictly matches every + * sentence. This also handles when the n-gram is a substring of a phrase. + */ + void SetRight(detail::Vertex &to, const Sentences &complete) { + Set(to, complete); + from_ = NULL; + } + + Sentence Current() const { + return *current_; + } + + bool Empty() const { + return current_ == last_; + } + + /* When this function returns: + * If Empty() then there's nothing left from this intersection. + * + * If Current() == to then to is part of the intersection. + * + * Otherwise, Current() > to. In this case, to is not part of the + * intersection and neither is anything < Current(). To determine if + * any value >= Current() is in the intersection, call LowerBound again + * with the value. + */ + void LowerBound(const Sentence to); + + private: + void Set(detail::Vertex &to, const Sentences &sentences); + + const Sentence *current_; + const Sentence *last_; + detail::Vertex *from_; +}; + +struct ArcGreater : public std::binary_function { + bool operator()(const Arc *first, const Arc *second) const { + return first->Current() > second->Current(); + } +}; + +class Vertex { + public: + Vertex() : current_(0) {} + + Sentence Current() const { + return current_; + } + + bool Empty() const { + return incoming_.empty(); + } + + void LowerBound(const Sentence to); + + private: + friend class Arc; + + void AddIncoming(Arc *arc) { + if (!arc->Empty()) incoming_.push(arc); + } + + unsigned int current_; + std::priority_queue, ArcGreater> incoming_; +}; + +void Arc::LowerBound(const Sentence to) { + current_ = std::lower_bound(current_, last_, to); + // If *current_ > to, don't advance from_. The intervening values of + // from_ may be useful for another one of its outgoing arcs. + if (!from_ || Empty() || (Current() > to)) return; + assert(Current() == to); + from_->LowerBound(to); + if (from_->Empty()) { + current_ = last_; + return; + } + assert(from_->Current() >= to); + if (from_->Current() > to) { + current_ = std::lower_bound(current_ + 1, last_, from_->Current()); + } +} + +void Arc::Set(Vertex &to, const Sentences &sentences) { + current_ = &*sentences.begin(); + last_ = &*sentences.end(); + to.AddIncoming(this); +} + +void Vertex::LowerBound(const Sentence to) { + if (Empty()) return; + // Union lower bound. + while (true) { + Arc *top = incoming_.top(); + if (top->Current() > to) { + current_ = top->Current(); + return; + } + // If top->Current() == to, we still need to verify that's an actual + // element and not just a bound. + incoming_.pop(); + top->LowerBound(to); + if (!top->Empty()) { + incoming_.push(top); + if (top->Current() == to) { + current_ = to; + return; + } + } else if (Empty()) { + return; + } + } +} + +} // namespace detail + +namespace { + +void BuildGraph(const Substrings &phrase, const std::vector &hashes, detail::Vertex *const vertices, detail::Arc *free_arc) { + using detail::Vertex; + using detail::Arc; + assert(!hashes.empty()); + + const Hash *const first_word = &*hashes.begin(); + const Hash *const last_word = &*hashes.end() - 1; + + Hash hash = 0; + const Sentences *found; + // Phrases starting at or before the first word in the n-gram. + { + Vertex *vertex = vertices; + for (const Hash *word = first_word; ; ++word, ++vertex) { + hash = util::MurmurHashNative(&hash, sizeof(uint64_t), *word); + // Now hash is [hashes.begin(), word]. + if (word == last_word) { + if (phrase.FindSubstring(hash, found)) + (free_arc++)->SetRight(*vertex, *found); + break; + } + if (!phrase.FindRight(hash, found)) break; + (free_arc++)->SetRight(*vertex, *found); + } + } + + // Phrases starting at the second or later word in the n-gram. + Vertex *vertex_from = vertices; + for (const Hash *word_from = first_word + 1; word_from != &*hashes.end(); ++word_from, ++vertex_from) { + hash = 0; + Vertex *vertex_to = vertex_from + 1; + for (const Hash *word_to = word_from; ; ++word_to, ++vertex_to) { + // Notice that word_to and vertex_to have the same index. + hash = util::MurmurHashNative(&hash, sizeof(uint64_t), *word_to); + // Now hash covers [word_from, word_to]. + if (word_to == last_word) { + if (phrase.FindLeft(hash, found)) + (free_arc++)->SetPhrase(*vertex_from, *vertex_to, *found); + break; + } + if (!phrase.FindPhrase(hash, found)) break; + (free_arc++)->SetPhrase(*vertex_from, *vertex_to, *found); + } + } +} + +} // namespace + +namespace detail { + +// Here instead of header due to forward declaration. +ConditionCommon::ConditionCommon(const Substrings &substrings) : substrings_(substrings) {} + +// Rest of the variables are temporaries anyway +ConditionCommon::ConditionCommon(const ConditionCommon &from) : substrings_(from.substrings_) {} + +ConditionCommon::~ConditionCommon() {} + +detail::Vertex &ConditionCommon::MakeGraph() { + assert(!hashes_.empty()); + vertices_.clear(); + vertices_.resize(hashes_.size()); + arcs_.clear(); + // One for every substring. + arcs_.resize(((hashes_.size() + 1) * hashes_.size()) / 2); + BuildGraph(substrings_, hashes_, &*vertices_.begin(), &*arcs_.begin()); + return vertices_[hashes_.size() - 1]; +} + +} // namespace detail + +bool Union::Evaluate() { + detail::Vertex &last_vertex = MakeGraph(); + unsigned int lower = 0; + while (true) { + last_vertex.LowerBound(lower); + if (last_vertex.Empty()) return false; + if (last_vertex.Current() == lower) return true; + lower = last_vertex.Current(); + } +} + +template void Multiple::Evaluate(const StringPiece &line, Output &output) { + detail::Vertex &last_vertex = MakeGraph(); + unsigned int lower = 0; + while (true) { + last_vertex.LowerBound(lower); + if (last_vertex.Empty()) return; + if (last_vertex.Current() == lower) { + output.SingleAddNGram(lower, line); + ++lower; + } else { + lower = last_vertex.Current(); + } + } +} + +template void Multiple::Evaluate(const StringPiece &line, CountFormat::Multiple &output); +template void Multiple::Evaluate(const StringPiece &line, ARPAFormat::Multiple &output); +template void Multiple::Evaluate(const StringPiece &line, MultipleOutputBuffer &output); + +} // namespace phrase +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/phrase.hh b/ctcdecode-master/third_party/kenlm/lm/filter/phrase.hh new file mode 100644 index 0000000..5227ab2 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/phrase.hh @@ -0,0 +1,168 @@ +#ifndef LM_FILTER_PHRASE_H +#define LM_FILTER_PHRASE_H + +#include "util/murmur_hash.hh" +#include "util/string_piece.hh" +#include "util/tokenize_piece.hh" + +#include + +#include +#include + +#define LM_FILTER_PHRASE_METHOD(caps, lower) \ +bool Find##caps(Hash key, const std::vector *&out) const {\ + Table::const_iterator i(table_.find(key));\ + if (i==table_.end()) return false; \ + out = &i->second.lower; \ + return true; \ +} + +namespace lm { +namespace phrase { + +typedef uint64_t Hash; + +class Substrings { + private: + /* This is the value in a hash table where the key is a string. It indicates + * four sets of sentences: + * substring is sentences with a phrase containing the key as a substring. + * left is sentencess with a phrase that begins with the key (left aligned). + * right is sentences with a phrase that ends with the key (right aligned). + * phrase is sentences where the key is a phrase. + * Each set is encoded as a vector of sentence ids in increasing order. + */ + struct SentenceRelation { + std::vector substring, left, right, phrase; + }; + /* Most of the CPU is hash table lookups, so let's not complicate it with + * vector equality comparisons. If a collision happens, the SentenceRelation + * structure will contain the union of sentence ids over the colliding strings. + * In that case, the filter will be slightly more permissive. + * The key here is the same as boost's hash of std::vector. + */ + typedef boost::unordered_map Table; + + public: + Substrings() {} + + /* If the string isn't a substring of any phrase, return NULL. Otherwise, + * return a pointer to std::vector listing sentences with + * matching phrases. This set may be empty for Left, Right, or Phrase. + * Example: const std::vector *FindSubstring(Hash key) + */ + LM_FILTER_PHRASE_METHOD(Substring, substring) + LM_FILTER_PHRASE_METHOD(Left, left) + LM_FILTER_PHRASE_METHOD(Right, right) + LM_FILTER_PHRASE_METHOD(Phrase, phrase) + +#pragma GCC diagnostic ignored "-Wuninitialized" // end != finish so there's always an initialization + // sentence_id must be non-decreasing. Iterators are over words in the phrase. + template void AddPhrase(unsigned int sentence_id, const Iterator &begin, const Iterator &end) { + // Iterate over all substrings. + for (Iterator start = begin; start != end; ++start) { + Hash hash = 0; + SentenceRelation *relation; + for (Iterator finish = start; finish != end; ++finish) { + hash = util::MurmurHashNative(&hash, sizeof(uint64_t), *finish); + // Now hash is of [start, finish]. + relation = &table_[hash]; + AppendSentence(relation->substring, sentence_id); + if (start == begin) AppendSentence(relation->left, sentence_id); + } + AppendSentence(relation->right, sentence_id); + if (start == begin) AppendSentence(relation->phrase, sentence_id); + } + } + + private: + void AppendSentence(std::vector &vec, unsigned int sentence_id) { + if (vec.empty() || vec.back() != sentence_id) vec.push_back(sentence_id); + } + + Table table_; +}; + +// Read a file with one sentence per line containing tab-delimited phrases of +// space-separated words. +unsigned int ReadMultiple(std::istream &in, Substrings &out); + +namespace detail { +extern const StringPiece kEndSentence; + +template void MakeHashes(Iterator i, const Iterator &end, std::vector &hashes) { + hashes.clear(); + if (i == end) return; + // TODO: check strict phrase boundaries after and before . For now, just skip tags. + if ((i->data()[0] == '<') && (i->data()[i->size() - 1] == '>')) { + ++i; + } + for (; i != end && (*i != kEndSentence); ++i) { + hashes.push_back(util::MurmurHashNative(i->data(), i->size())); + } +} + +class Vertex; +class Arc; + +class ConditionCommon { + protected: + ConditionCommon(const Substrings &substrings); + ConditionCommon(const ConditionCommon &from); + + ~ConditionCommon(); + + detail::Vertex &MakeGraph(); + + // Temporaries in PassNGram and Evaluate to avoid reallocation. + std::vector hashes_; + + private: + std::vector vertices_; + std::vector arcs_; + + const Substrings &substrings_; +}; + +} // namespace detail + +class Union : public detail::ConditionCommon { + public: + explicit Union(const Substrings &substrings) : detail::ConditionCommon(substrings) {} + + template bool PassNGram(const Iterator &begin, const Iterator &end) { + detail::MakeHashes(begin, end, hashes_); + return hashes_.empty() || Evaluate(); + } + + private: + bool Evaluate(); +}; + +class Multiple : public detail::ConditionCommon { + public: + explicit Multiple(const Substrings &substrings) : detail::ConditionCommon(substrings) {} + + template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line, Output &output) { + detail::MakeHashes(begin, end, hashes_); + if (hashes_.empty()) { + output.AddNGram(line); + } else { + Evaluate(line, output); + } + } + + template void AddNGram(const StringPiece &ngram, const StringPiece &line, Output &output) { + AddNGram(util::TokenIter(ngram, ' '), util::TokenIter::end(), line, output); + } + + void Flush() const {} + + private: + template void Evaluate(const StringPiece &line, Output &output); +}; + +} // namespace phrase +} // namespace lm +#endif // LM_FILTER_PHRASE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/phrase_table_vocab_main.cc b/ctcdecode-master/third_party/kenlm/lm/filter/phrase_table_vocab_main.cc new file mode 100644 index 0000000..9ffa35f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/phrase_table_vocab_main.cc @@ -0,0 +1,165 @@ +#include "util/file_stream.hh" +#include "util/file_piece.hh" +#include "util/murmur_hash.hh" +#include "util/pool.hh" +#include "util/string_piece.hh" +#include "util/string_piece_hash.hh" +#include "util/tokenize_piece.hh" + +#include +#include + +#include +#include + +namespace { + +struct MutablePiece { + mutable StringPiece behind; + bool operator==(const MutablePiece &other) const { + return behind == other.behind; + } +}; + +std::size_t hash_value(const MutablePiece &m) { + return hash_value(m.behind); +} + +class InternString { + public: + const char *Add(StringPiece str) { + MutablePiece mut; + mut.behind = str; + std::pair::iterator, bool> res(strs_.insert(mut)); + if (res.second) { + void *mem = backing_.Allocate(str.size() + 1); + memcpy(mem, str.data(), str.size()); + static_cast(mem)[str.size()] = 0; + res.first->behind = StringPiece(static_cast(mem), str.size()); + } + return res.first->behind.data(); + } + + private: + util::Pool backing_; + boost::unordered_set strs_; +}; + +class TargetWords { + public: + void Introduce(StringPiece source) { + vocab_.resize(vocab_.size() + 1); + std::vector temp(1, vocab_.size() - 1); + Add(temp, source); + } + + void Add(const std::vector &sentences, StringPiece target) { + if (sentences.empty()) return; + interns_.clear(); + for (util::TokenIter i(target, ' '); i; ++i) { + interns_.push_back(intern_.Add(*i)); + } + for (std::vector::const_iterator i(sentences.begin()); i != sentences.end(); ++i) { + boost::unordered_set &vocab = vocab_[*i]; + for (std::vector::const_iterator j = interns_.begin(); j != interns_.end(); ++j) { + vocab.insert(*j); + } + } + } + + void Print() const { + util::FileStream out(1); + for (std::vector >::const_iterator i = vocab_.begin(); i != vocab_.end(); ++i) { + for (boost::unordered_set::const_iterator j = i->begin(); j != i->end(); ++j) { + out << *j << ' '; + } + out << '\n'; + } + } + + private: + InternString intern_; + + std::vector > vocab_; + + // Temporary in Add. + std::vector interns_; +}; + +class Input { + public: + explicit Input(std::size_t max_length) + : max_length_(max_length), sentence_id_(0), empty_() {} + + void AddSentence(StringPiece sentence, TargetWords &targets) { + canonical_.clear(); + starts_.clear(); + starts_.push_back(0); + for (util::TokenIter i(sentence, StringPiece("\0 \t", 3)); i; ++i) { + canonical_.append(i->data(), i->size()); + canonical_ += ' '; + starts_.push_back(canonical_.size()); + } + targets.Introduce(canonical_); + for (std::size_t i = 0; i < starts_.size() - 1; ++i) { + std::size_t subtract = starts_[i]; + const char *start = &canonical_[subtract]; + for (std::size_t j = i + 1; j < std::min(starts_.size(), i + max_length_ + 1); ++j) { + map_[util::MurmurHash64A(start, &canonical_[starts_[j]] - start - 1)].push_back(sentence_id_); + } + } + ++sentence_id_; + } + + // Assumes single space-delimited phrase with no space at the beginning or end. + const std::vector &Matches(StringPiece phrase) const { + Map::const_iterator i = map_.find(util::MurmurHash64A(phrase.data(), phrase.size())); + return i == map_.end() ? empty_ : i->second; + } + + private: + const std::size_t max_length_; + + // hash of phrase is the key, array of sentences is the value. + typedef boost::unordered_map > Map; + Map map_; + + std::size_t sentence_id_; + + // Temporaries in AddSentence. + std::string canonical_; + std::vector starts_; + + const std::vector empty_; +}; + +} // namespace + +int main(int argc, char *argv[]) { + if (argc != 2) { + std::cerr << "Expected source text on the command line" << std::endl; + return 1; + } + Input input(7); + TargetWords targets; + try { + util::FilePiece inputs(argv[1], &std::cerr); + while (true) + input.AddSentence(inputs.ReadLine(), targets); + } catch (const util::EndOfFileException &e) {} + + util::FilePiece table(0, NULL, &std::cerr); + StringPiece line; + const StringPiece pipes("|||"); + while (true) { + try { + line = table.ReadLine(); + } catch (const util::EndOfFileException &e) { break; } + util::TokenIter it(line, pipes); + StringPiece source(*it); + if (!source.empty() && source[source.size() - 1] == ' ') + source.remove_suffix(1); + targets.Add(input.Matches(source), *++it); + } + targets.Print(); +} diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/thread.hh b/ctcdecode-master/third_party/kenlm/lm/filter/thread.hh new file mode 100644 index 0000000..88e069c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/thread.hh @@ -0,0 +1,167 @@ +#ifndef LM_FILTER_THREAD_H +#define LM_FILTER_THREAD_H + +#include "util/thread_pool.hh" + +#include + +#include +#include + +namespace lm { + +template class ThreadBatch { + public: + ThreadBatch() {} + + void Reserve(size_t size) { + input_.Reserve(size); + output_.Reserve(size); + } + + // File reading thread. + InputBuffer &Fill(uint64_t sequence) { + sequence_ = sequence; + // Why wait until now to clear instead of after output? free in the same + // thread as allocated. + input_.Clear(); + return input_; + } + + // Filter worker thread. + template void CallFilter(Filter &filter) { + input_.CallFilter(filter, output_); + } + + uint64_t Sequence() const { return sequence_; } + + // File writing thread. + template void Flush(RealOutput &output) { + output_.Flush(output); + } + + private: + InputBuffer input_; + OutputBuffer output_; + + uint64_t sequence_; +}; + +template class FilterWorker { + public: + typedef Batch *Request; + + FilterWorker(const Filter &filter, util::PCQueue &done) : filter_(filter), done_(done) {} + + void operator()(Request request) { + request->CallFilter(filter_); + done_.Produce(request); + } + + private: + Filter filter_; + + util::PCQueue &done_; +}; + +// There should only be one OutputWorker. +template class OutputWorker { + public: + typedef Batch *Request; + + OutputWorker(Output &output, util::PCQueue &done) : output_(output), done_(done), base_sequence_(0) {} + + void operator()(Request request) { + assert(request->Sequence() >= base_sequence_); + // Assemble the output in order. + uint64_t pos = request->Sequence() - base_sequence_; + if (pos >= ordering_.size()) { + ordering_.resize(pos + 1, NULL); + } + ordering_[pos] = request; + while (!ordering_.empty() && ordering_.front()) { + ordering_.front()->Flush(output_); + done_.Produce(ordering_.front()); + ordering_.pop_front(); + ++base_sequence_; + } + } + + private: + Output &output_; + + util::PCQueue &done_; + + std::deque ordering_; + + uint64_t base_sequence_; +}; + +template class Controller : boost::noncopyable { + private: + typedef ThreadBatch Batch; + + public: + Controller(size_t batch_size, size_t queue, size_t workers, const Filter &filter, RealOutput &output) + : batch_size_(batch_size), queue_size_(queue), + batches_(queue), + to_read_(queue), + output_(queue, 1, boost::in_place(boost::ref(output), boost::ref(to_read_)), NULL), + filter_(queue, workers, boost::in_place(boost::ref(filter), boost::ref(output_.In())), NULL), + sequence_(0) { + for (size_t i = 0; i < queue; ++i) { + batches_[i].Reserve(batch_size); + local_read_.push(&batches_[i]); + } + NewInput(); + } + + void AddNGram(const StringPiece &ngram, const StringPiece &line, RealOutput &output) { + input_->AddNGram(ngram, line, output); + if (input_->Size() == batch_size_) { + FlushInput(); + NewInput(); + } + } + + void Flush() { + FlushInput(); + while (local_read_.size() < queue_size_) { + MoveRead(); + } + NewInput(); + } + + private: + void FlushInput() { + if (input_->Empty()) return; + filter_.Produce(local_read_.top()); + local_read_.pop(); + if (local_read_.empty()) MoveRead(); + } + + void NewInput() { + input_ = &local_read_.top()->Fill(sequence_++); + } + + void MoveRead() { + local_read_.push(to_read_.Consume()); + } + + const size_t batch_size_; + const size_t queue_size_; + + std::vector batches_; + + util::PCQueue to_read_; + std::stack local_read_; + util::ThreadPool > output_; + util::ThreadPool > filter_; + + uint64_t sequence_; + InputBuffer *input_; +}; + +} // namespace lm + +#endif // LM_FILTER_THREAD_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/vocab.cc b/ctcdecode-master/third_party/kenlm/lm/filter/vocab.cc new file mode 100644 index 0000000..2aca4fc --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/vocab.cc @@ -0,0 +1,53 @@ +#include "lm/filter/vocab.hh" + +#include +#include + +#include + +namespace lm { +namespace vocab { + +void ReadSingle(std::istream &in, boost::unordered_set &out) { + in.exceptions(std::istream::badbit); + std::string word; + while (in >> word) { + out.insert(word); + } +} + +namespace { +bool IsLineEnd(std::istream &in) { + int got; + do { + got = in.get(); + if (!in) return true; + if (got == '\n') return true; + } while (isspace(got)); + in.unget(); + return false; +} +}// namespace + +// Read space separated words in enter separated lines. These lines can be +// very long, so don't read an entire line at a time. +unsigned int ReadMultiple(std::istream &in, boost::unordered_map > &out) { + in.exceptions(std::istream::badbit); + unsigned int sentence = 0; + bool used_id = false; + std::string word; + while (in >> word) { + used_id = true; + std::vector &posting = out[word]; + if (posting.empty() || (posting.back() != sentence)) + posting.push_back(sentence); + if (IsLineEnd(in)) { + ++sentence; + used_id = false; + } + } + return sentence + used_id; +} + +} // namespace vocab +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/vocab.hh b/ctcdecode-master/third_party/kenlm/lm/filter/vocab.hh new file mode 100644 index 0000000..397a932 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/vocab.hh @@ -0,0 +1,133 @@ +#ifndef LM_FILTER_VOCAB_H +#define LM_FILTER_VOCAB_H + +// Vocabulary-based filters for language models. + +#include "util/multi_intersection.hh" +#include "util/string_piece.hh" +#include "util/string_piece_hash.hh" +#include "util/tokenize_piece.hh" + +#include +#include +#include +#include + +#include +#include + +namespace lm { +namespace vocab { + +void ReadSingle(std::istream &in, boost::unordered_set &out); + +// Read one sentence vocabulary per line. Return the number of sentences. +unsigned int ReadMultiple(std::istream &in, boost::unordered_map > &out); + +/* Is this a special tag like or ? This actually includes anything + * surrounded with < and >, which most tokenizers separate for real words, so + * this should not catch real words as it looks at a single token. + */ +inline bool IsTag(const StringPiece &value) { + // The parser should never give an empty string. + assert(!value.empty()); + return (value.data()[0] == '<' && value.data()[value.size() - 1] == '>'); +} + +class Single { + public: + typedef boost::unordered_set Words; + + explicit Single(const Words &vocab) : vocab_(vocab) {} + + template bool PassNGram(const Iterator &begin, const Iterator &end) { + for (Iterator i = begin; i != end; ++i) { + if (IsTag(*i)) continue; + if (FindStringPiece(vocab_, *i) == vocab_.end()) return false; + } + return true; + } + + private: + const Words &vocab_; +}; + +class Union { + public: + typedef boost::unordered_map > Words; + + explicit Union(const Words &vocabs) : vocabs_(vocabs) {} + + template bool PassNGram(const Iterator &begin, const Iterator &end) { + sets_.clear(); + + for (Iterator i(begin); i != end; ++i) { + if (IsTag(*i)) continue; + Words::const_iterator found(FindStringPiece(vocabs_, *i)); + if (vocabs_.end() == found) return false; + sets_.push_back(boost::iterator_range(&*found->second.begin(), &*found->second.end())); + } + return (sets_.empty() || util::FirstIntersection(sets_)); + } + + private: + const Words &vocabs_; + + std::vector > sets_; +}; + +class Multiple { + public: + typedef boost::unordered_map > Words; + + Multiple(const Words &vocabs) : vocabs_(vocabs) {} + + private: + // Callback from AllIntersection that does AddNGram. + template class Callback { + public: + Callback(Output &out, const StringPiece &line) : out_(out), line_(line) {} + + void operator()(unsigned int index) { + out_.SingleAddNGram(index, line_); + } + + private: + Output &out_; + const StringPiece &line_; + }; + + public: + template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line, Output &output) { + sets_.clear(); + for (Iterator i(begin); i != end; ++i) { + if (IsTag(*i)) continue; + Words::const_iterator found(FindStringPiece(vocabs_, *i)); + if (vocabs_.end() == found) return; + sets_.push_back(boost::iterator_range(&*found->second.begin(), &*found->second.end())); + } + if (sets_.empty()) { + output.AddNGram(line); + return; + } + + Callback cb(output, line); + util::AllIntersection(sets_, cb); + } + + template void AddNGram(const StringPiece &ngram, const StringPiece &line, Output &output) { + AddNGram(util::TokenIter(ngram, ' '), util::TokenIter::end(), line, output); + } + + void Flush() const {} + + private: + const Words &vocabs_; + + std::vector > sets_; +}; + +} // namespace vocab +} // namespace lm + +#endif // LM_FILTER_VOCAB_H diff --git a/ctcdecode-master/third_party/kenlm/lm/filter/wrapper.hh b/ctcdecode-master/third_party/kenlm/lm/filter/wrapper.hh new file mode 100644 index 0000000..227ec8e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/filter/wrapper.hh @@ -0,0 +1,56 @@ +#ifndef LM_FILTER_WRAPPER_H +#define LM_FILTER_WRAPPER_H + +#include "util/string_piece.hh" + +#include +#include +#include + +namespace lm { + +// Provide a single-output filter with the same interface as a +// multiple-output filter so clients code against one interface. +template class BinaryFilter { + public: + // Binary modes are just references (and a set) and it makes the API cleaner to copy them. + explicit BinaryFilter(Binary binary) : binary_(binary) {} + + template void AddNGram(const Iterator &begin, const Iterator &end, const StringPiece &line, Output &output) { + if (binary_.PassNGram(begin, end)) + output.AddNGram(line); + } + + template void AddNGram(const StringPiece &ngram, const StringPiece &line, Output &output) { + AddNGram(util::TokenIter(ngram, ' '), util::TokenIter::end(), line, output); + } + + void Flush() const {} + + private: + Binary binary_; +}; + +// Wrap another filter to pay attention only to context words +template class ContextFilter { + public: + typedef FilterT Filter; + + explicit ContextFilter(Filter &backend) : backend_(backend) {} + + template void AddNGram(const StringPiece &ngram, const StringPiece &line, Output &output) { + // Find beginning of string or last space. + const char *last_space; + for (last_space = ngram.data() + ngram.size() - 1; last_space > ngram.data() && *last_space != ' '; --last_space) {} + backend_.AddNGram(StringPiece(ngram.data(), last_space - ngram.data()), line, output); + } + + void Flush() const {} + + private: + Filter backend_; +}; + +} // namespace lm + +#endif // LM_FILTER_WRAPPER_H diff --git a/ctcdecode-master/third_party/kenlm/lm/fragment_main.cc b/ctcdecode-master/third_party/kenlm/lm/fragment_main.cc new file mode 100644 index 0000000..0267cd4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/fragment_main.cc @@ -0,0 +1,37 @@ +#include "lm/binary_format.hh" +#include "lm/model.hh" +#include "lm/left.hh" +#include "util/tokenize_piece.hh" + +template void Query(const char *name) { + Model model(name); + std::string line; + lm::ngram::ChartState ignored; + while (getline(std::cin, line)) { + lm::ngram::RuleScore scorer(model, ignored); + for (util::TokenIter i(line, ' '); i; ++i) { + scorer.Terminal(model.GetVocabulary().Index(*i)); + } + std::cout << scorer.Finish() << '\n'; + } +} + +int main(int argc, char *argv[]) { + if (argc != 2) { + std::cerr << "Expected model file name." << std::endl; + return 1; + } + const char *name = argv[1]; + lm::ngram::ModelType model_type = lm::ngram::PROBING; + lm::ngram::RecognizeBinary(name, model_type); + switch (model_type) { + case lm::ngram::PROBING: + Query(name); + break; + case lm::ngram::REST_PROBING: + Query(name); + break; + default: + std::cerr << "Model type not supported yet." << std::endl; + } +} diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/lm/interpolate/CMakeLists.txt new file mode 100644 index 0000000..6a61786 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/CMakeLists.txt @@ -0,0 +1,70 @@ +find_package(Eigen3) + +if(EIGEN3_FOUND) + if (3.1.0 VERSION_LESS ${EIGEN3_VERSION}) + include_directories(${EIGEN3_INCLUDE_DIR}) + + set(KENLM_INTERPOLATE_SOURCE + backoff_reunification.cc + bounded_sequence_encoding.cc + merge_probabilities.cc + merge_vocab.cc + normalize.cc + pipeline.cc + split_worker.cc + tune_derivatives.cc + tune_instances.cc + tune_weights.cc + universal_vocab.cc) + + find_package(OpenMP) + if (OPENMP_FOUND) + set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + else() + message(STATUS "OpenMP support would be nice for parallelizing matrix operations.") + endif() + + add_library(kenlm_interpolate ${KENLM_INTERPOLATE_SOURCE}) + + set(KENLM_INTERPOLATE_EXES + interpolate + streaming_example) + + if (NOT MSVC) + set(THREADS pthread) + endif() + + set(KENLM_INTERPOLATE_LIBS + kenlm_interpolate kenlm kenlm_util ${Boost_LIBRARIES} ${THREADS}) + + AddExes(EXES ${KENLM_INTERPOLATE_EXES} + LIBRARIES ${KENLM_INTERPOLATE_LIBS}) + + if(BUILD_TESTING) + AddTests(TESTS backoff_reunification_test bounded_sequence_encoding_test normalize_test tune_derivatives_test + LIBRARIES ${KENLM_INTERPOLATE_LIBS} pthread) + + # tune_instances_test needs an extra command line parameter + KenLMAddTest(TEST tune_instances_test + LIBRARIES ${KENLM_INTERPOLATE_LIBS} + TEST_ARGS -- ${CMAKE_CURRENT_SOURCE_DIR}/../common/test_data/toy0.1) + + foreach(test_file test1 test2 test3 test_bad_order test_no_unk) + set(KENLM_MERGE_TESTS_PATH ${KENLM_MERGE_TESTS_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/merge_test/${test_file}) + endforeach(test_file) + + KenLMAddTest(TEST merge_vocab_test + LIBRARIES ${KENLM_INTERPOLATE_LIBS} + TEST_ARGS ${KENLM_MERGE_TESTS_PATH}) + endif() + else() + message(WARNING "Not building interpolation. You have an old version of Eigen3, ${EIGEN3_VERSION}, which has a race condition: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=466. Please install Eigen 3.1.0 or above.") + endif() +else() + message(WARNING "Not building interpolation. Eigen3 was not found.") + message(STATUS "To install Eigen3 in your home directory, copy paste this:\n" + "export EIGEN3_ROOT=$HOME/eigen-eigen-07105f7124f9\n" + "(cd $HOME; wget -O - https://bitbucket.org/eigen/eigen/get/3.2.8.tar.bz2 |tar xj)\n" + "rm CMakeCache.txt\n") +endif() diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_matrix.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_matrix.hh new file mode 100644 index 0000000..c7552df --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_matrix.hh @@ -0,0 +1,29 @@ +#ifndef LM_INTERPOLATE_BACKOFF_MATRIX_H +#define LM_INTERPOLATE_BACKOFF_MATRIX_H + +#include +#include + +namespace lm { namespace interpolate { + +class BackoffMatrix { + public: + BackoffMatrix(std::size_t num_models, std::size_t max_order) + : max_order_(max_order), backing_(num_models * max_order) {} + + float &Backoff(std::size_t model, std::size_t order_minus_1) { + return backing_[model * max_order_ + order_minus_1]; + } + + float Backoff(std::size_t model, std::size_t order_minus_1) const { + return backing_[model * max_order_ + order_minus_1]; + } + + private: + const std::size_t max_order_; + std::vector backing_; +}; + +}} // namespaces + +#endif // LM_INTERPOLATE_BACKOFF_MATRIX_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification.cc new file mode 100644 index 0000000..348c73d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification.cc @@ -0,0 +1,58 @@ +#include "lm/interpolate/backoff_reunification.hh" +#include "lm/common/model_buffer.hh" +#include "lm/common/ngram_stream.hh" +#include "lm/common/ngram.hh" +#include "lm/common/compare.hh" + +#include +#include + +namespace lm { +namespace interpolate { + +namespace { +class MergeWorker { +public: + MergeWorker(std::size_t order, const util::stream::ChainPosition &prob_pos, + const util::stream::ChainPosition &boff_pos) + : order_(order), prob_pos_(prob_pos), boff_pos_(boff_pos) { + // nothing + } + + void Run(const util::stream::ChainPosition &position) { + lm::NGramStream stream(position); + + lm::NGramStream prob_input(prob_pos_); + util::stream::Stream boff_input(boff_pos_); + for (; prob_input && boff_input; ++prob_input, ++boff_input, ++stream) { + std::copy(prob_input->begin(), prob_input->end(), stream->begin()); + stream->Value().prob = std::min(0.0f, prob_input->Value()); + stream->Value().backoff = *reinterpret_cast(boff_input.Get()); + } + UTIL_THROW_IF2(prob_input || boff_input, + "Streams were not the same size during merging"); + stream.Poison(); + } + +private: + std::size_t order_; + util::stream::ChainPosition prob_pos_; + util::stream::ChainPosition boff_pos_; +}; +} + +// Since we are *adding* something to the output chain here, we pass in the +// chain itself so that we can safely add a new step to the chain without +// creating a deadlock situation (since creating a new ChainPosition will +// make a new input/output pair---we want that position to be created +// *here*, not before). +void ReunifyBackoff(util::stream::ChainPositions &prob_pos, + util::stream::ChainPositions &boff_pos, + util::stream::Chains &output_chains) { + assert(prob_pos.size() == boff_pos.size()); + + for (size_t i = 0; i < prob_pos.size(); ++i) + output_chains[i] >> MergeWorker(i + 1, prob_pos[i], boff_pos[i]); +} +} +} diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification.hh new file mode 100644 index 0000000..327db65 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification.hh @@ -0,0 +1,27 @@ +#ifndef KENLM_INTERPOLATE_BACKOFF_REUNIFICATION_ +#define KENLM_INTERPOLATE_BACKOFF_REUNIFICATION_ + +#include "util/stream/stream.hh" +#include "util/stream/multi_stream.hh" + +namespace lm { +namespace interpolate { + +/** + * The third pass for the offline log-linear interpolation algorithm. This + * reads **suffix-ordered** probability values (ngram-id, float) and + * **suffix-ordered** backoff values (float) and writes the merged contents + * to the output. + * + * @param prob_pos The chain position for each order from which to read + * the probability values + * @param boff_pos The chain position for each order from which to read + * the backoff values + * @param output_chains The output chains for each order + */ +void ReunifyBackoff(util::stream::ChainPositions &prob_pos, + util::stream::ChainPositions &boff_pos, + util::stream::Chains &output_chains); +} +} +#endif diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification_test.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification_test.cc new file mode 100644 index 0000000..e1ec517 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/backoff_reunification_test.cc @@ -0,0 +1,158 @@ +#include "lm/interpolate/backoff_reunification.hh" +#include "lm/common/ngram_stream.hh" + +#define BOOST_TEST_MODULE InterpolateBackoffReunificationTest +#include + +namespace lm { +namespace interpolate { + +namespace { + +// none of this input actually makes sense, all we care about is making +// sure the merging works +template +struct Gram { + WordIndex ids[N]; + float prob; + float boff; +}; + +template +struct Grams { + const static Gram grams[]; +}; + +template <> +const Gram<1> Grams<1>::grams[] + = {{{0}, -0.1f, -0.1f}, {{1}, -0.4f, -0.2f}, {{2}, -0.5f, -0.1f}}; + +template <> +const Gram<2> Grams<2>::grams[] = {{{0, 0}, -0.05f, -0.05f}, + {{1, 0}, -0.05f, -0.02f}, + {{1, 1}, -0.2f, -0.04f}, + {{2, 2}, -0.2f, -0.01f}}; + +template <> +const Gram<3> Grams<3>::grams[] = {{{0, 0, 0}, -0.001f, -0.005f}, + {{1, 0, 0}, -0.001f, -0.002f}, + {{2, 0, 0}, -0.001f, -0.003f}, + {{0, 1, 0}, -0.1f, -0.008f}, + {{1, 1, 0}, -0.1f, -0.09f}, + {{1, 1, 1}, -0.2f, -0.08f}}; + +template +class WriteInput { +public: + void Run(const util::stream::ChainPosition &position) { + lm::NGramStream output(position); + + for (std::size_t i = 0; i < sizeof(Grams::grams) / sizeof(Gram); + ++i, ++output) { + std::copy(Grams::grams[i].ids, Grams::grams[i].ids + N, + output->begin()); + output->Value() = Grams::grams[i].prob; + } + output.Poison(); + } +}; + +template +class WriteBackoffs { +public: + void Run(const util::stream::ChainPosition &position) { + util::stream::Stream output(position); + + for (std::size_t i = 0; i < sizeof(Grams::grams) / sizeof(Gram); + ++i, ++output) { + *reinterpret_cast(output.Get()) = Grams::grams[i].boff; + } + output.Poison(); + } +}; + +template +class CheckOutput { +public: + void Run(const util::stream::ChainPosition &position) { + lm::NGramStream stream(position); + + std::size_t i = 0; + for (; stream; ++stream, ++i) { + std::stringstream ss; + for (WordIndex *idx = stream->begin(); idx != stream->end(); ++idx) + ss << "(" << *idx << ")"; + + BOOST_CHECK(std::equal(stream->begin(), stream->end(), Grams::grams[i].ids)); + //"Mismatched id in CheckOutput<" << (int)N << ">: " << ss.str(); + + BOOST_CHECK_EQUAL(stream->Value().prob, Grams::grams[i].prob); +/* "Mismatched probability in CheckOutput<" + << (int)N << ">, got " << stream->Value().prob + << ", expected " << Grams::grams[i].prob;*/ + + BOOST_CHECK_EQUAL(stream->Value().backoff, Grams::grams[i].boff); +/* "Mismatched backoff in CheckOutput<" + << (int)N << ">, got " << stream->Value().backoff + << ", expected " << Grams::grams[i].boff);*/ + } + BOOST_CHECK_EQUAL(i , sizeof(Grams::grams) / sizeof(Gram)); +/* "Did not get correct number of " + << (int)N << "-grams: expected " + << sizeof(Grams::grams) / sizeof(Gram) + << ", got " << i;*/ + } +}; +} + +BOOST_AUTO_TEST_CASE(BackoffReunificationTest) { + util::stream::ChainConfig config; + config.total_memory = 100; + config.block_count = 1; + + util::stream::Chains prob_chains(3); + config.entry_size = NGram::TotalSize(1); + prob_chains.push_back(config); + prob_chains.back() >> WriteInput<1>(); + + config.entry_size = NGram::TotalSize(2); + prob_chains.push_back(config); + prob_chains.back() >> WriteInput<2>(); + + config.entry_size = NGram::TotalSize(3); + prob_chains.push_back(config); + prob_chains.back() >> WriteInput<3>(); + + util::stream::Chains boff_chains(3); + config.entry_size = sizeof(float); + boff_chains.push_back(config); + boff_chains.back() >> WriteBackoffs<1>(); + + boff_chains.push_back(config); + boff_chains.back() >> WriteBackoffs<2>(); + + boff_chains.push_back(config); + boff_chains.back() >> WriteBackoffs<3>(); + + util::stream::ChainPositions prob_pos(prob_chains); + util::stream::ChainPositions boff_pos(boff_chains); + + util::stream::Chains output_chains(3); + for (std::size_t i = 0; i < 3; ++i) { + config.entry_size = NGram::TotalSize(i + 1); + output_chains.push_back(config); + } + + ReunifyBackoff(prob_pos, boff_pos, output_chains); + + output_chains[0] >> CheckOutput<1>(); + output_chains[1] >> CheckOutput<2>(); + output_chains[2] >> CheckOutput<3>(); + + prob_chains >> util::stream::kRecycle; + boff_chains >> util::stream::kRecycle; + + output_chains.Wait(); +} +} +} diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding.cc new file mode 100644 index 0000000..aca8ed7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding.cc @@ -0,0 +1,36 @@ +#include "lm/interpolate/bounded_sequence_encoding.hh" + +#include + +namespace lm { namespace interpolate { + +BoundedSequenceEncoding::BoundedSequenceEncoding(const unsigned char *bound_begin, const unsigned char *bound_end) + : entries_(bound_end - bound_begin) { + std::size_t full = 0; + Entry entry; + entry.shift = 0; + for (const unsigned char *i = bound_begin; i != bound_end; ++i) { + uint8_t length; + if (*i <= 1) { + length = 0; + } else { + length = sizeof(unsigned int) * 8 - __builtin_clz((unsigned int)*i); + } + entry.mask = (1ULL << length) - 1ULL; + if (entry.shift + length > 64) { + entry.shift = 0; + entry.next = true; + ++full; + } else { + entry.next = false; + } + entries_.push_back(entry); + entry.shift += length; + } + byte_length_ = full * sizeof(uint64_t) + (entry.shift + 7) / 8; + first_copy_ = std::min(byte_length_, sizeof(uint64_t)); + // Size of last uint64_t. Zero if empty, otherwise [1,8] depending on mod. + overhang_ = byte_length_ == 0 ? 0 : ((byte_length_ - 1) % 8 + 1); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding.hh new file mode 100644 index 0000000..84dd63a --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding.hh @@ -0,0 +1,76 @@ +#ifndef LM_INTERPOLATE_BOUNDED_SEQUENCE_ENCODING_H +#define LM_INTERPOLATE_BOUNDED_SEQUENCE_ENCODING_H + +/* Encodes fixed-length sequences of integers with known bounds on each entry. + * This is used to encode how far each model has backed off. + * TODO: make this class efficient. Bit-level packing or multiply by bound and + * add. + */ + +#include "util/exception.hh" +#include "util/fixed_array.hh" + +#if BYTE_ORDER != LITTLE_ENDIAN +#warning The interpolation code assumes little endian for now. +#endif + +#include +#include + +namespace lm { +namespace interpolate { + +class BoundedSequenceEncoding { + public: + // Encode [0, bound_begin[0]) x [0, bound_begin[1]) x [0, bound_begin[2]) x ... x [0, *(bound_end - 1)) for entries in the sequence + BoundedSequenceEncoding(const unsigned char *bound_begin, const unsigned char *bound_end); + + std::size_t Entries() const { return entries_.size(); } + + std::size_t EncodedLength() const { return byte_length_; } + + void Encode(const unsigned char *from, void *to_void) const { + uint8_t *to = static_cast(to_void); + uint64_t cur = 0; + for (const Entry *i = entries_.begin(); i != entries_.end(); ++i, ++from) { + if (UTIL_UNLIKELY(i->next)) { + std::memcpy(to, &cur, sizeof(uint64_t)); + to += sizeof(uint64_t); + cur = 0; + } + cur |= static_cast(*from) << i->shift; + } + memcpy(to, &cur, overhang_); + } + + void Decode(const void *from_void, unsigned char *to) const { + const uint8_t *from = static_cast(from_void); + uint64_t cur = 0; + memcpy(&cur, from, first_copy_); + for (const Entry *i = entries_.begin(); i != entries_.end(); ++i, ++to) { + if (UTIL_UNLIKELY(i->next)) { + from += sizeof(uint64_t); + cur = 0; + std::memcpy(&cur, from, + std::min(sizeof(uint64_t), static_cast(from_void) + byte_length_ - from)); + } + *to = (cur >> i->shift) & i->mask; + } + } + + private: + struct Entry { + bool next; + uint8_t shift; + uint64_t mask; + }; + util::FixedArray entries_; + std::size_t byte_length_; + std::size_t first_copy_; + std::size_t overhang_; +}; + + +}} // namespaces + +#endif // LM_INTERPOLATE_BOUNDED_SEQUENCE_ENCODING_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding_test.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding_test.cc new file mode 100644 index 0000000..2c4bbd9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/bounded_sequence_encoding_test.cc @@ -0,0 +1,86 @@ +#include "lm/interpolate/bounded_sequence_encoding.hh" + +#include "util/scoped.hh" + +#define BOOST_TEST_MODULE BoundedSequenceEncodingTest +#include + +namespace lm { +namespace interpolate { +namespace { + +void ExhaustiveTest(unsigned char *bound_begin, unsigned char *bound_end) { + BoundedSequenceEncoding enc(bound_begin, bound_end); + util::scoped_malloc backing(util::MallocOrThrow(enc.EncodedLength())); + std::vector values(bound_end - bound_begin), + out(bound_end - bound_begin); + while (true) { + enc.Encode(&values[0], backing.get()); + enc.Decode(backing.get(), &out[0]); + for (std::size_t i = 0; i != values.size(); ++i) { + BOOST_CHECK_EQUAL(values[i], out[i]); + } + for (std::size_t i = 0;; ++i) { + if (i == values.size()) return; + ++values[i]; + if (values[i] < bound_begin[i]) break; + values[i] = 0; + } + } +} + +void CheckEncodeDecode(unsigned char *bounds, unsigned char *input, + unsigned char *output, std::size_t len) { + BoundedSequenceEncoding encoder(bounds, bounds + len); + util::scoped_malloc backing(util::MallocOrThrow(encoder.EncodedLength())); + + encoder.Encode(input, backing.get()); + encoder.Decode(backing.get(), output); + + for (std::size_t i = 0; i < len; ++i) { + BOOST_CHECK_EQUAL(input[i], output[i]); + } +} + +BOOST_AUTO_TEST_CASE(Exhaustive) { + unsigned char bounds[] = {5, 2, 3, 9, 7, 20, 8}; + ExhaustiveTest(bounds, bounds + sizeof(bounds) / sizeof(unsigned char)); +} + +BOOST_AUTO_TEST_CASE(LessThan64) { + unsigned char bounds[] = {255, 255, 255, 255, 255, 255, 255, 3}; + unsigned char input[] = {172, 183, 254, 187, 96, 87, 65, 2}; + unsigned char output[] = {0, 0, 0, 0, 0, 0, 0, 0}; + + std::size_t len = sizeof(bounds) / sizeof(unsigned char); + assert(sizeof(input) / sizeof(unsigned char) == len); + assert(sizeof(output) / sizeof(unsigned char) == len); + + CheckEncodeDecode(bounds, input, output, len); +} + +BOOST_AUTO_TEST_CASE(Exactly64) { + unsigned char bounds[] = {255, 255, 255, 255, 255, 255, 255, 255}; + unsigned char input[] = {172, 183, 254, 187, 96, 87, 65, 16}; + unsigned char output[] = {0, 0, 0, 0, 0, 0, 0, 0}; + + std::size_t len = sizeof(bounds) / sizeof(unsigned char); + assert(sizeof(input) / sizeof(unsigned char) == len); + assert(sizeof(output) / sizeof(unsigned char) == len); + + CheckEncodeDecode(bounds, input, output, len); +} + +BOOST_AUTO_TEST_CASE(MoreThan64) { + unsigned char bounds[] = {255, 255, 255, 255, 255, 255, 255, 255, 255}; + unsigned char input[] = {172, 183, 254, 187, 96, 87, 65, 16, 137}; + unsigned char output[] = {0, 0, 0, 0, 0, 0, 0, 0, 0}; + + std::size_t len = sizeof(bounds) / sizeof(unsigned char); + assert(sizeof(input) / sizeof(unsigned char) == len); + assert(sizeof(output) / sizeof(unsigned char) == len); + + CheckEncodeDecode(bounds, input, output, len); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/interpolate_info.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/interpolate_info.hh new file mode 100644 index 0000000..ebecd92 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/interpolate_info.hh @@ -0,0 +1,35 @@ +#ifndef KENLM_INTERPOLATE_INTERPOLATE_INFO_H +#define KENLM_INTERPOLATE_INTERPOLATE_INFO_H + +#include +#include +#include + +namespace lm { +namespace interpolate { + +/** + * Stores relevant info for interpolating several language models, for use + * during the three-pass offline log-linear interpolation algorithm. + */ +struct InterpolateInfo { + /** + * @return the number of models being interpolated + */ + std::size_t Models() const { + return orders.size(); + } + + /** + * The lambda (interpolation weight) for each model. + */ + std::vector lambdas; + + /** + * The maximum ngram order for each model. + */ + std::vector orders; +}; +} +} +#endif diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/interpolate_main.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/interpolate_main.cc new file mode 100644 index 0000000..06161c5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/interpolate_main.cc @@ -0,0 +1,124 @@ +#include "lm/common/model_buffer.hh" +#include "lm/common/size_option.hh" +#include "lm/interpolate/pipeline.hh" +#include "lm/interpolate/tune_instances.hh" +#include "lm/interpolate/tune_weights.hh" +#include "util/fixed_array.hh" +#include "util/usage.hh" + +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wpragmas" // Older gcc doesn't have "-Wunused-local-typedefs" and complains. +#pragma GCC diagnostic ignored "-Wunused-local-typedefs" +#include +#pragma GCC diagnostic pop + +#include + +#include +#include + +namespace { +void MungeWeightArgs(int argc, char *argv[], std::vector &munged_args) { + // Boost program options doesn't -w 0.2 -0.1 because it thinks -0.1 is an + // option. There appears to be no standard way to fix this without breaking + // single-dash arguments. So here's a hack: put a -w before every number + // if it's within the scope of a weight argument. + munged_args.push_back(argv[0]); + char **inside_weights = NULL; + for (char **i = argv + 1; i < argv + argc; ++i) { + StringPiece arg(*i); + if (starts_with(arg, "-w") || starts_with(arg, "--w")) { + inside_weights = i; + } else if (inside_weights && arg.size() >= 2 && arg[0] == '-' && ((arg[1] >= '0' && arg[1] <= '9') || arg[1] == '.')) { + // If a negative number appears right after -w, don't add another -w. + // And do stay inside weights. + if (inside_weights + 1 != i) { + munged_args.push_back("-w"); + } + } else if (starts_with(arg, "-")) { + inside_weights = NULL; + } + munged_args.push_back(*i); + } +} +} // namespace + +int main(int argc, char *argv[]) { + try { + Eigen::initParallel(); + lm::interpolate::Config pipe_config; + lm::interpolate::InstancesConfig instances_config; + std::vector input_models; + std::string tuning_file; + + namespace po = boost::program_options; + po::options_description options("Log-linear interpolation options"); + options.add_options() + ("help,h", po::bool_switch(), "Show this help message") + ("model,m", po::value >(&input_models)->multitoken()->required(), "Models to interpolate, which must be in KenLM intermediate format. The intermediate format can be generated using the --intermediate argument to lmplz.") + ("weight,w", po::value >(&pipe_config.lambdas)->multitoken(), "Interpolation weights") + ("tuning,t", po::value(&tuning_file), "File to tune on: a text file with one sentence per line") + ("just_tune", po::bool_switch(), "Tune and print weights then quit") + ("temp_prefix,T", po::value(&pipe_config.sort.temp_prefix)->default_value("/tmp/lm"), "Temporary file prefix") + ("memory,S", lm::SizeOption(pipe_config.sort.total_memory, util::GuessPhysicalMemory() ? "50%" : "1G"), "Sorting memory: this is a very rough guide") + ("sort_block", lm::SizeOption(pipe_config.sort.buffer_size, "64M"), "Block size"); + po::variables_map vm; + + std::vector munged_args; + MungeWeightArgs(argc, argv, munged_args); + + po::store(po::parse_command_line((int)munged_args.size(), &*munged_args.begin(), options), vm); + if (argc == 1 || vm["help"].as()) { + std::cerr << "Interpolate multiple models\n" << options << std::endl; + return 1; + } + po::notify(vm); + instances_config.sort = pipe_config.sort; + instances_config.model_read_chain_mem = instances_config.sort.buffer_size; + instances_config.extension_write_chain_mem = instances_config.sort.total_memory; + instances_config.lazy_memory = instances_config.sort.total_memory; + + if (pipe_config.lambdas.empty() && tuning_file.empty()) { + std::cerr << "Provide a tuning file with -t xor weights with -w." << std::endl; + return 1; + } + if (!pipe_config.lambdas.empty() && !tuning_file.empty()) { + std::cerr << "Provide weights xor a tuning file, not both." << std::endl; + return 1; + } + + if (!tuning_file.empty()) { + // Tune weights + std::vector model_names; + for (std::vector::const_iterator i = input_models.begin(); i != input_models.end(); ++i) { + model_names.push_back(*i); + } + lm::interpolate::TuneWeights(util::OpenReadOrThrow(tuning_file.c_str()), model_names, instances_config, pipe_config.lambdas); + + std::cerr << "Final weights:"; + std::ostream &to = vm["just_tune"].as() ? std::cout : std::cerr; + for (std::vector::const_iterator i = pipe_config.lambdas.begin(); i != pipe_config.lambdas.end(); ++i) { + to << ' ' << *i; + } + to << std::endl; + } + if (vm["just_tune"].as()) { + return 0; + } + + if (pipe_config.lambdas.size() != input_models.size()) { + std::cerr << "Number of models (" << input_models.size() << ") should match the number of weights (" << pipe_config.lambdas.size() << ")." << std::endl; + return 1; + } + + util::FixedArray models(input_models.size()); + for (std::size_t i = 0; i < input_models.size(); ++i) { + models.push_back(input_models[i]); + } + lm::interpolate::Pipeline(models, pipe_config, 1); + } catch (const std::exception &e) { + std::cerr << e.what() < +#include +#include + +namespace lm { +namespace interpolate { + +/** + * Helper to generate the BoundedSequenceEncoding used for writing the + * from values. + */ +BoundedSequenceEncoding MakeEncoder(const InterpolateInfo &info, uint8_t order) { + util::FixedArray max_orders(info.orders.size()); + for (std::size_t i = 0; i < info.orders.size(); ++i) { + max_orders.push_back(std::min(order, info.orders[i])); + } + return BoundedSequenceEncoding(max_orders.begin(), max_orders.end()); +} + +namespace { + +/** + * A simple wrapper class that holds information needed to read and write + * the ngrams of a particular order. This class has the memory needed to + * buffer the data needed for the recursive process of computing the + * probabilities and "from" values for each component model. + * + * "From" values indicate, for each model, what order (as an index, so -1) + * was backed off to in order to arrive at a probability. For example, if a + * 5-gram model (order index 4) backed off twice, we would write a 2. + */ +class NGramHandler { +public: + NGramHandler(uint8_t order, const InterpolateInfo &ifo, + util::FixedArray &models_by_order) + : info(ifo), + encoder(MakeEncoder(info, order)), + out_record(order, encoder.EncodedLength()) { + std::size_t count_has_order = 0; + for (std::size_t i = 0; i < models_by_order.size(); ++i) { + count_has_order += (models_by_order[i].size() >= order); + } + inputs_.Init(count_has_order); + for (std::size_t i = 0; i < models_by_order.size(); ++i) { + if (models_by_order[i].size() < order) + continue; + inputs_.push_back(models_by_order[i][order - 1]); + if (inputs_.back()) { + active_.resize(active_.size() + 1); + active_.back().model = i; + active_.back().stream = &inputs_.back(); + } + } + + // have to init outside since NGramStreams doesn't forward to + // GenericStreams ctor given a ChainPositions + + probs.Init(info.Models()); + from.Init(info.Models()); + for (std::size_t i = 0; i < info.Models(); ++i) { + probs.push_back(0.0); + from.push_back(0); + } + } + + struct StreamIndex { + NGramStream *stream; + NGramStream &Stream() { return *stream; } + std::size_t model; + }; + + std::size_t ActiveSize() const { + return active_.size(); + } + + /** + * @return the input stream for a particular model that corresponds to + * this ngram order + */ + StreamIndex &operator[](std::size_t idx) { + return active_[idx]; + } + + void erase(std::size_t idx) { + active_.erase(active_.begin() + idx); + } + + const InterpolateInfo &info; + BoundedSequenceEncoding encoder; + PartialProbGamma out_record; + util::FixedArray probs; + util::FixedArray from; + +private: + std::vector active_; + NGramStreams inputs_; +}; + +/** + * A collection of NGramHandlers. + */ +class NGramHandlers : public util::FixedArray { +public: + explicit NGramHandlers(std::size_t num) + : util::FixedArray(num) { + } + + void push_back( + std::size_t order, const InterpolateInfo &info, + util::FixedArray &models_by_order) { + new (end()) NGramHandler(order, info, models_by_order); + Constructed(); + } +}; + +/** + * The recursive helper function that computes probability and "from" + * values for all ngrams matching a particular suffix. + * + * The current order can be computed as the suffix length + 1. Note that + * the suffix could be empty (suffix_begin == suffix_end == NULL), in which + * case we are handling unigrams with the UNK token as the fallback + * probability. + * + * @param handlers The full collection of handlers + * @param suffix_begin A start iterator for the suffix + * @param suffix_end An end iterator for the suffix + * @param fallback_probs The probabilities of this ngram if we need to + * back off (that is, the probability of the suffix) + * @param fallback_from The order that the corresponding fallback + * probability in the fallback_probs is from + * @param combined_fallback interpolated fallback_probs + * @param outputs The output streams, one for each order + */ +void HandleSuffix(NGramHandlers &handlers, WordIndex *suffix_begin, + WordIndex *suffix_end, + const util::FixedArray &fallback_probs, + const util::FixedArray &fallback_from, + float combined_fallback, + util::stream::Streams &outputs) { + uint8_t order = std::distance(suffix_begin, suffix_end) + 1; + if (order > outputs.size()) return; + + util::stream::Stream &output = outputs[order - 1]; + NGramHandler &handler = handlers[order - 1]; + + while (true) { + // find the next smallest ngram which matches our suffix + // TODO: priority queue driven. + WordIndex *minimum = NULL; + for (std::size_t i = 0; i < handler.ActiveSize(); ++i) { + if (!std::equal(suffix_begin, suffix_end, handler[i].Stream()->begin() + 1)) + continue; + + // if we either haven't set a minimum yet or this one is smaller than + // the minimum we found before, replace it + WordIndex *last = handler[i].Stream()->begin(); + if (!minimum || *last < *minimum) { minimum = handler[i].Stream()->begin(); } + } + + // no more ngrams of this order match our suffix, so we're done + if (!minimum) return; + + handler.out_record.ReBase(output.Get()); + std::copy(minimum, minimum + order, handler.out_record.begin()); + + // Default case is having backed off. + std::copy(fallback_probs.begin(), fallback_probs.end(), handler.probs.begin()); + std::copy(fallback_from.begin(), fallback_from.end(), handler.from.begin()); + + for (std::size_t i = 0; i < handler.ActiveSize();) { + if (std::equal(handler.out_record.begin(), handler.out_record.end(), + handler[i].Stream()->begin())) { + handler.probs[handler[i].model] = handler.info.lambdas[handler[i].model] * handler[i].Stream()->Value().prob; + handler.from[handler[i].model] = order - 1; + if (++handler[i].Stream()) { + ++i; + } else { + handler.erase(i); + } + } else { + ++i; + } + } + handler.out_record.Prob() = std::accumulate(handler.probs.begin(), handler.probs.end(), 0.0); + handler.out_record.LowerProb() = combined_fallback; + handler.encoder.Encode(handler.from.begin(), + handler.out_record.FromBegin()); + + // we've handled this particular ngram, so now recurse to the higher + // order using the current ngram as the suffix + HandleSuffix(handlers, handler.out_record.begin(), handler.out_record.end(), + handler.probs, handler.from, handler.out_record.Prob(), outputs); + // consume the output + ++output; + } +} + +/** + * Kicks off the recursion for computing the probabilities and "from" + * values for each ngram order. We begin by handling the UNK token that + * should be at the front of each of the unigram input streams. This is + * then output to the stream and it is used as the fallback for handling + * our unigram case, the unigram used as the fallback for the bigram case, + * etc. + */ +void HandleNGrams(NGramHandlers &handlers, util::stream::Streams &outputs) { + PartialProbGamma unk_record(1, 0); + // First: populate the unk probabilities by reading the first unigram + // from each stream + util::FixedArray unk_probs(handlers[0].info.Models()); + + // start by populating the ngram id from the first stream + lm::NGram ngram = *handlers[0][0].Stream(); + unk_record.ReBase(outputs[0].Get()); + std::copy(ngram.begin(), ngram.end(), unk_record.begin()); + unk_record.Prob() = 0; + + // then populate the probabilities into unk_probs while "multiply" the + // model probabilities together into the unk record + // + // note that from doesn't need to be set for unigrams + assert(handlers[0].ActiveSize() == handlers[0].info.Models()); + for (std::size_t i = 0; i < handlers[0].info.Models();) { + ngram = *handlers[0][i].Stream(); + unk_probs.push_back(handlers[0].info.lambdas[i] * ngram.Value().prob); + unk_record.Prob() += unk_probs[i]; + assert(*ngram.begin() == kUNK); + if (++handlers[0][i].Stream()) { + ++i; + } else { + handlers[0].erase(i); + } + } + float unk_combined = unk_record.Prob(); + unk_record.LowerProb() = unk_combined; + // flush the unk output record + ++outputs[0]; + + // Then, begin outputting everything in lexicographic order: first we'll + // get the unigram then the first bigram with that context, then the + // first trigram with that bigram context, etc., until we exhaust all of + // the ngrams, then all of the (n-1)grams, etc. + // + // This function is the "root" of this recursive process. + util::FixedArray unk_from(handlers[0].info.Models()); + for (std::size_t i = 0; i < handlers[0].info.Models(); ++i) { + unk_from.push_back(0); + } + + // the two nulls are to encode that our "fallback" word is the "0-gram" + // case, e.g. we "backed off" to UNK + // TODO: stop generating vocab ids and LowerProb for unigrams. + HandleSuffix(handlers, NULL, NULL, unk_probs, unk_from, unk_combined, outputs); + + // Verify we reached the end. And poison! + for (std::size_t i = 0; i < handlers.size(); ++i) { + UTIL_THROW_IF2(handlers[i].ActiveSize(), + "MergeProbabilities did not exhaust all ngram streams"); + outputs[i].Poison(); + } +} +} // namespace + +void MergeProbabilities::Run(const util::stream::ChainPositions &output_pos) { + NGramHandlers handlers(output_pos.size()); + for (std::size_t i = 0; i < output_pos.size(); ++i) { + handlers.push_back(i + 1, info_, models_by_order_); + } + + util::stream::Streams outputs(output_pos); + HandleNGrams(handlers, outputs); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_probabilities.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_probabilities.hh new file mode 100644 index 0000000..61940e3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_probabilities.hh @@ -0,0 +1,96 @@ +#ifndef LM_INTERPOLATE_MERGE_PROBABILITIES_H +#define LM_INTERPOLATE_MERGE_PROBABILITIES_H + +#include "lm/common/ngram.hh" +#include "lm/interpolate/bounded_sequence_encoding.hh" +#include "util/fixed_array.hh" +#include "util/stream/multi_stream.hh" + +#include + +namespace lm { +namespace interpolate { + +struct InterpolateInfo; + +/** + * Make the encoding of backoff values for a given order. This stores values + * in [PartialProbGamma::FromBegin(), PartialProbGamma::FromEnd()) + */ +BoundedSequenceEncoding MakeEncoder(const InterpolateInfo &info, uint8_t order); + +/** + * The first pass for the offline log-linear interpolation algorithm. This + * reads K **suffix-ordered** streams for each model, for each order, of + * ngram records (ngram-id, prob, backoff). It further assumes that the + * ngram-ids have been unified over all of the stream inputs. + * + * Its output is records of (ngram-id, prob-prod, backoff-level, + * backoff-level, ...) where the backoff-levels (of which there are K) are + * the context length (0 for unigrams) that the corresponding model had to + * back off to in order to obtain a probability for that ngram-id. Each of + * these streams is terminated with a record whose ngram-id is all + * maximum-integers for simplicity in implementation here. + * + * @param model_by_order An array of length N (max_i N_i) containing at + * the ChainPositions for the streams for order (i + 1). + * The Rus attached to output chains for each order (of length K) + */ +class MergeProbabilities { + public: + MergeProbabilities(const InterpolateInfo &info, util::FixedArray &models_by_order) + : info_(info), models_by_order_(models_by_order) {} + + void Run(const util::stream::ChainPositions &outputs); + + private: + const InterpolateInfo &info_; + util::FixedArray &models_by_order_; +}; + +/** + * This class represents the output payload for this pass, which consists + * of an ngram-id, a probability, and then a vector of orders from which + * each of the component models backed off to for this ngram, encoded + * using the BoundedSequenceEncoding class. + */ +class PartialProbGamma : public lm::NGramHeader { +public: + PartialProbGamma(std::size_t order, std::size_t backoff_bytes) + : lm::NGramHeader(NULL, order), backoff_bytes_(backoff_bytes) { + // nothing + } + + std::size_t TotalSize() const { + return sizeof(WordIndex) * Order() + sizeof(After) + backoff_bytes_; + } + + // TODO: cache bounded sequence encoding in the pipeline? + static std::size_t TotalSize(const InterpolateInfo &info, uint8_t order) { + return sizeof(WordIndex) * order + sizeof(After) + MakeEncoder(info, order).EncodedLength(); + } + + float &Prob() { return Pay().prob; } + float Prob() const { return Pay().prob; } + + float &LowerProb() { return Pay().lower_prob; } + float LowerProb() const { return Pay().lower_prob; } + + const uint8_t *FromBegin() const { return Pay().from; } + uint8_t *FromBegin() { return Pay().from; } + +private: + struct After { + // Note that backoff_and_normalize assumes this comes first. + float prob; + float lower_prob; + uint8_t from[]; + }; + const After &Pay() const { return *reinterpret_cast(end()); } + After &Pay() { return *reinterpret_cast(end()); } + + std::size_t backoff_bytes_; +}; + +}} // namespaces +#endif // LM_INTERPOLATE_MERGE_PROBABILITIES_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test1 b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test1 new file mode 100644 index 0000000000000000000000000000000000000000..08d114d967e530c1376e17694ed79a259e1c6304 GIT binary patch literal 26 fcmcC1&C9l9NMtC<$Sh_^E-hgIQfZk*#U%^?ZW9PH literal 0 HcmV?d00001 diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test2 b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test2 new file mode 100644 index 0000000000000000000000000000000000000000..fd3a38071a366e284850384591ed2cac6b909abc GIT binary patch literal 39 ocmcC1&C9l9$ShVU$;d2b01*m_3~8A~#U%>Kr6mlB3Sc$^00a^Y^8f$< literal 0 HcmV?d00001 diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test3 b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test3 new file mode 100644 index 0000000000000000000000000000000000000000..6c89d7f480cb92621b546657887aaeb7f96e8fe8 GIT binary patch literal 16 XcmcC1&C9l9$Sh{aWGGHePGJB5Db56~ literal 0 HcmV?d00001 diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test_bad_order b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test_bad_order new file mode 100644 index 0000000000000000000000000000000000000000..d50a5e8b3147b6a9a4a47cc98f6d3c78206c9195 GIT binary patch literal 14 VcmcC1&C9l9C{9gIVaP0I001I31VjJ; literal 0 HcmV?d00001 diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test_no_unk b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test_no_unk new file mode 100644 index 0000000..fbcf12d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_test/test_no_unk @@ -0,0 +1 @@ +toto diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab.cc new file mode 100644 index 0000000..077403f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab.cc @@ -0,0 +1,131 @@ +#include "lm/interpolate/merge_vocab.hh" + +#include "lm/enumerate_vocab.hh" +#include "lm/interpolate/universal_vocab.hh" +#include "lm/lm_exception.hh" +#include "lm/vocab.hh" +#include "util/file_piece.hh" + +#include +#include +#include +#include + +namespace lm { +namespace interpolate { +namespace { + +class VocabFileReader { + public: + explicit VocabFileReader(const int fd, size_t model_num, uint64_t offset = 0); + + VocabFileReader &operator++(); + operator bool() const { return !eof_; } + uint64_t operator*() const { return Value(); } + + uint64_t Value() const { return hash_value_; } + size_t ModelNum() const { return model_num_; } + WordIndex CurrentIndex() const { return current_index_; } + + StringPiece Word() const { return word_; } + + private: + uint64_t hash_value_; + WordIndex current_index_; + bool eof_; + size_t model_num_; + StringPiece word_; + util::FilePiece file_piece_; +}; + +VocabFileReader::VocabFileReader(const int fd, const size_t model_num, uint64_t offset) : + hash_value_(0), + current_index_(0), + eof_(false), + model_num_(model_num), + file_piece_(util::DupOrThrow(fd)) { + word_ = file_piece_.ReadLine('\0'); + UTIL_THROW_IF(word_ != "", + FormatLoadException, + "Vocabulary words are in the wrong place."); + // setup to initial value + ++*this; +} + +VocabFileReader &VocabFileReader::operator++() { + try { + word_ = file_piece_.ReadLine('\0'); + } catch(util::EndOfFileException &e) { + eof_ = true; + return *this; + } + uint64_t prev_hash_value = hash_value_; + hash_value_ = ngram::detail::HashForVocab(word_.data(), word_.size()); + + // hash values should be monotonically increasing + UTIL_THROW_IF(hash_value_ < prev_hash_value, FormatLoadException, + ": word index not monotonically increasing." + << " model_num: " << model_num_ + << " prev hash: " << prev_hash_value + << " new hash: " << hash_value_); + + ++current_index_; + return *this; +} + +class CompareFiles { +public: + bool operator()(const VocabFileReader* x, + const VocabFileReader* y) + { return x->Value() > y->Value(); } +}; + +class Readers : public util::FixedArray { + public: + Readers(std::size_t number) : util::FixedArray(number) {} + void push_back(int fd, std::size_t i) { + new(end()) VocabFileReader(fd, i); + Constructed(); + } +}; + +} // namespace + +WordIndex MergeVocab(util::FixedArray &files, UniversalVocab &vocab, EnumerateVocab &enumerate) { + typedef std::priority_queue, CompareFiles> HeapType; + HeapType heap; + Readers readers(files.size()); + for (size_t i = 0; i < files.size(); ++i) { + readers.push_back(files[i], i); + heap.push(&readers.back()); + // initialize first index to 0 for + vocab.InsertUniversalIdx(i, 0, 0); + } + + uint64_t prev_hash_value = 0; + // global_index starts with which is 0 + WordIndex global_index = 0; + + enumerate.Add(0, ""); + while (!heap.empty()) { + VocabFileReader* top_vocab_file = heap.top(); + if (top_vocab_file->Value() != prev_hash_value) { + enumerate.Add(++global_index, top_vocab_file->Word()); + } + vocab.InsertUniversalIdx(top_vocab_file->ModelNum(), + top_vocab_file->CurrentIndex(), + global_index); + + prev_hash_value = top_vocab_file->Value(); + + heap.pop(); + if (++(*top_vocab_file)) { + heap.push(top_vocab_file); + } + } + return global_index + 1; +} + +} // namespace interpolate +} // namespace lm + diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab.hh new file mode 100644 index 0000000..8807568 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab.hh @@ -0,0 +1,23 @@ +#ifndef LM_INTERPOLATE_MERGE_VOCAB_H +#define LM_INTERPOLATE_MERGE_VOCAB_H + +#include "lm/word_index.hh" +#include "util/file.hh" +#include "util/fixed_array.hh" + +namespace lm { + +class EnumerateVocab; + +namespace interpolate { + +class UniversalVocab; + +// The combined vocabulary is enumerated with enumerate. +// Returns the size of the combined vocabulary. +// Does not take ownership of vocab_files. +WordIndex MergeVocab(util::FixedArray &vocab_files, UniversalVocab &vocab, EnumerateVocab &enumerate); + +}} // namespaces + +#endif // LM_INTERPOLATE_MERGE_VOCAB_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab_test.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab_test.cc new file mode 100644 index 0000000..b7fa5ff --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/merge_vocab_test.cc @@ -0,0 +1,127 @@ +#define BOOST_TEST_MODULE InterpolateMergeVocabTest +#include + +#include "lm/enumerate_vocab.hh" +#include "lm/interpolate/merge_vocab.hh" +#include "lm/interpolate/universal_vocab.hh" +#include "lm/lm_exception.hh" +#include "lm/vocab.hh" +#include "lm/word_index.hh" +#include "util/file.hh" +#include "util/file_piece.hh" + +#include + +namespace lm { +namespace interpolate { +namespace { + +// Stupid bjam permutes the command line arguments randomly. +class TestFiles { + public: + TestFiles() { + char **argv = boost::unit_test::framework::master_test_suite().argv; + int argc = boost::unit_test::framework::master_test_suite().argc; + BOOST_REQUIRE_EQUAL(6, argc); + for (int i = 1; i < argc; ++i) { + EndsWithAssign(argv[i], "test1", test[0]); + EndsWithAssign(argv[i], "test2", test[1]); + EndsWithAssign(argv[i], "test3", test[2]); + EndsWithAssign(argv[i], "no_unk", no_unk); + EndsWithAssign(argv[i], "bad_order", bad_order); + } + } + + void EndsWithAssign(char *arg, StringPiece value, util::scoped_fd &to) { + StringPiece str(arg); + if (str.size() < value.size()) return; + if (std::memcmp(str.data() + str.size() - value.size(), value.data(), value.size())) return; + to.reset(util::OpenReadOrThrow(arg)); + } + + util::scoped_fd test[3], no_unk, bad_order; +}; + +class DoNothingEnumerate : public EnumerateVocab { + public: + void Add(WordIndex, const StringPiece &) {} +}; + +BOOST_AUTO_TEST_CASE(MergeVocabTest) { + TestFiles files; + + util::FixedArray used_files(3); + used_files.push_back(files.test[0].get()); + used_files.push_back(files.test[1].get()); + used_files.push_back(files.test[2].get()); + + std::vector model_max_idx; + model_max_idx.push_back(10); + model_max_idx.push_back(10); + model_max_idx.push_back(10); + + util::scoped_fd combined(util::MakeTemp("temporary")); + + UniversalVocab universal_vocab(model_max_idx); + { + ngram::ImmediateWriteWordsWrapper writer(NULL, combined.get(), 0); + MergeVocab(used_files, universal_vocab, writer); + } + + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(0, 0), 0); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(1, 0), 0); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(2, 0), 0); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(0, 1), 1); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(1, 1), 2); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(2, 1), 8); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(0, 5), 11); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(1, 3), 4); + BOOST_CHECK_EQUAL(universal_vocab.GetUniversalIdx(2, 3), 10); + + util::SeekOrThrow(combined.get(), 0); + util::FilePiece f(combined.release()); + BOOST_CHECK_EQUAL("", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("a", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("is this", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("this a", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("first cut", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("this", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("a first", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("cut", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("is", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("i", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("secd", f.ReadLine('\0')); + BOOST_CHECK_EQUAL("first", f.ReadLine('\0')); + BOOST_CHECK_THROW(f.ReadLine('\0'), util::EndOfFileException); +} + +BOOST_AUTO_TEST_CASE(MergeVocabNoUnkTest) { + TestFiles files; + util::FixedArray used_files(1); + used_files.push_back(files.no_unk.get()); + + std::vector model_max_idx; + model_max_idx.push_back(10); + + UniversalVocab universal_vocab(model_max_idx); + DoNothingEnumerate nothing; + BOOST_CHECK_THROW(MergeVocab(used_files, universal_vocab, nothing), FormatLoadException); +} + +BOOST_AUTO_TEST_CASE(MergeVocabWrongOrderTest) { + TestFiles files; + + util::FixedArray used_files(2); + used_files.push_back(files.test[0].get()); + used_files.push_back(files.bad_order.get()); + + std::vector model_max_idx; + model_max_idx.push_back(10); + model_max_idx.push_back(10); + + lm::interpolate::UniversalVocab universal_vocab(model_max_idx); + DoNothingEnumerate nothing; + BOOST_CHECK_THROW(MergeVocab(used_files, universal_vocab, nothing), FormatLoadException); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize.cc new file mode 100644 index 0000000..ceeb6c2 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize.cc @@ -0,0 +1,373 @@ +#include "lm/interpolate/normalize.hh" + +#include "lm/common/compare.hh" +#include "lm/common/ngram_stream.hh" +#include "lm/interpolate/backoff_matrix.hh" +#include "lm/interpolate/bounded_sequence_encoding.hh" +#include "lm/interpolate/interpolate_info.hh" +#include "lm/interpolate/merge_probabilities.hh" +#include "lm/weights.hh" +#include "lm/word_index.hh" +#include "util/fixed_array.hh" +#include "util/scoped.hh" +#include "util/stream/stream.hh" +#include "util/stream/rewindable_stream.hh" + +#include +#include +#include + +namespace lm { namespace interpolate { +namespace { + +class BackoffQueueEntry { + public: + BackoffQueueEntry(float &entry, const util::stream::ChainPosition &position) + : entry_(entry), stream_(position) { + entry_ = 0.0; + } + + operator bool() const { return stream_; } + + NGramHeader operator*() const { return *stream_; } + const NGramHeader *operator->() const { return &*stream_; } + + void Enter() { + entry_ = stream_->Value().backoff; + } + + BackoffQueueEntry &Next() { + entry_ = 0.0; + ++stream_; + return *this; + } + + private: + float &entry_; + NGramStream stream_; +}; + +struct PtrGreater : public std::binary_function { + bool operator()(const BackoffQueueEntry *first, const BackoffQueueEntry *second) const { + return SuffixLexicographicLess()(**second, **first); + } +}; + +class EntryOwner : public util::FixedArray { + public: + void push_back(float &entry, const util::stream::ChainPosition &position) { + new (end()) BackoffQueueEntry(entry, position); + Constructed(); + } +}; + +std::size_t MaxOrder(const util::FixedArray &model) { + std::size_t ret = 0; + for (const util::stream::ChainPositions *m = model.begin(); m != model.end(); ++m) { + ret = std::max(ret, m->size()); + } + return ret; +} + +class BackoffManager { + public: + explicit BackoffManager(const util::FixedArray &models) + : entered_(MaxOrder(models)), matrix_(models.size(), MaxOrder(models)), skip_write_(MaxOrder(models)) { + std::size_t total = 0; + for (const util::stream::ChainPositions *m = models.begin(); m != models.end(); ++m) { + total += m->size(); + } + for (std::size_t i = 0; i < MaxOrder(models); ++i) { + entered_.push_back(models.size()); + } + owner_.Init(total); + for (const util::stream::ChainPositions *m = models.begin(); m != models.end(); ++m) { + for (const util::stream::ChainPosition *j = m->begin(); j != m->end(); ++j) { + owner_.push_back(matrix_.Backoff(m - models.begin(), j - m->begin()), *j); + if (owner_.back()) { + queue_.push(&owner_.back()); + } + } + } + } + + void SetupSkip(std::size_t order, util::stream::Stream &stream) { + skip_write_[order - 2] = &stream; + } + + // Move up the backoffs for the given n-gram. The n-grams must be provided + // in suffix lexicographic order. + void Enter(const NGramHeader &to) { + // Check that we exited properly. + for (std::size_t i = to.Order() - 1; i < entered_.size(); ++i) { + assert(entered_[i].empty()); + } + SuffixLexicographicLess less; + while (!queue_.empty() && less(**queue_.top(), to)) + SkipRecord(); + while (TopMatches(to)) { + BackoffQueueEntry *matches = queue_.top(); + entered_[to.Order() - 1].push_back(matches); + matches->Enter(); + queue_.pop(); + } + } + + void Exit(std::size_t order_minus_1) { + for (BackoffQueueEntry **i = entered_[order_minus_1].begin(); i != entered_[order_minus_1].end(); ++i) { + if ((*i)->Next()) + queue_.push(*i); + } + entered_[order_minus_1].clear(); + } + + float Get(std::size_t model, std::size_t order_minus_1) const { + return matrix_.Backoff(model, order_minus_1); + } + + void Finish() { + while (!queue_.empty()) + SkipRecord(); + } + + private: + void SkipRecord() { + BackoffQueueEntry *top = queue_.top(); + queue_.pop(); + // Is this the last instance of the n-gram? + if (!TopMatches(**top)) { + // An n-gram is being skipped. Called once per skipped n-gram, + // regardless of how many models it comes from. + *reinterpret_cast(skip_write_[(*top)->Order() - 1]->Get()) = 0.0; + ++*skip_write_[(*top)->Order() - 1]; + } + if (top->Next()) + queue_.push(top); + } + + bool TopMatches(const NGramHeader &header) const { + return !queue_.empty() && (*queue_.top())->Order() == header.Order() && std::equal(header.begin(), header.end(), (*queue_.top())->begin()); + } + + EntryOwner owner_; + std::priority_queue, PtrGreater> queue_; + + // Indexed by order then just all the matching models. + util::FixedArray > entered_; + + BackoffMatrix matrix_; + + std::vector skip_write_; +}; + +typedef long double Accum; + +// Handles n-grams of the same order, using recursion to call another instance +// for higher orders. +class Recurse { + public: + Recurse( + const InterpolateInfo &info, // Must stay alive the entire time. + std::size_t order, + const util::stream::ChainPosition &merged_probs, + const util::stream::ChainPosition &prob_out, + const util::stream::ChainPosition &backoff_out, + BackoffManager &backoffs, + Recurse *higher) // higher is null for the highest order. + : order_(order), + encoding_(MakeEncoder(info, order)), + input_(merged_probs, PartialProbGamma(order, encoding_.EncodedLength())), + prob_out_(prob_out), + backoff_out_(backoff_out), + backoffs_(backoffs), + lambdas_(&*info.lambdas.begin()), + higher_(higher), + decoded_backoffs_(info.Models()), + extended_context_(order - 1) { + // This is only for bigrams and above. Summing unigrams is a much easier case. + assert(order >= 2); + } + + // context = w_1^{n-1} + // z_lower = Z(w_2^{n-1}) + // Input: + // Merged probabilities without backoff applied in input_. + // Backoffs via backoffs_. + // Calculates: + // Z(w_1^{n-1}): intermediate only. + // p_I(x | w_1^{n-1}) for all x: w_1^{n-1}x exists: Written to prob_out_. + // b_I(w_1^{n-1}): Written to backoff_out_. + void SameContext(const NGramHeader &context, Accum z_lower) { + assert(context.size() == order_ - 1); + backoffs_.Enter(context); + prob_out_.Mark(); + + // This is the backoff term that applies when one assumes everything backs off: + // \prod_i b_i(w_1^{n-1})^{\lambda_i}. + Accum backoff_once = 0.0; + for (std::size_t m = 0; m < decoded_backoffs_.size(); ++m) { + backoff_once += lambdas_[m] * backoffs_.Get(m, order_ - 2); + } + + Accum z_delta = 0.0; + std::size_t count = 0; + for (; input_ && std::equal(context.begin(), context.end(), input_->begin()); ++input_, ++prob_out_, ++count) { + // Apply backoffs to probabilities. + // TODO: change bounded sequence encoding to have an iterator for decoding instead of doing a copy here. + encoding_.Decode(input_->FromBegin(), &*decoded_backoffs_.begin()); + for (std::size_t m = 0; m < NumModels(); ++m) { + // Apply the backoffs as instructed for model m. + float accumulated = 0.0; + // Change backoffs for [order it backed off to, order - 1) except + // with 0-indexing. There is still the potential to charge backoff + // for order - 1, which is done later. The backoffs charged here + // are b_m(w_{n-1}^{n-1}) ... b_m(w_2^{n-1}) + for (unsigned char backed_to = decoded_backoffs_[m]; backed_to < order_ - 2; ++backed_to) { + accumulated += backoffs_.Get(m, backed_to); + } + float lambda = lambdas_[m]; + // Lower p(x | w_2^{n-1}) gets all the backoffs except the highest. + input_->LowerProb() += accumulated * lambda; + // Charge the backoff b(w_1^{n-1}) if applicable, but only to attain p(x | w_1^{n-1}) + if (decoded_backoffs_[m] < order_ - 1) { + accumulated += backoffs_.Get(m, order_ - 2); + } + input_->Prob() += accumulated * lambda; + } + // TODO: better precision/less operations here. + z_delta += pow(10.0, input_->Prob()) - pow(10.0, input_->LowerProb() + backoff_once); + + // Write unnormalized probability record. + std::copy(input_->begin(), input_->end(), reinterpret_cast(prob_out_.Get())); + ProbWrite() = input_->Prob(); + } + // TODO numerical precision. + Accum z = log10(pow(10.0, z_lower + backoff_once) + z_delta); + + // Normalize. + prob_out_.Rewind(); + for (std::size_t i = 0; i < count; ++i, ++prob_out_) { + ProbWrite() -= z; + } + // This allows the stream to release data. + prob_out_.Mark(); + + // Output backoff. + *reinterpret_cast(backoff_out_.Get()) = z_lower + backoff_once - z; + ++backoff_out_; + + if (higher_.get()) + higher_->ExtendContext(context, z); + + backoffs_.Exit(order_ - 2); + } + + // Call is given a context and z(context). + // Evaluates y context x for all y,x. + void ExtendContext(const NGramHeader &middle, Accum z_lower) { + assert(middle.size() == order_ - 2); + // Copy because the input will advance. TODO avoid this copy by sharing amongst classes. + std::copy(middle.begin(), middle.end(), extended_context_.begin() + 1); + while (input_ && std::equal(middle.begin(), middle.end(), input_->begin() + 1)) { + *extended_context_.begin() = *input_->begin(); + SameContext(NGramHeader(&*extended_context_.begin(), order_ - 1), z_lower); + } + } + + void Finish() { + assert(!input_); + prob_out_.Poison(); + backoff_out_.Poison(); + if (higher_.get()) + higher_->Finish(); + } + + // The BackoffManager class also injects backoffs when it skips ahead e.g. b() = 1 + util::stream::Stream &BackoffStream() { return backoff_out_; } + + private: + // Write the probability to the correct place in prob_out_. Should use a proxy but currently incompatible with RewindableStream. + float &ProbWrite() { + return *reinterpret_cast(reinterpret_cast(prob_out_.Get()) + order_ * sizeof(WordIndex)); + } + + std::size_t NumModels() const { return decoded_backoffs_.size(); } + + const std::size_t order_; + + const BoundedSequenceEncoding encoding_; + + ProxyStream input_; + util::stream::RewindableStream prob_out_; + util::stream::Stream backoff_out_; + + BackoffManager &backoffs_; + const float *const lambdas_; + + // Higher order instance of this same class. + util::scoped_ptr higher_; + + // Temporary in SameContext. + std::vector decoded_backoffs_; + // Temporary in ExtendContext. + std::vector extended_context_; +}; + +class Thread { + public: + Thread(const InterpolateInfo &info, util::FixedArray &models_by_order, util::stream::Chains &prob_out, util::stream::Chains &backoff_out) + : info_(info), models_by_order_(models_by_order), prob_out_(prob_out), backoff_out_(backoff_out) {} + + void Run(const util::stream::ChainPositions &merged_probabilities) { + // Unigrams do not have enocded backoff info. + ProxyStream in(merged_probabilities[0], PartialProbGamma(1, 0)); + util::stream::RewindableStream prob_write(prob_out_[0]); + Accum z = 0.0; + prob_write.Mark(); + WordIndex count = 0; + for (; in; ++in, ++prob_write, ++count) { + // Note assumption that probabilitity comes first + memcpy(prob_write.Get(), in.Get(), sizeof(WordIndex) + sizeof(float)); + z += pow(10.0, in->Prob()); + } + // TODO HACK TODO: lmplz outputs p() = 1 to get q to compute nicely. That will always result in 1.0 more than it should be. + z -= 1.0; + float log_z = log10(z); + prob_write.Rewind(); + // Normalize unigram probabilities. + for (WordIndex i = 0; i < count; ++i, ++prob_write) { + *reinterpret_cast(reinterpret_cast(prob_write.Get()) + sizeof(WordIndex)) -= log_z; + } + prob_write.Poison(); + + // Now setup the higher orders. + util::scoped_ptr higher_order; + BackoffManager backoffs(models_by_order_); + std::size_t max_order = merged_probabilities.size(); + for (std::size_t order = max_order; order >= 2; --order) { + higher_order.reset(new Recurse(info_, order, merged_probabilities[order - 1], prob_out_[order - 1], backoff_out_[order - 2], backoffs, higher_order.release())); + backoffs.SetupSkip(order, higher_order->BackoffStream()); + } + if (max_order > 1) { + higher_order->ExtendContext(NGramHeader(NULL, 0), log_z); + backoffs.Finish(); + higher_order->Finish(); + } + } + + private: + const InterpolateInfo info_; + util::FixedArray &models_by_order_; + util::stream::ChainPositions prob_out_; + util::stream::ChainPositions backoff_out_; +}; + +} // namespace + +void Normalize(const InterpolateInfo &info, util::FixedArray &models_by_order, util::stream::Chains &merged_probabilities, util::stream::Chains &prob_out, util::stream::Chains &backoff_out) { + assert(prob_out.size() == backoff_out.size() + 1); + // Arbitrarily put the thread on the merged_probabilities Chains. + merged_probabilities >> Thread(info, models_by_order, prob_out, backoff_out); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize.hh new file mode 100644 index 0000000..dd26b25 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize.hh @@ -0,0 +1,35 @@ +#ifndef LM_INTERPOLATE_NORMALIZE_H +#define LM_INTERPOLATE_NORMALIZE_H + +#include "util/fixed_array.hh" + +/* Pass 2: + * - Multiply backoff weights by the backed off probabilities from pass 1. + * - Compute the normalization factor Z. + * - Send Z to the next highest order. + * - Rewind and divide by Z. + */ + +namespace util { namespace stream { +class ChainPositions; +class Chains; +}} // namespaces + +namespace lm { namespace interpolate { + +struct InterpolateInfo; + +void Normalize( + const InterpolateInfo &info, + // Input full models for backoffs. Assumes that renumbering has been done. Suffix order. + util::FixedArray &models_by_order, + // Input PartialProbGamma from MergeProbabilities. Context order. + util::stream::Chains &merged_probabilities, + // Output NGram with normalized probabilities. Context order. + util::stream::Chains &probabilities_out, + // Output bare floats with backoffs. Note backoffs.size() == order - 1. Suffix order. + util::stream::Chains &backoffs_out); + +}} // namespaces + +#endif // LM_INTERPOLATE_NORMALIZE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize_test.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize_test.cc new file mode 100644 index 0000000..fe220f3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/normalize_test.cc @@ -0,0 +1,86 @@ +#include "lm/interpolate/normalize.hh" + +#include "lm/interpolate/interpolate_info.hh" +#include "lm/interpolate/merge_probabilities.hh" +#include "lm/common/ngram_stream.hh" +#include "util/stream/chain.hh" +#include "util/stream/multi_stream.hh" + +#define BOOST_TEST_MODULE NormalizeTest +#include + +namespace lm { namespace interpolate { namespace { + +// log without backoff +const float kInputs[] = {-0.3, 1.2, -9.8, 4.0, -7.0, 0.0}; + +class WriteInput { + public: + WriteInput() {} + void Run(const util::stream::ChainPosition &to) { + util::stream::Stream out(to); + for (WordIndex i = 0; i < sizeof(kInputs) / sizeof(float); ++i, ++out) { + memcpy(out.Get(), &i, sizeof(WordIndex)); + memcpy((uint8_t*)out.Get() + sizeof(WordIndex), &kInputs[i], sizeof(float)); + } + out.Poison(); + } +}; + +void CheckOutput(const util::stream::ChainPosition &from) { + NGramStream in(from); + float sum = 0.0; + for (WordIndex i = 0; i < sizeof(kInputs) / sizeof(float) - 1 /* at the end */; ++i) { + sum += pow(10.0, kInputs[i]); + } + sum = log10(sum); + BOOST_REQUIRE(in); + BOOST_CHECK_CLOSE(kInputs[0] - sum, in->Value(), 0.0001); + BOOST_REQUIRE(++in); + BOOST_CHECK_CLOSE(kInputs[1] - sum, in->Value(), 0.0001); + BOOST_REQUIRE(++in); + BOOST_CHECK_CLOSE(kInputs[2] - sum, in->Value(), 0.0001); + BOOST_REQUIRE(++in); + BOOST_CHECK_CLOSE(kInputs[3] - sum, in->Value(), 0.0001); + BOOST_REQUIRE(++in); + BOOST_CHECK_CLOSE(kInputs[4] - sum, in->Value(), 0.0001); + BOOST_REQUIRE(++in); + BOOST_CHECK_CLOSE(kInputs[5] - sum, in->Value(), 0.0001); + BOOST_CHECK(!++in); +} + +BOOST_AUTO_TEST_CASE(Unigrams) { + InterpolateInfo info; + info.lambdas.push_back(2.0); + info.lambdas.push_back(-0.1); + info.orders.push_back(1); + info.orders.push_back(1); + + BOOST_CHECK_EQUAL(0, MakeEncoder(info, 1).EncodedLength()); + + // No backoffs. + util::stream::Chains blank(0); + util::FixedArray models_by_order(2); + models_by_order.push_back(blank); + models_by_order.push_back(blank); + + util::stream::Chains merged_probabilities(1); + util::stream::Chains probabilities_out(1); + util::stream::Chains backoffs_out(0); + + merged_probabilities.push_back(util::stream::ChainConfig(sizeof(WordIndex) + sizeof(float) + sizeof(float), 2, 24)); + probabilities_out.push_back(util::stream::ChainConfig(sizeof(WordIndex) + sizeof(float), 2, 100)); + + merged_probabilities[0] >> WriteInput(); + Normalize(info, models_by_order, merged_probabilities, probabilities_out, backoffs_out); + + util::stream::ChainPosition checker(probabilities_out[0].Add()); + + merged_probabilities >> util::stream::kRecycle; + probabilities_out >> util::stream::kRecycle; + + CheckOutput(checker); + probabilities_out.Wait(); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/pipeline.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/pipeline.cc new file mode 100644 index 0000000..da7f857 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/pipeline.cc @@ -0,0 +1,187 @@ +#include "lm/interpolate/pipeline.hh" + +#include "lm/common/compare.hh" +#include "lm/common/print.hh" +#include "lm/common/renumber.hh" +#include "lm/vocab.hh" +#include "lm/interpolate/backoff_reunification.hh" +#include "lm/interpolate/interpolate_info.hh" +#include "lm/interpolate/merge_probabilities.hh" +#include "lm/interpolate/merge_vocab.hh" +#include "lm/interpolate/normalize.hh" +#include "lm/interpolate/universal_vocab.hh" +#include "util/stream/chain.hh" +#include "util/stream/count_records.hh" +#include "util/stream/io.hh" +#include "util/stream/multi_stream.hh" +#include "util/stream/sort.hh" +#include "util/fixed_array.hh" + +namespace lm { namespace interpolate { namespace { + +/* Put the original input files on chains and renumber them */ +void SetupInputs(std::size_t buffer_size, const UniversalVocab &vocab, util::FixedArray &models, bool exclude_highest, util::FixedArray &chains, util::FixedArray &positions) { + chains.clear(); + positions.clear(); + // TODO: much better memory sizing heuristics e.g. not making the chain larger than it will use. + util::stream::ChainConfig config(0, 2, buffer_size); + for (std::size_t i = 0; i < models.size(); ++i) { + chains.push_back(models[i].Order() - exclude_highest); + for (std::size_t j = 0; j < models[i].Order() - exclude_highest; ++j) { + config.entry_size = sizeof(WordIndex) * (j + 1) + sizeof(float) * 2; // TODO do not include wasteful backoff for highest. + chains.back().push_back(config); + } + if (i == models.size() - 1) + chains.back().back().ActivateProgress(); + models[i].Source(chains.back()); + for (std::size_t j = 0; j < models[i].Order() - exclude_highest; ++j) { + chains[i][j] >> Renumber(vocab.Mapping(i), j + 1); + } + } + for (std::size_t i = 0; i < chains.size(); ++i) { + positions.push_back(chains[i]); + } +} + +template void SinkSort(const util::stream::SortConfig &config, util::stream::Chains &chains, util::stream::Sorts &sorts) { + for (std::size_t i = 0; i < chains.size(); ++i) { + sorts.push_back(chains[i], config, Compare(i + 1)); + } +} + +template void SourceSort(util::stream::Chains &chains, util::stream::Sorts &sorts) { + // TODO memory management + for (std::size_t i = 0; i < sorts.size(); ++i) { + sorts[i].Merge(sorts[i].DefaultLazy()); + } + for (std::size_t i = 0; i < sorts.size(); ++i) { + sorts[i].Output(chains[i], sorts[i].DefaultLazy()); + } +} + +} // namespace + +void Pipeline(util::FixedArray &models, const Config &config, int write_file) { + // Setup InterpolateInfo and UniversalVocab. + InterpolateInfo info; + info.lambdas = config.lambdas; + std::vector vocab_sizes; + + util::scoped_fd vocab_null(util::MakeTemp(config.sort.temp_prefix)); + std::size_t max_order = 0; + util::FixedArray vocab_files(models.size()); + for (ModelBuffer *i = models.begin(); i != models.end(); ++i) { + info.orders.push_back(i->Order()); + vocab_sizes.push_back(i->Counts()[0]); + vocab_files.push_back(i->VocabFile()); + max_order = std::max(max_order, i->Order()); + } + util::scoped_ptr vocab(new UniversalVocab(vocab_sizes)); + { + ngram::ImmediateWriteWordsWrapper writer(NULL, vocab_null.get(), 0); + MergeVocab(vocab_files, *vocab, writer); + } + + std::cerr << "Merging probabilities." << std::endl; + // Pass 1: merge probabilities + util::FixedArray input_chains(models.size()); + util::FixedArray models_by_order(models.size()); + SetupInputs(config.BufferSize(), *vocab, models, false, input_chains, models_by_order); + + util::stream::Chains merged_probs(max_order); + for (std::size_t i = 0; i < max_order; ++i) { + merged_probs.push_back(util::stream::ChainConfig(PartialProbGamma::TotalSize(info, i + 1), 2, config.BufferSize())); // TODO: not buffer_size + } + merged_probs >> MergeProbabilities(info, models_by_order); + std::vector counts(max_order); + for (std::size_t i = 0; i < max_order; ++i) { + merged_probs[i] >> util::stream::CountRecords(&counts[i]); + } + for (util::stream::Chains *i = input_chains.begin(); i != input_chains.end(); ++i) { + *i >> util::stream::kRecycle; + } + + // Pass 2: normalize. + { + util::stream::Sorts sorts(merged_probs.size()); + SinkSort(config.sort, merged_probs, sorts); + merged_probs.Wait(true); + for (util::stream::Chains *i = input_chains.begin(); i != input_chains.end(); ++i) { + i->Wait(true); + } + SourceSort(merged_probs, sorts); + } + + std::cerr << "Normalizing" << std::endl; + SetupInputs(config.BufferSize(), *vocab, models, true, input_chains, models_by_order); + util::stream::Chains probabilities(max_order), backoffs(max_order - 1); + std::size_t block_count = 2; + for (std::size_t i = 0; i < max_order; ++i) { + // Careful accounting to ensure RewindableStream can fit the entire vocabulary. + block_count = std::max(block_count, 2); + // This much needs to fit in RewindableStream. + std::size_t fit = NGram::TotalSize(i + 1) * counts[0]; + // fit / (block_count - 1) rounded up + std::size_t min_block = (fit + block_count - 2) / (block_count - 1); + std::size_t specify = std::max(config.BufferSize(), min_block * block_count); + probabilities.push_back(util::stream::ChainConfig(NGram::TotalSize(i + 1), block_count, specify)); + } + for (std::size_t i = 0; i < max_order - 1; ++i) { + backoffs.push_back(util::stream::ChainConfig(sizeof(float), 2, config.BufferSize())); + } + Normalize(info, models_by_order, merged_probs, probabilities, backoffs); + util::FixedArray backoff_buffers(backoffs.size()); + for (std::size_t i = 0; i < max_order - 1; ++i) { + backoff_buffers.push_back(util::MakeTemp(config.sort.temp_prefix)); + backoffs[i] >> backoff_buffers.back().Sink() >> util::stream::kRecycle; + } + for (util::stream::Chains *i = input_chains.begin(); i != input_chains.end(); ++i) { + *i >> util::stream::kRecycle; + } + merged_probs >> util::stream::kRecycle; + + // Pass 3: backoffs in the right place. + { + util::stream::Sorts sorts(probabilities.size()); + SinkSort(config.sort, probabilities, sorts); + probabilities.Wait(true); + for (util::stream::Chains *i = input_chains.begin(); i != input_chains.end(); ++i) { + i->Wait(true); + } + backoffs.Wait(true); + merged_probs.Wait(true); + // destroy universal vocab to save RAM. + vocab.reset(); + SourceSort(probabilities, sorts); + } + + std::cerr << "Reunifying backoffs" << std::endl; + util::stream::ChainPositions prob_pos(max_order - 1); + util::stream::Chains combined(max_order - 1); + for (std::size_t i = 0; i < max_order - 1; ++i) { + if (i == max_order - 2) + backoffs[i].ActivateProgress(); + backoffs[i].SetProgressTarget(backoff_buffers[i].Size()); + backoffs[i] >> backoff_buffers[i].Source(true); + prob_pos.push_back(probabilities[i].Add()); + combined.push_back(util::stream::ChainConfig(NGram::TotalSize(i + 1), 2, config.BufferSize())); + } + util::stream::ChainPositions backoff_pos(backoffs); + + ReunifyBackoff(prob_pos, backoff_pos, combined); + + util::stream::ChainPositions output_pos(max_order); + for (std::size_t i = 0; i < max_order - 1; ++i) { + output_pos.push_back(combined[i].Add()); + } + output_pos.push_back(probabilities.back().Add()); + + probabilities >> util::stream::kRecycle; + backoffs >> util::stream::kRecycle; + combined >> util::stream::kRecycle; + + // TODO genericize to ModelBuffer etc. + PrintARPA(vocab_null.get(), write_file, counts).Run(output_pos); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/pipeline.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/pipeline.hh new file mode 100644 index 0000000..b200248 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/pipeline.hh @@ -0,0 +1,22 @@ +#ifndef LM_INTERPOLATE_PIPELINE_H +#define LM_INTERPOLATE_PIPELINE_H + +#include "lm/common/model_buffer.hh" +#include "util/fixed_array.hh" +#include "util/stream/config.hh" + +#include +#include + +namespace lm { namespace interpolate { + +struct Config { + std::vector lambdas; + util::stream::SortConfig sort; + std::size_t BufferSize() const { return sort.buffer_size; } +}; + +void Pipeline(util::FixedArray &models, const Config &config, int write_file); + +}} // namespaces +#endif // LM_INTERPOLATE_PIPELINE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/split_worker.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/split_worker.cc new file mode 100644 index 0000000..e777bf0 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/split_worker.cc @@ -0,0 +1,40 @@ +#include "lm/interpolate/split_worker.hh" +#include "lm/common/ngram.hh" + +namespace lm { +namespace interpolate { + +SplitWorker::SplitWorker(std::size_t order, util::stream::Chain &backoff_chain, + util::stream::Chain &sort_chain) + : order_(order) { + backoff_chain >> backoff_input_; + sort_chain >> sort_input_; +} + +void SplitWorker::Run(const util::stream::ChainPosition &position) { + // input: ngram record (id, prob, and backoff) + // output: a float to the backoff_input stream + // an ngram id and a float to the sort_input stream + for (util::stream::Stream stream(position); stream; ++stream) { + NGram ngram(stream.Get(), order_); + + // write id and prob to the sort stream + float prob = ngram.Value().prob; + lm::WordIndex *out = reinterpret_cast(sort_input_.Get()); + for (const lm::WordIndex *it = ngram.begin(); it != ngram.end(); ++it) { + *out++ = *it; + } + *reinterpret_cast(out) = prob; + ++sort_input_; + + // write backoff to the backoff output stream + float boff = ngram.Value().backoff; + *reinterpret_cast(backoff_input_.Get()) = boff; + ++backoff_input_; + } + sort_input_.Poison(); + backoff_input_.Poison(); +} + +} +} diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/split_worker.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/split_worker.hh new file mode 100644 index 0000000..15fae68 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/split_worker.hh @@ -0,0 +1,44 @@ +#ifndef KENLM_INTERPOLATE_SPLIT_WORKER_H_ +#define KENLM_INTERPOLATE_SPLIT_WORKER_H_ + +#include "util/stream/chain.hh" +#include "util/stream/stream.hh" + +namespace lm { +namespace interpolate { + +class SplitWorker { + public: + /** + * Constructs a split worker for a particular order. It writes the + * split-off backoff values to the backoff chain and the ngram id and + * probability to the sort chain for each ngram in the input. + */ + SplitWorker(std::size_t order, util::stream::Chain &backoff_chain, + util::stream::Chain &sort_chain); + + /** + * The callback invoked to handle the input from the ngram intermediate + * files. + */ + void Run(const util::stream::ChainPosition& position); + + private: + /** + * The ngram order we are reading/writing for. + */ + std::size_t order_; + + /** + * The stream to write to for the backoff values. + */ + util::stream::Stream backoff_input_; + + /** + * The stream to write to for the ngram id + probability values. + */ + util::stream::Stream sort_input_; +}; +} +} +#endif diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/streaming_example_main.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/streaming_example_main.cc new file mode 100644 index 0000000..1f543cb --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/streaming_example_main.cc @@ -0,0 +1,195 @@ +#include "lm/common/compare.hh" +#include "lm/common/model_buffer.hh" +#include "lm/common/ngram.hh" +#include "util/stream/chain.hh" +#include "util/stream/multi_stream.hh" +#include "util/stream/sort.hh" +#include "lm/interpolate/split_worker.hh" + +#include +#include + +#if defined(_WIN32) || defined(_WIN64) + +// Windows doesn't define +// +// So we define what we need here instead: +// +#define STDIN_FILENO = 0 +#define STDOUT_FILENO = 1 +#else // Huzzah for POSIX! +#include +#endif + +/* + * This is a simple example program that takes in intermediate + * suffix-sorted ngram files and outputs two sets of files: one for backoff + * probability values (raw numbers, in suffix order) and one for + * probability values (ngram id and probability, in *context* order) + */ +int main(int argc, char *argv[]) { + using namespace lm::interpolate; + + const std::size_t ONE_GB = 1 << 30; + const std::size_t SIXTY_FOUR_MB = 1 << 26; + const std::size_t NUMBER_OF_BLOCKS = 2; + + std::string FILE_NAME = "ngrams"; + std::string CONTEXT_SORTED_FILENAME = "csorted-ngrams"; + std::string BACKOFF_FILENAME = "backoffs"; + std::string TMP_DIR = "/tmp/"; + + try { + namespace po = boost::program_options; + po::options_description options("canhazinterp Pass-3 options"); + + options.add_options() + ("help,h", po::bool_switch(), "Show this help message") + ("ngrams,n", po::value(&FILE_NAME), "ngrams file") + ("csortngrams,c", po::value(&CONTEXT_SORTED_FILENAME), "context sorted ngrams file") + ("backoffs,b", po::value(&BACKOFF_FILENAME), "backoffs file") + ("tmpdir,t", po::value(&TMP_DIR), "tmp dir"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, options), vm); + + // Display help + if(vm["help"].as()) { + std::cerr << "Usage: " << options << std::endl; + return 1; + } + } + catch(const std::exception &e) { + + std::cerr << e.what() << std::endl; + return 1; + + } + + // The basic strategy here is to have three chains: + // - The first reads the ngram order inputs using ModelBuffer. Those are + // then stripped of their backoff values and fed into the third chain; + // the backoff values *themselves* are written to the second chain. + // + // - The second chain takes the backoff values and writes them out to a + // file (one for each order). + // + // - The third chain takes just the probability values and ngrams and + // writes them out, sorted in context-order, to a file (one for each + // order). + + // This will be used to read in the binary intermediate files. There is + // one file per order (e.g. ngrams.1, ngrams.2, ...) + lm::ModelBuffer buffer(FILE_NAME); + + // Create a separate chains for each ngram order for: + // - Input from the intermediate files + // - Output to the backoff file + // - Output to the (context-sorted) probability file + util::stream::Chains ngram_inputs(buffer.Order()); + util::stream::Chains backoff_chains(buffer.Order()); + util::stream::Chains prob_chains(buffer.Order()); + for (std::size_t i = 0; i < buffer.Order(); ++i) { + ngram_inputs.push_back(util::stream::ChainConfig( + lm::NGram::TotalSize(i + 1), NUMBER_OF_BLOCKS, ONE_GB)); + + backoff_chains.push_back( + util::stream::ChainConfig(sizeof(float), NUMBER_OF_BLOCKS, ONE_GB)); + + prob_chains.push_back(util::stream::ChainConfig( + sizeof(lm::WordIndex) * (i + 1) + sizeof(float), NUMBER_OF_BLOCKS, + ONE_GB)); + } + + // This sets the input for each of the ngram order chains to the + // appropriate file + buffer.Source(ngram_inputs); + + util::FixedArray > workers(buffer.Order()); + for (std::size_t i = 0; i < buffer.Order(); ++i) { + // Attach a SplitWorker to each of the ngram input chains, writing to the + // corresponding order's backoff and probability chains + workers.push_back( + new SplitWorker(i + 1, backoff_chains[i], prob_chains[i])); + ngram_inputs[i] >> boost::ref(*workers.back()); + } + + util::stream::SortConfig sort_cfg; + sort_cfg.temp_prefix = TMP_DIR; + sort_cfg.buffer_size = SIXTY_FOUR_MB; + sort_cfg.total_memory = ONE_GB; + + // This will parallel merge sort the individual order files, putting + // them in context-order instead of suffix-order. + // + // Two new threads will be running, each owned by the chains[i] object. + // - The first executes BlockSorter.Run() to sort the n-gram entries + // - The second executes WriteAndRecycle.Run() to write each sorted + // block to disk as a temporary file + util::stream::Sorts sorts(buffer.Order()); + for (std::size_t i = 0; i < prob_chains.size(); ++i) { + sorts.push_back(prob_chains[i], sort_cfg, lm::ContextOrder(i + 1)); + } + + // Set the sort output to be on the same chain + for (std::size_t i = 0; i < prob_chains.size(); ++i) { + // The following call to Chain::Wait() + // joins the threads owned by chains[i]. + // + // As such the following call won't return + // until all threads owned by chains[i] have completed. + // + // The following call also resets chain[i] + // so that it can be reused + // (including free'ing the memory previously used by the chain) + prob_chains[i].Wait(); + + // In an ideal world (without memory restrictions) + // we could merge all of the previously sorted blocks + // by reading them all completely into memory + // and then running merge sort over them. + // + // In the real world, we have memory restrictions; + // depending on how many blocks we have, + // and how much memory we can use to read from each block + // (sort_config.buffer_size) + // it may be the case that we have insufficient memory + // to read sort_config.buffer_size of data from each block from disk. + // + // If this occurs, then it will be necessary to perform one or more rounds + // of merge sort on disk; + // doing so will reduce the number of blocks that we will eventually + // need to read from + // when performing the final round of merge sort in memory. + // + // So, the following call determines whether it is necessary + // to perform one or more rounds of merge sort on disk; + // if such on-disk merge sorting is required, such sorting is performed. + // + // Finally, the following method launches a thread that calls + // OwningMergingReader.Run() + // to perform the final round of merge sort in memory. + // + // Merge sort could have be invoked directly + // so that merge sort memory doesn't coexist with Chain memory. + sorts[i].Output(prob_chains[i]); + } + + // Create another model buffer for our output on e.g. csorted-ngrams.1, + // csorted-ngrams.2, ... + lm::ModelBuffer output_buf(CONTEXT_SORTED_FILENAME, true, false); + output_buf.Sink(prob_chains, buffer.Counts()); + + // Create a third model buffer for our backoff output on e.g. backoff.1, + // backoff.2, ... + lm::ModelBuffer boff_buf(BACKOFF_FILENAME, true, false); + boff_buf.Sink(backoff_chains, buffer.Counts()); + + // Joins all threads that chains owns, + // and does a for loop over each chain object in chains, + // calling chain.Wait() on each such chain object + ngram_inputs.Wait(true); + backoff_chains.Wait(true); + prob_chains.Wait(true); + + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives.cc new file mode 100644 index 0000000..7cd915a --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives.cc @@ -0,0 +1,127 @@ +#include "lm/interpolate/tune_derivatives.hh" + +#include "lm/interpolate/tune_instances.hh" +#include "lm/interpolate/tune_matrix.hh" +#include "util/stream/chain.hh" +#include "util/stream/typed_stream.hh" + +#include + +namespace lm { namespace interpolate { + +Accum Derivatives(Instances &in, const Vector &weights, Vector &gradient, Matrix &hessian) { + gradient = in.CorrectGradientTerm(); + hessian = Matrix::Zero(weights.rows(), weights.rows()); + + // TODO: loop instead to force low-memory evaluation? + // Compute p_I(x)*Z_{\epsilon} i.e. the unnormalized probabilities + Vector weighted_uni((in.LNUnigrams() * weights).array().exp()); + // Even -inf doesn't work for because weights can be negative. Manually set it to zero. + weighted_uni(in.BOS()) = 0.0; + Accum Z_epsilon = weighted_uni.sum(); + // unigram_cross(i) = \sum_{all x} p_I(x) ln p_i(x) + Vector unigram_cross(in.LNUnigrams().transpose() * weighted_uni / Z_epsilon); + + Accum sum_B_I = 0.0; + Accum sum_ln_Z_context = 0.0; + + // Temporaries used each cycle of the loop. + Matrix convolve; + Vector full_cross; + Matrix hessian_missing_Z_context; + // Backed off ln p_i(x)B_i(context) + Vector ln_p_i_backed; + // Full ln p_i(x | context) + Vector ln_p_i_full; + + // TODO make configurable memory size. + util::stream::Chain chain(util::stream::ChainConfig(in.ReadExtensionsEntrySize(), 2, 64 << 20)); + chain.ActivateProgress(); + in.ReadExtensions(chain); + util::stream::TypedStream extensions(chain.Add()); + chain >> util::stream::kRecycle; + + // Loop over instances (words in the tuning data). + for (InstanceIndex n = 0; n < in.NumInstances(); ++n) { + assert(extensions); + Accum weighted_backoffs = exp(in.LNBackoffs(n).dot(weights)); + + // Compute \sum_{x: model does not back off to unigram} p_I(x)Z(epsilon) + Accum unnormalized_sum_x_p_I = 0.0; + // Compute \sum_{x: model does not back off to unigram} p_I(x | context)Z(context) + Accum unnormalized_sum_x_p_I_full = 0.0; + + // This should be divided by Z_context then added to the Hessian. + hessian_missing_Z_context = Matrix::Zero(weights.rows(), weights.rows()); + + full_cross = Vector::Zero(weights.rows()); + + // Loop over words within an instance for which extension exists. An extension happens when any model matches more than a unigram in the tuning instance. + while (extensions && extensions->instance == n) { + const WordIndex word = extensions->word; + unnormalized_sum_x_p_I += weighted_uni(word); + + ln_p_i_backed = in.LNUnigrams().row(word) + in.LNBackoffs(n); + + // Calculate ln_p_i_full(i) = ln p_i(word | context) by filling in unigrams then overwriting with extensions. + ln_p_i_full = ln_p_i_backed; + // Loop over all models that have an extension for the same word namely p_i(word | context) matches at least a bigram. + for (; extensions && extensions->word == word && extensions->instance == n; ++extensions) { + ln_p_i_full(extensions->model) = extensions->ln_prob; + } + + // This is the weighted product of probabilities. In other words, p_I(word | context) * Z(context) = exp(\sum_i w_i * p_i(word | context)). + Accum weighted = exp(ln_p_i_full.dot(weights)); + unnormalized_sum_x_p_I_full += weighted; + + // These aren't normalized by Z_context (happens later) + full_cross.noalias() += + weighted * ln_p_i_full + - weighted_uni(word) * weighted_backoffs /* we'll divide by Z_context later to form B_I */ * in.LNUnigrams().row(word).transpose(); + + // This will get multiplied by Z_context then added to the Hessian. + hessian_missing_Z_context.noalias() += + // Replacement terms. + weighted * ln_p_i_full * ln_p_i_full.transpose() + // Presumed unigrams. Z_epsilon * weighted_backoffs will turn into B_I once all of this is divided by Z_context. + - weighted_uni(word) * weighted_backoffs * ln_p_i_backed * ln_p_i_backed.transpose(); + } + + Accum Z_context = + weighted_backoffs * (Z_epsilon - unnormalized_sum_x_p_I) // Back off and unnormalize the unigrams for which there is no extension. + + unnormalized_sum_x_p_I_full; // Add the extensions. + sum_ln_Z_context += log(Z_context); + Accum B_I = Z_epsilon / Z_context * weighted_backoffs; + sum_B_I += B_I; + + // This is the gradient term for this instance except for -log p_i(w_n | w_1^{n-1}) which was accounted for as part of neg_correct_sum_. + // full_cross(i) is \sum_{all x} p_I(x | context) log p_i(x | context) + // Prior terms excluded dividing by Z_context because it wasn't known at the time. + full_cross /= Z_context; + full_cross += + // Uncorrected term + B_I * (in.LNBackoffs(n).transpose() + unigram_cross) + // Subtract values that should not have been charged. + - unnormalized_sum_x_p_I / Z_epsilon * B_I * in.LNBackoffs(n).transpose(); + gradient += full_cross; + + convolve = unigram_cross * in.LNBackoffs(n); + // There's one missing term here, which is independent of context and done at the end. + hessian.noalias() += + // First term of Hessian, assuming all models back off to unigram. + B_I * (convolve + convolve.transpose() + in.LNBackoffs(n).transpose() * in.LNBackoffs(n)) + // Error in the first term, correcting from unigram to full probabilities. + + hessian_missing_Z_context / Z_context + // Second term of Hessian, with correct full probabilities. + - full_cross * full_cross.transpose(); + } + + for (Matrix::Index x = 0; x < weighted_uni.rows(); ++x) { + // \sum_{contexts} B_I(context) \sum_x p_I(x) log p_i(x) log p_j(x) + // TODO can this be optimized? It's summing over the entire vocab which should be a matrix operation. + hessian.noalias() += sum_B_I * weighted_uni(x) / Z_epsilon * in.LNUnigrams().row(x).transpose() * in.LNUnigrams().row(x); + } + return exp((in.CorrectGradientTerm().dot(weights) + sum_ln_Z_context) / static_cast(in.NumInstances())); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives.hh new file mode 100644 index 0000000..e3bedd3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives.hh @@ -0,0 +1,20 @@ +#ifndef LM_INTERPOLATE_TUNE_DERIVATIVES_H +#define LM_INTERPOLATE_TUNE_DERIVATIVES_H + +#include "lm/interpolate/tune_matrix.hh" + +#include +#include + +namespace lm { namespace interpolate { + +class Instances; + +// Given tuning instances and model weights, computes the objective function (log probability), gradient, and Hessian. +// Returns log probability / number of instances. +Accum Derivatives(Instances &instances /* Doesn't modify but ReadExtensions is lazy */, const Vector &weights, Vector &gradient, Matrix &hessian); + +}} // namespaces + +#endif // LM_INTERPOLATE_TUNE_DERIVATIVES_H + diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives_test.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives_test.cc new file mode 100644 index 0000000..2a43abf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_derivatives_test.cc @@ -0,0 +1,138 @@ +#include "lm/interpolate/tune_derivatives.hh" + +#include "lm/interpolate/tune_instances.hh" + +#include "util/stream/config.hh" +#include "util/stream/chain.hh" +#include "util/stream/io.hh" +#include "util/stream/typed_stream.hh" + +#define BOOST_TEST_MODULE DerivativeTest +#include + +namespace lm { namespace interpolate { + +class MockInstances : public Instances { + public: + MockInstances() : chain_(util::stream::ChainConfig(ReadExtensionsEntrySize(), 2, 100)), write_(chain_.Add()) { + extensions_subsequent_.reset(new util::stream::FileBuffer(util::MakeTemp("/tmp/"))); + chain_ >> extensions_subsequent_->Sink() >> util::stream::kRecycle; + } + + Matrix &LNUnigrams() { return ln_unigrams_; } + + BackoffMatrix &LNBackoffs() { return ln_backoffs_; } + + WordIndex &BOS() { return bos_; } + + Vector &NegLNCorrectSum() { return neg_ln_correct_sum_; } + + // Extensions must be provided sorted! + void AddExtension(const Extension &extension) { + *write_ = extension; + ++write_; + } + + void DoneExtending() { + write_.Poison(); + chain_.Wait(true); + } + + private: + util::stream::Chain chain_; + util::stream::TypedStream write_; +}; + +namespace { + +BOOST_AUTO_TEST_CASE(Small) { + MockInstances mock; + + { + // Three vocabulary words plus , two models. + Matrix unigrams(4, 2); + unigrams << + 0.1, 0.6, + 0.4, 0.3, + 0.5, 0.1, + // + 1.0, 1.0; + mock.LNUnigrams() = unigrams.array().log(); + } + mock.BOS() = 3; + + // One instance + mock.LNBackoffs().resize(1, 2); + mock.LNBackoffs() << 0.2, 0.4; + mock.LNBackoffs() = mock.LNBackoffs().array().log(); + + // Sparse extensions: model 0 word 2 and model 1 word 1. + + // Assuming that model 1 only matches word 1, this is p_1(1 | context) + Accum model_1_word_1 = 1.0 - .6 * .4 - .1 * .4; + + mock.NegLNCorrectSum().resize(2); + // We'll suppose correct has WordIndex 1, which backs off in model 0, and matches in model 1 + mock.NegLNCorrectSum() << (0.4 * 0.2), model_1_word_1; + mock.NegLNCorrectSum() = -mock.NegLNCorrectSum().array().log(); + + Accum model_0_word_2 = 1.0 - .1 * .2 - .4 * .2; + + Extension ext; + + ext.instance = 0; + ext.word = 1; + ext.model = 1; + ext.ln_prob = log(model_1_word_1); + mock.AddExtension(ext); + + ext.instance = 0; + ext.word = 2; + ext.model = 0; + ext.ln_prob = log(model_0_word_2); + mock.AddExtension(ext); + + mock.DoneExtending(); + + Vector weights(2); + weights << 0.9, 1.2; + + Vector gradient(2); + Matrix hessian(2,2); + Derivatives(mock, weights, gradient, hessian); + // TODO: check perplexity value coming out. + + // p_I(x | context) + Vector p_I(3); + p_I << + pow(0.1 * 0.2, 0.9) * pow(0.6 * 0.4, 1.2), + pow(0.4 * 0.2, 0.9) * pow(model_1_word_1, 1.2), + pow(model_0_word_2, 0.9) * pow(0.1 * 0.4, 1.2); + p_I /= p_I.sum(); + + Vector expected_gradient = mock.NegLNCorrectSum(); + expected_gradient(0) += p_I(0) * log(0.1 * 0.2); + expected_gradient(0) += p_I(1) * log(0.4 * 0.2); + expected_gradient(0) += p_I(2) * log(model_0_word_2); + BOOST_CHECK_CLOSE(expected_gradient(0), gradient(0), 0.01); + + expected_gradient(1) += p_I(0) * log(0.6 * 0.4); + expected_gradient(1) += p_I(1) * log(model_1_word_1); + expected_gradient(1) += p_I(2) * log(0.1 * 0.4); + BOOST_CHECK_CLOSE(expected_gradient(1), gradient(1), 0.01); + + Matrix expected_hessian(2, 2); + expected_hessian(1, 0) = + // First term + p_I(0) * log(0.1 * 0.2) * log(0.6 * 0.4) + + p_I(1) * log(0.4 * 0.2) * log(model_1_word_1) + + p_I(2) * log(model_0_word_2) * log(0.1 * 0.4); + expected_hessian(1, 0) -= + (p_I(0) * log(0.1 * 0.2) + p_I(1) * log(0.4 * 0.2) + p_I(2) * log(model_0_word_2)) * + (p_I(0) * log(0.6 * 0.4) + p_I(1) * log(model_1_word_1) + p_I(2) * log(0.1 * 0.4)); + expected_hessian(0, 1) = expected_hessian(1, 0); + BOOST_CHECK_CLOSE(expected_hessian(1, 0), hessian(1, 0), 0.01); + BOOST_CHECK_CLOSE(expected_hessian(0, 1), hessian(0, 1), 0.01); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances.cc new file mode 100644 index 0000000..6f41bd2 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances.cc @@ -0,0 +1,501 @@ +/* Load tuning instances and filter underlying models to them. A tuning + * instance is an n-gram in the tuning file. To tune towards these, we want + * the correct probability p_i(w_n | w_1^{n-1}) from each model as well as + * all the denominators p_i(v | w_1^{n-1}) that appear in normalization. + * + * In other words, we filter the models to only those n-grams whose context + * appears in the tuning data. This can be divided into two categories: + * - All unigrams. This goes into Instances::ln_unigrams_ + * - Bigrams and above whose context appears in the tuning data. These are + * known as extensions. We only care about the longest extension for each + * w_1^{n-1}v since that is what will be used for the probability. + * Because there is a large number of extensions (we tried keeping them in RAM + * and ran out), the streaming framework is used to keep track of extensions + * and sort them so they can be streamed in. Downstream code + * (tune_derivatives.hh) takes a stream of extensions ordered by tuning + * instance, the word v, and the model the extension came from. + */ +#include "lm/interpolate/tune_instances.hh" + +#include "lm/common/compare.hh" +#include "lm/common/joint_order.hh" +#include "lm/common/model_buffer.hh" +#include "lm/common/ngram_stream.hh" +#include "lm/common/renumber.hh" +#include "lm/enumerate_vocab.hh" +#include "lm/interpolate/merge_vocab.hh" +#include "lm/interpolate/universal_vocab.hh" +#include "lm/lm_exception.hh" +#include "util/file_piece.hh" +#include "util/murmur_hash.hh" +#include "util/stream/chain.hh" +#include "util/stream/io.hh" +#include "util/stream/sort.hh" +#include "util/tokenize_piece.hh" + +#include +#include + +#include +#include +#include + +namespace lm { namespace interpolate { + +// gcc 4.6 complains about uninitialized when sort code is generated for a 4-byte POD. But that sort code is never used. +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wuninitialized" +bool Extension::operator<(const Extension &other) const { + if (instance != other.instance) + return instance < other.instance; + if (word != other.word) + return word < other.word; + if (model != other.model) + return model < other.model; + return false; +} +#pragma GCC diagnostic pop + +namespace { + +// An extension without backoff weights applied yet. +#pragma pack(push) +#pragma pack(1) +struct InitialExtension { + Extension ext; + // Order from which it came. + uint8_t order; +}; +#pragma pack(pop) + +struct InitialExtensionCompare { + bool operator()(const void *first, const void *second) const { + return reinterpret_cast(first)->ext < reinterpret_cast(second)->ext; + } +}; + +// Intended use +// For each model: +// stream through orders jointly in suffix order: +// Call MatchedBackoff for full matches. +// Call Exit when the context matches. +// Call FinishModel with the unigram probability of the correct word, get full +// probability in return. +// Use backoffs_out to adjust records that were written to the stream. +// backoffs_out(model, order - 1) is the penalty for matching order. +class InstanceMatch { + public: + InstanceMatch(Matrix &backoffs_out, const WordIndex correct) + : seen_(std::numeric_limits::max()), + backoffs_(backoffs_out), + correct_(correct), correct_from_(1), correct_ln_prob_(std::numeric_limits::quiet_NaN()) {} + + void MatchedBackoff(ModelIndex model, uint8_t order, float ln_backoff) { + backoffs_(model, order - 1) = ln_backoff; + } + + // We only want the highest-order matches, which are the first to be exited for a given word. + void Exit(const InitialExtension &from, util::stream::Stream &out) { + if (from.ext.word == seen_) return; + seen_ = from.ext.word; + *static_cast(out.Get()) = from; + ++out; + if (UTIL_UNLIKELY(correct_ == from.ext.word)) { + correct_from_ = from.order; + correct_ln_prob_ = from.ext.ln_prob; + } + } + + WordIndex Correct() const { return correct_; } + + // Call this after each model has been passed through. Provide the unigram + // probability of the correct word (which follows the given context). + // This function will return the fully-backed-off probability of the correct + // word. + float FinishModel(ModelIndex model, float correct_ln_unigram) { + seen_ = std::numeric_limits::max(); + // Turn backoffs into multiplied values (added in log space). + // So backoffs_(model, order - 1) is the penalty for matching order. + float accum = 0.0; + for (int order = backoffs_.cols() - 1; order >= 0; --order) { + accum += backoffs_(model, order); + backoffs_(model, order) = accum; + } + if (correct_from_ == 1) { + correct_ln_prob_ = correct_ln_unigram; + } + if (correct_from_ - 1 < backoffs_.cols()) { + correct_ln_prob_ += backoffs_(model, correct_from_ - 1); + } + correct_from_ = 1; + return correct_ln_prob_; + } + + private: + // What's the last word we've seen? Used to act only on exiting the longest match. + WordIndex seen_; + + Matrix &backoffs_; + + const WordIndex correct_; + + // These only apply to the most recent model. + uint8_t correct_from_; + + float correct_ln_prob_; +}; + +// Forward information to multiple instances of a context. So if the tuning +// set contains +// a b c d e +// a b c d e +// there's one DispatchContext for a b c d which calls two InstanceMatch, one +// for each tuning instance. This might be to inform them about a b c d g in +// one of the models. +class DispatchContext { + public: + void Register(InstanceMatch &context) { + registered_.push_back(&context); + } + + void MatchedBackoff(ModelIndex model, uint8_t order, float ln_backoff) { + for (std::vector::iterator i = registered_.begin(); i != registered_.end(); ++i) + (*i)->MatchedBackoff(model, order, ln_backoff); + } + + void Exit(InitialExtension &from, util::stream::Stream &out, const InstanceMatch *base_instance) { + for (std::vector::iterator i = registered_.begin(); i != registered_.end(); ++i) { + from.ext.instance = *i - base_instance; + (*i)->Exit(from, out); + } + } + + private: + // TODO make these offsets in a big array rather than separately allocated. + std::vector registered_; +}; + +// Map from n-gram hash to contexts in the tuning data. TODO: probing hash table? +typedef boost::unordered_map ContextMap; + +// Handle all the orders of a single model at once. +class JointOrderCallback { + public: + JointOrderCallback( + std::size_t model, + std::size_t full_order_minus_1, + ContextMap &contexts, + util::stream::Stream &out, + const InstanceMatch *base_instance) + : full_order_minus_1_(full_order_minus_1), + contexts_(contexts), + out_(out), + base_instance_(base_instance) { + ext_.ext.model = model; + } + + void Enter(std::size_t order_minus_1, const void *data) {} + + void Exit(std::size_t order_minus_1, void *data) { + // Match the full n-gram for backoffs. + if (order_minus_1 != full_order_minus_1_) { + NGram gram(data, order_minus_1 + 1); + ContextMap::iterator i = contexts_.find(util::MurmurHashNative(gram.begin(), gram.Order() * sizeof(WordIndex))); + if (UTIL_UNLIKELY(i != contexts_.end())) { + i->second.MatchedBackoff(ext_.ext.model, gram.Order(), gram.Value().backoff * M_LN10); + } + } + // Match the context of the n-gram to indicate it's an extension. + ContextMap::iterator i = contexts_.find(util::MurmurHashNative(data, order_minus_1 * sizeof(WordIndex))); + if (UTIL_UNLIKELY(i != contexts_.end())) { + NGram gram(data, order_minus_1 + 1); + // model is already set. + // instance is set by DispatchContext. + // That leaves word, ln_prob, and order. + ext_.ext.word = *(gram.end() - 1); + ext_.ext.ln_prob = gram.Value().prob * M_LN10; + ext_.order = order_minus_1 + 1; + // model was already set in the constructor. + // ext_.ext.instance is set by the Exit call. + i->second.Exit(ext_, out_, base_instance_); + } + } + + void Run(const util::stream::ChainPositions &positions) { + JointOrder(positions, *this); + } + + private: + const std::size_t full_order_minus_1_; + + // Mapping is constant but values are being manipulated to tell them about + // n-grams. + ContextMap &contexts_; + + // Reused variable. model is set correctly. + InitialExtension ext_; + + util::stream::Stream &out_; + + const InstanceMatch *const base_instance_; +}; + +// This populates the ln_unigrams_ matrix. It can (and should for efficiency) +// be run in the same scan as JointOrderCallback. +class ReadUnigrams { + public: + explicit ReadUnigrams(Matrix::ColXpr out) : out_(out) {} + + // Read renumbered unigrams, fill with otherwise. + void Run(const util::stream::ChainPosition &position) { + NGramStream stream(position); + assert(stream); + Accum unk = stream->Value().prob * M_LN10; + WordIndex previous = 0; + for (; stream; ++stream) { + WordIndex word = *stream->begin(); + out_.segment(previous, word - previous) = Vector::Constant(word - previous, unk); + out_(word) = stream->Value().prob * M_LN10; + //backoffs are used by JointOrderCallback. + previous = word + 1; + } + out_.segment(previous, out_.rows() - previous) = Vector::Constant(out_.rows() - previous, unk); + } + + private: + Matrix::ColXpr out_; +}; + +// Read tuning data into an array of vocab ids. The vocab ids are agreed with MergeVocab. +class IdentifyTuning : public EnumerateVocab { + public: + IdentifyTuning(int tuning_file, std::vector &out) : indices_(out) { + indices_.clear(); + StringPiece line; + std::size_t counter = 0; + std::vector &eos = words_[util::MurmurHashNative("", 4)]; + for (util::FilePiece f(tuning_file); f.ReadLineOrEOF(line);) { + for (util::TokenIter word(line, util::kSpaces); word; ++word) { + UTIL_THROW_IF(*word == "" || *word == "", FormatLoadException, "Illegal word in tuning data: " << *word); + words_[util::MurmurHashNative(word->data(), word->size())].push_back(counter++); + } + eos.push_back(counter++); + } + // Also get + indices_.resize(counter + 1); + words_[util::MurmurHashNative("", 3)].push_back(indices_.size() - 1); + } + + // Apply ids as they come out of MergeVocab if they match. + void Add(WordIndex id, const StringPiece &str) { + boost::unordered_map >::iterator i = words_.find(util::MurmurHashNative(str.data(), str.size())); + if (i != words_.end()) { + for (std::vector::iterator j = i->second.begin(); j != i->second.end(); ++j) { + indices_[*j] = id; + } + } + } + + WordIndex FinishGetBOS() { + WordIndex ret = indices_.back(); + indices_.pop_back(); + return ret; + } + + private: + // array of words in tuning data. + std::vector &indices_; + + // map from hash(string) to offsets in indices_. + boost::unordered_map > words_; +}; + +} // namespace + +// Store information about the first iteration. +class ExtensionsFirstIteration { + public: + explicit ExtensionsFirstIteration(std::size_t instances, std::size_t models, std::size_t max_order, util::stream::Chain &extension_input, const util::stream::SortConfig &config) + : backoffs_by_instance_(new std::vector(instances)), sort_(extension_input, config) { + // Initialize all the backoff matrices to zeros. + for (std::vector::iterator i = backoffs_by_instance_->begin(); i != backoffs_by_instance_->end(); ++i) { + *i = Matrix::Zero(models, max_order); + } + } + + Matrix &WriteBackoffs(std::size_t instance) { + return (*backoffs_by_instance_)[instance]; + } + + // Get the backoff all the way to unigram for a particular tuning instance and model. + Accum FullBackoff(std::size_t instance, std::size_t model) const { + return (*backoffs_by_instance_)[instance](model, 0); + } + + void Merge(std::size_t lazy_memory) { + sort_.Merge(lazy_memory); + lazy_memory_ = lazy_memory; + } + + void Output(util::stream::Chain &chain) { + sort_.Output(chain, lazy_memory_); + chain >> ApplyBackoffs(backoffs_by_instance_); + } + + private: + class ApplyBackoffs { + public: + explicit ApplyBackoffs(boost::shared_ptr > backoffs_by_instance) + : backoffs_by_instance_(backoffs_by_instance) {} + + void Run(const util::stream::ChainPosition &position) { + // There should always be tuning instances. + const std::vector &backoffs = *backoffs_by_instance_; + assert(!backoffs.empty()); + uint8_t max_order = backoffs.front().cols(); + for (util::stream::Stream stream(position); stream; ++stream) { + InitialExtension &ini = *reinterpret_cast(stream.Get()); + assert(ini.order > 1); // If it's an extension, it should be higher than a unigram. + if (ini.order != max_order) { + ini.ext.ln_prob += backoffs[ini.ext.instance](ini.ext.model, ini.order - 1); + } + } + } + + private: + boost::shared_ptr > backoffs_by_instance_; + }; + + // Array of complete backoff matrices by instance. + // Each matrix is by model, then by order. + // Would have liked to use a tensor but it's not that well supported. + // This is a shared pointer so that ApplyBackoffs can run after this class is gone. + boost::shared_ptr > backoffs_by_instance_; + + // This sorts and stores all the InitialExtensions. + util::stream::Sort sort_; + + std::size_t lazy_memory_; +}; + +Instances::Instances(int tune_file, const std::vector &model_names, const InstancesConfig &config) : temp_prefix_(config.sort.temp_prefix) { + // All the memory from stack variables here should go away before merge sort of the instances. + { + util::FixedArray models(model_names.size()); + + // Load tuning set and join vocabulary. + std::vector vocab_sizes; + vocab_sizes.reserve(model_names.size()); + util::FixedArray vocab_files(model_names.size()); + std::size_t max_order = 0; + for (std::vector::const_iterator i = model_names.begin(); i != model_names.end(); ++i) { + models.push_back(*i); + vocab_sizes.push_back(models.back().Counts()[0]); + vocab_files.push_back(models.back().VocabFile()); + max_order = std::max(max_order, models.back().Order()); + } + UniversalVocab vocab(vocab_sizes); + std::vector tuning_words; + WordIndex combined_vocab_size; + { + IdentifyTuning identify(tune_file, tuning_words); + combined_vocab_size = MergeVocab(vocab_files, vocab, identify); + bos_ = identify.FinishGetBOS(); + } + + // Setup the initial extensions storage: a chain going to a sort with a stream in the middle for writing. + util::stream::Chain extensions_chain(util::stream::ChainConfig(sizeof(InitialExtension), 2, config.extension_write_chain_mem)); + util::stream::Stream extensions_write(extensions_chain.Add()); + extensions_first_.reset(new ExtensionsFirstIteration(tuning_words.size(), model_names.size(), max_order, extensions_chain, config.sort)); + + // Populate the ContextMap from contexts to instances. + ContextMap cmap; + util::FixedArray instances(tuning_words.size()); + { + UTIL_THROW_IF2(tuning_words.empty(), "Empty tuning data"); + const WordIndex eos = tuning_words.back(); + std::vector context; + context.push_back(bos_); + for (std::size_t i = 0; i < tuning_words.size(); ++i) { + instances.push_back(boost::ref(extensions_first_->WriteBackoffs(i)), tuning_words[i]); + for (std::size_t j = 0; j < context.size(); ++j) { + cmap[util::MurmurHashNative(&context[j], sizeof(WordIndex) * (context.size() - j))].Register(instances.back()); + } + // Prepare for next word by starting a new sentence or shifting context. + if (tuning_words[i] == eos) { + context.clear(); + context.push_back(bos_); + } else { + if (context.size() == max_order) { + context.erase(context.begin()); + } + context.push_back(tuning_words[i]); + } + } + } + + // Go through each model. Populate: + // ln_backoffs_ + ln_backoffs_.resize(instances.size(), models.size()); + // neg_ln_correct_sum_ + neg_ln_correct_sum_.resize(models.size()); + // ln_unigrams_ + ln_unigrams_.resize(combined_vocab_size, models.size()); + // The backoffs in extensions_first_ + for (std::size_t m = 0; m < models.size(); ++m) { + std::cerr << "Processing model " << m << '/' << models.size() << ": " << model_names[m] << std::endl; + util::stream::Chains chains(models[m].Order()); + for (std::size_t i = 0; i < models[m].Order(); ++i) { + // TODO: stop wasting space for backoffs of highest order. + chains.push_back(util::stream::ChainConfig(NGram::TotalSize(i + 1), 2, config.model_read_chain_mem)); + } + chains.back().ActivateProgress(); + models[m].Source(chains); + for (std::size_t i = 0; i < models[m].Order(); ++i) { + chains[i] >> Renumber(vocab.Mapping(m), i + 1); + } + + // Populate ln_unigrams_. + chains[0] >> ReadUnigrams(ln_unigrams_.col(m)); + + // Send extensions into extensions_first_ and give data to the instances about backoffs/extensions. + chains >> JointOrderCallback(m, models[m].Order() - 1, cmap, extensions_write, instances.begin()); + + chains >> util::stream::kRecycle; + chains.Wait(true); + neg_ln_correct_sum_(m) = 0.0; + for (InstanceMatch *i = instances.begin(); i != instances.end(); ++i) { + neg_ln_correct_sum_(m) -= i->FinishModel(m, ln_unigrams_(i->Correct(), m)); + ln_backoffs_(i - instances.begin(), m) = extensions_first_->FullBackoff(i - instances.begin(), m); + } + ln_unigrams_(bos_, m) = 0; // Does not matter as long as it does not produce nans since tune_derivatives will overwrite the output. + } + extensions_write.Poison(); + } + extensions_first_->Merge(config.lazy_memory); +} + +Instances::~Instances() {} + +// TODO: size reduction by excluding order for subsequent passes. +std::size_t Instances::ReadExtensionsEntrySize() const { + return sizeof(InitialExtension); +} + +void Instances::ReadExtensions(util::stream::Chain &on) { + if (extensions_first_.get()) { + // Lazy sort and save a sorted copy to disk. TODO: cut down on record size by stripping out order information. + extensions_first_->Output(on); + extensions_first_.reset(); // Relevant data will continue to live in workers. + extensions_subsequent_.reset(new util::stream::FileBuffer(util::MakeTemp(temp_prefix_))); + on >> extensions_subsequent_->Sink(); + } else { + on.SetProgressTarget(extensions_subsequent_->Size()); + on >> extensions_subsequent_->Source(); + } +} + +// Back door. +Instances::Instances() {} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances.hh new file mode 100644 index 0000000..3ecde6b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances.hh @@ -0,0 +1,102 @@ +#ifndef LM_INTERPOLATE_TUNE_INSTANCE_H +#define LM_INTERPOLATE_TUNE_INSTANCE_H + +#include "lm/interpolate/tune_matrix.hh" +#include "lm/word_index.hh" +#include "util/scoped.hh" +#include "util/stream/config.hh" +#include "util/string_piece.hh" + +#include + +#include + +namespace util { namespace stream { +class Chain; +class FileBuffer; +}} // namespaces + +namespace lm { namespace interpolate { + +typedef uint32_t InstanceIndex; +typedef uint32_t ModelIndex; + +struct Extension { + // Which tuning instance does this belong to? + InstanceIndex instance; + WordIndex word; + ModelIndex model; + // ln p_{model} (word | context(instance)) + float ln_prob; + + bool operator<(const Extension &other) const; +}; + +class ExtensionsFirstIteration; + +struct InstancesConfig { + // For batching the model reads. This is per order. + std::size_t model_read_chain_mem; + // This is being sorted, make it larger. + std::size_t extension_write_chain_mem; + std::size_t lazy_memory; + util::stream::SortConfig sort; +}; + +class Instances { + private: + typedef Eigen::Matrix BackoffMatrix; + + public: + Instances(int tune_file, const std::vector &model_names, const InstancesConfig &config); + + // For destruction of forward-declared classes. + ~Instances(); + + // Full backoff from unigram for each model. + typedef BackoffMatrix::ConstRowXpr FullBackoffs; + FullBackoffs LNBackoffs(InstanceIndex instance) const { + return ln_backoffs_.row(instance); + } + + InstanceIndex NumInstances() const { return ln_backoffs_.rows(); } + + const Vector &CorrectGradientTerm() const { return neg_ln_correct_sum_; } + + const Matrix &LNUnigrams() const { return ln_unigrams_; } + + // Entry size to use to configure the chain (since in practice order is needed). + std::size_t ReadExtensionsEntrySize() const; + void ReadExtensions(util::stream::Chain &chain); + + // Vocab id of the beginning of sentence. Used to ignore it for normalization. + WordIndex BOS() const { return bos_; } + + private: + // Allow the derivatives test to get access. + friend class MockInstances; + Instances(); + + // backoffs_(instance, model) is the backoff all the way to unigrams. + BackoffMatrix ln_backoffs_; + + // neg_correct_sum_(model) = -\sum_{instances} ln p_{model}(correct(instance) | context(instance)). + // This appears as a term in the gradient. + Vector neg_ln_correct_sum_; + + // ln_unigrams_(word, model) = ln p_{model}(word). + Matrix ln_unigrams_; + + // This is the source of data for the first iteration. + util::scoped_ptr extensions_first_; + + // Source of data for subsequent iterations. This contains already-sorted data. + util::scoped_ptr extensions_subsequent_; + + WordIndex bos_; + + std::string temp_prefix_; +}; + +}} // namespaces +#endif // LM_INTERPOLATE_TUNE_INSTANCE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances_test.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances_test.cc new file mode 100644 index 0000000..7bbf640 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_instances_test.cc @@ -0,0 +1,132 @@ +#include "lm/interpolate/tune_instances.hh" + +#include "util/file.hh" +#include "util/file_stream.hh" +#include "util/stream/chain.hh" +#include "util/stream/config.hh" +#include "util/stream/typed_stream.hh" +#include "util/string_piece.hh" + +#define BOOST_TEST_MODULE InstanceTest +#include + +#include + +#include + +namespace lm { namespace interpolate { namespace { + +BOOST_AUTO_TEST_CASE(Toy) { + util::scoped_fd test_input(util::MakeTemp("temporary")); + util::FileStream(test_input.get()) << "c\n"; + + StringPiece dir("../common/test_data/"); + if (boost::unit_test::framework::master_test_suite().argc == 2) { + StringPiece zero_file(boost::unit_test::framework::master_test_suite().argv[1]); + BOOST_REQUIRE(zero_file.size() > strlen("toy0.1")); + BOOST_REQUIRE_EQUAL("toy0.1", StringPiece(zero_file.data() + zero_file.size() - 6, 6)); + dir = StringPiece(zero_file.data(), zero_file.size() - 6); + } + + std::vector model_names; + std::string full0 = std::string(dir.data(), dir.size()) + "toy0"; + std::string full1 = std::string(dir.data(), dir.size()) + "toy1"; + model_names.push_back(full0); + model_names.push_back(full1); + + // Tiny buffer sizes. + InstancesConfig config; + config.model_read_chain_mem = 100; + config.extension_write_chain_mem = 100; + config.lazy_memory = 100; + config.sort.temp_prefix = "temporary"; + config.sort.buffer_size = 100; + config.sort.total_memory = 1024; + + util::SeekOrThrow(test_input.get(), 0); + + Instances inst(test_input.release(), model_names, config); + + BOOST_CHECK_EQUAL(1, inst.BOS()); + const Matrix &ln_unigrams = inst.LNUnigrams(); + + // =0 + BOOST_CHECK_CLOSE(-0.90309 * M_LN10, ln_unigrams(0, 0), 0.001); + BOOST_CHECK_CLOSE(-1 * M_LN10, ln_unigrams(0, 1), 0.001); + // =1 doesn't matter as long as it doesn't cause NaNs. + BOOST_CHECK(!isnan(ln_unigrams(1, 0))); + BOOST_CHECK(!isnan(ln_unigrams(1, 1))); + // a = 2 + BOOST_CHECK_CLOSE(-0.46943438 * M_LN10, ln_unigrams(2, 0), 0.001); + BOOST_CHECK_CLOSE(-0.6146491 * M_LN10, ln_unigrams(2, 1), 0.001); + // = 3 + BOOST_CHECK_CLOSE(-0.5720968 * M_LN10, ln_unigrams(3, 0), 0.001); + BOOST_CHECK_CLOSE(-0.6146491 * M_LN10, ln_unigrams(3, 1), 0.001); + // c = 4 + BOOST_CHECK_CLOSE(-0.90309 * M_LN10, ln_unigrams(4, 0), 0.001); // + BOOST_CHECK_CLOSE(-0.7659168 * M_LN10, ln_unigrams(4, 1), 0.001); + // too lazy to do b = 5. + + // Two instances: + // predicts c + // c predicts + BOOST_REQUIRE_EQUAL(2, inst.NumInstances()); + BOOST_CHECK_CLOSE(-0.30103 * M_LN10, inst.LNBackoffs(0)(0), 0.001); + BOOST_CHECK_CLOSE(-0.30103 * M_LN10, inst.LNBackoffs(0)(1), 0.001); + + + // Backoffs of c + BOOST_CHECK_CLOSE(0.0, inst.LNBackoffs(1)(0), 0.001); + BOOST_CHECK_CLOSE((-0.30103 - 0.30103) * M_LN10, inst.LNBackoffs(1)(1), 0.001); + + util::stream::Chain extensions(util::stream::ChainConfig(inst.ReadExtensionsEntrySize(), 2, 300)); + inst.ReadExtensions(extensions); + util::stream::TypedStream stream(extensions.Add()); + extensions >> util::stream::kRecycle; + + // The extensions are (in order of instance, vocab id, and model as they should be sorted): + // a from both models 0 and 1 (so two instances) + // c from model 1 + // b from model 0 + // c from model 1 + // Magic probabilities come from querying the models directly. + + // a from model 0 + BOOST_REQUIRE(stream); + BOOST_CHECK_EQUAL(0, stream->instance); + BOOST_CHECK_EQUAL(2 /* a */, stream->word); + BOOST_CHECK_EQUAL(0, stream->model); + BOOST_CHECK_CLOSE(-0.37712017 * M_LN10, stream->ln_prob, 0.001); + + // a from model 1 + BOOST_REQUIRE(++stream); + BOOST_CHECK_EQUAL(0, stream->instance); + BOOST_CHECK_EQUAL(2 /* a */, stream->word); + BOOST_CHECK_EQUAL(1, stream->model); + BOOST_CHECK_CLOSE(-0.4301247 * M_LN10, stream->ln_prob, 0.001); + + // c from model 1 + BOOST_REQUIRE(++stream); + BOOST_CHECK_EQUAL(0, stream->instance); + BOOST_CHECK_EQUAL(4 /* c */, stream->word); + BOOST_CHECK_EQUAL(1, stream->model); + BOOST_CHECK_CLOSE(-0.4740302 * M_LN10, stream->ln_prob, 0.001); + + // b from model 0 + BOOST_REQUIRE(++stream); + BOOST_CHECK_EQUAL(0, stream->instance); + BOOST_CHECK_EQUAL(5 /* b */, stream->word); + BOOST_CHECK_EQUAL(0, stream->model); + BOOST_CHECK_CLOSE(-0.41574955 * M_LN10, stream->ln_prob, 0.001); + + // c from model 1 + BOOST_REQUIRE(++stream); + BOOST_CHECK_EQUAL(1, stream->instance); + BOOST_CHECK_EQUAL(3 /* */, stream->word); + BOOST_CHECK_EQUAL(1, stream->model); + BOOST_CHECK_CLOSE(-0.09113217 * M_LN10, stream->ln_prob, 0.001); + + BOOST_CHECK(!++stream); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_matrix.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_matrix.hh new file mode 100644 index 0000000..a15986a --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_matrix.hh @@ -0,0 +1,18 @@ +#ifndef LM_INTERPOLATE_TUNE_MATRIX_H +#define LM_INTERPOLATE_TUNE_MATRIX_H + +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wpragmas" // Older gcc doesn't have "-Wunused-local-typedefs" and complains. +#pragma GCC diagnostic ignored "-Wunused-local-typedefs" +#include +#pragma GCC diagnostic pop + +namespace lm { namespace interpolate { + +typedef Eigen::MatrixXf Matrix; +typedef Eigen::VectorXf Vector; + +typedef Matrix::Scalar Accum; + +}} // namespaces +#endif // LM_INTERPOLATE_TUNE_MATRIX_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_weights.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_weights.cc new file mode 100644 index 0000000..72c93da --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_weights.cc @@ -0,0 +1,33 @@ +#include "lm/interpolate/tune_weights.hh" + +#include "lm/interpolate/tune_derivatives.hh" +#include "lm/interpolate/tune_instances.hh" + +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wpragmas" // Older gcc doesn't have "-Wunused-local-typedefs" and complains. +#pragma GCC diagnostic ignored "-Wunused-local-typedefs" +#include +#pragma GCC diagnostic pop +#include + +#include + +namespace lm { namespace interpolate { +void TuneWeights(int tune_file, const std::vector &model_names, const InstancesConfig &config, std::vector &weights_out) { + Instances instances(tune_file, model_names, config); + Vector weights = Vector::Constant(model_names.size(), 1.0 / model_names.size()); + Vector gradient; + Matrix hessian; + for (std::size_t iteration = 0; iteration < 10 /*TODO fancy stopping criteria */; ++iteration) { + std::cerr << "Iteration " << iteration << ": weights ="; + for (Vector::Index i = 0; i < weights.rows(); ++i) { + std::cerr << ' ' << weights(i); + } + std::cerr << std::endl; + std::cerr << "Perplexity = " << Derivatives(instances, weights, gradient, hessian) << std::endl; + // TODO: 1.0 step size was too big and it kept getting unstable. More math. + weights -= 0.7 * hessian.inverse() * gradient; + } + weights_out.assign(weights.data(), weights.data() + weights.size()); +} +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_weights.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_weights.hh new file mode 100644 index 0000000..43e7e1a --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/tune_weights.hh @@ -0,0 +1,15 @@ +#ifndef LM_INTERPOLATE_TUNE_WEIGHTS_H +#define LM_INTERPOLATE_TUNE_WEIGHTS_H + +#include "util/string_piece.hh" + +#include + +namespace lm { namespace interpolate { +struct InstancesConfig; + +// Run a tuning loop, producing weights as output. +void TuneWeights(int tune_file, const std::vector &model_names, const InstancesConfig &config, std::vector &weights); + +}} // namespaces +#endif // LM_INTERPOLATE_TUNE_WEIGHTS_H diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/universal_vocab.cc b/ctcdecode-master/third_party/kenlm/lm/interpolate/universal_vocab.cc new file mode 100644 index 0000000..5cdf41e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/universal_vocab.cc @@ -0,0 +1,13 @@ +#include "lm/interpolate/universal_vocab.hh" + +namespace lm { +namespace interpolate { + +UniversalVocab::UniversalVocab(const std::vector& model_vocab_sizes) { + model_index_map_.resize(model_vocab_sizes.size()); + for (size_t i = 0; i < model_vocab_sizes.size(); ++i) { + model_index_map_[i].resize(model_vocab_sizes[i]); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/interpolate/universal_vocab.hh b/ctcdecode-master/third_party/kenlm/lm/interpolate/universal_vocab.hh new file mode 100644 index 0000000..61af27a --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/interpolate/universal_vocab.hh @@ -0,0 +1,44 @@ +#ifndef LM_INTERPOLATE_UNIVERSAL_VOCAB_H +#define LM_INTERPOLATE_UNIVERSAL_VOCAB_H + +#include "lm/word_index.hh" + +#include +#include + +namespace lm { +namespace interpolate { + +class UniversalVocab { +public: + explicit UniversalVocab(const std::vector& model_vocab_sizes); + + // GetUniversalIndex takes the model number and index for the specific + // model and returns the universal model number + WordIndex GetUniversalIdx(std::size_t model_num, WordIndex model_word_index) const { + return model_index_map_[model_num][model_word_index]; + } + + const WordIndex *Mapping(std::size_t model) const { + return &*model_index_map_[model].begin(); + } + + WordIndex SlowConvertToModel(std::size_t model, WordIndex index) const { + std::vector::const_iterator i = lower_bound(model_index_map_[model].begin(), model_index_map_[model].end(), index); + if (i == model_index_map_[model].end() || *i != index) return 0; + return i - model_index_map_[model].begin(); + } + + void InsertUniversalIdx(std::size_t model_num, WordIndex word_index, + WordIndex universal_word_index) { + model_index_map_[model_num][word_index] = universal_word_index; + } + +private: + std::vector > model_index_map_; +}; + +} // namespace interpolate +} // namespace lm + +#endif // LM_INTERPOLATE_UNIVERSAL_VOCAB_H diff --git a/ctcdecode-master/third_party/kenlm/lm/kenlm_benchmark_main.cc b/ctcdecode-master/third_party/kenlm/lm/kenlm_benchmark_main.cc new file mode 100644 index 0000000..3b048e3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/kenlm_benchmark_main.cc @@ -0,0 +1,232 @@ +#include "lm/model.hh" +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/file_piece.hh" +#include "util/usage.hh" +#include "util/thread_pool.hh" + +#include +#include + +#include + +#include + +namespace { + +template void ConvertToBytes(const Model &model, int fd_in) { + util::FilePiece in(fd_in); + util::FileStream out(1); + Width width; + StringPiece word; + const Width end_sentence = (Width)model.GetVocabulary().EndSentence(); + while (true) { + while (in.ReadWordSameLine(word)) { + width = (Width)model.GetVocabulary().Index(word); + out.write(&width, sizeof(Width)); + } + if (!in.ReadLineOrEOF(word)) break; + out.write(&end_sentence, sizeof(Width)); + } +} + +template class Worker { + public: + explicit Worker(const Model &model, double &add_total) : model_(model), total_(0.0), add_total_(add_total) {} + + // Destructors happen in the main thread, so there's no race for add_total_. + ~Worker() { add_total_ += total_; } + + typedef boost::iterator_range Request; + + void operator()(Request request) { + const lm::ngram::State *const begin_state = &model_.BeginSentenceState(); + const lm::ngram::State *next_state = begin_state; + const Width kEOS = model_.GetVocabulary().EndSentence(); + float sum = 0.0; + // Do even stuff first. + const Width *even_end = request.begin() + (request.size() & ~1); + // Alternating states + const Width *i; + for (i = request.begin(); i != even_end;) { + sum += model_.FullScore(*next_state, *i, state_[1]).prob; + next_state = (*i++ == kEOS) ? begin_state : &state_[1]; + sum += model_.FullScore(*next_state, *i, state_[0]).prob; + next_state = (*i++ == kEOS) ? begin_state : &state_[0]; + } + // Odd corner case. + if (request.size() & 1) { + sum += model_.FullScore(*next_state, *i, state_[2]).prob; + next_state = (*i++ == kEOS) ? begin_state : &state_[2]; + } + total_ += sum; + } + + private: + const Model &model_; + double total_; + double &add_total_; + + lm::ngram::State state_[3]; +}; + +struct Config { + int fd_in; + std::size_t threads; + std::size_t buf_per_thread; + bool query; +}; + +template void QueryFromBytes(const Model &model, const Config &config) { + util::FileStream out(1); + out << "Threads: " << config.threads << '\n'; + const Width kEOS = model.GetVocabulary().EndSentence(); + double total = 0.0; + // Number of items to have in queue in addition to everything in flight. + const std::size_t kInQueue = 3; + std::size_t total_queue = config.threads + kInQueue; + std::vector backing(config.buf_per_thread * total_queue); + double loaded_cpu; + double loaded_wall; + uint64_t queries = 0; + { + util::RecyclingThreadPool > pool(total_queue, config.threads, Worker(model, total), boost::iterator_range((Width*)0, (Width*)0)); + + for (std::size_t i = 0; i < total_queue; ++i) { + pool.PopulateRecycling(boost::iterator_range(&backing[i * config.buf_per_thread], &backing[i * config.buf_per_thread])); + } + + loaded_cpu = util::CPUTime(); + loaded_wall = util::WallTime(); + out << "To Load, CPU: " << loaded_cpu << " Wall: " << loaded_wall << '\n'; + boost::iterator_range overhang((Width*)0, (Width*)0); + while (true) { + boost::iterator_range buf = pool.Consume(); + std::memmove(buf.begin(), overhang.begin(), overhang.size() * sizeof(Width)); + std::size_t got = util::ReadOrEOF(config.fd_in, buf.begin() + overhang.size(), (config.buf_per_thread - overhang.size()) * sizeof(Width)); + if (!got && overhang.empty()) break; + UTIL_THROW_IF2(got % sizeof(Width), "File size not a multiple of vocab id size " << sizeof(Width)); + Width *read_end = buf.begin() + overhang.size() + got / sizeof(Width); + Width *last_eos; + for (last_eos = read_end - 1; ; --last_eos) { + UTIL_THROW_IF2(last_eos <= buf.begin(), "Encountered a sentence longer than the buffer size of " << config.buf_per_thread << " words. Rerun with increased buffer size. TODO: adaptable buffer"); + if (*last_eos == kEOS) break; + } + buf = boost::iterator_range(buf.begin(), last_eos + 1); + overhang = boost::iterator_range(last_eos + 1, read_end); + queries += buf.size(); + pool.Produce(buf); + } + } // Drain pool. + + double after_cpu = util::CPUTime(); + double after_wall = util::WallTime(); + util::FileStream(2, 70) << "Probability sum: " << total << '\n'; + out << "Queries: " << queries << '\n'; + out << "Excluding load, CPU: " << (after_cpu - loaded_cpu) << " Wall: " << (after_wall - loaded_wall) << '\n'; + double cpu_per_entry = ((after_cpu - loaded_cpu) / static_cast(queries)); + double wall_per_entry = ((after_wall - loaded_wall) / static_cast(queries)); + out << "Seconds per query excluding load, CPU: " << cpu_per_entry << " Wall: " << wall_per_entry << '\n'; + out << "Queries per second excluding load, CPU: " << (1.0/cpu_per_entry) << " Wall: " << (1.0/wall_per_entry) << '\n'; + out << "RSSMax: " << util::RSSMax() << '\n'; +} + +template void DispatchFunction(const Model &model, const Config &config) { + if (config.query) { + QueryFromBytes(model, config); + } else { + ConvertToBytes(model, config.fd_in); + } +} + +template void DispatchWidth(const char *file, const Config &config) { + lm::ngram::Config model_config; + model_config.load_method = util::READ; + Model model(file, model_config); + uint64_t bound = model.GetVocabulary().Bound(); + if (bound <= 256) { + DispatchFunction(model, config); + } else if (bound <= 65536) { + DispatchFunction(model, config); + } else if (bound <= (1ULL << 32)) { + DispatchFunction(model, config); + } else { + DispatchFunction(model, config); + } +} + +void Dispatch(const char *file, const Config &config) { + using namespace lm::ngram; + lm::ngram::ModelType model_type; + if (lm::ngram::RecognizeBinary(file, model_type)) { + switch(model_type) { + case PROBING: + DispatchWidth(file, config); + break; + case REST_PROBING: + DispatchWidth(file, config); + break; + case TRIE: + DispatchWidth(file, config); + break; + case QUANT_TRIE: + DispatchWidth(file, config); + break; + case ARRAY_TRIE: + DispatchWidth(file, config); + break; + case QUANT_ARRAY_TRIE: + DispatchWidth(file, config); + break; + default: + UTIL_THROW(util::Exception, "Unrecognized kenlm model type " << model_type); + } + } else { + UTIL_THROW(util::Exception, "Binarize before running benchmarks."); + } +} + +} // namespace + +int main(int argc, char *argv[]) { + try { + Config config; + config.fd_in = 0; + std::string model; + namespace po = boost::program_options; + po::options_description options("Benchmark options"); + options.add_options() + ("help,h", po::bool_switch(), "Show help message") + ("model,m", po::value(&model)->required(), "Model to query or convert vocab ids") + ("threads,t", po::value(&config.threads)->default_value(boost::thread::hardware_concurrency()), "Threads to use (querying only; TODO vocab conversion)") + ("buffer,b", po::value(&config.buf_per_thread)->default_value(4096), "Number of words to buffer per task.") + ("vocab,v", po::bool_switch(), "Convert strings to vocab ids") + ("query,q", po::bool_switch(), "Query from vocab ids"); + po::variables_map vm; + po::store(po::parse_command_line(argc, argv, options), vm); + if (argc == 1 || vm["help"].as()) { + std::cerr << "Benchmark program for KenLM. Intended usage:\n" + << "#Convert text to vocabulary ids offline. These ids are tied to a model.\n" + << argv[0] << " -v -m $model <$text >$text.vocab\n" + << "#Ensure files are in RAM.\n" + << "cat $text.vocab $model >/dev/null\n" + << "#Timed query against the model.\n" + << argv[0] << " -q -m $model <$text.vocab\n"; + return 0; + } + po::notify(vm); + if (!(vm["vocab"].as() ^ vm["query"].as())) { + std::cerr << "Specify exactly one of -v (vocab conversion) or -q (query)." << std::endl; + return 0; + } + config.query = vm["query"].as(); + if (!config.threads) { + std::cerr << "Specify a non-zero number of threads with -t." << std::endl; + } + Dispatch(model.c_str(), config); + } catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + return 1; + } + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/lm/left.hh b/ctcdecode-master/third_party/kenlm/lm/left.hh new file mode 100644 index 0000000..4d49686 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/left.hh @@ -0,0 +1,216 @@ +/* Efficient left and right language model state for sentence fragments. + * Intended usage: + * Store ChartState with every chart entry. + * To do a rule application: + * 1. Make a ChartState object for your new entry. + * 2. Construct RuleScore. + * 3. Going from left to right, call Terminal or NonTerminal. + * For terminals, just pass the vocab id. + * For non-terminals, pass that non-terminal's ChartState. + * If your decoder expects scores inclusive of subtree scores (i.e. you + * label entries with the highest-scoring path), pass the non-terminal's + * score as prob. + * If your decoder expects relative scores and will walk the chart later, + * pass prob = 0.0. + * In other words, the only effect of prob is that it gets added to the + * returned log probability. + * 4. Call Finish. It returns the log probability. + * + * There's a couple more details: + * Do not pass to Terminal as it is formally not a word in the sentence, + * only context. Instead, call BeginSentence. If called, it should be the + * first call after RuleScore is constructed (since is always the + * leftmost). + * + * If the leftmost RHS is a non-terminal, it's faster to call BeginNonTerminal. + * + * Hashing and sorting comparison operators are provided. All state objects + * are POD. If you intend to use memcmp on raw state objects, you must call + * ZeroRemaining first, as the value of array entries beyond length is + * otherwise undefined. + * + * Usage is of course not limited to chart decoding. Anything that generates + * sentence fragments missing left context could benefit. For example, a + * phrase-based decoder could pre-score phrases, storing ChartState with each + * phrase, even if hypotheses are generated left-to-right. + */ + +#ifndef LM_LEFT_H +#define LM_LEFT_H + +#include "lm/max_order.hh" +#include "lm/state.hh" +#include "lm/return.hh" + +#include "util/murmur_hash.hh" + +#include + +namespace lm { +namespace ngram { + +template class RuleScore { + public: + explicit RuleScore(const M &model, ChartState &out) : model_(model), out_(&out), left_done_(false), prob_(0.0) { + out.left.length = 0; + out.right.length = 0; + } + + void BeginSentence() { + out_->right = model_.BeginSentenceState(); + // out_->left is empty. + left_done_ = true; + } + + void Terminal(WordIndex word) { + State copy(out_->right); + FullScoreReturn ret(model_.FullScore(copy, word, out_->right)); + if (left_done_) { prob_ += ret.prob; return; } + if (ret.independent_left) { + prob_ += ret.prob; + left_done_ = true; + return; + } + out_->left.pointers[out_->left.length++] = ret.extend_left; + prob_ += ret.rest; + if (out_->right.length != copy.length + 1) + left_done_ = true; + } + + // Faster version of NonTerminal for the case where the rule begins with a non-terminal. + void BeginNonTerminal(const ChartState &in, float prob = 0.0) { + prob_ = prob; + *out_ = in; + left_done_ = in.left.full; + } + + void NonTerminal(const ChartState &in, float prob = 0.0) { + prob_ += prob; + + if (!in.left.length) { + if (in.left.full) { + for (const float *i = out_->right.backoff; i < out_->right.backoff + out_->right.length; ++i) prob_ += *i; + left_done_ = true; + out_->right = in.right; + } + return; + } + + if (!out_->right.length) { + out_->right = in.right; + if (left_done_) { + prob_ += model_.UnRest(in.left.pointers, in.left.pointers + in.left.length, 1); + return; + } + if (out_->left.length) { + left_done_ = true; + } else { + out_->left = in.left; + left_done_ = in.left.full; + } + return; + } + + float backoffs[KENLM_MAX_ORDER - 1], backoffs2[KENLM_MAX_ORDER - 1]; + float *back = backoffs, *back2 = backoffs2; + unsigned char next_use = out_->right.length; + + // First word + if (ExtendLeft(in, next_use, 1, out_->right.backoff, back)) return; + + // Words after the first, so extending a bigram to begin with + for (unsigned char extend_length = 2; extend_length <= in.left.length; ++extend_length) { + if (ExtendLeft(in, next_use, extend_length, back, back2)) return; + std::swap(back, back2); + } + + if (in.left.full) { + for (const float *i = back; i != back + next_use; ++i) prob_ += *i; + left_done_ = true; + out_->right = in.right; + return; + } + + // Right state was minimized, so it's already independent of the new words to the left. + if (in.right.length < in.left.length) { + out_->right = in.right; + return; + } + + // Shift exisiting words down. + for (WordIndex *i = out_->right.words + next_use - 1; i >= out_->right.words; --i) { + *(i + in.right.length) = *i; + } + // Add words from in.right. + std::copy(in.right.words, in.right.words + in.right.length, out_->right.words); + // Assemble backoff composed on the existing state's backoff followed by the new state's backoff. + std::copy(in.right.backoff, in.right.backoff + in.right.length, out_->right.backoff); + std::copy(back, back + next_use, out_->right.backoff + in.right.length); + out_->right.length = in.right.length + next_use; + } + + float Finish() { + // A N-1-gram might extend left and right but we should still set full to true because it's an N-1-gram. + out_->left.full = left_done_ || (out_->left.length == model_.Order() - 1); + return prob_; + } + + void Reset() { + prob_ = 0.0; + left_done_ = false; + out_->left.length = 0; + out_->right.length = 0; + } + void Reset(ChartState &replacement) { + out_ = &replacement; + Reset(); + } + + private: + bool ExtendLeft(const ChartState &in, unsigned char &next_use, unsigned char extend_length, const float *back_in, float *back_out) { + ProcessRet(model_.ExtendLeft( + out_->right.words, out_->right.words + next_use, // Words to extend into + back_in, // Backoffs to use + in.left.pointers[extend_length - 1], extend_length, // Words to be extended + back_out, // Backoffs for the next score + next_use)); // Length of n-gram to use in next scoring. + if (next_use != out_->right.length) { + left_done_ = true; + if (!next_use) { + // Early exit. + out_->right = in.right; + prob_ += model_.UnRest(in.left.pointers + extend_length, in.left.pointers + in.left.length, extend_length + 1); + return true; + } + } + // Continue scoring. + return false; + } + + void ProcessRet(const FullScoreReturn &ret) { + if (left_done_) { + prob_ += ret.prob; + return; + } + if (ret.independent_left) { + prob_ += ret.prob; + left_done_ = true; + return; + } + out_->left.pointers[out_->left.length++] = ret.extend_left; + prob_ += ret.rest; + } + + const M &model_; + + ChartState *out_; + + bool left_done_; + + float prob_; +}; + +} // namespace ngram +} // namespace lm + +#endif // LM_LEFT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/left_test.cc b/ctcdecode-master/third_party/kenlm/lm/left_test.cc new file mode 100644 index 0000000..fdb6416 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/left_test.cc @@ -0,0 +1,397 @@ +#include "lm/left.hh" +#include "lm/model.hh" + +#include "util/tokenize_piece.hh" + +#include + +#define BOOST_TEST_MODULE LeftTest +#include +#include + +namespace lm { +namespace ngram { +namespace { + +#define Term(word) score.Terminal(m.GetVocabulary().Index(word)); +#define VCheck(word, value) BOOST_CHECK_EQUAL(m.GetVocabulary().Index(word), value); + +// Apparently some Boost versions use templates and are pretty strict about types matching. +#define SLOPPY_CHECK_CLOSE(ref, value, tol) BOOST_CHECK_CLOSE(static_cast(ref), static_cast(value), static_cast(tol)); + +template void Short(const M &m) { + ChartState base; + { + RuleScore score(m, base); + Term("more"); + Term("loin"); + SLOPPY_CHECK_CLOSE(-1.206319 - 0.3561665, score.Finish(), 0.001); + } + BOOST_CHECK(base.left.full); + BOOST_CHECK_EQUAL(2, base.left.length); + BOOST_CHECK_EQUAL(1, base.right.length); + VCheck("loin", base.right.words[0]); + + ChartState more_left; + { + RuleScore score(m, more_left); + Term("little"); + score.NonTerminal(base, -1.206319 - 0.3561665); + // p(little more loin | null context) + SLOPPY_CHECK_CLOSE(-1.56538, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(3, more_left.left.length); + BOOST_CHECK_EQUAL(1, more_left.right.length); + VCheck("loin", more_left.right.words[0]); + BOOST_CHECK(more_left.left.full); + + ChartState shorter; + { + RuleScore score(m, shorter); + Term("to"); + score.NonTerminal(base, -1.206319 - 0.3561665); + SLOPPY_CHECK_CLOSE(-0.30103 - 1.687872 - 1.206319 - 0.3561665, score.Finish(), 0.01); + } + BOOST_CHECK_EQUAL(1, shorter.left.length); + BOOST_CHECK_EQUAL(1, shorter.right.length); + VCheck("loin", shorter.right.words[0]); + BOOST_CHECK(shorter.left.full); +} + +template void Charge(const M &m) { + ChartState base; + { + RuleScore score(m, base); + Term("on"); + Term("more"); + SLOPPY_CHECK_CLOSE(-1.509559 -0.4771212 -1.206319, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(1, base.left.length); + BOOST_CHECK_EQUAL(1, base.right.length); + VCheck("more", base.right.words[0]); + BOOST_CHECK(base.left.full); + + ChartState extend; + { + RuleScore score(m, extend); + Term("looking"); + score.NonTerminal(base, -1.509559 -0.4771212 -1.206319); + SLOPPY_CHECK_CLOSE(-3.91039, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(2, extend.left.length); + BOOST_CHECK_EQUAL(1, extend.right.length); + VCheck("more", extend.right.words[0]); + BOOST_CHECK(extend.left.full); + + ChartState tobos; + { + RuleScore score(m, tobos); + score.BeginSentence(); + score.NonTerminal(extend, -3.91039); + SLOPPY_CHECK_CLOSE(-3.471169, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(0, tobos.left.length); + BOOST_CHECK_EQUAL(1, tobos.right.length); +} + +template float LeftToRight(const M &m, const std::vector &words, bool begin_sentence = false) { + float ret = 0.0; + State right = begin_sentence ? m.BeginSentenceState() : m.NullContextState(); + for (std::vector::const_iterator i = words.begin(); i != words.end(); ++i) { + State copy(right); + ret += m.Score(copy, *i, right); + } + return ret; +} + +template float RightToLeft(const M &m, const std::vector &words, bool begin_sentence = false) { + float ret = 0.0; + ChartState state; + state.left.length = 0; + state.right.length = 0; + state.left.full = false; + for (std::vector::const_reverse_iterator i = words.rbegin(); i != words.rend(); ++i) { + ChartState copy(state); + RuleScore score(m, state); + score.Terminal(*i); + score.NonTerminal(copy, ret); + ret = score.Finish(); + } + if (begin_sentence) { + ChartState copy(state); + RuleScore score(m, state); + score.BeginSentence(); + score.NonTerminal(copy, ret); + ret = score.Finish(); + } + return ret; +} + +template float TreeMiddle(const M &m, const std::vector &words, bool begin_sentence = false) { + std::vector > states(words.size()); + for (unsigned int i = 0; i < words.size(); ++i) { + RuleScore score(m, states[i].first); + score.Terminal(words[i]); + states[i].second = score.Finish(); + } + while (states.size() > 1) { + std::vector > upper((states.size() + 1) / 2); + for (unsigned int i = 0; i < states.size() / 2; ++i) { + RuleScore score(m, upper[i].first); + score.NonTerminal(states[i*2].first, states[i*2].second); + score.NonTerminal(states[i*2+1].first, states[i*2+1].second); + upper[i].second = score.Finish(); + } + if (states.size() % 2) { + upper.back() = states.back(); + } + std::swap(states, upper); + } + + if (states.empty()) return 0.0; + + if (begin_sentence) { + ChartState ignored; + RuleScore score(m, ignored); + score.BeginSentence(); + score.NonTerminal(states.front().first, states.front().second); + return score.Finish(); + } else { + return states.front().second; + } + +} + +template void LookupVocab(const M &m, const StringPiece &str, std::vector &out) { + out.clear(); + for (util::TokenIter i(str, ' '); i; ++i) { + out.push_back(m.GetVocabulary().Index(*i)); + } +} + +#define TEXT_TEST(str) \ + LookupVocab(m, str, words); \ + expect = LeftToRight(m, words, rest); \ + SLOPPY_CHECK_CLOSE(expect, RightToLeft(m, words, rest), 0.001); \ + SLOPPY_CHECK_CLOSE(expect, TreeMiddle(m, words, rest), 0.001); \ + +// Build sentences, or parts thereof, from right to left. +template void GrowBig(const M &m, bool rest = false) { + std::vector words; + float expect; + TEXT_TEST("in biarritz watching considering looking . on a little more loin also would consider higher to look good unknown the screening foo bar , unknown however unknown "); + TEXT_TEST("on a little more loin also would consider higher to look good unknown the screening foo bar , unknown however unknown "); + TEXT_TEST("on a little more loin also would consider higher to look good"); + TEXT_TEST("more loin also would consider higher to look good"); + TEXT_TEST("more loin also would consider higher to look"); + TEXT_TEST("also would consider higher to look"); + TEXT_TEST("also would consider higher"); + TEXT_TEST("would consider higher to look"); + TEXT_TEST("consider higher to look"); + TEXT_TEST("consider higher to"); + TEXT_TEST("consider higher"); +} + +template void GrowSmall(const M &m, bool rest = false) { + std::vector words; + float expect; + TEXT_TEST("in biarritz watching considering looking . "); + TEXT_TEST("in biarritz watching considering looking ."); + TEXT_TEST("in biarritz"); +} + +template void AlsoWouldConsiderHigher(const M &m) { + ChartState also; + { + RuleScore score(m, also); + score.Terminal(m.GetVocabulary().Index("also")); + SLOPPY_CHECK_CLOSE(-1.687872, score.Finish(), 0.001); + } + ChartState would; + { + RuleScore score(m, would); + score.Terminal(m.GetVocabulary().Index("would")); + SLOPPY_CHECK_CLOSE(-1.687872, score.Finish(), 0.001); + } + ChartState combine_also_would; + { + RuleScore score(m, combine_also_would); + score.NonTerminal(also, -1.687872); + score.NonTerminal(would, -1.687872); + SLOPPY_CHECK_CLOSE(-1.687872 - 2.0, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(2, combine_also_would.right.length); + + ChartState also_would; + { + RuleScore score(m, also_would); + score.Terminal(m.GetVocabulary().Index("also")); + score.Terminal(m.GetVocabulary().Index("would")); + SLOPPY_CHECK_CLOSE(-1.687872 - 2.0, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(2, also_would.right.length); + + ChartState consider; + { + RuleScore score(m, consider); + score.Terminal(m.GetVocabulary().Index("consider")); + SLOPPY_CHECK_CLOSE(-1.687872, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(1, consider.left.length); + BOOST_CHECK_EQUAL(1, consider.right.length); + BOOST_CHECK(!consider.left.full); + + ChartState higher; + float higher_score; + { + RuleScore score(m, higher); + score.Terminal(m.GetVocabulary().Index("higher")); + higher_score = score.Finish(); + } + SLOPPY_CHECK_CLOSE(-1.509559, higher_score, 0.001); + BOOST_CHECK_EQUAL(1, higher.left.length); + BOOST_CHECK_EQUAL(1, higher.right.length); + BOOST_CHECK(!higher.left.full); + VCheck("higher", higher.right.words[0]); + SLOPPY_CHECK_CLOSE(-0.30103, higher.right.backoff[0], 0.001); + + ChartState consider_higher; + { + RuleScore score(m, consider_higher); + score.NonTerminal(consider, -1.687872); + score.NonTerminal(higher, higher_score); + SLOPPY_CHECK_CLOSE(-1.509559 - 1.687872 - 0.30103, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(2, consider_higher.left.length); + BOOST_CHECK(!consider_higher.left.full); + + ChartState full; + { + RuleScore score(m, full); + score.NonTerminal(combine_also_would, -1.687872 - 2.0); + score.NonTerminal(consider_higher, -1.509559 - 1.687872 - 0.30103); + SLOPPY_CHECK_CLOSE(-10.6879, score.Finish(), 0.001); + } + BOOST_CHECK_EQUAL(4, full.right.length); +} + +#define CHECK_SCORE(str, val) \ +{ \ + float got = val; \ + std::vector indices; \ + LookupVocab(m, str, indices); \ + SLOPPY_CHECK_CLOSE(LeftToRight(m, indices), got, 0.001); \ +} + +template void FullGrow(const M &m) { + std::vector words; + LookupVocab(m, "in biarritz watching considering looking . ", words); + + ChartState lexical[7]; + float lexical_scores[7]; + for (unsigned int i = 0; i < 7; ++i) { + RuleScore score(m, lexical[i]); + score.Terminal(words[i]); + lexical_scores[i] = score.Finish(); + } + CHECK_SCORE("in", lexical_scores[0]); + CHECK_SCORE("biarritz", lexical_scores[1]); + CHECK_SCORE("watching", lexical_scores[2]); + CHECK_SCORE("", lexical_scores[6]); + + ChartState l1[4]; + float l1_scores[4]; + { + RuleScore score(m, l1[0]); + score.NonTerminal(lexical[0], lexical_scores[0]); + score.NonTerminal(lexical[1], lexical_scores[1]); + CHECK_SCORE("in biarritz", l1_scores[0] = score.Finish()); + } + { + RuleScore score(m, l1[1]); + score.NonTerminal(lexical[2], lexical_scores[2]); + score.NonTerminal(lexical[3], lexical_scores[3]); + CHECK_SCORE("watching considering", l1_scores[1] = score.Finish()); + } + { + RuleScore score(m, l1[2]); + score.NonTerminal(lexical[4], lexical_scores[4]); + score.NonTerminal(lexical[5], lexical_scores[5]); + CHECK_SCORE("looking .", l1_scores[2] = score.Finish()); + } + BOOST_CHECK_EQUAL(l1[2].left.length, 1); + l1[3] = lexical[6]; + l1_scores[3] = lexical_scores[6]; + + ChartState l2[2]; + float l2_scores[2]; + { + RuleScore score(m, l2[0]); + score.NonTerminal(l1[0], l1_scores[0]); + score.NonTerminal(l1[1], l1_scores[1]); + CHECK_SCORE("in biarritz watching considering", l2_scores[0] = score.Finish()); + } + { + RuleScore score(m, l2[1]); + score.NonTerminal(l1[2], l1_scores[2]); + score.NonTerminal(l1[3], l1_scores[3]); + CHECK_SCORE("looking . ", l2_scores[1] = score.Finish()); + } + BOOST_CHECK_EQUAL(l2[1].left.length, 1); + BOOST_CHECK(l2[1].left.full); + + ChartState top; + { + RuleScore score(m, top); + score.NonTerminal(l2[0], l2_scores[0]); + score.NonTerminal(l2[1], l2_scores[1]); + CHECK_SCORE("in biarritz watching considering looking . ", score.Finish()); + } +} + +const char *FileLocation() { + if (boost::unit_test::framework::master_test_suite().argc < 2) { + return "test.arpa"; + } + return boost::unit_test::framework::master_test_suite().argv[1]; +} + +template void Everything() { + Config config; + config.messages = NULL; + M m(FileLocation(), config); + + Short(m); + Charge(m); + GrowBig(m); + AlsoWouldConsiderHigher(m); + GrowSmall(m); + FullGrow(m); +} + +BOOST_AUTO_TEST_CASE(ProbingAll) { + Everything(); +} +BOOST_AUTO_TEST_CASE(TrieAll) { + Everything(); +} +BOOST_AUTO_TEST_CASE(QuantTrieAll) { + Everything(); +} +BOOST_AUTO_TEST_CASE(ArrayQuantTrieAll) { + Everything(); +} +BOOST_AUTO_TEST_CASE(ArrayTrieAll) { + Everything(); +} + +BOOST_AUTO_TEST_CASE(RestProbing) { + Config config; + config.messages = NULL; + RestProbingModel m(FileLocation(), config); + GrowBig(m, true); +} + +} // namespace +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/lm_exception.cc b/ctcdecode-master/third_party/kenlm/lm/lm_exception.cc new file mode 100644 index 0000000..58d468f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/lm_exception.cc @@ -0,0 +1,23 @@ +#include "lm/lm_exception.hh" + +#include +#include + +namespace lm { + +ConfigException::ConfigException() throw() {} +ConfigException::~ConfigException() throw() {} + +LoadException::LoadException() throw() {} +LoadException::~LoadException() throw() {} + +FormatLoadException::FormatLoadException() throw() {} +FormatLoadException::~FormatLoadException() throw() {} + +VocabLoadException::VocabLoadException() throw() {} +VocabLoadException::~VocabLoadException() throw() {} + +SpecialWordMissingException::SpecialWordMissingException() throw() {} +SpecialWordMissingException::~SpecialWordMissingException() throw() {} + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/lm_exception.hh b/ctcdecode-master/third_party/kenlm/lm/lm_exception.hh new file mode 100644 index 0000000..85a5738 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/lm_exception.hh @@ -0,0 +1,50 @@ +#ifndef LM_LM_EXCEPTION_H +#define LM_LM_EXCEPTION_H + +// Named to avoid conflict with util/exception.hh. + +#include "util/exception.hh" +#include "util/string_piece.hh" + +#include +#include + +namespace lm { + +typedef enum {THROW_UP, COMPLAIN, SILENT} WarningAction; + +class ConfigException : public util::Exception { + public: + ConfigException() throw(); + ~ConfigException() throw(); +}; + +class LoadException : public util::Exception { + public: + virtual ~LoadException() throw(); + + protected: + LoadException() throw(); +}; + +class FormatLoadException : public LoadException { + public: + FormatLoadException() throw(); + ~FormatLoadException() throw(); +}; + +class VocabLoadException : public LoadException { + public: + virtual ~VocabLoadException() throw(); + VocabLoadException() throw(); +}; + +class SpecialWordMissingException : public VocabLoadException { + public: + explicit SpecialWordMissingException() throw(); + ~SpecialWordMissingException() throw(); +}; + +} // namespace lm + +#endif // LM_LM_EXCEPTION diff --git a/ctcdecode-master/third_party/kenlm/lm/max_order.hh b/ctcdecode-master/third_party/kenlm/lm/max_order.hh new file mode 100644 index 0000000..0ad1379 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/max_order.hh @@ -0,0 +1,13 @@ +#ifndef LM_MAX_ORDER_H +#define LM_MAX_ORDER_H +/* IF YOUR BUILD SYSTEM PASSES -DKENLM_MAX_ORDER, THEN CHANGE THE BUILD SYSTEM. + * If not, this is the default maximum order. + * Having this limit means that State can be + * (kMaxOrder - 1) * sizeof(float) bytes instead of + * sizeof(float*) + (kMaxOrder - 1) * sizeof(float) + malloc overhead + */ +#ifndef KENLM_ORDER_MESSAGE +#define KENLM_ORDER_MESSAGE "If your build system supports changing KENLM_MAX_ORDER, change it there and recompile. In the KenLM tarball or Moses, use e.g. `bjam --max-kenlm-order=6 -a'. Otherwise, edit lm/max_order.hh." +#endif + +#endif // LM_MAX_ORDER_H diff --git a/ctcdecode-master/third_party/kenlm/lm/model.cc b/ctcdecode-master/third_party/kenlm/lm/model.cc new file mode 100644 index 0000000..a5a16bf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/model.cc @@ -0,0 +1,349 @@ +#include "lm/model.hh" + +#include "lm/blank.hh" +#include "lm/lm_exception.hh" +#include "lm/search_hashed.hh" +#include "lm/search_trie.hh" +#include "lm/read_arpa.hh" +#include "util/have.hh" +#include "util/murmur_hash.hh" + +#include +#include +#include +#include +#include + +namespace lm { +namespace ngram { +namespace detail { + +template const ModelType GenericModel::kModelType = Search::kModelType; + +template uint64_t GenericModel::Size(const std::vector &counts, const Config &config) { + return VocabularyT::Size(counts[0], config) + Search::Size(counts, config); +} + +template void GenericModel::SetupMemory(void *base, const std::vector &counts, const Config &config) { + size_t goal_size = util::CheckOverflow(Size(counts, config)); + uint8_t *start = static_cast(base); + size_t allocated = VocabularyT::Size(counts[0], config); + vocab_.SetupMemory(start, allocated, counts[0], config); + start += allocated; + start = search_.SetupMemory(start, counts, config); + if (static_cast(start - static_cast(base)) != goal_size) UTIL_THROW(FormatLoadException, "The data structures took " << (start - static_cast(base)) << " but Size says they should take " << goal_size); +} + +namespace { +void ComplainAboutARPA(const Config &config, ModelType model_type) { + if (config.write_mmap || !config.messages) return; + if (config.arpa_complain == Config::ALL) { + *config.messages << "Loading the LM will be faster if you build a binary file." << std::endl; + } else if (config.arpa_complain == Config::EXPENSIVE && + (model_type == TRIE || model_type == QUANT_TRIE || model_type == ARRAY_TRIE || model_type == QUANT_ARRAY_TRIE)) { + *config.messages << "Building " << kModelNames[model_type] << " from ARPA is expensive. Save time by building a binary format." << std::endl; + } +} + +void CheckCounts(const std::vector &counts) { + UTIL_THROW_IF(counts.size() > KENLM_MAX_ORDER, FormatLoadException, "This model has order " << counts.size() << " but KenLM was compiled to support up to " << KENLM_MAX_ORDER << ". " << KENLM_ORDER_MESSAGE); + if (sizeof(uint64_t) > sizeof(std::size_t)) { + for (std::vector::const_iterator i = counts.begin(); i != counts.end(); ++i) { + UTIL_THROW_IF(*i > static_cast(std::numeric_limits::max()), util::OverflowException, "This model has " << *i << " " << (i - counts.begin() + 1) << "-grams which is too many for 32-bit machines."); + } + } +} + +} // namespace + +template GenericModel::GenericModel(const char *file, const Config &init_config) : backing_(init_config) { + util::scoped_fd fd(util::OpenReadOrThrow(file)); + if (IsBinaryFormat(fd.get())) { + Parameters parameters; + int fd_shallow = fd.release(); + backing_.InitializeBinary(fd_shallow, kModelType, kVersion, parameters); + CheckCounts(parameters.counts); + + Config new_config(init_config); + new_config.probing_multiplier = parameters.fixed.probing_multiplier; + Search::UpdateConfigFromBinary(backing_, parameters.counts, VocabularyT::Size(parameters.counts[0], new_config), new_config); + UTIL_THROW_IF(new_config.enumerate_vocab && !parameters.fixed.has_vocabulary, FormatLoadException, "The decoder requested all the vocabulary strings, but this binary file does not have them. You may need to rebuild the binary file with an updated version of build_binary."); + + SetupMemory(backing_.LoadBinary(Size(parameters.counts, new_config)), parameters.counts, new_config); + vocab_.LoadedBinary(parameters.fixed.has_vocabulary, fd_shallow, new_config.enumerate_vocab, backing_.VocabStringReadingOffset()); + } else { + ComplainAboutARPA(init_config, kModelType); + InitializeFromARPA(fd.release(), file, init_config); + } + + // g++ prints warnings unless these are fully initialized. + State begin_sentence = State(); + begin_sentence.length = 1; + begin_sentence.words[0] = vocab_.BeginSentence(); + typename Search::Node ignored_node; + bool ignored_independent_left; + uint64_t ignored_extend_left; + begin_sentence.backoff[0] = search_.LookupUnigram(begin_sentence.words[0], ignored_node, ignored_independent_left, ignored_extend_left).Backoff(); + State null_context = State(); + null_context.length = 0; + P::Init(begin_sentence, null_context, vocab_, search_.Order()); +} + +template void GenericModel::InitializeFromARPA(int fd, const char *file, const Config &config) { + // Backing file is the ARPA. + util::FilePiece f(fd, file, config.ProgressMessages()); + try { + std::vector counts; + // File counts do not include pruned trigrams that extend to quadgrams etc. These will be fixed by search_. + ReadARPACounts(f, counts); + CheckCounts(counts); + if (counts.size() < 2) UTIL_THROW(FormatLoadException, "This ngram implementation assumes at least a bigram model."); + if (config.probing_multiplier <= 1.0) UTIL_THROW(ConfigException, "probing multiplier must be > 1.0"); + + std::size_t vocab_size = util::CheckOverflow(VocabularyT::Size(counts[0], config)); + // Setup the binary file for writing the vocab lookup table. The search_ is responsible for growing the binary file to its needs. + vocab_.SetupMemory(backing_.SetupJustVocab(vocab_size, counts.size()), vocab_size, counts[0], config); + + if (config.write_mmap && config.include_vocab) { + WriteWordsWrapper wrap(config.enumerate_vocab); + vocab_.ConfigureEnumerate(&wrap, counts[0]); + search_.InitializeFromARPA(file, f, counts, config, vocab_, backing_); + void *vocab_rebase, *search_rebase; + backing_.WriteVocabWords(wrap.Buffer(), vocab_rebase, search_rebase); + // Due to writing at the end of file, mmap may have relocated data. So remap. + vocab_.Relocate(vocab_rebase); + search_.SetupMemory(reinterpret_cast(search_rebase), counts, config); + } else { + vocab_.ConfigureEnumerate(config.enumerate_vocab, counts[0]); + search_.InitializeFromARPA(file, f, counts, config, vocab_, backing_); + } + + if (!vocab_.SawUnk()) { + assert(config.unknown_missing != THROW_UP); + // Default probabilities for unknown. + search_.UnknownUnigram().backoff = 0.0; + search_.UnknownUnigram().prob = config.unknown_missing_logprob; + } + backing_.FinishFile(config, kModelType, kVersion, counts); + } catch (util::Exception &e) { + e << " Byte: " << f.Offset(); + throw; + } +} + +template FullScoreReturn GenericModel::FullScore(const State &in_state, const WordIndex new_word, State &out_state) const { + FullScoreReturn ret = ScoreExceptBackoff(in_state.words, in_state.words + in_state.length, new_word, out_state); + for (const float *i = in_state.backoff + ret.ngram_length - 1; i < in_state.backoff + in_state.length; ++i) { + ret.prob += *i; + } + return ret; +} + +template FullScoreReturn GenericModel::FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const { + context_rend = std::min(context_rend, context_rbegin + P::Order() - 1); + FullScoreReturn ret = ScoreExceptBackoff(context_rbegin, context_rend, new_word, out_state); + + // Add the backoff weights for n-grams of order start to (context_rend - context_rbegin). + unsigned char start = ret.ngram_length; + if (context_rend - context_rbegin < static_cast(start)) return ret; + + bool independent_left; + uint64_t extend_left; + typename Search::Node node; + if (start <= 1) { + ret.prob += search_.LookupUnigram(*context_rbegin, node, independent_left, extend_left).Backoff(); + start = 2; + } else if (!search_.FastMakeNode(context_rbegin, context_rbegin + start - 1, node)) { + return ret; + } + // i is the order of the backoff we're looking for. + unsigned char order_minus_2 = start - 2; + for (const WordIndex *i = context_rbegin + start - 1; i < context_rend; ++i, ++order_minus_2) { + typename Search::MiddlePointer p(search_.LookupMiddle(order_minus_2, *i, node, independent_left, extend_left)); + if (!p.Found()) break; + ret.prob += p.Backoff(); + } + return ret; +} + +template void GenericModel::GetState(const WordIndex *context_rbegin, const WordIndex *context_rend, State &out_state) const { + // Generate a state from context. + context_rend = std::min(context_rend, context_rbegin + P::Order() - 1); + if (context_rend == context_rbegin) { + out_state.length = 0; + return; + } + typename Search::Node node; + bool independent_left; + uint64_t extend_left; + out_state.backoff[0] = search_.LookupUnigram(*context_rbegin, node, independent_left, extend_left).Backoff(); + out_state.length = HasExtension(out_state.backoff[0]) ? 1 : 0; + float *backoff_out = out_state.backoff + 1; + unsigned char order_minus_2 = 0; + for (const WordIndex *i = context_rbegin + 1; i < context_rend; ++i, ++backoff_out, ++order_minus_2) { + typename Search::MiddlePointer p(search_.LookupMiddle(order_minus_2, *i, node, independent_left, extend_left)); + if (!p.Found()) { + std::copy(context_rbegin, context_rbegin + out_state.length, out_state.words); + return; + } + *backoff_out = p.Backoff(); + if (HasExtension(*backoff_out)) out_state.length = i - context_rbegin + 1; + } + std::copy(context_rbegin, context_rbegin + out_state.length, out_state.words); +} + +template FullScoreReturn GenericModel::ExtendLeft( + const WordIndex *add_rbegin, const WordIndex *add_rend, + const float *backoff_in, + uint64_t extend_pointer, + unsigned char extend_length, + float *backoff_out, + unsigned char &next_use) const { + FullScoreReturn ret; + typename Search::Node node; + if (extend_length == 1) { + typename Search::UnigramPointer ptr(search_.LookupUnigram(static_cast(extend_pointer), node, ret.independent_left, ret.extend_left)); + ret.rest = ptr.Rest(); + ret.prob = ptr.Prob(); + assert(!ret.independent_left); + } else { + typename Search::MiddlePointer ptr(search_.Unpack(extend_pointer, extend_length, node)); + ret.rest = ptr.Rest(); + ret.prob = ptr.Prob(); + ret.extend_left = extend_pointer; + // If this function is called, then it does depend on left words. + ret.independent_left = false; + } + float subtract_me = ret.rest; + ret.ngram_length = extend_length; + next_use = extend_length; + ResumeScore(add_rbegin, add_rend, extend_length - 1, node, backoff_out, next_use, ret); + next_use -= extend_length; + // Charge backoffs. + for (const float *b = backoff_in + ret.ngram_length - extend_length; b < backoff_in + (add_rend - add_rbegin); ++b) ret.prob += *b; + ret.prob -= subtract_me; + ret.rest -= subtract_me; + return ret; +} + +namespace { +// Do a paraonoid copy of history, assuming new_word has already been copied +// (hence the -1). out_state.length could be zero so I avoided using +// std::copy. +void CopyRemainingHistory(const WordIndex *from, State &out_state) { + WordIndex *out = out_state.words + 1; + const WordIndex *in_end = from + static_cast(out_state.length) - 1; + for (const WordIndex *in = from; in < in_end; ++in, ++out) *out = *in; +} +} // namespace + +/* Ugly optimized function. Produce a score excluding backoff. + * The search goes in increasing order of ngram length. + * Context goes backward, so context_begin is the word immediately preceeding + * new_word. + */ +template FullScoreReturn GenericModel::ScoreExceptBackoff( + const WordIndex *const context_rbegin, + const WordIndex *const context_rend, + const WordIndex new_word, + State &out_state) const { + assert(new_word < vocab_.Bound()); + FullScoreReturn ret; + // ret.ngram_length contains the last known non-blank ngram length. + ret.ngram_length = 1; + + typename Search::Node node; + typename Search::UnigramPointer uni(search_.LookupUnigram(new_word, node, ret.independent_left, ret.extend_left)); + out_state.backoff[0] = uni.Backoff(); + ret.prob = uni.Prob(); + ret.rest = uni.Rest(); + + // This is the length of the context that should be used for continuation to the right. + out_state.length = HasExtension(out_state.backoff[0]) ? 1 : 0; + // We'll write the word anyway since it will probably be used and does no harm being there. + out_state.words[0] = new_word; + if (context_rbegin == context_rend) return ret; + + ResumeScore(context_rbegin, context_rend, 0, node, out_state.backoff + 1, out_state.length, ret); + CopyRemainingHistory(context_rbegin, out_state); + return ret; +} + +template void GenericModel::ResumeScore(const WordIndex *hist_iter, const WordIndex *const context_rend, unsigned char order_minus_2, typename Search::Node &node, float *backoff_out, unsigned char &next_use, FullScoreReturn &ret) const { + for (; ; ++order_minus_2, ++hist_iter, ++backoff_out) { + if (hist_iter == context_rend) return; + if (ret.independent_left) return; + if (order_minus_2 == P::Order() - 2) break; + + typename Search::MiddlePointer pointer(search_.LookupMiddle(order_minus_2, *hist_iter, node, ret.independent_left, ret.extend_left)); + if (!pointer.Found()) return; + *backoff_out = pointer.Backoff(); + ret.prob = pointer.Prob(); + ret.rest = pointer.Rest(); + ret.ngram_length = order_minus_2 + 2; + if (HasExtension(*backoff_out)) { + next_use = ret.ngram_length; + } + } + ret.independent_left = true; + typename Search::LongestPointer longest(search_.LookupLongest(*hist_iter, node)); + if (longest.Found()) { + ret.prob = longest.Prob(); + ret.rest = ret.prob; + // There is no blank in longest_. + ret.ngram_length = P::Order(); + } +} + +template float GenericModel::InternalUnRest(const uint64_t *pointers_begin, const uint64_t *pointers_end, unsigned char first_length) const { + float ret; + typename Search::Node node; + if (first_length == 1) { + if (pointers_begin >= pointers_end) return 0.0; + bool independent_left; + uint64_t extend_left; + typename Search::UnigramPointer ptr(search_.LookupUnigram(static_cast(*pointers_begin), node, independent_left, extend_left)); + ret = ptr.Prob() - ptr.Rest(); + ++first_length; + ++pointers_begin; + } else { + ret = 0.0; + } + for (const uint64_t *i = pointers_begin; i < pointers_end; ++i, ++first_length) { + typename Search::MiddlePointer ptr(search_.Unpack(*i, first_length, node)); + ret += ptr.Prob() - ptr.Rest(); + } + return ret; +} + +template class GenericModel, ProbingVocabulary>; +template class GenericModel, ProbingVocabulary>; +template class GenericModel, SortedVocabulary>; +template class GenericModel, SortedVocabulary>; +template class GenericModel, SortedVocabulary>; +template class GenericModel, SortedVocabulary>; + +} // namespace detail + +base::Model *LoadVirtual(const char *file_name, const Config &config, ModelType model_type) { + RecognizeBinary(file_name, model_type); + switch (model_type) { + case PROBING: + return new ProbingModel(file_name, config); + case REST_PROBING: + return new RestProbingModel(file_name, config); + case TRIE: + return new TrieModel(file_name, config); + case QUANT_TRIE: + return new QuantTrieModel(file_name, config); + case ARRAY_TRIE: + return new ArrayTrieModel(file_name, config); + case QUANT_ARRAY_TRIE: + return new QuantArrayTrieModel(file_name, config); + default: + UTIL_THROW(FormatLoadException, "Confused by model type " << model_type); + } +} + +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/model.hh b/ctcdecode-master/third_party/kenlm/lm/model.hh new file mode 100644 index 0000000..b2bbe39 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/model.hh @@ -0,0 +1,155 @@ +#ifndef LM_MODEL_H +#define LM_MODEL_H + +#include "lm/bhiksha.hh" +#include "lm/binary_format.hh" +#include "lm/config.hh" +#include "lm/facade.hh" +#include "lm/quantize.hh" +#include "lm/search_hashed.hh" +#include "lm/search_trie.hh" +#include "lm/state.hh" +#include "lm/value.hh" +#include "lm/vocab.hh" +#include "lm/weights.hh" + +#include "util/murmur_hash.hh" + +#include +#include +#include + +namespace util { class FilePiece; } + +namespace lm { +namespace ngram { +namespace detail { + +// Should return the same results as SRI. +// ModelFacade typedefs Vocabulary so we use VocabularyT to avoid naming conflicts. +template class GenericModel : public base::ModelFacade, State, VocabularyT> { + private: + typedef base::ModelFacade, State, VocabularyT> P; + public: + // This is the model type returned by RecognizeBinary. + static const ModelType kModelType; + + static const unsigned int kVersion = Search::kVersion; + + /* Get the size of memory that will be mapped given ngram counts. This + * does not include small non-mapped control structures, such as this class + * itself. + */ + static uint64_t Size(const std::vector &counts, const Config &config = Config()); + + /* Load the model from a file. It may be an ARPA or binary file. Binary + * files must have the format expected by this class or you'll get an + * exception. So TrieModel can only load ARPA or binary created by + * TrieModel. To classify binary files, call RecognizeBinary in + * lm/binary_format.hh. + */ + explicit GenericModel(const char *file, const Config &config = Config()); + + /* Score p(new_word | in_state) and incorporate new_word into out_state. + * Note that in_state and out_state must be different references: + * &in_state != &out_state. + */ + FullScoreReturn FullScore(const State &in_state, const WordIndex new_word, State &out_state) const; + + /* Slower call without in_state. Try to remember state, but sometimes it + * would cost too much memory or your decoder isn't setup properly. + * To use this function, make an array of WordIndex containing the context + * vocabulary ids in reverse order. Then, pass the bounds of the array: + * [context_rbegin, context_rend). The new_word is not part of the context + * array unless you intend to repeat words. + */ + FullScoreReturn FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const; + + /* Get the state for a context. Don't use this if you can avoid it. Use + * BeginSentenceState or NullContextState and extend from those. If + * you're only going to use this state to call FullScore once, use + * FullScoreForgotState. + * To use this function, make an array of WordIndex containing the context + * vocabulary ids in reverse order. Then, pass the bounds of the array: + * [context_rbegin, context_rend). + */ + void GetState(const WordIndex *context_rbegin, const WordIndex *context_rend, State &out_state) const; + + /* More efficient version of FullScore where a partial n-gram has already + * been scored. + * NOTE: THE RETURNED .rest AND .prob ARE RELATIVE TO THE .rest RETURNED BEFORE. + */ + FullScoreReturn ExtendLeft( + // Additional context in reverse order. This will update add_rend to + const WordIndex *add_rbegin, const WordIndex *add_rend, + // Backoff weights to use. + const float *backoff_in, + // extend_left returned by a previous query. + uint64_t extend_pointer, + // Length of n-gram that the pointer corresponds to. + unsigned char extend_length, + // Where to write additional backoffs for [extend_length + 1, min(Order() - 1, return.ngram_length)] + float *backoff_out, + // Amount of additional content that should be considered by the next call. + unsigned char &next_use) const; + + /* Return probabilities minus rest costs for an array of pointers. The + * first length should be the length of the n-gram to which pointers_begin + * points. + */ + float UnRest(const uint64_t *pointers_begin, const uint64_t *pointers_end, unsigned char first_length) const { + // Compiler should optimize this if away. + return Search::kDifferentRest ? InternalUnRest(pointers_begin, pointers_end, first_length) : 0.0; + } + + private: + FullScoreReturn ScoreExceptBackoff(const WordIndex *const context_rbegin, const WordIndex *const context_rend, const WordIndex new_word, State &out_state) const; + + // Score bigrams and above. Do not include backoff. + void ResumeScore(const WordIndex *context_rbegin, const WordIndex *const context_rend, unsigned char starting_order_minus_2, typename Search::Node &node, float *backoff_out, unsigned char &next_use, FullScoreReturn &ret) const; + + // Appears after Size in the cc file. + void SetupMemory(void *start, const std::vector &counts, const Config &config); + + void InitializeFromARPA(int fd, const char *file, const Config &config); + + float InternalUnRest(const uint64_t *pointers_begin, const uint64_t *pointers_end, unsigned char first_length) const; + + BinaryFormat backing_; + + VocabularyT vocab_; + + Search search_; +}; + +} // namespace detail + +// Instead of typedef, inherit. This allows the Model etc to be forward declared. +// Oh the joys of C and C++. +#define LM_COMMA() , +#define LM_NAME_MODEL(name, from)\ +class name : public from {\ + public:\ + name(const char *file, const Config &config = Config()) : from(file, config) {}\ +}; + +LM_NAME_MODEL(ProbingModel, detail::GenericModel LM_COMMA() ProbingVocabulary>); +LM_NAME_MODEL(RestProbingModel, detail::GenericModel LM_COMMA() ProbingVocabulary>); +LM_NAME_MODEL(TrieModel, detail::GenericModel LM_COMMA() SortedVocabulary>); +LM_NAME_MODEL(ArrayTrieModel, detail::GenericModel LM_COMMA() SortedVocabulary>); +LM_NAME_MODEL(QuantTrieModel, detail::GenericModel LM_COMMA() SortedVocabulary>); +LM_NAME_MODEL(QuantArrayTrieModel, detail::GenericModel LM_COMMA() SortedVocabulary>); + +// Default implementation. No real reason for it to be the default. +typedef ::lm::ngram::ProbingVocabulary Vocabulary; +typedef ProbingModel Model; + +/* Autorecognize the file type, load, and return the virtual base class. Don't + * use the virtual base class if you can avoid it. Instead, use the above + * classes as template arguments to your own virtual feature function.*/ +base::Model *LoadVirtual(const char *file_name, const Config &config = Config(), ModelType if_arpa = PROBING); + +} // namespace ngram +} // namespace lm + +#endif // LM_MODEL_H diff --git a/ctcdecode-master/third_party/kenlm/lm/model_test.cc b/ctcdecode-master/third_party/kenlm/lm/model_test.cc new file mode 100644 index 0000000..d408d6f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/model_test.cc @@ -0,0 +1,448 @@ +#include "lm/model.hh" + +#include +#include + +#define BOOST_TEST_MODULE ModelTest +#include +#include + +// Apparently some Boost versions use templates and are pretty strict about types matching. +#define SLOPPY_CHECK_CLOSE(ref, value, tol) BOOST_CHECK_CLOSE(static_cast(ref), static_cast(value), static_cast(tol)); + +namespace lm { +namespace ngram { + +std::ostream &operator<<(std::ostream &o, const State &state) { + o << "State length " << static_cast(state.length) << ':'; + for (const WordIndex *i = state.words; i < state.words + state.length; ++i) { + o << ' ' << *i; + } + return o; +} + +namespace { + +// Stupid bjam reverses the command line arguments randomly. +const char *TestLocation() { + if (boost::unit_test::framework::master_test_suite().argc < 3) { + return "test.arpa"; + } + char **argv = boost::unit_test::framework::master_test_suite().argv; + return argv[strstr(argv[1], "nounk") ? 2 : 1]; +} +const char *TestNoUnkLocation() { + if (boost::unit_test::framework::master_test_suite().argc < 3) { + return "test_nounk.arpa"; + } + char **argv = boost::unit_test::framework::master_test_suite().argv; + return argv[strstr(argv[1], "nounk") ? 1 : 2]; +} + +template State GetState(const Model &model, const char *word, const State &in) { + WordIndex context[in.length + 1]; + context[0] = model.GetVocabulary().Index(word); + std::copy(in.words, in.words + in.length, context + 1); + State ret; + model.GetState(context, context + in.length + 1, ret); + return ret; +} + +#define StartTest(word, ngram, score, indep_left) \ + ret = model.FullScore( \ + state, \ + model.GetVocabulary().Index(word), \ + out);\ + SLOPPY_CHECK_CLOSE(score, ret.prob, 0.001); \ + BOOST_CHECK_EQUAL(static_cast(ngram), ret.ngram_length); \ + BOOST_CHECK_GE(std::min(ngram, 5 - 1), out.length); \ + BOOST_CHECK_EQUAL(indep_left, ret.independent_left); \ + BOOST_CHECK_EQUAL(out, GetState(model, word, state)); + +#define AppendTest(word, ngram, score, indep_left) \ + StartTest(word, ngram, score, indep_left) \ + state = out; + +template void Starters(const M &model) { + FullScoreReturn ret; + Model::State state(model.BeginSentenceState()); + Model::State out; + + StartTest("looking", 2, -0.4846522, true); + + // , probability plus backoff + StartTest(",", 1, -1.383514 + -0.4149733, true); + // probability plus backoff + StartTest("this_is_not_found", 1, -1.995635 + -0.4149733, true); +} + +template void Continuation(const M &model) { + FullScoreReturn ret; + Model::State state(model.BeginSentenceState()); + Model::State out; + + AppendTest("looking", 2, -0.484652, true); + AppendTest("on", 3, -0.348837, true); + AppendTest("a", 4, -0.0155266, true); + AppendTest("little", 5, -0.00306122, true); + State preserve = state; + AppendTest("the", 1, -4.04005, true); + AppendTest("biarritz", 1, -1.9889, true); + AppendTest("not_found", 1, -2.29666, true); + AppendTest("more", 1, -1.20632 - 20.0, true); + AppendTest(".", 2, -0.51363, true); + AppendTest("", 3, -0.0191651, true); + BOOST_CHECK_EQUAL(0, state.length); + + state = preserve; + AppendTest("more", 5, -0.00181395, true); + BOOST_CHECK_EQUAL(4, state.length); + AppendTest("loin", 5, -0.0432557, true); + BOOST_CHECK_EQUAL(1, state.length); +} + +template void Blanks(const M &model) { + FullScoreReturn ret; + State state(model.NullContextState()); + State out; + AppendTest("also", 1, -1.687872, false); + AppendTest("would", 2, -2, true); + AppendTest("consider", 3, -3, true); + State preserve = state; + AppendTest("higher", 4, -4, true); + AppendTest("looking", 5, -5, true); + BOOST_CHECK_EQUAL(1, state.length); + + state = preserve; + // also would consider not_found + AppendTest("not_found", 1, -1.995635 - 7.0 - 0.30103, true); + + state = model.NullContextState(); + // higher looking is a blank. + AppendTest("higher", 1, -1.509559, false); + AppendTest("looking", 2, -1.285941 - 0.30103, false); + + State higher_looking = state; + + BOOST_CHECK_EQUAL(1, state.length); + AppendTest("not_found", 1, -1.995635 - 0.4771212, true); + + state = higher_looking; + // higher looking consider + AppendTest("consider", 1, -1.687872 - 0.4771212, true); + + state = model.NullContextState(); + AppendTest("would", 1, -1.687872, false); + BOOST_CHECK_EQUAL(1, state.length); + AppendTest("consider", 2, -1.687872 -0.30103, false); + BOOST_CHECK_EQUAL(2, state.length); + AppendTest("higher", 3, -1.509559 - 0.30103, false); + BOOST_CHECK_EQUAL(3, state.length); + AppendTest("looking", 4, -1.285941 - 0.30103, false); +} + +template void Unknowns(const M &model) { + FullScoreReturn ret; + State state(model.NullContextState()); + State out; + + AppendTest("not_found", 1, -1.995635, false); + State preserve = state; + AppendTest("not_found2", 2, -15.0, true); + AppendTest("not_found3", 2, -15.0 - 2.0, true); + + state = preserve; + AppendTest("however", 2, -4, true); + AppendTest("not_found3", 3, -6, true); +} + +template void MinimalState(const M &model) { + FullScoreReturn ret; + State state(model.NullContextState()); + State out; + + AppendTest("baz", 1, -6.535897, true); + BOOST_CHECK_EQUAL(0, state.length); + state = model.NullContextState(); + AppendTest("foo", 1, -3.141592, true); + BOOST_CHECK_EQUAL(1, state.length); + AppendTest("bar", 2, -6.0, true); + // Has to include the backoff weight. + BOOST_CHECK_EQUAL(1, state.length); + AppendTest("bar", 1, -2.718281 + 3.0, true); + BOOST_CHECK_EQUAL(1, state.length); + + state = model.NullContextState(); + AppendTest("to", 1, -1.687872, false); + AppendTest("look", 2, -0.2922095, true); + BOOST_CHECK_EQUAL(2, state.length); + AppendTest("a", 3, -7, true); +} + +template void ExtendLeftTest(const M &model) { + State right; + FullScoreReturn little(model.FullScore(model.NullContextState(), model.GetVocabulary().Index("little"), right)); + const float kLittleProb = -1.285941; + SLOPPY_CHECK_CLOSE(kLittleProb, little.prob, 0.001); + unsigned char next_use; + float backoff_out[4]; + + FullScoreReturn extend_none(model.ExtendLeft(NULL, NULL, NULL, little.extend_left, 1, NULL, next_use)); + BOOST_CHECK_EQUAL(0, next_use); + BOOST_CHECK_EQUAL(little.extend_left, extend_none.extend_left); + SLOPPY_CHECK_CLOSE(little.prob - little.rest, extend_none.prob, 0.001); + BOOST_CHECK_EQUAL(1, extend_none.ngram_length); + + const WordIndex a = model.GetVocabulary().Index("a"); + float backoff_in = 3.14; + // a little + FullScoreReturn extend_a(model.ExtendLeft(&a, &a + 1, &backoff_in, little.extend_left, 1, backoff_out, next_use)); + BOOST_CHECK_EQUAL(1, next_use); + SLOPPY_CHECK_CLOSE(-0.69897, backoff_out[0], 0.001); + SLOPPY_CHECK_CLOSE(-0.09132547 - little.rest, extend_a.prob, 0.001); + BOOST_CHECK_EQUAL(2, extend_a.ngram_length); + BOOST_CHECK(!extend_a.independent_left); + + const WordIndex on = model.GetVocabulary().Index("on"); + FullScoreReturn extend_on(model.ExtendLeft(&on, &on + 1, &backoff_in, extend_a.extend_left, 2, backoff_out, next_use)); + BOOST_CHECK_EQUAL(1, next_use); + SLOPPY_CHECK_CLOSE(-0.4771212, backoff_out[0], 0.001); + SLOPPY_CHECK_CLOSE(-0.0283603 - (extend_a.rest + little.rest), extend_on.prob, 0.001); + BOOST_CHECK_EQUAL(3, extend_on.ngram_length); + BOOST_CHECK(!extend_on.independent_left); + + const WordIndex both[2] = {a, on}; + float backoff_in_arr[4]; + FullScoreReturn extend_both(model.ExtendLeft(both, both + 2, backoff_in_arr, little.extend_left, 1, backoff_out, next_use)); + BOOST_CHECK_EQUAL(2, next_use); + SLOPPY_CHECK_CLOSE(-0.69897, backoff_out[0], 0.001); + SLOPPY_CHECK_CLOSE(-0.4771212, backoff_out[1], 0.001); + SLOPPY_CHECK_CLOSE(-0.0283603 - little.rest, extend_both.prob, 0.001); + BOOST_CHECK_EQUAL(3, extend_both.ngram_length); + BOOST_CHECK(!extend_both.independent_left); + BOOST_CHECK_EQUAL(extend_on.extend_left, extend_both.extend_left); +} + +#define StatelessTest(word, provide, ngram, score) \ + ret = model.FullScoreForgotState(indices + num_words - word, indices + num_words - word + provide, indices[num_words - word - 1], state); \ + SLOPPY_CHECK_CLOSE(score, ret.prob, 0.001); \ + BOOST_CHECK_EQUAL(static_cast(ngram), ret.ngram_length); \ + model.GetState(indices + num_words - word, indices + num_words - word + provide, before); \ + ret = model.FullScore(before, indices[num_words - word - 1], out); \ + BOOST_CHECK(state == out); \ + SLOPPY_CHECK_CLOSE(score, ret.prob, 0.001); \ + BOOST_CHECK_EQUAL(static_cast(ngram), ret.ngram_length); + +template void Stateless(const M &model) { + const char *words[] = {"", "looking", "on", "a", "little", "the", "biarritz", "not_found", "more", ".", ""}; + const size_t num_words = sizeof(words) / sizeof(const char*); + // Silience "array subscript is above array bounds" when extracting end pointer. + WordIndex indices[num_words + 1]; + for (unsigned int i = 0; i < num_words; ++i) { + indices[num_words - 1 - i] = model.GetVocabulary().Index(words[i]); + } + FullScoreReturn ret; + State state, out, before; + + ret = model.FullScoreForgotState(indices + num_words - 1, indices + num_words, indices[num_words - 2], state); + SLOPPY_CHECK_CLOSE(-0.484652, ret.prob, 0.001); + StatelessTest(1, 1, 2, -0.484652); + + // looking + StatelessTest(1, 2, 2, -0.484652); + // on + AppendTest("on", 3, -0.348837, true); + StatelessTest(2, 3, 3, -0.348837); + StatelessTest(2, 2, 3, -0.348837); + StatelessTest(2, 1, 2, -0.4638903); + // a + StatelessTest(3, 4, 4, -0.0155266); + // little + AppendTest("little", 5, -0.00306122, true); + StatelessTest(4, 5, 5, -0.00306122); + // the + AppendTest("the", 1, -4.04005, true); + StatelessTest(5, 5, 1, -4.04005); + // No context of the. + StatelessTest(5, 0, 1, -1.687872); + // biarritz + StatelessTest(6, 1, 1, -1.9889); + // not found + StatelessTest(7, 1, 1, -2.29666); + StatelessTest(7, 0, 1, -1.995635); + + WordIndex unk[1]; + unk[0] = 0; + model.GetState(unk, unk + 1, state); + BOOST_CHECK_EQUAL(1, state.length); + BOOST_CHECK_EQUAL(static_cast(0), state.words[0]); +} + +template void NoUnkCheck(const M &model) { + WordIndex unk_index = 0; + State state; + + FullScoreReturn ret = model.FullScoreForgotState(&unk_index, &unk_index + 1, unk_index, state); + SLOPPY_CHECK_CLOSE(-100.0, ret.prob, 0.001); +} + +template void Everything(const M &m) { + Starters(m); + Continuation(m); + Blanks(m); + Unknowns(m); + MinimalState(m); + ExtendLeftTest(m); + Stateless(m); +} + +class ExpectEnumerateVocab : public EnumerateVocab { + public: + ExpectEnumerateVocab() {} + + void Add(WordIndex index, const StringPiece &str) { + BOOST_CHECK_EQUAL(seen.size(), index); + seen.push_back(std::string(str.data(), str.length())); + } + + void Check(const base::Vocabulary &vocab) { + BOOST_CHECK_EQUAL(37ULL, seen.size()); + BOOST_REQUIRE(!seen.empty()); + BOOST_CHECK_EQUAL("", seen[0]); + for (WordIndex i = 0; i < seen.size(); ++i) { + BOOST_CHECK_EQUAL(i, vocab.Index(seen[i])); + } + } + + void Clear() { + seen.clear(); + } + + std::vector seen; +}; + +template void LoadingTest() { + Config config; + config.arpa_complain = Config::NONE; + config.messages = NULL; + config.probing_multiplier = 2.0; + { + ExpectEnumerateVocab enumerate; + config.enumerate_vocab = &enumerate; + ModelT m(TestLocation(), config); + enumerate.Check(m.GetVocabulary()); + BOOST_CHECK_EQUAL((WordIndex)37, m.GetVocabulary().Bound()); + Everything(m); + } + { + ExpectEnumerateVocab enumerate; + config.enumerate_vocab = &enumerate; + ModelT m(TestNoUnkLocation(), config); + enumerate.Check(m.GetVocabulary()); + BOOST_CHECK_EQUAL((WordIndex)37, m.GetVocabulary().Bound()); + NoUnkCheck(m); + } +} + +BOOST_AUTO_TEST_CASE(probing) { + LoadingTest(); +} +BOOST_AUTO_TEST_CASE(trie) { + LoadingTest(); +} +BOOST_AUTO_TEST_CASE(quant_trie) { + LoadingTest(); +} +BOOST_AUTO_TEST_CASE(bhiksha_trie) { + LoadingTest(); +} +BOOST_AUTO_TEST_CASE(quant_bhiksha_trie) { + LoadingTest(); +} + +template void BinaryTest(Config::WriteMethod write_method) { + Config config; + config.write_mmap = "test.binary"; + config.messages = NULL; + config.write_method = write_method; + ExpectEnumerateVocab enumerate; + config.enumerate_vocab = &enumerate; + + { + ModelT copy_model(TestLocation(), config); + enumerate.Check(copy_model.GetVocabulary()); + enumerate.Clear(); + Everything(copy_model); + } + + config.write_mmap = NULL; + + ModelType type; + BOOST_REQUIRE(RecognizeBinary("test.binary", type)); + BOOST_CHECK_EQUAL(ModelT::kModelType, type); + + { + ModelT binary("test.binary", config); + enumerate.Check(binary.GetVocabulary()); + Everything(binary); + } + unlink("test.binary"); + + // Now test without . + config.write_mmap = "test_nounk.binary"; + config.messages = NULL; + enumerate.Clear(); + { + ModelT copy_model(TestNoUnkLocation(), config); + enumerate.Check(copy_model.GetVocabulary()); + enumerate.Clear(); + NoUnkCheck(copy_model); + } + config.write_mmap = NULL; + { + ModelT binary(TestNoUnkLocation(), config); + enumerate.Check(binary.GetVocabulary()); + NoUnkCheck(binary); + } + unlink("test_nounk.binary"); +} + +template void BinaryTest() { + BinaryTest(Config::WRITE_MMAP); + BinaryTest(Config::WRITE_AFTER); +} + +BOOST_AUTO_TEST_CASE(write_and_read_probing) { + BinaryTest(); +} +BOOST_AUTO_TEST_CASE(write_and_read_rest_probing) { + BinaryTest(); +} +BOOST_AUTO_TEST_CASE(write_and_read_trie) { + BinaryTest(); +} +BOOST_AUTO_TEST_CASE(write_and_read_quant_trie) { + BinaryTest(); +} +BOOST_AUTO_TEST_CASE(write_and_read_array_trie) { + BinaryTest(); +} +BOOST_AUTO_TEST_CASE(write_and_read_quant_array_trie) { + BinaryTest(); +} + +BOOST_AUTO_TEST_CASE(rest_max) { + Config config; + config.arpa_complain = Config::NONE; + config.messages = NULL; + + RestProbingModel model(TestLocation(), config); + State state, out; + FullScoreReturn ret(model.FullScore(model.NullContextState(), model.GetVocabulary().Index("."), state)); + SLOPPY_CHECK_CLOSE(-0.2705918, ret.rest, 0.001); + SLOPPY_CHECK_CLOSE(-0.01916512, model.FullScore(state, model.GetVocabulary().EndSentence(), out).rest, 0.001); +} + +} // namespace +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/model_type.hh b/ctcdecode-master/third_party/kenlm/lm/model_type.hh new file mode 100644 index 0000000..dcdc6ac --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/model_type.hh @@ -0,0 +1,23 @@ +#ifndef LM_MODEL_TYPE_H +#define LM_MODEL_TYPE_H + +namespace lm { +namespace ngram { + +/* Not the best numbering system, but it grew this way for historical reasons + * and I want to preserve existing binary files. */ +typedef enum {PROBING=0, REST_PROBING=1, TRIE=2, QUANT_TRIE=3, ARRAY_TRIE=4, QUANT_ARRAY_TRIE=5} ModelType; + +// Historical names. +const ModelType HASH_PROBING = PROBING; +const ModelType TRIE_SORTED = TRIE; +const ModelType QUANT_TRIE_SORTED = QUANT_TRIE; +const ModelType ARRAY_TRIE_SORTED = ARRAY_TRIE; +const ModelType QUANT_ARRAY_TRIE_SORTED = QUANT_ARRAY_TRIE; + +const static ModelType kQuantAdd = static_cast(QUANT_TRIE - TRIE); +const static ModelType kArrayAdd = static_cast(ARRAY_TRIE - TRIE); + +} // namespace ngram +} // namespace lm +#endif // LM_MODEL_TYPE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/ngram_query.hh b/ctcdecode-master/third_party/kenlm/lm/ngram_query.hh new file mode 100644 index 0000000..4430841 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/ngram_query.hh @@ -0,0 +1,113 @@ +#ifndef LM_NGRAM_QUERY_H +#define LM_NGRAM_QUERY_H + +#include "lm/enumerate_vocab.hh" +#include "lm/model.hh" +#include "util/file_stream.hh" +#include "util/file_piece.hh" +#include "util/usage.hh" + +#include +#include +#include + +namespace lm { +namespace ngram { + +class QueryPrinter { + public: + QueryPrinter(int fd, bool print_word, bool print_line, bool print_summary, bool flush) + : out_(fd), print_word_(print_word), print_line_(print_line), print_summary_(print_summary), flush_(flush) {} + + void Word(StringPiece surface, WordIndex vocab, const FullScoreReturn &ret) { + if (!print_word_) return; + out_ << surface << '=' << vocab << ' ' << static_cast(ret.ngram_length) << ' ' << ret.prob << '\t'; + if (flush_) out_.flush(); + } + + void Line(uint64_t oov, float total) { + if (!print_line_) return; + out_ << "Total: " << total << " OOV: " << oov << '\n'; + if (flush_) out_.flush(); + } + + void Summary(double ppl_including_oov, double ppl_excluding_oov, uint64_t corpus_oov, uint64_t corpus_tokens) { + if (!print_summary_) return; + out_ << + "Perplexity including OOVs:\t" << ppl_including_oov << "\n" + "Perplexity excluding OOVs:\t" << ppl_excluding_oov << "\n" + "OOVs:\t" << corpus_oov << "\n" + "Tokens:\t" << corpus_tokens << '\n'; + out_.flush(); + } + + private: + util::FileStream out_; + bool print_word_; + bool print_line_; + bool print_summary_; + bool flush_; +}; + +template void Query(const Model &model, bool sentence_context, Printer &printer) { + typename Model::State state, out; + lm::FullScoreReturn ret; + StringPiece word; + + util::FilePiece in(0); + + double corpus_total = 0.0; + double corpus_total_oov_only = 0.0; + uint64_t corpus_oov = 0; + uint64_t corpus_tokens = 0; + + while (true) { + state = sentence_context ? model.BeginSentenceState() : model.NullContextState(); + float total = 0.0; + uint64_t oov = 0; + + while (in.ReadWordSameLine(word)) { + lm::WordIndex vocab = model.GetVocabulary().Index(word); + ret = model.FullScore(state, vocab, out); + if (vocab == model.GetVocabulary().NotFound()) { + ++oov; + corpus_total_oov_only += ret.prob; + } + total += ret.prob; + printer.Word(word, vocab, ret); + ++corpus_tokens; + state = out; + } + // If people don't have a newline after their last query, this won't add a . + // Sue me. + try { + UTIL_THROW_IF('\n' != in.get(), util::Exception, "FilePiece is confused."); + } catch (const util::EndOfFileException &e) { break; } + if (sentence_context) { + ret = model.FullScore(state, model.GetVocabulary().EndSentence(), out); + total += ret.prob; + ++corpus_tokens; + printer.Word("", model.GetVocabulary().EndSentence(), ret); + } + printer.Line(oov, total); + corpus_total += total; + corpus_oov += oov; + } + printer.Summary( + pow(10.0, -(corpus_total / static_cast(corpus_tokens))), // PPL including OOVs + pow(10.0, -((corpus_total - corpus_total_oov_only) / static_cast(corpus_tokens - corpus_oov))), // PPL excluding OOVs + corpus_oov, + corpus_tokens); +} + +template void Query(const char *file, const Config &config, bool sentence_context, QueryPrinter &printer) { + Model model(file, config); + Query(model, sentence_context, printer); +} + +} // namespace ngram +} // namespace lm + +#endif // LM_NGRAM_QUERY_H + + diff --git a/ctcdecode-master/third_party/kenlm/lm/partial.hh b/ctcdecode-master/third_party/kenlm/lm/partial.hh new file mode 100644 index 0000000..9e4e352 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/partial.hh @@ -0,0 +1,166 @@ +#ifndef LM_PARTIAL_H +#define LM_PARTIAL_H + +#include "lm/return.hh" +#include "lm/state.hh" + +#include +#include + +namespace lm { +namespace ngram { + +struct ExtendReturn { + float adjust; + bool make_full; + unsigned char next_use; +}; + +template ExtendReturn ExtendLoop( + const Model &model, + unsigned char seen, const WordIndex *add_rbegin, const WordIndex *add_rend, const float *backoff_start, + const uint64_t *pointers, const uint64_t *pointers_end, + uint64_t *&pointers_write, + float *backoff_write) { + unsigned char add_length = add_rend - add_rbegin; + + float backoff_buf[2][KENLM_MAX_ORDER - 1]; + float *backoff_in = backoff_buf[0], *backoff_out = backoff_buf[1]; + std::copy(backoff_start, backoff_start + add_length, backoff_in); + + ExtendReturn value; + value.make_full = false; + value.adjust = 0.0; + value.next_use = add_length; + + unsigned char i = 0; + unsigned char length = pointers_end - pointers; + // pointers_write is NULL means that the existing left state is full, so we should use completed probabilities. + if (pointers_write) { + // Using full context, writing to new left state. + for (; i < length; ++i) { + FullScoreReturn ret(model.ExtendLeft( + add_rbegin, add_rbegin + value.next_use, + backoff_in, + pointers[i], i + seen + 1, + backoff_out, + value.next_use)); + std::swap(backoff_in, backoff_out); + if (ret.independent_left) { + value.adjust += ret.prob; + value.make_full = true; + ++i; + break; + } + value.adjust += ret.rest; + *pointers_write++ = ret.extend_left; + if (value.next_use != add_length) { + value.make_full = true; + ++i; + break; + } + } + } + // Using some of the new context. + for (; i < length && value.next_use; ++i) { + FullScoreReturn ret(model.ExtendLeft( + add_rbegin, add_rbegin + value.next_use, + backoff_in, + pointers[i], i + seen + 1, + backoff_out, + value.next_use)); + std::swap(backoff_in, backoff_out); + value.adjust += ret.prob; + } + float unrest = model.UnRest(pointers + i, pointers_end, i + seen + 1); + // Using none of the new context. + value.adjust += unrest; + + std::copy(backoff_in, backoff_in + value.next_use, backoff_write); + return value; +} + +template float RevealBefore(const Model &model, const Right &reveal, const unsigned char seen, bool reveal_full, Left &left, Right &right) { + assert(seen < reveal.length || reveal_full); + uint64_t *pointers_write = reveal_full ? NULL : left.pointers; + float backoff_buffer[KENLM_MAX_ORDER - 1]; + ExtendReturn value(ExtendLoop( + model, + seen, reveal.words + seen, reveal.words + reveal.length, reveal.backoff + seen, + left.pointers, left.pointers + left.length, + pointers_write, + left.full ? backoff_buffer : (right.backoff + right.length))); + if (reveal_full) { + left.length = 0; + value.make_full = true; + } else { + left.length = pointers_write - left.pointers; + value.make_full |= (left.length == model.Order() - 1); + } + if (left.full) { + for (unsigned char i = 0; i < value.next_use; ++i) value.adjust += backoff_buffer[i]; + } else { + // If left wasn't full when it came in, put words into right state. + std::copy(reveal.words + seen, reveal.words + seen + value.next_use, right.words + right.length); + right.length += value.next_use; + left.full = value.make_full || (right.length == model.Order() - 1); + } + return value.adjust; +} + +template float RevealAfter(const Model &model, Left &left, Right &right, const Left &reveal, unsigned char seen) { + assert(seen < reveal.length || reveal.full); + uint64_t *pointers_write = left.full ? NULL : (left.pointers + left.length); + ExtendReturn value(ExtendLoop( + model, + seen, right.words, right.words + right.length, right.backoff, + reveal.pointers + seen, reveal.pointers + reveal.length, + pointers_write, + right.backoff)); + if (reveal.full) { + for (unsigned char i = 0; i < value.next_use; ++i) value.adjust += right.backoff[i]; + right.length = 0; + value.make_full = true; + } else { + right.length = value.next_use; + value.make_full |= (right.length == model.Order() - 1); + } + if (!left.full) { + left.length = pointers_write - left.pointers; + left.full = value.make_full || (left.length == model.Order() - 1); + } + return value.adjust; +} + +template float Subsume(const Model &model, Left &first_left, const Right &first_right, const Left &second_left, Right &second_right, const unsigned int between_length) { + assert(first_right.length < KENLM_MAX_ORDER); + assert(second_left.length < KENLM_MAX_ORDER); + assert(between_length < KENLM_MAX_ORDER - 1); + uint64_t *pointers_write = first_left.full ? NULL : (first_left.pointers + first_left.length); + float backoff_buffer[KENLM_MAX_ORDER - 1]; + ExtendReturn value(ExtendLoop( + model, + between_length, first_right.words, first_right.words + first_right.length, first_right.backoff, + second_left.pointers, second_left.pointers + second_left.length, + pointers_write, + second_left.full ? backoff_buffer : (second_right.backoff + second_right.length))); + if (second_left.full) { + for (unsigned char i = 0; i < value.next_use; ++i) value.adjust += backoff_buffer[i]; + } else { + std::copy(first_right.words, first_right.words + value.next_use, second_right.words + second_right.length); + second_right.length += value.next_use; + value.make_full |= (second_right.length == model.Order() - 1); + } + if (!first_left.full) { + first_left.length = pointers_write - first_left.pointers; + first_left.full = value.make_full || second_left.full || (first_left.length == model.Order() - 1); + } + assert(first_left.length < KENLM_MAX_ORDER); + assert(second_right.length < KENLM_MAX_ORDER); + return value.adjust; +} + +} // namespace ngram +} // namespace lm + +#endif // LM_PARTIAL_H diff --git a/ctcdecode-master/third_party/kenlm/lm/partial_test.cc b/ctcdecode-master/third_party/kenlm/lm/partial_test.cc new file mode 100644 index 0000000..adb644f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/partial_test.cc @@ -0,0 +1,199 @@ +#include "lm/partial.hh" + +#include "lm/left.hh" +#include "lm/model.hh" +#include "util/tokenize_piece.hh" + +#define BOOST_TEST_MODULE PartialTest +#include +#include + +namespace lm { +namespace ngram { +namespace { + +const char *TestLocation() { + if (boost::unit_test::framework::master_test_suite().argc < 2) { + return "test.arpa"; + } + return boost::unit_test::framework::master_test_suite().argv[1]; +} + +Config SilentConfig() { + Config config; + config.arpa_complain = Config::NONE; + config.messages = NULL; + return config; +} + +struct ModelFixture { + ModelFixture() : m(TestLocation(), SilentConfig()) {} + + RestProbingModel m; +}; + +BOOST_FIXTURE_TEST_SUITE(suite, ModelFixture) + +BOOST_AUTO_TEST_CASE(SimpleBefore) { + Left left; + left.full = false; + left.length = 0; + Right right; + right.length = 0; + + Right reveal; + reveal.length = 1; + WordIndex period = m.GetVocabulary().Index("."); + reveal.words[0] = period; + reveal.backoff[0] = -0.845098; + + BOOST_CHECK_CLOSE(0.0, RevealBefore(m, reveal, 0, false, left, right), 0.001); + BOOST_CHECK_EQUAL(0, left.length); + BOOST_CHECK(!left.full); + BOOST_CHECK_EQUAL(1, right.length); + BOOST_CHECK_EQUAL(period, right.words[0]); + BOOST_CHECK_CLOSE(-0.845098, right.backoff[0], 0.001); + + WordIndex more = m.GetVocabulary().Index("more"); + reveal.words[1] = more; + reveal.backoff[1] = -0.4771212; + reveal.length = 2; + BOOST_CHECK_CLOSE(0.0, RevealBefore(m, reveal, 1, false, left, right), 0.001); + BOOST_CHECK_EQUAL(0, left.length); + BOOST_CHECK(!left.full); + BOOST_CHECK_EQUAL(2, right.length); + BOOST_CHECK_EQUAL(period, right.words[0]); + BOOST_CHECK_EQUAL(more, right.words[1]); + BOOST_CHECK_CLOSE(-0.845098, right.backoff[0], 0.001); + BOOST_CHECK_CLOSE(-0.4771212, right.backoff[1], 0.001); +} + +BOOST_AUTO_TEST_CASE(AlsoWouldConsider) { + WordIndex would = m.GetVocabulary().Index("would"); + WordIndex consider = m.GetVocabulary().Index("consider"); + + ChartState current; + current.left.length = 1; + current.left.pointers[0] = would; + current.left.full = false; + current.right.length = 1; + current.right.words[0] = would; + current.right.backoff[0] = -0.30103; + + Left after; + after.full = false; + after.length = 1; + after.pointers[0] = consider; + + // adjustment for would consider + BOOST_CHECK_CLOSE(-1.687872 - -0.2922095 - 0.30103, RevealAfter(m, current.left, current.right, after, 0), 0.001); + + BOOST_CHECK_EQUAL(2, current.left.length); + BOOST_CHECK_EQUAL(would, current.left.pointers[0]); + BOOST_CHECK_EQUAL(false, current.left.full); + + WordIndex also = m.GetVocabulary().Index("also"); + Right before; + before.length = 1; + before.words[0] = also; + before.backoff[0] = -0.30103; + // r(would) = -0.2922095 [i would], r(would -> consider) = -1.988902 [b(would) + p(consider)] + // p(also -> would) = -2, p(also would -> consider) = -3 + BOOST_CHECK_CLOSE(-2 + 0.2922095 -3 + 1.988902, RevealBefore(m, before, 0, false, current.left, current.right), 0.001); + BOOST_CHECK_EQUAL(0, current.left.length); + BOOST_CHECK(current.left.full); + BOOST_CHECK_EQUAL(2, current.right.length); + BOOST_CHECK_EQUAL(would, current.right.words[0]); + BOOST_CHECK_EQUAL(also, current.right.words[1]); +} + +BOOST_AUTO_TEST_CASE(EndSentence) { + WordIndex loin = m.GetVocabulary().Index("loin"); + WordIndex period = m.GetVocabulary().Index("."); + WordIndex eos = m.GetVocabulary().EndSentence(); + + ChartState between; + between.left.length = 1; + between.left.pointers[0] = eos; + between.left.full = true; + between.right.length = 0; + + Right before; + before.words[0] = period; + before.words[1] = loin; + before.backoff[0] = -0.845098; + before.backoff[1] = 0.0; + + before.length = 1; + BOOST_CHECK_CLOSE(-0.0410707, RevealBefore(m, before, 0, true, between.left, between.right), 0.001); + BOOST_CHECK_EQUAL(0, between.left.length); +} + +float ScoreFragment(const RestProbingModel &model, unsigned int *begin, unsigned int *end, ChartState &out) { + RuleScore scorer(model, out); + for (unsigned int *i = begin; i < end; ++i) { + scorer.Terminal(*i); + } + return scorer.Finish(); +} + +void CheckAdjustment(const RestProbingModel &model, float expect, const Right &before_in, bool before_full, ChartState between, const Left &after_in) { + Right before(before_in); + Left after(after_in); + after.full = false; + float got = 0.0; + for (unsigned int i = 1; i < 5; ++i) { + if (before_in.length >= i) { + before.length = i; + got += RevealBefore(model, before, i - 1, false, between.left, between.right); + } + if (after_in.length >= i) { + after.length = i; + got += RevealAfter(model, between.left, between.right, after, i - 1); + } + } + if (after_in.full) { + after.full = true; + got += RevealAfter(model, between.left, between.right, after, after.length); + } + if (before_full) { + got += RevealBefore(model, before, before.length, true, between.left, between.right); + } + // Sometimes they're zero and BOOST_CHECK_CLOSE fails for this. + BOOST_CHECK(fabs(expect - got) < 0.001); +} + +void FullDivide(const RestProbingModel &model, StringPiece str) { + std::vector indices; + for (util::TokenIter i(str, ' '); i; ++i) { + indices.push_back(model.GetVocabulary().Index(*i)); + } + ChartState full_state; + float full = ScoreFragment(model, &indices.front(), &indices.back() + 1, full_state); + + ChartState before_state; + before_state.left.full = false; + RuleScore before_scorer(model, before_state); + float before_score = 0.0; + for (unsigned int before = 0; before < indices.size(); ++before) { + for (unsigned int after = before; after <= indices.size(); ++after) { + ChartState after_state, between_state; + float after_score = ScoreFragment(model, &indices.front() + after, &indices.front() + indices.size(), after_state); + float between_score = ScoreFragment(model, &indices.front() + before, &indices.front() + after, between_state); + CheckAdjustment(model, full - before_score - after_score - between_score, before_state.right, before_state.left.full, between_state, after_state.left); + } + before_scorer.Terminal(indices[before]); + before_score = before_scorer.Finish(); + } +} + +BOOST_AUTO_TEST_CASE(Strings) { + FullDivide(m, "also would consider"); + FullDivide(m, "looking on a little more loin . "); + FullDivide(m, "in biarritz watching considering looking . on a little more loin also would consider higher to look good unknown the screening foo bar , unknown however unknown "); +} + +BOOST_AUTO_TEST_SUITE_END() +} // namespace +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/quantize.cc b/ctcdecode-master/third_party/kenlm/lm/quantize.cc new file mode 100644 index 0000000..02b5dbc --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/quantize.cc @@ -0,0 +1,93 @@ +/* Quantize into bins of equal size as described in + * M. Federico and N. Bertoldi. 2006. How many bits are needed + * to store probabilities for phrase-based translation? In Proc. + * of the Workshop on Statistical Machine Translation, pages + * 94–101, New York City, June. Association for Computa- + * tional Linguistics. + */ + +#include "lm/quantize.hh" + +#include "lm/binary_format.hh" +#include "lm/lm_exception.hh" +#include "util/file.hh" + +#include +#include + +namespace lm { +namespace ngram { + +namespace { + +void MakeBins(std::vector &values, float *centers, uint32_t bins) { + std::sort(values.begin(), values.end()); + std::vector::const_iterator start = values.begin(), finish; + for (uint32_t i = 0; i < bins; ++i, ++centers, start = finish) { + finish = values.begin() + ((values.size() * static_cast(i + 1)) / bins); + if (finish == start) { + // zero length bucket. + *centers = i ? *(centers - 1) : -std::numeric_limits::infinity(); + } else { + *centers = std::accumulate(start, finish, 0.0) / static_cast(finish - start); + } + } +} + +const char kSeparatelyQuantizeVersion = 2; + +} // namespace + +void SeparatelyQuantize::UpdateConfigFromBinary(const BinaryFormat &file, uint64_t offset, Config &config) { + unsigned char buffer[3]; + file.ReadForConfig(buffer, 3, offset); + char version = buffer[0]; + config.prob_bits = buffer[1]; + config.backoff_bits = buffer[2]; + if (version != kSeparatelyQuantizeVersion) UTIL_THROW(FormatLoadException, "This file has quantization version " << (unsigned)version << " but the code expects version " << (unsigned)kSeparatelyQuantizeVersion); +} + +void SeparatelyQuantize::SetupMemory(void *base, unsigned char order, const Config &config) { + prob_bits_ = config.prob_bits; + backoff_bits_ = config.backoff_bits; + // We need the reserved values. + if (config.prob_bits == 0) UTIL_THROW(ConfigException, "You can't quantize probability to zero"); + if (config.backoff_bits == 0) UTIL_THROW(ConfigException, "You can't quantize backoff to zero"); + if (config.prob_bits > 25) UTIL_THROW(ConfigException, "For efficiency reasons, quantizing probability supports at most 25 bits. Currently you have requested " << static_cast(config.prob_bits) << " bits."); + if (config.backoff_bits > 25) UTIL_THROW(ConfigException, "For efficiency reasons, quantizing backoff supports at most 25 bits. Currently you have requested " << static_cast(config.backoff_bits) << " bits."); + // Reserve 8 byte header for bit counts. + actual_base_ = static_cast(base); + float *start = reinterpret_cast(actual_base_ + 8); + for (unsigned char i = 0; i < order - 2; ++i) { + tables_[i][0] = Bins(prob_bits_, start); + start += (1ULL << prob_bits_); + tables_[i][1] = Bins(backoff_bits_, start); + start += (1ULL << backoff_bits_); + } + longest_ = tables_[order - 2][0] = Bins(prob_bits_, start); +} + +void SeparatelyQuantize::Train(uint8_t order, std::vector &prob, std::vector &backoff) { + TrainProb(order, prob); + + // Backoff + float *centers = tables_[order - 2][1].Populate(); + *(centers++) = kNoExtensionBackoff; + *(centers++) = kExtensionBackoff; + MakeBins(backoff, centers, (1ULL << backoff_bits_) - 2); +} + +void SeparatelyQuantize::TrainProb(uint8_t order, std::vector &prob) { + float *centers = tables_[order - 2][0].Populate(); + MakeBins(prob, centers, (1ULL << prob_bits_)); +} + +void SeparatelyQuantize::FinishedLoading(const Config &config) { + uint8_t *actual_base = actual_base_; + *(actual_base++) = kSeparatelyQuantizeVersion; // version + *(actual_base++) = config.prob_bits; + *(actual_base++) = config.backoff_bits; +} + +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/quantize.hh b/ctcdecode-master/third_party/kenlm/lm/quantize.hh new file mode 100644 index 0000000..8500ace --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/quantize.hh @@ -0,0 +1,233 @@ +#ifndef LM_QUANTIZE_H +#define LM_QUANTIZE_H + +#include "lm/blank.hh" +#include "lm/config.hh" +#include "lm/max_order.hh" +#include "lm/model_type.hh" +#include "util/bit_packing.hh" + +#include +#include + +#include + +#include + +namespace lm { +namespace ngram { + +struct Config; +class BinaryFormat; + +/* Store values directly and don't quantize. */ +class DontQuantize { + public: + static const ModelType kModelTypeAdd = static_cast(0); + static void UpdateConfigFromBinary(const BinaryFormat &, uint64_t, Config &) {} + static uint64_t Size(uint8_t /*order*/, const Config &/*config*/) { return 0; } + static uint8_t MiddleBits(const Config &/*config*/) { return 63; } + static uint8_t LongestBits(const Config &/*config*/) { return 31; } + + class MiddlePointer { + public: + MiddlePointer(const DontQuantize & /*quant*/, unsigned char /*order_minus_2*/, util::BitAddress address) : address_(address) {} + + MiddlePointer() : address_(NULL, 0) {} + + bool Found() const { + return address_.base != NULL; + } + + float Prob() const { + return util::ReadNonPositiveFloat31(address_.base, address_.offset); + } + + float Backoff() const { + return util::ReadFloat32(address_.base, address_.offset + 31); + } + + float Rest() const { return Prob(); } + + void Write(float prob, float backoff) { + util::WriteNonPositiveFloat31(address_.base, address_.offset, prob); + util::WriteFloat32(address_.base, address_.offset + 31, backoff); + } + + private: + util::BitAddress address_; + }; + + class LongestPointer { + public: + explicit LongestPointer(const DontQuantize &/*quant*/, util::BitAddress address) : address_(address) {} + + LongestPointer() : address_(NULL, 0) {} + + bool Found() const { + return address_.base != NULL; + } + + float Prob() const { + return util::ReadNonPositiveFloat31(address_.base, address_.offset); + } + + void Write(float prob) { + util::WriteNonPositiveFloat31(address_.base, address_.offset, prob); + } + + private: + util::BitAddress address_; + }; + + DontQuantize() {} + + void SetupMemory(void * /*start*/, unsigned char /*order*/, const Config & /*config*/) {} + + static const bool kTrain = false; + // These should never be called because kTrain is false. + void Train(uint8_t /*order*/, std::vector &/*prob*/, std::vector &/*backoff*/) {} + void TrainProb(uint8_t, std::vector &/*prob*/) {} + + void FinishedLoading(const Config &) {} +}; + +class SeparatelyQuantize { + private: + class Bins { + public: + // Sigh C++ default constructor + Bins() {} + + Bins(uint8_t bits, float *begin) : begin_(begin), end_(begin_ + (1ULL << bits)), bits_(bits), mask_((1ULL << bits) - 1) {} + + float *Populate() { return begin_; } + + uint64_t EncodeProb(float value) const { + return Encode(value, 0); + } + + uint64_t EncodeBackoff(float value) const { + if (value == 0.0) { + return HasExtension(value) ? kExtensionQuant : kNoExtensionQuant; + } + return Encode(value, 2); + } + + float Decode(std::size_t off) const { return begin_[off]; } + + uint8_t Bits() const { return bits_; } + + uint64_t Mask() const { return mask_; } + + private: + uint64_t Encode(float value, size_t reserved) const { + const float *above = std::lower_bound(static_cast(begin_) + reserved, end_, value); + if (above == begin_ + reserved) return reserved; + if (above == end_) return end_ - begin_ - 1; + return above - begin_ - (value - *(above - 1) < *above - value); + } + + float *begin_; + const float *end_; + uint8_t bits_; + uint64_t mask_; + }; + + public: + static const ModelType kModelTypeAdd = kQuantAdd; + + static void UpdateConfigFromBinary(const BinaryFormat &file, uint64_t offset, Config &config); + + static uint64_t Size(uint8_t order, const Config &config) { + uint64_t longest_table = (static_cast(1) << static_cast(config.prob_bits)) * sizeof(float); + uint64_t middle_table = (static_cast(1) << static_cast(config.backoff_bits)) * sizeof(float) + longest_table; + // unigrams are currently not quantized so no need for a table. + return (order - 2) * middle_table + longest_table + /* for the bit counts and alignment padding) */ 8; + } + + static uint8_t MiddleBits(const Config &config) { return config.prob_bits + config.backoff_bits; } + static uint8_t LongestBits(const Config &config) { return config.prob_bits; } + + class MiddlePointer { + public: + MiddlePointer(const SeparatelyQuantize &quant, unsigned char order_minus_2, const util::BitAddress &address) : bins_(quant.GetTables(order_minus_2)), address_(address) {} + + MiddlePointer() : address_(NULL, 0) {} + + bool Found() const { return address_.base != NULL; } + + float Prob() const { + return ProbBins().Decode(util::ReadInt25(address_.base, address_.offset + BackoffBins().Bits(), ProbBins().Bits(), ProbBins().Mask())); + } + + float Backoff() const { + return BackoffBins().Decode(util::ReadInt25(address_.base, address_.offset, BackoffBins().Bits(), BackoffBins().Mask())); + } + + float Rest() const { return Prob(); } + + void Write(float prob, float backoff) const { + util::WriteInt57(address_.base, address_.offset, ProbBins().Bits() + BackoffBins().Bits(), + (ProbBins().EncodeProb(prob) << BackoffBins().Bits()) | BackoffBins().EncodeBackoff(backoff)); + } + + private: + const Bins &ProbBins() const { return bins_[0]; } + const Bins &BackoffBins() const { return bins_[1]; } + const Bins *bins_; + + util::BitAddress address_; + }; + + class LongestPointer { + public: + LongestPointer(const SeparatelyQuantize &quant, const util::BitAddress &address) : table_(&quant.LongestTable()), address_(address) {} + + LongestPointer() : address_(NULL, 0) {} + + bool Found() const { return address_.base != NULL; } + + void Write(float prob) const { + util::WriteInt25(address_.base, address_.offset, table_->Bits(), table_->EncodeProb(prob)); + } + + float Prob() const { + return table_->Decode(util::ReadInt25(address_.base, address_.offset, table_->Bits(), table_->Mask())); + } + + private: + const Bins *table_; + util::BitAddress address_; + }; + + SeparatelyQuantize() {} + + void SetupMemory(void *start, unsigned char order, const Config &config); + + static const bool kTrain = true; + // Assumes 0.0 is removed from backoff. + void Train(uint8_t order, std::vector &prob, std::vector &backoff); + // Train just probabilities (for longest order). + void TrainProb(uint8_t order, std::vector &prob); + + void FinishedLoading(const Config &config); + + const Bins *GetTables(unsigned char order_minus_2) const { return tables_[order_minus_2]; } + + const Bins &LongestTable() const { return longest_; } + + private: + Bins tables_[KENLM_MAX_ORDER - 1][2]; + + Bins longest_; + + uint8_t *actual_base_; + + uint8_t prob_bits_, backoff_bits_; +}; + +} // namespace ngram +} // namespace lm + +#endif // LM_QUANTIZE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/query_main.cc b/ctcdecode-master/third_party/kenlm/lm/query_main.cc new file mode 100644 index 0000000..f3ca6e6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/query_main.cc @@ -0,0 +1,121 @@ +#include "lm/ngram_query.hh" +#include "util/getopt.hh" + +#ifdef WITH_NPLM +#include "lm/wrappers/nplm.hh" +#endif + +#include + +void Usage(const char *name) { + std::cerr << + "KenLM was compiled with maximum order " << KENLM_MAX_ORDER << ".\n" + "Usage: " << name << " [-b] [-n] [-w] [-s] lm_file\n" + "-b: Do not buffer output.\n" + "-n: Do not wrap the input in and .\n" + "-v summary|sentence|word: Level of verbosity\n" + "-l lazy|populate|read|parallel: Load lazily, with populate, or malloc+read\n" + "The default loading method is populate on Linux and read on others.\n\n" + "Each word in the output is formatted as:\n" + " word=vocab_id ngram_length log10(p(word|context))\n" + "where ngram_length is the length of n-gram matched. A vocab_id of 0 indicates\n" + "indicates the unknown word. Sentence-level output includes log10 probability of\n" + "the sentence and OOV count.\n"; + exit(1); +} + +int main(int argc, char *argv[]) { + if (argc == 1 || (argc == 2 && !strcmp(argv[1], "--help"))) + Usage(argv[0]); + + lm::ngram::Config config; + bool sentence_context = true; + unsigned int verbosity = 2; + bool flush = false; + + int opt; + while ((opt = getopt(argc, argv, "bnv:l:")) != -1) { + switch (opt) { + case 'b': + flush = true; + break; + case 'n': + sentence_context = false; + break; + case 'v': + if (!strcmp(optarg, "word") || !strcmp(optarg, "2")) { + verbosity = 2; + } else if (!strcmp(optarg, "sentence") || !strcmp(optarg, "1")) { + verbosity = 1; + } else if (!strcmp(optarg, "summary") || !strcmp(optarg, "0")) { + verbosity = 0; + } else { + Usage(argv[0]); + } + break; + case 'l': + if (!strcmp(optarg, "lazy")) { + config.load_method = util::LAZY; + } else if (!strcmp(optarg, "populate")) { + config.load_method = util::POPULATE_OR_READ; + } else if (!strcmp(optarg, "read")) { + config.load_method = util::READ; + } else if (!strcmp(optarg, "parallel")) { + config.load_method = util::PARALLEL_READ; + } else { + Usage(argv[0]); + } + break; + case 'h': + default: + Usage(argv[0]); + } + } + if (optind + 1 != argc) + Usage(argv[0]); + lm::ngram::QueryPrinter printer(1, verbosity >= 2, verbosity >= 1, true, flush); + const char *file = argv[optind]; + try { + using namespace lm::ngram; + ModelType model_type; + if (RecognizeBinary(file, model_type)) { + std::cerr << "This binary file contains " << lm::ngram::kModelNames[model_type] << "." << std::endl; + switch(model_type) { + case PROBING: + Query(file, config, sentence_context, printer); + break; + case REST_PROBING: + Query(file, config, sentence_context, printer); + break; + case TRIE: + Query(file, config, sentence_context, printer); + break; + case QUANT_TRIE: + Query(file, config, sentence_context, printer); + break; + case ARRAY_TRIE: + Query(file, config, sentence_context, printer); + break; + case QUANT_ARRAY_TRIE: + Query(file, config, sentence_context, printer); + break; + default: + std::cerr << "Unrecognized kenlm model type " << model_type << std::endl; + abort(); + } +#ifdef WITH_NPLM + } else if (lm::np::Model::Recognize(file)) { + lm::np::Model model(file); + Query(model, sentence_context, printer); + Query(model, sentence_context, printer); +#endif + } else { + Query(file, config, sentence_context, printer); + } + util::PrintUsage(std::cerr); + } catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + return 1; + } + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/lm/read_arpa.cc b/ctcdecode-master/third_party/kenlm/lm/read_arpa.cc new file mode 100644 index 0000000..dc05a65 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/read_arpa.cc @@ -0,0 +1,161 @@ +#include "lm/read_arpa.hh" + +#include "lm/blank.hh" +#include "util/file.hh" + +#include +#include +#include +#include +#include + +#include +#include +#include + +#ifdef WIN32 +#include +#endif + +namespace lm { + +// 1 for '\t', '\n', and ' '. This is stricter than isspace. +const bool kARPASpaces[256] = {0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}; + +namespace { + +bool IsEntirelyWhiteSpace(const StringPiece &line) { + for (size_t i = 0; i < static_cast(line.size()); ++i) { + if (!isspace(line.data()[i])) return false; + } + return true; +} + +const char kBinaryMagic[] = "mmap lm http://kheafield.com/code"; + +// strtoull isn't portable enough :-( +uint64_t ReadCount(const std::string &from) { + std::stringstream stream(from); + uint64_t ret; + stream >> ret; + UTIL_THROW_IF(!stream, FormatLoadException, "Bad count " << from); + return ret; +} + +} // namespace + +void ReadARPACounts(util::FilePiece &in, std::vector &number) { + number.clear(); + StringPiece line = in.ReadLine(); + // In general, ARPA files can have arbitrary text before "\data\" + // But in KenLM, we require such lines to start with "#", so that + // we can do stricter error checking + while (IsEntirelyWhiteSpace(line) || starts_with(line, "#")) { + line = in.ReadLine(); + } + + if (line != "\\data\\") { + if ((line.size() >= 2) && (line.data()[0] == 0x1f) && (static_cast(line.data()[1]) == 0x8b)) { + UTIL_THROW(FormatLoadException, "Looks like a gzip file. If this is an ARPA file, pipe " << in.FileName() << " through zcat. If this already in binary format, you need to decompress it because mmap doesn't work on top of gzip."); + } + if (static_cast(line.size()) >= strlen(kBinaryMagic) && StringPiece(line.data(), strlen(kBinaryMagic)) == kBinaryMagic) + UTIL_THROW(FormatLoadException, "This looks like a binary file but got sent to the ARPA parser. Did you compress the binary file or pass a binary file where only ARPA files are accepted?"); + UTIL_THROW_IF(line.size() >= 4 && StringPiece(line.data(), 4) == "blmt", FormatLoadException, "This looks like an IRSTLM binary file. Did you forget to pass --text yes to compile-lm?"); + UTIL_THROW_IF(line == "iARPA", FormatLoadException, "This looks like an IRSTLM iARPA file. You need an ARPA file. Run\n compile-lm --text yes " << in.FileName() << " " << in.FileName() << ".arpa\nfirst."); + UTIL_THROW(FormatLoadException, "first non-empty line was \"" << line << "\" not \\data\\."); + } + while (!IsEntirelyWhiteSpace(line = in.ReadLine())) { + if (line.size() < 6 || strncmp(line.data(), "ngram ", 6)) UTIL_THROW(FormatLoadException, "count line \"" << line << "\"doesn't begin with \"ngram \""); + // So strtol doesn't go off the end of line. + std::string remaining(line.data() + 6, line.size() - 6); + char *end_ptr; + unsigned int length = std::strtol(remaining.c_str(), &end_ptr, 10); + if ((end_ptr == remaining.c_str()) || (length - 1 != number.size())) UTIL_THROW(FormatLoadException, "ngram count lengths should be consecutive starting with 1: " << line); + if (*end_ptr != '=') UTIL_THROW(FormatLoadException, "Expected = immediately following the first number in the count line " << line); + ++end_ptr; + number.push_back(ReadCount(end_ptr)); + } +} + +void ReadNGramHeader(util::FilePiece &in, unsigned int length) { + StringPiece line; + while (IsEntirelyWhiteSpace(line = in.ReadLine())) {} + std::stringstream expected; + expected << '\\' << length << "-grams:"; + if (line != expected.str()) UTIL_THROW(FormatLoadException, "Was expecting n-gram header " << expected.str() << " but got " << line << " instead"); +} + +void ReadBackoff(util::FilePiece &in, Prob &/*weights*/) { + switch (in.get()) { + case '\t': + { + float got = in.ReadFloat(); + if (got != 0.0) + UTIL_THROW(FormatLoadException, "Non-zero backoff " << got << " provided for an n-gram that should have no backoff"); + } + break; + case '\n': + break; + default: + UTIL_THROW(FormatLoadException, "Expected tab or newline for backoff"); + } +} + +void ReadBackoff(util::FilePiece &in, float &backoff) { + // Always make zero negative. + // Negative zero means that no (n+1)-gram has this n-gram as context. + // Therefore the hypothesis state can be shorter. Of course, many n-grams + // are context for (n+1)-grams. An algorithm in the data structure will go + // back and set the backoff to positive zero in these cases. + switch (in.get()) { + case '\t': + backoff = in.ReadFloat(); + if (backoff == ngram::kExtensionBackoff) backoff = ngram::kNoExtensionBackoff; + { +#if defined(WIN32) && !defined(__MINGW32__) + int float_class = _fpclass(backoff); + UTIL_THROW_IF(float_class == _FPCLASS_SNAN || float_class == _FPCLASS_QNAN || float_class == _FPCLASS_NINF || float_class == _FPCLASS_PINF, FormatLoadException, "Bad backoff " << backoff); +#else + int float_class = std::fpclassify(backoff); + UTIL_THROW_IF(float_class == FP_NAN || float_class == FP_INFINITE, FormatLoadException, "Bad backoff " << backoff); +#endif + } + UTIL_THROW_IF(in.get() != '\n', FormatLoadException, "Expected newline after backoff"); + break; + case '\n': + backoff = ngram::kNoExtensionBackoff; + break; + default: + UTIL_THROW(FormatLoadException, "Expected tab or newline for backoff"); + } +} + +void ReadEnd(util::FilePiece &in) { + StringPiece line; + do { + line = in.ReadLine(); + } while (IsEntirelyWhiteSpace(line)); + if (line != "\\end\\") UTIL_THROW(FormatLoadException, "Expected \\end\\ but the ARPA file has " << line); + + try { + while (true) { + line = in.ReadLine(); + if (!IsEntirelyWhiteSpace(line)) UTIL_THROW(FormatLoadException, "Trailing line " << line); + } + } catch (const util::EndOfFileException &e) {} +} + +void PositiveProbWarn::Warn(float prob) { + switch (action_) { + case THROW_UP: + UTIL_THROW(FormatLoadException, "Positive log probability " << prob << " in the model. This is a bug in IRSTLM; you can set config.positive_log_probability = SILENT or pass -i to build_binary to substitute 0.0 for the log probability. Error"); + case COMPLAIN: + std::cerr << "There's a positive log probability " << prob << " in the APRA file, probably because of a bug in IRSTLM. This and subsequent entires will be mapped to 0 log probability." << std::endl; + action_ = SILENT; + break; + case SILENT: + break; + } +} + +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/read_arpa.hh b/ctcdecode-master/third_party/kenlm/lm/read_arpa.hh new file mode 100644 index 0000000..64eeef3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/read_arpa.hh @@ -0,0 +1,95 @@ +#ifndef LM_READ_ARPA_H +#define LM_READ_ARPA_H + +#include "lm/lm_exception.hh" +#include "lm/word_index.hh" +#include "lm/weights.hh" +#include "util/file_piece.hh" + +#include +#include +#include + +namespace lm { + +void ReadARPACounts(util::FilePiece &in, std::vector &number); +void ReadNGramHeader(util::FilePiece &in, unsigned int length); + +void ReadBackoff(util::FilePiece &in, Prob &weights); +void ReadBackoff(util::FilePiece &in, float &backoff); +inline void ReadBackoff(util::FilePiece &in, ProbBackoff &weights) { + ReadBackoff(in, weights.backoff); +} +inline void ReadBackoff(util::FilePiece &in, RestWeights &weights) { + ReadBackoff(in, weights.backoff); +} + +void ReadEnd(util::FilePiece &in); + +extern const bool kARPASpaces[256]; + +// Positive log probability warning. +class PositiveProbWarn { + public: + PositiveProbWarn() : action_(THROW_UP) {} + + explicit PositiveProbWarn(WarningAction action) : action_(action) {} + + void Warn(float prob); + + private: + WarningAction action_; +}; + +template void Read1Gram(util::FilePiece &f, Voc &vocab, Weights *unigrams, PositiveProbWarn &warn) { + try { + float prob = f.ReadFloat(); + if (prob > 0.0) { + warn.Warn(prob); + prob = 0.0; + } + UTIL_THROW_IF(f.get() != '\t', FormatLoadException, "Expected tab after probability"); + WordIndex word = vocab.Insert(f.ReadDelimited(kARPASpaces)); + Weights &w = unigrams[word]; + w.prob = prob; + ReadBackoff(f, w); + } catch(util::Exception &e) { + e << " in the 1-gram at byte " << f.Offset(); + throw; + } +} + +template void Read1Grams(util::FilePiece &f, std::size_t count, Voc &vocab, Weights *unigrams, PositiveProbWarn &warn) { + ReadNGramHeader(f, 1); + for (std::size_t i = 0; i < count; ++i) { + Read1Gram(f, vocab, unigrams, warn); + } + vocab.FinishedLoading(unigrams); +} + +// Read ngram, write vocab ids to indices_out. +template void ReadNGram(util::FilePiece &f, const unsigned char n, const Voc &vocab, Iterator indices_out, Weights &weights, PositiveProbWarn &warn) { + try { + weights.prob = f.ReadFloat(); + if (weights.prob > 0.0) { + warn.Warn(weights.prob); + weights.prob = 0.0; + } + for (unsigned char i = 0; i < n; ++i, ++indices_out) { + StringPiece word(f.ReadDelimited(kARPASpaces)); + WordIndex index = vocab.Index(word); + *indices_out = index; + // Check for words mapped to that are not the string . + UTIL_THROW_IF(index == 0 /* mapped to */ && (word != StringPiece("", 5)) && (word != StringPiece("", 5)), + FormatLoadException, "Word " << word << " was not seen in the unigrams (which are supposed to list the entire vocabulary) but appears"); + } + ReadBackoff(f, weights); + } catch(util::Exception &e) { + e << " in the " << static_cast(n) << "-gram at byte " << f.Offset(); + throw; + } +} + +} // namespace lm + +#endif // LM_READ_ARPA_H diff --git a/ctcdecode-master/third_party/kenlm/lm/return.hh b/ctcdecode-master/third_party/kenlm/lm/return.hh new file mode 100644 index 0000000..ee1f25e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/return.hh @@ -0,0 +1,42 @@ +#ifndef LM_RETURN_H +#define LM_RETURN_H + +#include + +namespace lm { +/* Structure returned by scoring routines. */ +struct FullScoreReturn { + // log10 probability + float prob; + + /* The length of n-gram matched. Do not use this for recombination. + * Consider a model containing only the following n-grams: + * -1 foo + * -3.14 bar + * -2.718 baz -5 + * -6 foo bar + * + * If you score ``bar'' then ngram_length is 1 and recombination state is the + * empty string because bar has zero backoff and does not extend to the + * right. + * If you score ``foo'' then ngram_length is 1 and recombination state is + * ``foo''. + * + * Ideally, keep output states around and compare them. Failing that, + * get out_state.ValidLength() and use that length for recombination. + */ + unsigned char ngram_length; + + /* Left extension information. If independent_left is set, then prob is + * independent of words to the left (up to additional backoff). Otherwise, + * extend_left indicates how to efficiently extend further to the left. + */ + bool independent_left; + uint64_t extend_left; // Defined only if independent_left + + // Rest cost for extension to the left. + float rest; +}; + +} // namespace lm +#endif // LM_RETURN_H diff --git a/ctcdecode-master/third_party/kenlm/lm/search_hashed.cc b/ctcdecode-master/third_party/kenlm/lm/search_hashed.cc new file mode 100644 index 0000000..7e63e00 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/search_hashed.cc @@ -0,0 +1,298 @@ +#include "lm/search_hashed.hh" + +#include "lm/binary_format.hh" +#include "lm/blank.hh" +#include "lm/lm_exception.hh" +#include "lm/model.hh" +#include "lm/read_arpa.hh" +#include "lm/value.hh" +#include "lm/vocab.hh" + +#include "util/bit_packing.hh" +#include "util/file_piece.hh" + +#include + +namespace lm { +namespace ngram { + +class ProbingModel; + +namespace { + +/* These are passed to ReadNGrams so that n-grams with zero backoff that appear as context will still be used in state. */ +template class ActivateLowerMiddle { + public: + explicit ActivateLowerMiddle(Middle &middle) : modify_(middle) {} + + void operator()(const WordIndex *vocab_ids, const unsigned int n) { + uint64_t hash = static_cast(vocab_ids[1]); + for (const WordIndex *i = vocab_ids + 2; i < vocab_ids + n; ++i) { + hash = detail::CombineWordHash(hash, *i); + } + typename Middle::MutableIterator i; + // TODO: somehow get text of n-gram for this error message. + if (!modify_.UnsafeMutableFind(hash, i)) + UTIL_THROW(FormatLoadException, "The context of every " << n << "-gram should appear as a " << (n-1) << "-gram"); + SetExtension(i->value.backoff); + } + + private: + Middle &modify_; +}; + +template class ActivateUnigram { + public: + explicit ActivateUnigram(Weights *unigram) : modify_(unigram) {} + + void operator()(const WordIndex *vocab_ids, const unsigned int /*n*/) { + // assert(n == 2); + SetExtension(modify_[vocab_ids[1]].backoff); + } + + private: + Weights *modify_; +}; + +// Find the lower order entry, inserting blanks along the way as necessary. +template void FindLower( + const std::vector &keys, + typename Value::Weights &unigram, + std::vector > &middle, + std::vector &between) { + typename util::ProbingHashTable::MutableIterator iter; + typename Value::ProbingEntry entry; + // Backoff will always be 0.0. We'll get the probability and rest in another pass. + entry.value.backoff = kNoExtensionBackoff; + // Go back and find the longest right-aligned entry, informing it that it extends left. Normally this will match immediately, but sometimes SRI is dumb. + for (int lower = keys.size() - 2; ; --lower) { + if (lower == -1) { + between.push_back(&unigram); + return; + } + entry.key = keys[lower]; + bool found = middle[lower].FindOrInsert(entry, iter); + between.push_back(&iter->value); + if (found) return; + } +} + +// Between usually has single entry, the value to adjust. But sometimes SRI stupidly pruned entries so it has unitialized blank values to be set here. +template void AdjustLower( + const Added &added, + const Build &build, + std::vector &between, + const unsigned int n, + const std::vector &vocab_ids, + typename Build::Value::Weights *unigrams, + std::vector > &middle) { + typedef typename Build::Value Value; + if (between.size() == 1) { + build.MarkExtends(*between.front(), added); + return; + } + typedef util::ProbingHashTable Middle; + float prob = -fabs(between.back()->prob); + // Order of the n-gram on which probabilities are based. + unsigned char basis = n - between.size(); + assert(basis != 0); + typename Build::Value::Weights **change = &between.back(); + // Skip the basis. + --change; + if (basis == 1) { + // Hallucinate a bigram based on a unigram's backoff and a unigram probability. + float &backoff = unigrams[vocab_ids[1]].backoff; + SetExtension(backoff); + prob += backoff; + (*change)->prob = prob; + build.SetRest(&*vocab_ids.begin(), 2, **change); + basis = 2; + --change; + } + uint64_t backoff_hash = static_cast(vocab_ids[1]); + for (unsigned char i = 2; i <= basis; ++i) { + backoff_hash = detail::CombineWordHash(backoff_hash, vocab_ids[i]); + } + for (; basis < n - 1; ++basis, --change) { + typename Middle::MutableIterator gotit; + if (middle[basis - 2].UnsafeMutableFind(backoff_hash, gotit)) { + float &backoff = gotit->value.backoff; + SetExtension(backoff); + prob += backoff; + } + (*change)->prob = prob; + build.SetRest(&*vocab_ids.begin(), basis + 1, **change); + backoff_hash = detail::CombineWordHash(backoff_hash, vocab_ids[basis+1]); + } + + typename std::vector::const_iterator i(between.begin()); + build.MarkExtends(**i, added); + const typename Value::Weights *longer = *i; + // Everything has probability but is not marked as extending. + for (++i; i != between.end(); ++i) { + build.MarkExtends(**i, *longer); + longer = *i; + } +} + +// Continue marking lower entries even they know that they extend left. This is used for upper/lower bounds. +template void MarkLower( + const std::vector &keys, + const Build &build, + typename Build::Value::Weights &unigram, + std::vector > &middle, + int start_order, + const typename Build::Value::Weights &longer) { + if (start_order == 0) return; + // Hopefully the compiler will realize that if MarkExtends always returns false, it can simplify this code. + for (int even_lower = start_order - 2 /* index in middle */; ; --even_lower) { + if (even_lower == -1) { + build.MarkExtends(unigram, longer); + return; + } + if (!build.MarkExtends( + middle[even_lower].UnsafeMutableMustFind(keys[even_lower])->value, + longer)) return; + } +} + +template void ReadNGrams( + util::FilePiece &f, + const unsigned int n, + const size_t count, + const ProbingVocabulary &vocab, + const Build &build, + typename Build::Value::Weights *unigrams, + std::vector > &middle, + Activate activate, + Store &store, + PositiveProbWarn &warn) { + typedef typename Build::Value Value; + assert(n >= 2); + ReadNGramHeader(f, n); + + // Both vocab_ids and keys are non-empty because n >= 2. + // vocab ids of words in reverse order. + std::vector vocab_ids(n); + std::vector keys(n-1); + typename Store::Entry entry; + std::vector between; + for (size_t i = 0; i < count; ++i) { + ReadNGram(f, n, vocab, vocab_ids.rbegin(), entry.value, warn); + build.SetRest(&*vocab_ids.begin(), n, entry.value); + + keys[0] = detail::CombineWordHash(static_cast(vocab_ids.front()), vocab_ids[1]); + for (unsigned int h = 1; h < n - 1; ++h) { + keys[h] = detail::CombineWordHash(keys[h-1], vocab_ids[h+1]); + } + // Initially the sign bit is on, indicating it does not extend left. Most already have this but there might +0.0. + util::SetSign(entry.value.prob); + entry.key = keys[n-2]; + + store.Insert(entry); + between.clear(); + FindLower(keys, unigrams[vocab_ids.front()], middle, between); + AdjustLower(entry.value, build, between, n, vocab_ids, unigrams, middle); + if (Build::kMarkEvenLower) MarkLower(keys, build, unigrams[vocab_ids.front()], middle, n - between.size() - 1, *between.back()); + activate(&*vocab_ids.begin(), n); + } + + store.FinishedInserting(); +} + +} // namespace +namespace detail { + +template uint8_t *HashedSearch::SetupMemory(uint8_t *start, const std::vector &counts, const Config &config) { + unigram_ = Unigram(start, counts[0]); + start += Unigram::Size(counts[0]); + std::size_t allocated; + middle_.clear(); + for (unsigned int n = 2; n < counts.size(); ++n) { + allocated = Middle::Size(counts[n - 1], config.probing_multiplier); + middle_.push_back(Middle(start, allocated)); + start += allocated; + } + allocated = Longest::Size(counts.back(), config.probing_multiplier); + longest_ = Longest(start, allocated); + start += allocated; + return start; +} + +/*template void HashedSearch::Relocate(uint8_t *start, const std::vector &counts, const Config &config) { + unigram_ = Unigram(start, counts[0]); + start += Unigram::Size(counts[0]); + for (unsigned int n = 2; n < counts.size(); ++n) { + middle[n-2].Relocate(start); + start += Middle::Size(counts[n - 1], config.probing_multiplier) + } + longest_.Relocate(start); +}*/ + +template void HashedSearch::InitializeFromARPA(const char * /*file*/, util::FilePiece &f, const std::vector &counts, const Config &config, ProbingVocabulary &vocab, BinaryFormat &backing) { + void *vocab_rebase; + void *search_base = backing.GrowForSearch(Size(counts, config), vocab.UnkCountChangePadding(), vocab_rebase); + vocab.Relocate(vocab_rebase); + SetupMemory(reinterpret_cast(search_base), counts, config); + + PositiveProbWarn warn(config.positive_log_probability); + Read1Grams(f, counts[0], vocab, unigram_.Raw(), warn); + CheckSpecials(config, vocab); + DispatchBuild(f, counts, config, vocab, warn); +} + +template <> void HashedSearch::DispatchBuild(util::FilePiece &f, const std::vector &counts, const Config &config, const ProbingVocabulary &vocab, PositiveProbWarn &warn) { + NoRestBuild build; + ApplyBuild(f, counts, vocab, warn, build); +} + +template <> void HashedSearch::DispatchBuild(util::FilePiece &f, const std::vector &counts, const Config &config, const ProbingVocabulary &vocab, PositiveProbWarn &warn) { + switch (config.rest_function) { + case Config::REST_MAX: + { + MaxRestBuild build; + ApplyBuild(f, counts, vocab, warn, build); + } + break; + case Config::REST_LOWER: + { + LowerRestBuild build(config, counts.size(), vocab); + ApplyBuild(f, counts, vocab, warn, build); + } + break; + } +} + +template template void HashedSearch::ApplyBuild(util::FilePiece &f, const std::vector &counts, const ProbingVocabulary &vocab, PositiveProbWarn &warn, const Build &build) { + for (WordIndex i = 0; i < counts[0]; ++i) { + build.SetRest(&i, (unsigned int)1, unigram_.Raw()[i]); + } + + try { + if (counts.size() > 2) { + ReadNGrams, Middle>( + f, 2, counts[1], vocab, build, unigram_.Raw(), middle_, ActivateUnigram(unigram_.Raw()), middle_[0], warn); + } + for (unsigned int n = 3; n < counts.size(); ++n) { + ReadNGrams, Middle>( + f, n, counts[n-1], vocab, build, unigram_.Raw(), middle_, ActivateLowerMiddle(middle_[n-3]), middle_[n-2], warn); + } + if (counts.size() > 2) { + ReadNGrams, Longest>( + f, counts.size(), counts[counts.size() - 1], vocab, build, unigram_.Raw(), middle_, ActivateLowerMiddle(middle_.back()), longest_, warn); + } else { + ReadNGrams, Longest>( + f, counts.size(), counts[counts.size() - 1], vocab, build, unigram_.Raw(), middle_, ActivateUnigram(unigram_.Raw()), longest_, warn); + } + } catch (util::ProbingSizeException &e) { + UTIL_THROW(util::ProbingSizeException, "Avoid pruning n-grams like \"bar baz quux\" when \"foo bar baz quux\" is still in the model. KenLM will work when this pruning happens, but the probing model assumes these events are rare enough that using blank space in the probing hash table will cover all of them. Increase probing_multiplier (-p to build_binary) to add more blank spaces.\n"); + } + ReadEnd(f); +} + +template class HashedSearch; +template class HashedSearch; + +} // namespace detail +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/search_hashed.hh b/ctcdecode-master/third_party/kenlm/lm/search_hashed.hh new file mode 100644 index 0000000..9dc8445 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/search_hashed.hh @@ -0,0 +1,192 @@ +#ifndef LM_SEARCH_HASHED_H +#define LM_SEARCH_HASHED_H + +#include "lm/model_type.hh" +#include "lm/config.hh" +#include "lm/read_arpa.hh" +#include "lm/return.hh" +#include "lm/weights.hh" + +#include "util/bit_packing.hh" +#include "util/probing_hash_table.hh" + +#include +#include +#include + +namespace util { class FilePiece; } + +namespace lm { +namespace ngram { +class BinaryFormat; +class ProbingVocabulary; +namespace detail { + +inline uint64_t CombineWordHash(uint64_t current, const WordIndex next) { + uint64_t ret = (current * 8978948897894561157ULL) ^ (static_cast(1 + next) * 17894857484156487943ULL); + return ret; +} + +#pragma pack(push) +#pragma pack(4) +struct ProbEntry { + uint64_t key; + Prob value; + typedef uint64_t Key; + typedef Prob Value; + uint64_t GetKey() const { + return key; + } +}; + +#pragma pack(pop) + +class LongestPointer { + public: + explicit LongestPointer(const float &to) : to_(&to) {} + + LongestPointer() : to_(NULL) {} + + bool Found() const { + return to_ != NULL; + } + + float Prob() const { + return *to_; + } + + private: + const float *to_; +}; + +template class HashedSearch { + public: + typedef uint64_t Node; + + typedef typename Value::ProbingProxy UnigramPointer; + typedef typename Value::ProbingProxy MiddlePointer; + typedef ::lm::ngram::detail::LongestPointer LongestPointer; + + static const ModelType kModelType = Value::kProbingModelType; + static const bool kDifferentRest = Value::kDifferentRest; + static const unsigned int kVersion = 0; + + // TODO: move probing_multiplier here with next binary file format update. + static void UpdateConfigFromBinary(const BinaryFormat &, const std::vector &, uint64_t, Config &) {} + + static uint64_t Size(const std::vector &counts, const Config &config) { + uint64_t ret = Unigram::Size(counts[0]); + for (unsigned char n = 1; n < counts.size() - 1; ++n) { + ret += Middle::Size(counts[n], config.probing_multiplier); + } + return ret + Longest::Size(counts.back(), config.probing_multiplier); + } + + uint8_t *SetupMemory(uint8_t *start, const std::vector &counts, const Config &config); + + void InitializeFromARPA(const char *file, util::FilePiece &f, const std::vector &counts, const Config &config, ProbingVocabulary &vocab, BinaryFormat &backing); + + unsigned char Order() const { + return middle_.size() + 2; + } + + typename Value::Weights &UnknownUnigram() { return unigram_.Unknown(); } + + UnigramPointer LookupUnigram(WordIndex word, Node &next, bool &independent_left, uint64_t &extend_left) const { + extend_left = static_cast(word); + next = extend_left; + UnigramPointer ret(unigram_.Lookup(word)); + independent_left = ret.IndependentLeft(); + return ret; + } + + MiddlePointer Unpack(uint64_t extend_pointer, unsigned char extend_length, Node &node) const { + node = extend_pointer; + return MiddlePointer(middle_[extend_length - 2].MustFind(extend_pointer)->value); + } + + MiddlePointer LookupMiddle(unsigned char order_minus_2, WordIndex word, Node &node, bool &independent_left, uint64_t &extend_pointer) const { + node = CombineWordHash(node, word); + typename Middle::ConstIterator found; + if (!middle_[order_minus_2].Find(node, found)) { + independent_left = true; + return MiddlePointer(); + } + extend_pointer = node; + MiddlePointer ret(found->value); + independent_left = ret.IndependentLeft(); + return ret; + } + + LongestPointer LookupLongest(WordIndex word, const Node &node) const { + // Sign bit is always on because longest n-grams do not extend left. + typename Longest::ConstIterator found; + if (!longest_.Find(CombineWordHash(node, word), found)) return LongestPointer(); + return LongestPointer(found->value.prob); + } + + // Generate a node without necessarily checking that it actually exists. + // Optionally return false if it's know to not exist. + bool FastMakeNode(const WordIndex *begin, const WordIndex *end, Node &node) const { + assert(begin != end); + node = static_cast(*begin); + for (const WordIndex *i = begin + 1; i < end; ++i) { + node = CombineWordHash(node, *i); + } + return true; + } + + private: + // Interpret config's rest cost build policy and pass the right template argument to ApplyBuild. + void DispatchBuild(util::FilePiece &f, const std::vector &counts, const Config &config, const ProbingVocabulary &vocab, PositiveProbWarn &warn); + + template void ApplyBuild(util::FilePiece &f, const std::vector &counts, const ProbingVocabulary &vocab, PositiveProbWarn &warn, const Build &build); + + class Unigram { + public: + Unigram() {} + + Unigram(void *start, uint64_t count) : + unigram_(static_cast(start)) +#ifdef DEBUG + , count_(count) +#endif + {} + + static uint64_t Size(uint64_t count) { + return (count + 1) * sizeof(typename Value::Weights); // +1 for hallucinate + } + + const typename Value::Weights &Lookup(WordIndex index) const { +#ifdef DEBUG + assert(index < count_); +#endif + return unigram_[index]; + } + + typename Value::Weights &Unknown() { return unigram_[0]; } + + // For building. + typename Value::Weights *Raw() { return unigram_; } + + private: + typename Value::Weights *unigram_; +#ifdef DEBUG + uint64_t count_; +#endif + }; + + Unigram unigram_; + + typedef util::ProbingHashTable Middle; + std::vector middle_; + + typedef util::ProbingHashTable Longest; + Longest longest_; +}; + +} // namespace detail +} // namespace ngram +} // namespace lm + +#endif // LM_SEARCH_HASHED_H diff --git a/ctcdecode-master/third_party/kenlm/lm/search_trie.cc b/ctcdecode-master/third_party/kenlm/lm/search_trie.cc new file mode 100644 index 0000000..3d3f5d5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/search_trie.cc @@ -0,0 +1,597 @@ +/* This is where the trie is built. It's on-disk. */ +#include "lm/search_trie.hh" + +#include "lm/bhiksha.hh" +#include "lm/binary_format.hh" +#include "lm/blank.hh" +#include "lm/lm_exception.hh" +#include "lm/max_order.hh" +#include "lm/quantize.hh" +#include "lm/trie.hh" +#include "lm/trie_sort.hh" +#include "lm/vocab.hh" +#include "lm/weights.hh" +#include "lm/word_index.hh" +#include "util/ersatz_progress.hh" +#include "util/mmap.hh" +#include "util/proxy_iterator.hh" +#include "util/scoped.hh" +#include "util/sized_iterator.hh" + +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) || defined(_WIN64) +#include +#endif + +namespace lm { +namespace ngram { +namespace trie { +namespace { + +void ReadOrThrow(FILE *from, void *data, size_t size) { + UTIL_THROW_IF(1 != std::fread(data, size, 1, from), util::ErrnoException, "Short read"); +} + +int Compare(unsigned char order, const void *first_void, const void *second_void) { + const WordIndex *first = reinterpret_cast(first_void), *second = reinterpret_cast(second_void); + const WordIndex *end = first + order; + for (; first != end; ++first, ++second) { + if (*first < *second) return -1; + if (*first > *second) return 1; + } + return 0; +} + +struct ProbPointer { + unsigned char array; + uint64_t index; +}; + +// Array of n-grams and float indices. +class BackoffMessages { + public: + void Init(std::size_t entry_size) { + current_ = NULL; + allocated_ = NULL; + entry_size_ = entry_size; + } + + void Add(const WordIndex *to, ProbPointer index) { + while (current_ + entry_size_ > allocated_) { + std::size_t allocated_size = allocated_ - (uint8_t*)backing_.get(); + Resize(std::max(allocated_size * 2, entry_size_)); + } + memcpy(current_, to, entry_size_ - sizeof(ProbPointer)); + *reinterpret_cast(current_ + entry_size_ - sizeof(ProbPointer)) = index; + current_ += entry_size_; + } + + void Apply(float *const *const base, FILE *unigrams) { + FinishedAdding(); + if (current_ == allocated_) return; + rewind(unigrams); + ProbBackoff weights; + WordIndex unigram = 0; + ReadOrThrow(unigrams, &weights, sizeof(weights)); + for (; current_ != allocated_; current_ += entry_size_) { + const WordIndex &cur_word = *reinterpret_cast(current_); + for (; unigram < cur_word; ++unigram) { + ReadOrThrow(unigrams, &weights, sizeof(weights)); + } + if (!HasExtension(weights.backoff)) { + weights.backoff = kExtensionBackoff; + UTIL_THROW_IF(fseek(unigrams, -sizeof(weights), SEEK_CUR), util::ErrnoException, "Seeking backwards to denote unigram extension failed."); + util::WriteOrThrow(unigrams, &weights, sizeof(weights)); + } + const ProbPointer &write_to = *reinterpret_cast(current_ + sizeof(WordIndex)); + base[write_to.array][write_to.index] += weights.backoff; + } + backing_.reset(); + } + + void Apply(float *const *const base, RecordReader &reader) { + FinishedAdding(); + if (current_ == allocated_) return; + // We'll also use the same buffer to record messages to blanks that they extend. + WordIndex *extend_out = reinterpret_cast(current_); + const unsigned char order = (entry_size_ - sizeof(ProbPointer)) / sizeof(WordIndex); + for (reader.Rewind(); reader && (current_ != allocated_); ) { + switch (Compare(order, reader.Data(), current_)) { + case -1: + ++reader; + break; + case 1: + // Message but nobody to receive it. Write it down at the beginning of the buffer so we can inform this blank that it extends. + for (const WordIndex *w = reinterpret_cast(current_); w != reinterpret_cast(current_) + order; ++w, ++extend_out) *extend_out = *w; + current_ += entry_size_; + break; + case 0: + float &backoff = reinterpret_cast((uint8_t*)reader.Data() + order * sizeof(WordIndex))->backoff; + if (!HasExtension(backoff)) { + backoff = kExtensionBackoff; + reader.Overwrite(&backoff, sizeof(float)); + } else { + const ProbPointer &write_to = *reinterpret_cast(current_ + entry_size_ - sizeof(ProbPointer)); + base[write_to.array][write_to.index] += backoff; + } + current_ += entry_size_; + break; + } + } + // Now this is a list of blanks that extend right. + entry_size_ = sizeof(WordIndex) * order; + Resize(sizeof(WordIndex) * (extend_out - (const WordIndex*)backing_.get())); + current_ = (uint8_t*)backing_.get(); + } + + // Call after Apply + bool Extends(unsigned char order, const WordIndex *words) { + if (current_ == allocated_) return false; + assert(order * sizeof(WordIndex) == entry_size_); + while (true) { + switch(Compare(order, words, current_)) { + case 1: + current_ += entry_size_; + if (current_ == allocated_) return false; + break; + case -1: + return false; + case 0: + return true; + } + } + } + + private: + void FinishedAdding() { + Resize(current_ - (uint8_t*)backing_.get()); + // Sort requests in same order as files. + util::SizedSort(backing_.get(), current_, entry_size_, EntryCompare((entry_size_ - sizeof(ProbPointer)) / sizeof(WordIndex))); + current_ = (uint8_t*)backing_.get(); + } + + void Resize(std::size_t to) { + std::size_t current = current_ - (uint8_t*)backing_.get(); + backing_.call_realloc(to); + current_ = (uint8_t*)backing_.get() + current; + allocated_ = (uint8_t*)backing_.get() + to; + } + + util::scoped_malloc backing_; + + uint8_t *current_, *allocated_; + + std::size_t entry_size_; +}; + +const float kBadProb = std::numeric_limits::infinity(); + +class SRISucks { + public: + SRISucks() { + for (BackoffMessages *i = messages_; i != messages_ + KENLM_MAX_ORDER - 1; ++i) + i->Init(sizeof(ProbPointer) + sizeof(WordIndex) * (i - messages_ + 1)); + } + + void Send(unsigned char begin, unsigned char order, const WordIndex *to, float prob_basis) { + assert(prob_basis != kBadProb); + ProbPointer pointer; + pointer.array = order - 1; + pointer.index = values_[order - 1].size(); + for (unsigned char i = begin; i < order; ++i) { + messages_[i - 1].Add(to, pointer); + } + values_[order - 1].push_back(prob_basis); + } + + void ObtainBackoffs(unsigned char total_order, FILE *unigram_file, RecordReader *reader) { + for (unsigned char i = 0; i < KENLM_MAX_ORDER - 1; ++i) { + it_[i] = values_[i].empty() ? NULL : &*values_[i].begin(); + } + messages_[0].Apply(it_, unigram_file); + BackoffMessages *messages = messages_ + 1; + const RecordReader *end = reader + total_order - 2 /* exclude unigrams and longest order */; + for (; reader != end; ++messages, ++reader) { + messages->Apply(it_, *reader); + } + } + + ProbBackoff GetBlank(unsigned char total_order, unsigned char order, const WordIndex *indices) { + assert(order > 1); + ProbBackoff ret; + ret.prob = *(it_[order - 1]++); + ret.backoff = ((order != total_order - 1) && messages_[order - 1].Extends(order, indices)) ? kExtensionBackoff : kNoExtensionBackoff; + return ret; + } + + const std::vector &Values(unsigned char order) const { + return values_[order - 1]; + } + + private: + // This used to be one array. Then I needed to separate it by order for quantization to work. + std::vector values_[KENLM_MAX_ORDER - 1]; + BackoffMessages messages_[KENLM_MAX_ORDER - 1]; + + float *it_[KENLM_MAX_ORDER - 1]; +}; + +class FindBlanks { + public: + FindBlanks(unsigned char order, const ProbBackoff *unigrams, SRISucks &messages) + : counts_(order), unigrams_(unigrams), sri_(messages) {} + + float UnigramProb(WordIndex index) const { + return unigrams_[index].prob; + } + + void Unigram(WordIndex /*index*/) { + ++counts_[0]; + } + + void MiddleBlank(const unsigned char order, const WordIndex *indices, unsigned char lower, float prob_basis) { + sri_.Send(lower, order, indices + 1, prob_basis); + ++counts_[order - 1]; + } + + void Middle(const unsigned char order, const void * /*data*/) { + ++counts_[order - 1]; + } + + void Longest(const void * /*data*/) { + ++counts_.back(); + } + + const std::vector &Counts() const { + return counts_; + } + + private: + std::vector counts_; + + const ProbBackoff *unigrams_; + + SRISucks &sri_; +}; + +// Phase to actually write n-grams to the trie. +template class WriteEntries { + public: + WriteEntries(RecordReader *contexts, const Quant &quant, UnigramValue *unigrams, BitPackedMiddle *middle, BitPackedLongest &longest, unsigned char order, SRISucks &sri) : + contexts_(contexts), + quant_(quant), + unigrams_(unigrams), + middle_(middle), + longest_(longest), + bigram_pack_((order == 2) ? static_cast(longest_) : static_cast(*middle_)), + order_(order), + sri_(sri) {} + + float UnigramProb(WordIndex index) const { return unigrams_[index].weights.prob; } + + void Unigram(WordIndex word) { + unigrams_[word].next = bigram_pack_.InsertIndex(); + } + + void MiddleBlank(const unsigned char order, const WordIndex *indices, unsigned char /*lower*/, float /*prob_base*/) { + ProbBackoff weights = sri_.GetBlank(order_, order, indices); + typename Quant::MiddlePointer(quant_, order - 2, middle_[order - 2].Insert(indices[order - 1])).Write(weights.prob, weights.backoff); + } + + void Middle(const unsigned char order, const void *data) { + RecordReader &context = contexts_[order - 1]; + const WordIndex *words = reinterpret_cast(data); + ProbBackoff weights = *reinterpret_cast(words + order); + if (context && !memcmp(data, context.Data(), sizeof(WordIndex) * order)) { + SetExtension(weights.backoff); + ++context; + } + typename Quant::MiddlePointer(quant_, order - 2, middle_[order - 2].Insert(words[order - 1])).Write(weights.prob, weights.backoff); + } + + void Longest(const void *data) { + const WordIndex *words = reinterpret_cast(data); + typename Quant::LongestPointer(quant_, longest_.Insert(words[order_ - 1])).Write(reinterpret_cast(words + order_)->prob); + } + + private: + RecordReader *contexts_; + const Quant &quant_; + UnigramValue *const unigrams_; + BitPackedMiddle *const middle_; + BitPackedLongest &longest_; + BitPacked &bigram_pack_; + const unsigned char order_; + SRISucks &sri_; +}; + +struct Gram { + Gram(const WordIndex *in_begin, unsigned char order) : begin(in_begin), end(in_begin + order) {} + + const WordIndex *begin, *end; + + // For queue, this is the direction we want. + bool operator<(const Gram &other) const { + return std::lexicographical_compare(other.begin, other.end, begin, end); + } +}; + +template class BlankManager { + public: + BlankManager(unsigned char total_order, Doing &doing) : total_order_(total_order), been_length_(0), doing_(doing) { + for (float *i = basis_; i != basis_ + KENLM_MAX_ORDER - 1; ++i) *i = kBadProb; + } + + void Visit(const WordIndex *to, unsigned char length, float prob) { + basis_[length - 1] = prob; + unsigned char overlap = std::min(length - 1, been_length_); + const WordIndex *cur; + WordIndex *pre; + for (cur = to, pre = been_; cur != to + overlap; ++cur, ++pre) { + if (*pre != *cur) break; + } + if (cur == to + length - 1) { + *pre = *cur; + been_length_ = length; + return; + } + // There are blanks to insert starting with order blank. + unsigned char blank = cur - to + 1; + UTIL_THROW_IF(blank == 1, FormatLoadException, "Missing a unigram that appears as context."); + const float *lower_basis; + for (lower_basis = basis_ + blank - 2; *lower_basis == kBadProb; --lower_basis) {} + unsigned char based_on = lower_basis - basis_ + 1; + for (; cur != to + length - 1; ++blank, ++cur, ++pre) { + assert(*lower_basis != kBadProb); + doing_.MiddleBlank(blank, to, based_on, *lower_basis); + *pre = *cur; + // Mark that the probability is a blank so it shouldn't be used as the basis for a later n-gram. + basis_[blank - 1] = kBadProb; + } + *pre = *cur; + been_length_ = length; + } + + private: + const unsigned char total_order_; + + WordIndex been_[KENLM_MAX_ORDER]; + unsigned char been_length_; + + float basis_[KENLM_MAX_ORDER]; + + Doing &doing_; +}; + +template void RecursiveInsert(const unsigned char total_order, const WordIndex unigram_count, RecordReader *input, std::ostream *progress_out, const char *message, Doing &doing) { + util::ErsatzProgress progress(unigram_count + 1, progress_out, message); + WordIndex unigram = 0; + std::priority_queue grams; + if (unigram_count) grams.push(Gram(&unigram, 1)); + for (unsigned char i = 2; i <= total_order; ++i) { + if (input[i-2]) grams.push(Gram(reinterpret_cast(input[i-2].Data()), i)); + } + + BlankManager blank(total_order, doing); + + while (!grams.empty()) { + Gram top = grams.top(); + grams.pop(); + unsigned char order = top.end - top.begin; + if (order == 1) { + blank.Visit(&unigram, 1, doing.UnigramProb(unigram)); + doing.Unigram(unigram); + progress.Set(unigram); + if (++unigram < unigram_count) grams.push(top); + } else { + if (order == total_order) { + blank.Visit(top.begin, order, reinterpret_cast(top.end)->prob); + doing.Longest(top.begin); + } else { + blank.Visit(top.begin, order, reinterpret_cast(top.end)->prob); + doing.Middle(order, top.begin); + } + RecordReader &reader = input[order - 2]; + if (++reader) grams.push(top); + } + } +} + +void SanityCheckCounts(const std::vector &initial, const std::vector &fixed) { + if (fixed[0] != initial[0]) UTIL_THROW(util::Exception, "Unigram count should be constant but initial is " << initial[0] << " and recounted is " << fixed[0]); + if (fixed.back() != initial.back()) UTIL_THROW(util::Exception, "Longest count should be constant but it changed from " << initial.back() << " to " << fixed.back()); + for (unsigned char i = 0; i < initial.size(); ++i) { + if (fixed[i] < initial[i]) UTIL_THROW(util::Exception, "Counts came out lower than expected. This shouldn't happen"); + } +} + +template void TrainQuantizer(uint8_t order, uint64_t count, const std::vector &additional, RecordReader &reader, util::ErsatzProgress &progress, Quant &quant) { + std::vector probs(additional), backoffs; + probs.reserve(count + additional.size()); + backoffs.reserve(count); + for (reader.Rewind(); reader; ++reader) { + const ProbBackoff &weights = *reinterpret_cast(reinterpret_cast(reader.Data()) + sizeof(WordIndex) * order); + probs.push_back(weights.prob); + if (weights.backoff != 0.0) backoffs.push_back(weights.backoff); + ++progress; + } + quant.Train(order, probs, backoffs); +} + +template void TrainProbQuantizer(uint8_t order, uint64_t count, RecordReader &reader, util::ErsatzProgress &progress, Quant &quant) { + std::vector probs, backoffs; + probs.reserve(count); + for (reader.Rewind(); reader; ++reader) { + const Prob &weights = *reinterpret_cast(reinterpret_cast(reader.Data()) + sizeof(WordIndex) * order); + probs.push_back(weights.prob); + ++progress; + } + quant.TrainProb(order, probs); +} + +void PopulateUnigramWeights(FILE *file, WordIndex unigram_count, RecordReader &contexts, UnigramValue *unigrams) { + // Fill unigram probabilities. + try { + rewind(file); + for (WordIndex i = 0; i < unigram_count; ++i) { + ReadOrThrow(file, &unigrams[i].weights, sizeof(ProbBackoff)); + if (contexts && *reinterpret_cast(contexts.Data()) == i) { + SetExtension(unigrams[i].weights.backoff); + ++contexts; + } + } + } catch (util::Exception &e) { + e << " while re-reading unigram probabilities"; + throw; + } +} + +} // namespace + +template void BuildTrie(SortedFiles &files, std::vector &counts, const Config &config, TrieSearch &out, Quant &quant, SortedVocabulary &vocab, BinaryFormat &backing) { + RecordReader inputs[KENLM_MAX_ORDER - 1]; + RecordReader contexts[KENLM_MAX_ORDER - 1]; + + for (unsigned char i = 2; i <= counts.size(); ++i) { + inputs[i-2].Init(files.Full(i), i * sizeof(WordIndex) + (i == counts.size() ? sizeof(Prob) : sizeof(ProbBackoff))); + contexts[i-2].Init(files.Context(i), (i-1) * sizeof(WordIndex)); + } + + SRISucks sri; + std::vector fixed_counts; + util::scoped_FILE unigram_file; + util::scoped_fd unigram_fd(files.StealUnigram()); + { + util::scoped_memory unigrams; + MapRead(util::POPULATE_OR_READ, unigram_fd.get(), 0, counts[0] * sizeof(ProbBackoff), unigrams); + FindBlanks finder(counts.size(), reinterpret_cast(unigrams.get()), sri); + RecursiveInsert(counts.size(), counts[0], inputs, config.ProgressMessages(), "Identifying n-grams omitted by SRI", finder); + fixed_counts = finder.Counts(); + } + unigram_file.reset(util::FDOpenOrThrow(unigram_fd)); + for (const RecordReader *i = inputs; i != inputs + counts.size() - 2; ++i) { + if (*i) UTIL_THROW(FormatLoadException, "There's a bug in the trie implementation: the " << (i - inputs + 2) << "-gram table did not complete reading"); + } + SanityCheckCounts(counts, fixed_counts); + counts = fixed_counts; + + sri.ObtainBackoffs(counts.size(), unigram_file.get(), inputs); + + void *vocab_relocate; + void *search_base = backing.GrowForSearch(TrieSearch::Size(fixed_counts, config), vocab.UnkCountChangePadding(), vocab_relocate); + vocab.Relocate(vocab_relocate); + out.SetupMemory(reinterpret_cast(search_base), fixed_counts, config); + + for (unsigned char i = 2; i <= counts.size(); ++i) { + inputs[i-2].Rewind(); + } + if (Quant::kTrain) { + util::ErsatzProgress progress(std::accumulate(counts.begin() + 1, counts.end(), 0), + config.ProgressMessages(), "Quantizing"); + for (unsigned char i = 2; i < counts.size(); ++i) { + TrainQuantizer(i, counts[i-1], sri.Values(i), inputs[i-2], progress, quant); + } + TrainProbQuantizer(counts.size(), counts.back(), inputs[counts.size() - 2], progress, quant); + quant.FinishedLoading(config); + } + + UnigramValue *unigrams = out.unigram_.Raw(); + PopulateUnigramWeights(unigram_file.get(), counts[0], contexts[0], unigrams); + unigram_file.reset(); + + for (unsigned char i = 2; i <= counts.size(); ++i) { + inputs[i-2].Rewind(); + } + // Fill entries except unigram probabilities. + { + WriteEntries writer(contexts, quant, unigrams, out.middle_begin_, out.longest_, counts.size(), sri); + RecursiveInsert(counts.size(), counts[0], inputs, config.ProgressMessages(), "Writing trie", writer); + // Write the last unigram entry, which is the end pointer for the bigrams. + writer.Unigram(counts[0]); + } + + // Do not disable this error message or else too little state will be returned. Both WriteEntries::Middle and returning state based on found n-grams will need to be fixed to handle this situation. + for (unsigned char order = 2; order <= counts.size(); ++order) { + const RecordReader &context = contexts[order - 2]; + if (context) { + FormatLoadException e; + e << "A " << static_cast(order) << "-gram has context"; + const WordIndex *ctx = reinterpret_cast(context.Data()); + for (const WordIndex *i = ctx; i != ctx + order - 1; ++i) { + e << ' ' << *i; + } + e << " so this context must appear in the model as a " << static_cast(order - 1) << "-gram but it does not"; + throw e; + } + } + + /* Set ending offsets so the last entry will be sized properly */ + // Last entry for unigrams was already set. + if (out.middle_begin_ != out.middle_end_) { + for (typename TrieSearch::Middle *i = out.middle_begin_; i != out.middle_end_ - 1; ++i) { + i->FinishedLoading((i+1)->InsertIndex(), config); + } + (out.middle_end_ - 1)->FinishedLoading(out.longest_.InsertIndex(), config); + } +} + +template uint8_t *TrieSearch::SetupMemory(uint8_t *start, const std::vector &counts, const Config &config) { + quant_.SetupMemory(start, counts.size(), config); + start += Quant::Size(counts.size(), config); + unigram_.Init(start); + start += Unigram::Size(counts[0]); + FreeMiddles(); + middle_begin_ = static_cast(malloc(sizeof(Middle) * (counts.size() - 2))); + middle_end_ = middle_begin_ + (counts.size() - 2); + std::vector middle_starts(counts.size() - 2); + for (unsigned char i = 2; i < counts.size(); ++i) { + middle_starts[i-2] = start; + start += Middle::Size(Quant::MiddleBits(config), counts[i-1], counts[0], counts[i], config); + } + // Crazy backwards thing so we initialize using pointers to ones that have already been initialized + for (unsigned char i = counts.size() - 1; i >= 2; --i) { + // use "placement new" syntax to initalize Middle in an already-allocated memory location + new (middle_begin_ + i - 2) Middle( + middle_starts[i-2], + quant_.MiddleBits(config), + counts[i-1], + counts[0], + counts[i], + (i == counts.size() - 1) ? static_cast(longest_) : static_cast(middle_begin_[i-1]), + config); + } + longest_.Init(start, quant_.LongestBits(config), counts[0]); + return start + Longest::Size(Quant::LongestBits(config), counts.back(), counts[0]); +} + +template void TrieSearch::InitializeFromARPA(const char *file, util::FilePiece &f, std::vector &counts, const Config &config, SortedVocabulary &vocab, BinaryFormat &backing) { + std::string temporary_prefix; + if (!config.temporary_directory_prefix.empty()) { + temporary_prefix = config.temporary_directory_prefix; + } else if (config.write_mmap) { + temporary_prefix = config.write_mmap; + } else { + temporary_prefix = file; + } + // At least 1MB sorting memory. + SortedFiles sorted(config, f, counts, std::max(config.building_memory, 1048576), temporary_prefix, vocab); + + BuildTrie(sorted, counts, config, *this, quant_, vocab, backing); +} + +template class TrieSearch; +template class TrieSearch; +template class TrieSearch; +template class TrieSearch; + +} // namespace trie +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/search_trie.hh b/ctcdecode-master/third_party/kenlm/lm/search_trie.hh new file mode 100644 index 0000000..1adba6e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/search_trie.hh @@ -0,0 +1,129 @@ +#ifndef LM_SEARCH_TRIE_H +#define LM_SEARCH_TRIE_H + +#include "lm/config.hh" +#include "lm/model_type.hh" +#include "lm/return.hh" +#include "lm/trie.hh" +#include "lm/weights.hh" + +#include "util/file.hh" +#include "util/file_piece.hh" + +#include +#include +#include + +namespace lm { +namespace ngram { +class BinaryFormat; +class SortedVocabulary; +namespace trie { + +template class TrieSearch; +class SortedFiles; +template void BuildTrie(SortedFiles &files, std::vector &counts, const Config &config, TrieSearch &out, Quant &quant, SortedVocabulary &vocab, BinaryFormat &backing); + +template class TrieSearch { + public: + typedef NodeRange Node; + + typedef ::lm::ngram::trie::UnigramPointer UnigramPointer; + typedef typename Quant::MiddlePointer MiddlePointer; + typedef typename Quant::LongestPointer LongestPointer; + + static const bool kDifferentRest = false; + + static const ModelType kModelType = static_cast(TRIE_SORTED + Quant::kModelTypeAdd + Bhiksha::kModelTypeAdd); + + static const unsigned int kVersion = 1; + + static void UpdateConfigFromBinary(const BinaryFormat &file, const std::vector &counts, uint64_t offset, Config &config) { + Quant::UpdateConfigFromBinary(file, offset, config); + // Currently the unigram pointers are not compresssed, so there will only be a header for order > 2. + if (counts.size() > 2) + Bhiksha::UpdateConfigFromBinary(file, offset + Quant::Size(counts.size(), config) + Unigram::Size(counts[0]), config); + } + + static uint64_t Size(const std::vector &counts, const Config &config) { + uint64_t ret = Quant::Size(counts.size(), config) + Unigram::Size(counts[0]); + for (unsigned char i = 1; i < counts.size() - 1; ++i) { + ret += Middle::Size(Quant::MiddleBits(config), counts[i], counts[0], counts[i+1], config); + } + return ret + Longest::Size(Quant::LongestBits(config), counts.back(), counts[0]); + } + + TrieSearch() : middle_begin_(NULL), middle_end_(NULL) {} + + ~TrieSearch() { FreeMiddles(); } + + uint8_t *SetupMemory(uint8_t *start, const std::vector &counts, const Config &config); + + void InitializeFromARPA(const char *file, util::FilePiece &f, std::vector &counts, const Config &config, SortedVocabulary &vocab, BinaryFormat &backing); + + unsigned char Order() const { + return middle_end_ - middle_begin_ + 2; + } + + ProbBackoff &UnknownUnigram() { return unigram_.Unknown(); } + + UnigramPointer LookupUnigram(WordIndex word, Node &next, bool &independent_left, uint64_t &extend_left) const { + extend_left = static_cast(word); + UnigramPointer ret(unigram_.Find(word, next)); + independent_left = (next.begin == next.end); + return ret; + } + + MiddlePointer Unpack(uint64_t extend_pointer, unsigned char extend_length, Node &node) const { + return MiddlePointer(quant_, extend_length - 2, middle_begin_[extend_length - 2].ReadEntry(extend_pointer, node)); + } + + MiddlePointer LookupMiddle(unsigned char order_minus_2, WordIndex word, Node &node, bool &independent_left, uint64_t &extend_left) const { + util::BitAddress address(middle_begin_[order_minus_2].Find(word, node, extend_left)); + independent_left = (address.base == NULL) || (node.begin == node.end); + return MiddlePointer(quant_, order_minus_2, address); + } + + LongestPointer LookupLongest(WordIndex word, const Node &node) const { + return LongestPointer(quant_, longest_.Find(word, node)); + } + + bool FastMakeNode(const WordIndex *begin, const WordIndex *end, Node &node) const { + assert(begin != end); + bool independent_left; + uint64_t ignored; + LookupUnigram(*begin, node, independent_left, ignored); + for (const WordIndex *i = begin + 1; i < end; ++i) { + if (independent_left || !LookupMiddle(i - begin - 1, *i, node, independent_left, ignored).Found()) return false; + } + return true; + } + + private: + friend void BuildTrie(SortedFiles &files, std::vector &counts, const Config &config, TrieSearch &out, Quant &quant, SortedVocabulary &vocab, BinaryFormat &backing); + + // Middles are managed manually so we can delay construction and they don't have to be copyable. + void FreeMiddles() { + for (const Middle *i = middle_begin_; i != middle_end_; ++i) { + i->~Middle(); + } + std::free(middle_begin_); + } + + typedef trie::BitPackedMiddle Middle; + + typedef trie::BitPackedLongest Longest; + Longest longest_; + + Middle *middle_begin_, *middle_end_; + Quant quant_; + + typedef ::lm::ngram::trie::Unigram Unigram; + Unigram unigram_; +}; + +} // namespace trie +} // namespace ngram +} // namespace lm + +#endif // LM_SEARCH_TRIE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/sizes.cc b/ctcdecode-master/third_party/kenlm/lm/sizes.cc new file mode 100644 index 0000000..dd831c5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/sizes.cc @@ -0,0 +1,63 @@ +#include "lm/sizes.hh" +#include "lm/model.hh" +#include "util/file_piece.hh" + +#include +#include + +namespace lm { +namespace ngram { + +void ShowSizes(const std::vector &counts, const lm::ngram::Config &config) { + uint64_t sizes[6]; + sizes[0] = ProbingModel::Size(counts, config); + sizes[1] = RestProbingModel::Size(counts, config); + sizes[2] = TrieModel::Size(counts, config); + sizes[3] = QuantTrieModel::Size(counts, config); + sizes[4] = ArrayTrieModel::Size(counts, config); + sizes[5] = QuantArrayTrieModel::Size(counts, config); + uint64_t max_length = *std::max_element(sizes, sizes + sizeof(sizes) / sizeof(uint64_t)); + uint64_t min_length = *std::min_element(sizes, sizes + sizeof(sizes) / sizeof(uint64_t)); + uint64_t divide; + char prefix; + if (min_length < (1 << 10) * 10) { + prefix = ' '; + divide = 1; + } else if (min_length < (1 << 20) * 10) { + prefix = 'k'; + divide = 1 << 10; + } else if (min_length < (1ULL << 30) * 10) { + prefix = 'M'; + divide = 1 << 20; + } else { + prefix = 'G'; + divide = 1 << 30; + } + long int length = std::max(2, static_cast(ceil(log10((double) max_length / divide)))); + std::cerr << "Memory estimate for binary LM:\ntype "; + + // right align bytes. + for (long int i = 0; i < length - 2; ++i) std::cerr << ' '; + + std::cerr << prefix << "B\n" + "probing " << std::setw(length) << (sizes[0] / divide) << " assuming -p " << config.probing_multiplier << "\n" + "probing " << std::setw(length) << (sizes[1] / divide) << " assuming -r models -p " << config.probing_multiplier << "\n" + "trie " << std::setw(length) << (sizes[2] / divide) << " without quantization\n" + "trie " << std::setw(length) << (sizes[3] / divide) << " assuming -q " << (unsigned)config.prob_bits << " -b " << (unsigned)config.backoff_bits << " quantization \n" + "trie " << std::setw(length) << (sizes[4] / divide) << " assuming -a " << (unsigned)config.pointer_bhiksha_bits << " array pointer compression\n" + "trie " << std::setw(length) << (sizes[5] / divide) << " assuming -a " << (unsigned)config.pointer_bhiksha_bits << " -q " << (unsigned)config.prob_bits << " -b " << (unsigned)config.backoff_bits<< " array pointer compression and quantization\n"; +} + +void ShowSizes(const std::vector &counts) { + lm::ngram::Config config; + ShowSizes(counts, config); +} + +void ShowSizes(const char *file, const lm::ngram::Config &config) { + std::vector counts; + util::FilePiece f(file); + lm::ReadARPACounts(f, counts); + ShowSizes(counts, config); +} + +}} //namespaces diff --git a/ctcdecode-master/third_party/kenlm/lm/sizes.hh b/ctcdecode-master/third_party/kenlm/lm/sizes.hh new file mode 100644 index 0000000..eb7e99d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/sizes.hh @@ -0,0 +1,17 @@ +#ifndef LM_SIZES_H +#define LM_SIZES_H + +#include + +#include + +namespace lm { namespace ngram { + +struct Config; + +void ShowSizes(const std::vector &counts, const lm::ngram::Config &config); +void ShowSizes(const std::vector &counts); +void ShowSizes(const char *file, const lm::ngram::Config &config); + +}} // namespaces +#endif // LM_SIZES_H diff --git a/ctcdecode-master/third_party/kenlm/lm/state.hh b/ctcdecode-master/third_party/kenlm/lm/state.hh new file mode 100644 index 0000000..2195dee --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/state.hh @@ -0,0 +1,125 @@ +#ifndef LM_STATE_H +#define LM_STATE_H + +#include "lm/max_order.hh" +#include "lm/word_index.hh" +#include "util/murmur_hash.hh" + +#include + +namespace lm { +namespace ngram { + +// This is a POD but if you want memcmp to return the same as operator==, call +// ZeroRemaining first. +class State { + public: + bool operator==(const State &other) const { + if (length != other.length) return false; + return !memcmp(words, other.words, length * sizeof(WordIndex)); + } + + // Three way comparison function. + int Compare(const State &other) const { + if (length != other.length) return length < other.length ? -1 : 1; + return memcmp(words, other.words, length * sizeof(WordIndex)); + } + + bool operator<(const State &other) const { + if (length != other.length) return length < other.length; + return memcmp(words, other.words, length * sizeof(WordIndex)) < 0; + } + + // Call this before using raw memcmp. + void ZeroRemaining() { + for (unsigned char i = length; i < KENLM_MAX_ORDER - 1; ++i) { + words[i] = 0; + backoff[i] = 0.0; + } + } + + unsigned char Length() const { return length; } + + // You shouldn't need to touch anything below this line, but the members are public so FullState will qualify as a POD. + // This order minimizes total size of the struct if WordIndex is 64 bit, float is 32 bit, and alignment of 64 bit integers is 64 bit. + WordIndex words[KENLM_MAX_ORDER - 1]; + float backoff[KENLM_MAX_ORDER - 1]; + unsigned char length; +}; + +typedef State Right; + +inline uint64_t hash_value(const State &state, uint64_t seed = 0) { + return util::MurmurHashNative(state.words, sizeof(WordIndex) * state.length, seed); +} + +struct Left { + bool operator==(const Left &other) const { + return + length == other.length && + (!length || (pointers[length - 1] == other.pointers[length - 1] && full == other.full)); + } + + int Compare(const Left &other) const { + if (length < other.length) return -1; + if (length > other.length) return 1; + if (length == 0) return 0; // Must be full. + if (pointers[length - 1] > other.pointers[length - 1]) return 1; + if (pointers[length - 1] < other.pointers[length - 1]) return -1; + return (int)full - (int)other.full; + } + + bool operator<(const Left &other) const { + return Compare(other) == -1; + } + + void ZeroRemaining() { + for (uint64_t * i = pointers + length; i < pointers + KENLM_MAX_ORDER - 1; ++i) + *i = 0; + } + + uint64_t pointers[KENLM_MAX_ORDER - 1]; + unsigned char length; + bool full; +}; + +inline uint64_t hash_value(const Left &left) { + unsigned char add[2]; + add[0] = left.length; + add[1] = left.full; + return util::MurmurHashNative(add, 2, left.length ? left.pointers[left.length - 1] : 0); +} + +struct ChartState { + bool operator==(const ChartState &other) const { + return (right == other.right) && (left == other.left); + } + + int Compare(const ChartState &other) const { + int lres = left.Compare(other.left); + if (lres) return lres; + return right.Compare(other.right); + } + + bool operator<(const ChartState &other) const { + return Compare(other) < 0; + } + + void ZeroRemaining() { + left.ZeroRemaining(); + right.ZeroRemaining(); + } + + Left left; + State right; +}; + +inline uint64_t hash_value(const ChartState &state) { + return hash_value(state.right, hash_value(state.left)); +} + + +} // namespace ngram +} // namespace lm + +#endif // LM_STATE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/test.arpa b/ctcdecode-master/third_party/kenlm/lm/test.arpa new file mode 100644 index 0000000..c4d2e6d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/test.arpa @@ -0,0 +1,124 @@ + +\data\ +ngram 1=37 +ngram 2=47 +ngram 3=11 +ngram 4=6 +ngram 5=4 + +\1-grams: +-1.383514 , -0.30103 +-1.139057 . -0.845098 +-1.029493 +-99 -0.4149733 +-1.995635 -20 +-1.285941 a -0.69897 +-1.687872 also -0.30103 +-1.687872 beyond -0.30103 +-1.687872 biarritz -0.30103 +-1.687872 call -0.30103 +-1.687872 concerns -0.30103 +-1.687872 consider -0.30103 +-1.687872 considering -0.30103 +-1.687872 for -0.30103 +-1.509559 higher -0.30103 +-1.687872 however -0.30103 +-1.687872 i -0.30103 +-1.687872 immediate -0.30103 +-1.687872 in -0.30103 +-1.687872 is -0.30103 +-1.285941 little -0.69897 +-1.383514 loin -0.30103 +-1.687872 look -0.30103 +-1.285941 looking -0.4771212 +-1.206319 more -0.544068 +-1.509559 on -0.4771212 +-1.509559 screening -0.4771212 +-1.687872 small -0.30103 +-1.687872 the -0.30103 +-1.687872 to -0.30103 +-1.687872 watch -0.30103 +-1.687872 watching -0.30103 +-1.687872 what -0.30103 +-1.687872 would -0.30103 +-3.141592 foo +-2.718281 bar 3.0 +-6.535897 baz -0.0 + +\2-grams: +-0.6925742 , . +-0.7522095 , however +-0.7522095 , is +-0.0602359 . +-0.4846522 looking -0.4771214 +-1.051485 screening +-1.07153 the +-1.07153 watching +-1.07153 what +-0.09132547 a little -0.69897 +-0.2922095 also call +-0.2922095 beyond immediate +-0.2705918 biarritz . +-0.2922095 call for +-0.2922095 concerns in +-0.2922095 consider watch +-0.2922095 considering consider +-0.2834328 for , +-0.5511513 higher more +-0.5845945 higher small +-0.2834328 however , +-0.2922095 i would +-0.2922095 immediate concerns +-0.2922095 in biarritz +-0.2922095 is to +-0.09021038 little more -0.1998621 +-0.7273645 loin , +-0.6925742 loin . +-0.6708385 loin +-0.2922095 look beyond +-0.4638903 looking higher +-0.4638903 looking on -0.4771212 +-0.5136299 more . -0.4771212 +-0.3561665 more loin +-0.1649931 on a -0.4771213 +-0.1649931 screening a -0.4771213 +-0.2705918 small . +-0.287799 the screening +-0.2922095 to look +-0.2622373 watch +-0.2922095 watching considering +-0.2922095 what i +-0.2922095 would also +-2 also would -6 +-15 -2 +-4 however -1 +-6 foo bar + +\3-grams: +-0.01916512 more . +-0.0283603 on a little -0.4771212 +-0.0283603 screening a little -0.4771212 +-0.01660496 a little more -0.09409451 +-0.3488368 looking higher +-0.3488368 looking on -0.4771212 +-0.1892331 little more loin +-0.04835128 looking on a -0.4771212 +-3 also would consider -7 +-6 however -12 +-7 to look a + +\4-grams: +-0.009249173 looking on a little -0.4771212 +-0.005464747 on a little more -0.4771212 +-0.005464747 screening a little more +-0.1453306 a little more loin +-0.01552657 looking on a -0.4771212 +-4 also would consider higher -8 + +\5-grams: +-0.003061223 looking on a little +-0.001813953 looking on a little more +-0.0432557 on a little more loin +-5 also would consider higher looking + +\end\ diff --git a/ctcdecode-master/third_party/kenlm/lm/test_nounk.arpa b/ctcdecode-master/third_party/kenlm/lm/test_nounk.arpa new file mode 100644 index 0000000..e38fc85 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/test_nounk.arpa @@ -0,0 +1,120 @@ + +\data\ +ngram 1=36 +ngram 2=45 +ngram 3=10 +ngram 4=6 +ngram 5=4 + +\1-grams: +-1.383514 , -0.30103 +-1.139057 . -0.845098 +-1.029493 +-99 -0.4149733 +-1.285941 a -0.69897 +-1.687872 also -0.30103 +-1.687872 beyond -0.30103 +-1.687872 biarritz -0.30103 +-1.687872 call -0.30103 +-1.687872 concerns -0.30103 +-1.687872 consider -0.30103 +-1.687872 considering -0.30103 +-1.687872 for -0.30103 +-1.509559 higher -0.30103 +-1.687872 however -0.30103 +-1.687872 i -0.30103 +-1.687872 immediate -0.30103 +-1.687872 in -0.30103 +-1.687872 is -0.30103 +-1.285941 little -0.69897 +-1.383514 loin -0.30103 +-1.687872 look -0.30103 +-1.285941 looking -0.4771212 +-1.206319 more -0.544068 +-1.509559 on -0.4771212 +-1.509559 screening -0.4771212 +-1.687872 small -0.30103 +-1.687872 the -0.30103 +-1.687872 to -0.30103 +-1.687872 watch -0.30103 +-1.687872 watching -0.30103 +-1.687872 what -0.30103 +-1.687872 would -0.30103 +-3.141592 foo +-2.718281 bar 3.0 +-6.535897 baz -0.0 + +\2-grams: +-0.6925742 , . +-0.7522095 , however +-0.7522095 , is +-0.0602359 . +-0.4846522 looking -0.4771214 +-1.051485 screening +-1.07153 the +-1.07153 watching +-1.07153 what +-0.09132547 a little -0.69897 +-0.2922095 also call +-0.2922095 beyond immediate +-0.2705918 biarritz . +-0.2922095 call for +-0.2922095 concerns in +-0.2922095 consider watch +-0.2922095 considering consider +-0.2834328 for , +-0.5511513 higher more +-0.5845945 higher small +-0.2834328 however , +-0.2922095 i would +-0.2922095 immediate concerns +-0.2922095 in biarritz +-0.2922095 is to +-0.09021038 little more -0.1998621 +-0.7273645 loin , +-0.6925742 loin . +-0.6708385 loin +-0.2922095 look beyond +-0.4638903 looking higher +-0.4638903 looking on -0.4771212 +-0.5136299 more . -0.4771212 +-0.3561665 more loin +-0.1649931 on a -0.4771213 +-0.1649931 screening a -0.4771213 +-0.2705918 small . +-0.287799 the screening +-0.2922095 to look +-0.2622373 watch +-0.2922095 watching considering +-0.2922095 what i +-0.2922095 would also +-2 also would -6 +-6 foo bar + +\3-grams: +-0.01916512 more . +-0.0283603 on a little -0.4771212 +-0.0283603 screening a little -0.4771212 +-0.01660496 a little more -0.09409451 +-0.3488368 looking higher +-0.3488368 looking on -0.4771212 +-0.1892331 little more loin +-0.04835128 looking on a -0.4771212 +-3 also would consider -7 +-7 to look a + +\4-grams: +-0.009249173 looking on a little -0.4771212 +-0.005464747 on a little more -0.4771212 +-0.005464747 screening a little more +-0.1453306 a little more loin +-0.01552657 looking on a -0.4771212 +-4 also would consider higher -8 + +\5-grams: +-0.003061223 looking on a little +-0.001813953 looking on a little more +-0.0432557 on a little more loin +-5 also would consider higher looking + +\end\ diff --git a/ctcdecode-master/third_party/kenlm/lm/trie.cc b/ctcdecode-master/third_party/kenlm/lm/trie.cc new file mode 100644 index 0000000..72ad544 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/trie.cc @@ -0,0 +1,131 @@ +#include "lm/trie.hh" + +#include "lm/bhiksha.hh" +#include "util/bit_packing.hh" +#include "util/exception.hh" +#include "util/sorted_uniform.hh" + +#include + +namespace lm { +namespace ngram { +namespace trie { +namespace { + +class KeyAccessor { + public: + KeyAccessor(const void *base, uint64_t key_mask, uint8_t key_bits, uint8_t total_bits) + : base_(reinterpret_cast(base)), key_mask_(key_mask), key_bits_(key_bits), total_bits_(total_bits) {} + + typedef uint64_t Key; + + Key operator()(uint64_t index) const { + return util::ReadInt57(base_, index * static_cast(total_bits_), key_bits_, key_mask_); + } + + private: + const uint8_t *const base_; + const WordIndex key_mask_; + const uint8_t key_bits_, total_bits_; +}; + +bool FindBitPacked(const void *base, uint64_t key_mask, uint8_t key_bits, uint8_t total_bits, uint64_t begin_index, uint64_t end_index, const uint64_t max_vocab, const uint64_t key, uint64_t &at_index) { + KeyAccessor accessor(base, key_mask, key_bits, total_bits); + if (!util::BoundedSortedUniformFind::T>(accessor, begin_index - 1, (uint64_t)0, end_index, max_vocab, key, at_index)) return false; + return true; +} +} // namespace + +uint64_t BitPacked::BaseSize(uint64_t entries, uint64_t max_vocab, uint8_t remaining_bits) { + uint8_t total_bits = util::RequiredBits(max_vocab) + remaining_bits; + // Extra entry for next pointer at the end. + // +7 then / 8 to round up bits and convert to bytes + // +sizeof(uint64_t) so that ReadInt57 etc don't go segfault. + // Note that this waste is O(order), not O(number of ngrams). + return ((1 + entries) * total_bits + 7) / 8 + sizeof(uint64_t); +} + +void BitPacked::BaseInit(void *base, uint64_t max_vocab, uint8_t remaining_bits) { + util::BitPackingSanity(); + word_bits_ = util::RequiredBits(max_vocab); + word_mask_ = (1ULL << word_bits_) - 1ULL; + if (word_bits_ > 57) UTIL_THROW(util::Exception, "Sorry, word indices more than " << (1ULL << 57) << " are not implemented. Edit util/bit_packing.hh and fix the bit packing functions."); + total_bits_ = word_bits_ + remaining_bits; + + base_ = static_cast(base); + insert_index_ = 0; + max_vocab_ = max_vocab; +} + +template uint64_t BitPackedMiddle::Size(uint8_t quant_bits, uint64_t entries, uint64_t max_vocab, uint64_t max_ptr, const Config &config) { + return Bhiksha::Size(entries + 1, max_ptr, config) + BaseSize(entries, max_vocab, quant_bits + Bhiksha::InlineBits(entries + 1, max_ptr, config)); +} + +template BitPackedMiddle::BitPackedMiddle(void *base, uint8_t quant_bits, uint64_t entries, uint64_t max_vocab, uint64_t max_next, const BitPacked &next_source, const Config &config) : + BitPacked(), + quant_bits_(quant_bits), + // If the offset of the method changes, also change TrieSearch::UpdateConfigFromBinary. + bhiksha_(base, entries + 1, max_next, config), + next_source_(&next_source) { + if (entries + 1 >= (1ULL << 57) || (max_next >= (1ULL << 57))) UTIL_THROW(util::Exception, "Sorry, this does not support more than " << (1ULL << 57) << " n-grams of a particular order. Edit util/bit_packing.hh and fix the bit packing functions."); + BaseInit(reinterpret_cast(base) + Bhiksha::Size(entries + 1, max_next, config), max_vocab, quant_bits_ + bhiksha_.InlineBits()); +} + +template util::BitAddress BitPackedMiddle::Insert(WordIndex word) { + assert(word <= word_mask_); + uint64_t at_pointer = insert_index_ * total_bits_; + + util::WriteInt57(base_, at_pointer, word_bits_, word); + at_pointer += word_bits_; + util::BitAddress ret(base_, at_pointer); + at_pointer += quant_bits_; + uint64_t next = next_source_->InsertIndex(); + bhiksha_.WriteNext(base_, at_pointer, insert_index_, next); + ++insert_index_; + return ret; +} + +template util::BitAddress BitPackedMiddle::Find(WordIndex word, NodeRange &range, uint64_t &pointer) const { + uint64_t at_pointer; + if (!FindBitPacked(base_, word_mask_, word_bits_, total_bits_, range.begin, range.end, max_vocab_, word, at_pointer)) { + return util::BitAddress(NULL, 0); + } + pointer = at_pointer; + at_pointer *= total_bits_; + at_pointer += word_bits_; + bhiksha_.ReadNext(base_, at_pointer + quant_bits_, pointer, total_bits_, range); + + return util::BitAddress(base_, at_pointer); +} + +template void BitPackedMiddle::FinishedLoading(uint64_t next_end, const Config &config) { + // Write at insert_index. . . + uint64_t last_next_write = insert_index_ * total_bits_ + + // at the offset where the next pointers are stored. + (total_bits_ - bhiksha_.InlineBits()); + bhiksha_.WriteNext(base_, last_next_write, insert_index_, next_end); + bhiksha_.FinishedLoading(config); +} + +util::BitAddress BitPackedLongest::Insert(WordIndex index) { + assert(index <= word_mask_); + uint64_t at_pointer = insert_index_ * total_bits_; + util::WriteInt57(base_, at_pointer, word_bits_, index); + at_pointer += word_bits_; + ++insert_index_; + return util::BitAddress(base_, at_pointer); +} + +util::BitAddress BitPackedLongest::Find(WordIndex word, const NodeRange &range) const { + uint64_t at_pointer; + if (!FindBitPacked(base_, word_mask_, word_bits_, total_bits_, range.begin, range.end, max_vocab_, word, at_pointer)) return util::BitAddress(NULL, 0); + at_pointer = at_pointer * total_bits_ + word_bits_; + return util::BitAddress(base_, at_pointer); +} + +template class BitPackedMiddle; +template class BitPackedMiddle; + +} // namespace trie +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/trie.hh b/ctcdecode-master/third_party/kenlm/lm/trie.hh new file mode 100644 index 0000000..b7f0458 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/trie.hh @@ -0,0 +1,146 @@ +#ifndef LM_TRIE_H +#define LM_TRIE_H + +#include "lm/weights.hh" +#include "lm/word_index.hh" +#include "util/bit_packing.hh" + +#include + +#include + +namespace lm { +namespace ngram { +struct Config; +namespace trie { + +struct NodeRange { + uint64_t begin, end; +}; + +// TODO: if the number of unigrams is a concern, also bit pack these records. +struct UnigramValue { + ProbBackoff weights; + uint64_t next; + uint64_t Next() const { return next; } +}; + +class UnigramPointer { + public: + explicit UnigramPointer(const ProbBackoff &to) : to_(&to) {} + + UnigramPointer() : to_(NULL) {} + + bool Found() const { return to_ != NULL; } + + float Prob() const { return to_->prob; } + float Backoff() const { return to_->backoff; } + float Rest() const { return Prob(); } + + private: + const ProbBackoff *to_; +}; + +class Unigram { + public: + Unigram() {} + + void Init(void *start) { + unigram_ = static_cast(start); + } + + static uint64_t Size(uint64_t count) { + // +1 in case unknown doesn't appear. +1 for the final next. + return (count + 2) * sizeof(UnigramValue); + } + + const ProbBackoff &Lookup(WordIndex index) const { return unigram_[index].weights; } + + ProbBackoff &Unknown() { return unigram_[0].weights; } + + UnigramValue *Raw() { + return unigram_; + } + + UnigramPointer Find(WordIndex word, NodeRange &next) const { + UnigramValue *val = unigram_ + word; + next.begin = val->next; + next.end = (val+1)->next; + return UnigramPointer(val->weights); + } + + private: + UnigramValue *unigram_; +}; + +class BitPacked { + public: + BitPacked() {} + + uint64_t InsertIndex() const { + return insert_index_; + } + + protected: + static uint64_t BaseSize(uint64_t entries, uint64_t max_vocab, uint8_t remaining_bits); + + void BaseInit(void *base, uint64_t max_vocab, uint8_t remaining_bits); + + uint8_t word_bits_; + uint8_t total_bits_; + uint64_t word_mask_; + + uint8_t *base_; + + uint64_t insert_index_, max_vocab_; +}; + +template class BitPackedMiddle : public BitPacked { + public: + static uint64_t Size(uint8_t quant_bits, uint64_t entries, uint64_t max_vocab, uint64_t max_next, const Config &config); + + // next_source need not be initialized. + BitPackedMiddle(void *base, uint8_t quant_bits, uint64_t entries, uint64_t max_vocab, uint64_t max_next, const BitPacked &next_source, const Config &config); + + util::BitAddress Insert(WordIndex word); + + void FinishedLoading(uint64_t next_end, const Config &config); + + util::BitAddress Find(WordIndex word, NodeRange &range, uint64_t &pointer) const; + + util::BitAddress ReadEntry(uint64_t pointer, NodeRange &range) { + uint64_t addr = pointer * total_bits_; + addr += word_bits_; + bhiksha_.ReadNext(base_, addr + quant_bits_, pointer, total_bits_, range); + return util::BitAddress(base_, addr); + } + + private: + uint8_t quant_bits_; + Bhiksha bhiksha_; + + const BitPacked *next_source_; +}; + +class BitPackedLongest : public BitPacked { + public: + static uint64_t Size(uint8_t quant_bits, uint64_t entries, uint64_t max_vocab) { + return BaseSize(entries, max_vocab, quant_bits); + } + + BitPackedLongest() {} + + void Init(void *base, uint8_t quant_bits, uint64_t max_vocab) { + BaseInit(base, max_vocab, quant_bits); + } + + util::BitAddress Insert(WordIndex word); + + util::BitAddress Find(WordIndex word, const NodeRange &node) const; +}; + +} // namespace trie +} // namespace ngram +} // namespace lm + +#endif // LM_TRIE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/trie_sort.cc b/ctcdecode-master/third_party/kenlm/lm/trie_sort.cc new file mode 100644 index 0000000..73728c1 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/trie_sort.cc @@ -0,0 +1,311 @@ +#include "lm/trie_sort.hh" + +#include "lm/config.hh" +#include "lm/lm_exception.hh" +#include "lm/read_arpa.hh" +#include "lm/vocab.hh" +#include "lm/weights.hh" +#include "lm/word_index.hh" +#include "util/file_piece.hh" +#include "util/mmap.hh" +#include "util/pool.hh" +#include "util/proxy_iterator.hh" +#include "util/sized_iterator.hh" + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace lm { +namespace ngram { +namespace trie { +namespace { + +typedef util::SizedIterator NGramIter; + +// Proxy for an entry except there is some extra cruft between the entries. This is used to sort (n-1)-grams using the same memory as the sorted n-grams. +class PartialViewProxy { + public: + PartialViewProxy() : attention_size_(0), inner_() {} + + PartialViewProxy(void *ptr, std::size_t block_size, util::FreePool &pool) : attention_size_(pool.ElementSize()), inner_(ptr, block_size), pool_(&pool) {} + + operator util::ValueBlock() const { + return util::ValueBlock(inner_.Data(), *pool_); + } + + PartialViewProxy &operator=(const PartialViewProxy &from) { + memcpy(inner_.Data(), from.inner_.Data(), attention_size_); + return *this; + } + + PartialViewProxy &operator=(const util::ValueBlock &from) { + memcpy(inner_.Data(), from.Data(), attention_size_); + return *this; + } + + const void *Data() const { return inner_.Data(); } + void *Data() { return inner_.Data(); } + + friend void swap(PartialViewProxy first, PartialViewProxy second); + + private: + friend class util::ProxyIterator; + + typedef util::ValueBlock value_type; + + const std::size_t attention_size_; + + typedef util::SizedInnerIterator InnerIterator; + InnerIterator &Inner() { return inner_; } + const InnerIterator &Inner() const { return inner_; } + InnerIterator inner_; + + util::FreePool *pool_; +}; + +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wunused-function" +#endif +void swap(PartialViewProxy first, PartialViewProxy second) { + std::swap_ranges(reinterpret_cast(first.Data()), reinterpret_cast(first.Data()) + first.attention_size_, reinterpret_cast(second.Data())); +} +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +typedef util::ProxyIterator PartialIter; + +FILE *DiskFlush(const void *mem_begin, const void *mem_end, const std::string &temp_prefix) { + util::scoped_fd file(util::MakeTemp(temp_prefix)); + util::WriteOrThrow(file.get(), mem_begin, (uint8_t*)mem_end - (uint8_t*)mem_begin); + return util::FDOpenOrThrow(file); +} + +FILE *WriteContextFile(uint8_t *begin, uint8_t *end, const std::string &temp_prefix, std::size_t entry_size, unsigned char order) { + const size_t context_size = sizeof(WordIndex) * (order - 1); + + util::FreePool pool(context_size); + // Sort just the contexts using the same memory. + PartialIter context_begin(PartialViewProxy(begin + sizeof(WordIndex), entry_size, pool)); + PartialIter context_end(PartialViewProxy(end + sizeof(WordIndex), entry_size, pool)); + +#if defined(_WIN32) || defined(_WIN64) + std::stable_sort +#else + std::sort +#endif + (context_begin, context_end, util::SizedCompare(EntryCompare(order - 1))); + + util::scoped_FILE out(util::FMakeTemp(temp_prefix)); + + // Write out to file and uniqueify at the same time. Could have used unique_copy if there was an appropriate OutputIterator. + if (context_begin == context_end) return out.release(); + PartialIter i(context_begin); + util::WriteOrThrow(out.get(), i->Data(), context_size); + const void *previous = i->Data(); + ++i; + for (; i != context_end; ++i) { + if (memcmp(previous, i->Data(), context_size)) { + util::WriteOrThrow(out.get(), i->Data(), context_size); + previous = i->Data(); + } + } + return out.release(); +} + +struct ThrowCombine { + void operator()(std::size_t entry_size, unsigned char order, const void *first, const void *second, FILE * /*out*/) const { + const WordIndex *base = reinterpret_cast(first); + FormatLoadException e; + e << "Duplicate n-gram detected with vocab ids"; + for (const WordIndex *i = base; i != base + order; ++i) { + e << ' ' << *i; + } + throw e; + } +}; + +// Useful for context files that just contain records with no value. +struct FirstCombine { + void operator()(std::size_t entry_size, unsigned char /*order*/, const void *first, const void * /*second*/, FILE *out) const { + util::WriteOrThrow(out, first, entry_size); + } +}; + +template FILE *MergeSortedFiles(FILE *first_file, FILE *second_file, const std::string &temp_prefix, std::size_t weights_size, unsigned char order, const Combine &combine) { + std::size_t entry_size = sizeof(WordIndex) * order + weights_size; + RecordReader first, second; + first.Init(first_file, entry_size); + second.Init(second_file, entry_size); + util::scoped_FILE out_file(util::FMakeTemp(temp_prefix)); + EntryCompare less(order); + while (first && second) { + if (less(first.Data(), second.Data())) { + util::WriteOrThrow(out_file.get(), first.Data(), entry_size); + ++first; + } else if (less(second.Data(), first.Data())) { + util::WriteOrThrow(out_file.get(), second.Data(), entry_size); + ++second; + } else { + combine(entry_size, order, first.Data(), second.Data(), out_file.get()); + ++first; ++second; + } + } + for (RecordReader &remains = (first ? first : second); remains; ++remains) { + util::WriteOrThrow(out_file.get(), remains.Data(), entry_size); + } + return out_file.release(); +} + +} // namespace + +void RecordReader::Init(FILE *file, std::size_t entry_size) { + entry_size_ = entry_size; + data_.reset(malloc(entry_size)); + UTIL_THROW_IF(!data_.get(), util::ErrnoException, "Failed to malloc read buffer"); + file_ = file; + if (file) { + rewind(file); + remains_ = true; + ++*this; + } else { + remains_ = false; + } +} + +void RecordReader::Overwrite(const void *start, std::size_t amount) { + long internal = (uint8_t*)start - (uint8_t*)data_.get(); + UTIL_THROW_IF(fseek(file_, internal - entry_size_, SEEK_CUR), util::ErrnoException, "Couldn't seek backwards for revision"); + util::WriteOrThrow(file_, start, amount); + long forward = entry_size_ - internal - amount; +#if !defined(_WIN32) && !defined(_WIN64) + if (forward) +#endif + UTIL_THROW_IF(fseek(file_, forward, SEEK_CUR), util::ErrnoException, "Couldn't seek forwards past revision"); +} + +void RecordReader::Rewind() { + if (file_) { + rewind(file_); + remains_ = true; + ++*this; + } else { + remains_ = false; + } +} + +SortedFiles::SortedFiles(const Config &config, util::FilePiece &f, std::vector &counts, size_t buffer, const std::string &file_prefix, SortedVocabulary &vocab) { + PositiveProbWarn warn(config.positive_log_probability); + unigram_.reset(util::MakeTemp(file_prefix)); + { + // In case appears. + size_t size_out = (counts[0] + 1) * sizeof(ProbBackoff); + util::scoped_mmap unigram_mmap(util::MapZeroedWrite(unigram_.get(), size_out), size_out); + Read1Grams(f, counts[0], vocab, reinterpret_cast(unigram_mmap.get()), warn); + CheckSpecials(config, vocab); + if (!vocab.SawUnk()) ++counts[0]; + } + + // Only use as much buffer as we need. + size_t buffer_use = 0; + for (unsigned int order = 2; order < counts.size(); ++order) { + buffer_use = std::max(buffer_use, static_cast((sizeof(WordIndex) * order + 2 * sizeof(float)) * counts[order - 1])); + } + buffer_use = std::max(buffer_use, static_cast((sizeof(WordIndex) * counts.size() + sizeof(float)) * counts.back())); + buffer = std::min(buffer, buffer_use); + + util::scoped_malloc mem; + mem.reset(malloc(buffer)); + if (!mem.get()) UTIL_THROW(util::ErrnoException, "malloc failed for sort buffer size " << buffer); + + for (unsigned char order = 2; order <= counts.size(); ++order) { + ConvertToSorted(f, vocab, counts, file_prefix, order, warn, mem.get(), buffer); + } + ReadEnd(f); +} + +namespace { +class Closer { + public: + explicit Closer(std::deque &files) : files_(files) {} + + ~Closer() { + for (std::deque::iterator i = files_.begin(); i != files_.end(); ++i) { + util::scoped_FILE deleter(*i); + } + } + + void PopFront() { + util::scoped_FILE deleter(files_.front()); + files_.pop_front(); + } + private: + std::deque &files_; +}; +} // namespace + +void SortedFiles::ConvertToSorted(util::FilePiece &f, const SortedVocabulary &vocab, const std::vector &counts, const std::string &file_prefix, unsigned char order, PositiveProbWarn &warn, void *mem, std::size_t mem_size) { + ReadNGramHeader(f, order); + const size_t count = counts[order - 1]; + // Size of weights. Does it include backoff? + const size_t words_size = sizeof(WordIndex) * order; + const size_t weights_size = sizeof(float) + ((order == counts.size()) ? 0 : sizeof(float)); + const size_t entry_size = words_size + weights_size; + const size_t batch_size = std::min(count, mem_size / entry_size); + uint8_t *const begin = reinterpret_cast(mem); + + std::deque files, contexts; + Closer files_closer(files), contexts_closer(contexts); + + for (std::size_t batch = 0, done = 0; done < count; ++batch) { + uint8_t *out = begin; + uint8_t *out_end = out + std::min(count - done, batch_size) * entry_size; + if (order == counts.size()) { + for (; out != out_end; out += entry_size) { + std::reverse_iterator it(reinterpret_cast(out) + order); + ReadNGram(f, order, vocab, it, *reinterpret_cast(out + words_size), warn); + } + } else { + for (; out != out_end; out += entry_size) { + std::reverse_iterator it(reinterpret_cast(out) + order); + ReadNGram(f, order, vocab, it, *reinterpret_cast(out + words_size), warn); + } + } + // Sort full records by full n-gram. + util::SizedSort(begin, out_end, entry_size, EntryCompare(order)); + files.push_back(DiskFlush(begin, out_end, file_prefix)); + contexts.push_back(WriteContextFile(begin, out_end, file_prefix, entry_size, order)); + + done += (out_end - begin) / entry_size; + } + + // All individual files created. Merge them. + + while (files.size() > 1) { + files.push_back(MergeSortedFiles(files[0], files[1], file_prefix, weights_size, order, ThrowCombine())); + files_closer.PopFront(); + files_closer.PopFront(); + contexts.push_back(MergeSortedFiles(contexts[0], contexts[1], file_prefix, 0, order - 1, FirstCombine())); + contexts_closer.PopFront(); + contexts_closer.PopFront(); + } + + if (!files.empty()) { + // Steal from closers. + full_[order - 2].reset(files.front()); + files.pop_front(); + context_[order - 2].reset(contexts.front()); + contexts.pop_front(); + } +} + +} // namespace trie +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/trie_sort.hh b/ctcdecode-master/third_party/kenlm/lm/trie_sort.hh new file mode 100644 index 0000000..594efee --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/trie_sort.hh @@ -0,0 +1,114 @@ +// Step of trie builder: create sorted files. + +#ifndef LM_TRIE_SORT_H +#define LM_TRIE_SORT_H + +#include "lm/max_order.hh" +#include "lm/word_index.hh" + +#include "util/file.hh" +#include "util/scoped.hh" + +#include +#include +#include +#include + +#include + +namespace util { +class FilePiece; +} // namespace util + +namespace lm { +class PositiveProbWarn; +namespace ngram { +class SortedVocabulary; +struct Config; + +namespace trie { + +class EntryCompare : public std::binary_function { + public: + explicit EntryCompare(unsigned char order) : order_(order) {} + + bool operator()(const void *first_void, const void *second_void) const { + const WordIndex *first = static_cast(first_void); + const WordIndex *second = static_cast(second_void); + const WordIndex *end = first + order_; + for (; first != end; ++first, ++second) { + if (*first < *second) return true; + if (*first > *second) return false; + } + return false; + } + private: + unsigned char order_; +}; + +class RecordReader { + public: + RecordReader() : remains_(true) {} + + void Init(FILE *file, std::size_t entry_size); + + void *Data() { return data_.get(); } + const void *Data() const { return data_.get(); } + + RecordReader &operator++() { + std::size_t ret = fread(data_.get(), entry_size_, 1, file_); + if (!ret) { + UTIL_THROW_IF(!feof(file_), util::ErrnoException, "Error reading temporary file"); + remains_ = false; + } + return *this; + } + + operator bool() const { return remains_; } + + void Rewind(); + + std::size_t EntrySize() const { return entry_size_; } + + void Overwrite(const void *start, std::size_t amount); + + private: + FILE *file_; + + util::scoped_malloc data_; + + bool remains_; + + std::size_t entry_size_; +}; + +class SortedFiles { + public: + // Build from ARPA + SortedFiles(const Config &config, util::FilePiece &f, std::vector &counts, std::size_t buffer, const std::string &file_prefix, SortedVocabulary &vocab); + + int StealUnigram() { + return unigram_.release(); + } + + FILE *Full(unsigned char order) { + return full_[order - 2].get(); + } + + FILE *Context(unsigned char of_order) { + return context_[of_order - 2].get(); + } + + private: + void ConvertToSorted(util::FilePiece &f, const SortedVocabulary &vocab, const std::vector &counts, const std::string &prefix, unsigned char order, PositiveProbWarn &warn, void *mem, std::size_t mem_size); + + util::scoped_fd unigram_; + + util::scoped_FILE full_[KENLM_MAX_ORDER - 1], context_[KENLM_MAX_ORDER - 1]; +}; + +} // namespace trie +} // namespace ngram +} // namespace lm + +#endif // LM_TRIE_SORT_H diff --git a/ctcdecode-master/third_party/kenlm/lm/value.hh b/ctcdecode-master/third_party/kenlm/lm/value.hh new file mode 100644 index 0000000..d2425cc --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/value.hh @@ -0,0 +1,158 @@ +#ifndef LM_VALUE_H +#define LM_VALUE_H + +#include "lm/config.hh" +#include "lm/model_type.hh" +#include "lm/value_build.hh" +#include "lm/weights.hh" +#include "util/bit_packing.hh" + +#include + +namespace lm { +namespace ngram { + +// Template proxy for probing unigrams and middle. +template class GenericProbingProxy { + public: + explicit GenericProbingProxy(const Weights &to) : to_(&to) {} + + GenericProbingProxy() : to_(0) {} + + bool Found() const { return to_ != 0; } + + float Prob() const { + util::FloatEnc enc; + enc.f = to_->prob; + enc.i |= util::kSignBit; + return enc.f; + } + + float Backoff() const { return to_->backoff; } + + bool IndependentLeft() const { + util::FloatEnc enc; + enc.f = to_->prob; + return enc.i & util::kSignBit; + } + + protected: + const Weights *to_; +}; + +// Basic proxy for trie unigrams. +template class GenericTrieUnigramProxy { + public: + explicit GenericTrieUnigramProxy(const Weights &to) : to_(&to) {} + + GenericTrieUnigramProxy() : to_(0) {} + + bool Found() const { return to_ != 0; } + float Prob() const { return to_->prob; } + float Backoff() const { return to_->backoff; } + float Rest() const { return Prob(); } + + protected: + const Weights *to_; +}; + +struct BackoffValue { + typedef ProbBackoff Weights; + static const ModelType kProbingModelType = PROBING; + + class ProbingProxy : public GenericProbingProxy { + public: + explicit ProbingProxy(const Weights &to) : GenericProbingProxy(to) {} + ProbingProxy() {} + float Rest() const { return Prob(); } + }; + + class TrieUnigramProxy : public GenericTrieUnigramProxy { + public: + explicit TrieUnigramProxy(const Weights &to) : GenericTrieUnigramProxy(to) {} + TrieUnigramProxy() {} + float Rest() const { return Prob(); } + }; + + struct ProbingEntry { + typedef uint64_t Key; + typedef Weights Value; + uint64_t key; + ProbBackoff value; + uint64_t GetKey() const { return key; } + }; + + struct TrieUnigramValue { + Weights weights; + uint64_t next; + uint64_t Next() const { return next; } + }; + + const static bool kDifferentRest = false; + + template void Callback(const Config &, unsigned int, typename Model::Vocabulary &, C &callback) { + NoRestBuild build; + callback(build); + } +}; + +struct RestValue { + typedef RestWeights Weights; + static const ModelType kProbingModelType = REST_PROBING; + + class ProbingProxy : public GenericProbingProxy { + public: + explicit ProbingProxy(const Weights &to) : GenericProbingProxy(to) {} + ProbingProxy() {} + float Rest() const { return to_->rest; } + }; + + class TrieUnigramProxy : public GenericTrieUnigramProxy { + public: + explicit TrieUnigramProxy(const Weights &to) : GenericTrieUnigramProxy(to) {} + TrieUnigramProxy() {} + float Rest() const { return to_->rest; } + }; + +// gcc 4.1 doesn't properly back dependent types :-(. +#pragma pack(push) +#pragma pack(4) + struct ProbingEntry { + typedef uint64_t Key; + typedef Weights Value; + Key key; + Value value; + Key GetKey() const { return key; } + }; + + struct TrieUnigramValue { + Weights weights; + uint64_t next; + uint64_t Next() const { return next; } + }; +#pragma pack(pop) + + const static bool kDifferentRest = true; + + template void Callback(const Config &config, unsigned int order, typename Model::Vocabulary &vocab, C &callback) { + switch (config.rest_function) { + case Config::REST_MAX: + { + MaxRestBuild build; + callback(build); + } + break; + case Config::REST_LOWER: + { + LowerRestBuild build(config, order, vocab); + callback(build); + } + break; + } + } +}; + +} // namespace ngram +} // namespace lm + +#endif // LM_VALUE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/value_build.cc b/ctcdecode-master/third_party/kenlm/lm/value_build.cc new file mode 100644 index 0000000..ac623a6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/value_build.cc @@ -0,0 +1,59 @@ +#include "lm/value_build.hh" + +#include "lm/model.hh" +#include "lm/read_arpa.hh" + +namespace lm { +namespace ngram { + +template LowerRestBuild::LowerRestBuild(const Config &config, unsigned int order, const typename Model::Vocabulary &vocab) { + UTIL_THROW_IF(config.rest_lower_files.size() != order - 1, ConfigException, "This model has order " << order << " so there should be " << (order - 1) << " lower-order models for rest cost purposes."); + Config for_lower = config; + for_lower.write_mmap = NULL; + for_lower.rest_lower_files.clear(); + + // Unigram models aren't supported, so this is a custom loader. + // TODO: optimize the unigram loading? + { + util::FilePiece uni(config.rest_lower_files[0].c_str()); + std::vector number; + ReadARPACounts(uni, number); + UTIL_THROW_IF(number.size() != 1, FormatLoadException, "Expected the unigram model to have order 1, not " << number.size()); + ReadNGramHeader(uni, 1); + unigrams_.resize(number[0]); + unigrams_[0] = config.unknown_missing_logprob; + PositiveProbWarn warn; + for (uint64_t i = 0; i < number[0]; ++i) { + WordIndex w; + Prob entry; + ReadNGram(uni, 1, vocab, &w, entry, warn); + unigrams_[w] = entry.prob; + } + } + + try { + for (unsigned int i = 2; i < order; ++i) { + models_.push_back(new Model(config.rest_lower_files[i - 1].c_str(), for_lower)); + UTIL_THROW_IF(models_.back()->Order() != i, FormatLoadException, "Lower order file " << config.rest_lower_files[i-1] << " should have order " << i); + } + } catch (...) { + for (typename std::vector::const_iterator i = models_.begin(); i != models_.end(); ++i) { + delete *i; + } + models_.clear(); + throw; + } + + // TODO: force/check same vocab. +} + +template LowerRestBuild::~LowerRestBuild() { + for (typename std::vector::const_iterator i = models_.begin(); i != models_.end(); ++i) { + delete *i; + } +} + +template class LowerRestBuild; + +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/value_build.hh b/ctcdecode-master/third_party/kenlm/lm/value_build.hh new file mode 100644 index 0000000..49989ab --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/value_build.hh @@ -0,0 +1,97 @@ +#ifndef LM_VALUE_BUILD_H +#define LM_VALUE_BUILD_H + +#include "lm/weights.hh" +#include "lm/word_index.hh" +#include "util/bit_packing.hh" + +#include + +namespace lm { +namespace ngram { + +struct Config; +struct BackoffValue; +struct RestValue; + +class NoRestBuild { + public: + typedef BackoffValue Value; + + NoRestBuild() {} + + void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {} + void SetRest(const WordIndex *, unsigned int, const ProbBackoff &) const {} + + template bool MarkExtends(ProbBackoff &weights, const Second &) const { + util::UnsetSign(weights.prob); + return false; + } + + // Probing doesn't need to go back to unigram. + const static bool kMarkEvenLower = false; +}; + +class MaxRestBuild { + public: + typedef RestValue Value; + + MaxRestBuild() {} + + void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {} + void SetRest(const WordIndex *, unsigned int, RestWeights &weights) const { + weights.rest = weights.prob; + util::SetSign(weights.rest); + } + + bool MarkExtends(RestWeights &weights, const RestWeights &to) const { + util::UnsetSign(weights.prob); + if (weights.rest >= to.rest) return false; + weights.rest = to.rest; + return true; + } + bool MarkExtends(RestWeights &weights, const Prob &to) const { + util::UnsetSign(weights.prob); + if (weights.rest >= to.prob) return false; + weights.rest = to.prob; + return true; + } + + // Probing does need to go back to unigram. + const static bool kMarkEvenLower = true; +}; + +template class LowerRestBuild { + public: + typedef RestValue Value; + + LowerRestBuild(const Config &config, unsigned int order, const typename Model::Vocabulary &vocab); + + ~LowerRestBuild(); + + void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {} + void SetRest(const WordIndex *vocab_ids, unsigned int n, RestWeights &weights) const { + typename Model::State ignored; + if (n == 1) { + weights.rest = unigrams_[*vocab_ids]; + } else { + weights.rest = models_[n-2]->FullScoreForgotState(vocab_ids + 1, vocab_ids + n, *vocab_ids, ignored).prob; + } + } + + template bool MarkExtends(RestWeights &weights, const Second &) const { + util::UnsetSign(weights.prob); + return false; + } + + const static bool kMarkEvenLower = false; + + std::vector unigrams_; + + std::vector models_; +}; + +} // namespace ngram +} // namespace lm + +#endif // LM_VALUE_BUILD_H diff --git a/ctcdecode-master/third_party/kenlm/lm/virtual_interface.cc b/ctcdecode-master/third_party/kenlm/lm/virtual_interface.cc new file mode 100644 index 0000000..17a74c3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/virtual_interface.cc @@ -0,0 +1,19 @@ +#include "lm/virtual_interface.hh" + +#include "lm/lm_exception.hh" + +namespace lm { +namespace base { + +Vocabulary::~Vocabulary() {} + +void Vocabulary::SetSpecial(WordIndex begin_sentence, WordIndex end_sentence, WordIndex not_found) { + begin_sentence_ = begin_sentence; + end_sentence_ = end_sentence; + not_found_ = not_found; +} + +Model::~Model() {} + +} // namespace base +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/virtual_interface.hh b/ctcdecode-master/third_party/kenlm/lm/virtual_interface.hh new file mode 100644 index 0000000..ea491fb --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/virtual_interface.hh @@ -0,0 +1,160 @@ +#ifndef LM_VIRTUAL_INTERFACE_H +#define LM_VIRTUAL_INTERFACE_H + +#include "lm/return.hh" +#include "lm/word_index.hh" +#include "util/string_piece.hh" + +#include +#include + +namespace lm { +namespace base { + +template class ModelFacade; + +/* Vocabulary interface. Call Index(string) and get a word index for use in + * calling Model. It provides faster convenience functions for , , and + * although you can also find these using Index. + * + * Some models do not load the mapping from index to string. If you need this, + * check if the model Vocabulary class implements such a function and access it + * directly. + * + * The Vocabulary object is always owned by the Model and can be retrieved from + * the Model using BaseVocabulary() for this abstract interface or + * GetVocabulary() for the actual implementation (in which case you'll need the + * actual implementation of the Model too). + */ +class Vocabulary { + public: + virtual ~Vocabulary(); + + WordIndex BeginSentence() const { return begin_sentence_; } + WordIndex EndSentence() const { return end_sentence_; } + WordIndex NotFound() const { return not_found_; } + + /* Most implementations allow StringPiece lookups and need only override + * Index(StringPiece). SRI requires null termination and overrides all + * three methods. + */ + virtual WordIndex Index(const StringPiece &str) const = 0; + virtual WordIndex Index(const std::string &str) const { + return Index(StringPiece(str)); + } + virtual WordIndex Index(const char *str) const { + return Index(StringPiece(str)); + } + + protected: + // Call SetSpecial afterward. + Vocabulary() {} + + Vocabulary(WordIndex begin_sentence, WordIndex end_sentence, WordIndex not_found) { + SetSpecial(begin_sentence, end_sentence, not_found); + } + + void SetSpecial(WordIndex begin_sentence, WordIndex end_sentence, WordIndex not_found); + + WordIndex begin_sentence_, end_sentence_, not_found_; + + private: + // Disable copy constructors. They're private and undefined. + // Ersatz boost::noncopyable. + Vocabulary(const Vocabulary &); + Vocabulary &operator=(const Vocabulary &); +}; + +/* There are two ways to access a Model. + * + * + * OPTION 1: Access the Model directly (e.g. lm::ngram::Model in model.hh). + * + * Every Model implements the scoring function: + * float Score( + * const Model::State &in_state, + * const WordIndex new_word, + * Model::State &out_state) const; + * + * It can also return the length of n-gram matched by the model: + * FullScoreReturn FullScore( + * const Model::State &in_state, + * const WordIndex new_word, + * Model::State &out_state) const; + * + * + * There are also accessor functions: + * const State &BeginSentenceState() const; + * const State &NullContextState() const; + * const Vocabulary &GetVocabulary() const; + * unsigned int Order() const; + * + * NB: In case you're wondering why the model implementation looks like it's + * missing these methods, see facade.hh. + * + * This is the fastest way to use a model and presents a normal State class to + * be included in a hypothesis state structure. + * + * + * OPTION 2: Use the virtual interface below. + * + * The virtual interface allow you to decide which Model to use at runtime + * without templatizing everything on the Model type. However, each Model has + * its own State class, so a single State cannot be efficiently provided (it + * would require using the maximum memory of any Model's State or memory + * allocation with each lookup). This means you become responsible for + * allocating memory with size StateSize() and passing it to the Score or + * FullScore functions provided here. + * + * For example, cdec has a std::string containing the entire state of a + * hypothesis. It can reserve StateSize bytes in this string for the model + * state. + * + * All the State objects are POD, so it's ok to use raw memory for storing + * State. + * in_state and out_state must not have the same address. + */ +class Model { + public: + virtual ~Model(); + + size_t StateSize() const { return state_size_; } + const void *BeginSentenceMemory() const { return begin_sentence_memory_; } + void BeginSentenceWrite(void *to) const { memcpy(to, begin_sentence_memory_, StateSize()); } + const void *NullContextMemory() const { return null_context_memory_; } + void NullContextWrite(void *to) const { memcpy(to, null_context_memory_, StateSize()); } + + // Requires in_state != out_state + virtual float BaseScore(const void *in_state, const WordIndex new_word, void *out_state) const = 0; + + // Requires in_state != out_state + virtual FullScoreReturn BaseFullScore(const void *in_state, const WordIndex new_word, void *out_state) const = 0; + + // Prefer to use FullScore. The context words should be provided in reverse order. + virtual FullScoreReturn BaseFullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, void *out_state) const = 0; + + unsigned char Order() const { return order_; } + + const Vocabulary &BaseVocabulary() const { return *base_vocab_; } + + private: + template friend class ModelFacade; + explicit Model(size_t state_size) : state_size_(state_size) {} + + const size_t state_size_; + const void *begin_sentence_memory_, *null_context_memory_; + + const Vocabulary *base_vocab_; + + unsigned char order_; + + // Disable copy constructors. They're private and undefined. + // Ersatz boost::noncopyable. + Model(const Model &); + Model &operator=(const Model &); +}; + +} // mamespace base +} // namespace lm + +#endif // LM_VIRTUAL_INTERFACE_H diff --git a/ctcdecode-master/third_party/kenlm/lm/vocab.cc b/ctcdecode-master/third_party/kenlm/lm/vocab.cc new file mode 100644 index 0000000..db5c9f9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/vocab.cc @@ -0,0 +1,310 @@ +#include "lm/vocab.hh" + +#include "lm/binary_format.hh" +#include "lm/enumerate_vocab.hh" +#include "lm/lm_exception.hh" +#include "lm/config.hh" +#include "lm/weights.hh" +#include "util/exception.hh" +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/joint_sort.hh" +#include "util/murmur_hash.hh" +#include "util/probing_hash_table.hh" + +#include +#include + +namespace lm { +namespace ngram { + +namespace detail { +uint64_t HashForVocab(const char *str, std::size_t len) { + // This proved faster than Boost's hash in speed trials: total load time Murmur 67090000, Boost 72210000 + // Chose to use 64A instead of native so binary format will be portable across 64 and 32 bit. + return util::MurmurHash64A(str, len, 0); +} +} // namespace detail + +namespace { +// Normally static initialization is a bad idea but MurmurHash is pure arithmetic, so this is ok. +const uint64_t kUnknownHash = detail::HashForVocab("", 5); +// Sadly some LMs have . +const uint64_t kUnknownCapHash = detail::HashForVocab("", 5); + +void ReadWords(int fd, EnumerateVocab *enumerate, WordIndex expected_count, uint64_t offset) { + util::SeekOrThrow(fd, offset); + // Check that we're at the right place by reading which is always first. + char check_unk[6]; + util::ReadOrThrow(fd, check_unk, 6); + UTIL_THROW_IF( + memcmp(check_unk, "", 6), + FormatLoadException, + "Vocabulary words are in the wrong place. This could be because the binary file was built with stale gcc and old kenlm. Stale gcc, including the gcc distributed with RedHat and OS X, has a bug that ignores pragma pack for template-dependent types. New kenlm works around this, so you'll save memory but have to rebuild any binary files using the probing data structure."); + if (!enumerate) return; + enumerate->Add(0, ""); + + WordIndex index = 1; // Read already. + util::FilePiece in(util::DupOrThrow(fd)); + for (util::LineIterator w(in, '\0'); w; ++w, ++index) { + enumerate->Add(index, *w); + } + UTIL_THROW_IF(expected_count != index, FormatLoadException, "The binary file has the wrong number of words at the end. This could be caused by a truncated binary file."); +} + +// Constructor ordering madness. +int SeekAndReturn(int fd, uint64_t start) { + util::SeekOrThrow(fd, start); + return fd; +} +} // namespace + +ImmediateWriteWordsWrapper::ImmediateWriteWordsWrapper(EnumerateVocab *inner, int fd, uint64_t start) + : inner_(inner), stream_(SeekAndReturn(fd, start)) {} + +WriteWordsWrapper::WriteWordsWrapper(EnumerateVocab *inner) : inner_(inner) {} + +void WriteWordsWrapper::Add(WordIndex index, const StringPiece &str) { + if (inner_) inner_->Add(index, str); + buffer_.append(str.data(), str.size()); + buffer_.push_back(0); +} + +void WriteWordsWrapper::Write(int fd, uint64_t start) { + util::SeekOrThrow(fd, start); + util::WriteOrThrow(fd, buffer_.data(), buffer_.size()); + // Free memory from the string. + std::string for_swap; + std::swap(buffer_, for_swap); +} + +SortedVocabulary::SortedVocabulary() : begin_(NULL), end_(NULL), enumerate_(NULL) {} + +uint64_t SortedVocabulary::Size(uint64_t entries, const Config &/*config*/) { + // Lead with the number of entries. + return sizeof(uint64_t) + sizeof(uint64_t) * entries; +} + +void SortedVocabulary::SetupMemory(void *start, std::size_t allocated, std::size_t entries, const Config &config) { + assert(allocated >= Size(entries, config)); + // Leave space for number of entries. + begin_ = reinterpret_cast(start) + 1; + end_ = begin_; + saw_unk_ = false; +} + +void SortedVocabulary::Relocate(void *new_start) { + std::size_t delta = end_ - begin_; + begin_ = reinterpret_cast(new_start) + 1; + end_ = begin_ + delta; +} + +void SortedVocabulary::ConfigureEnumerate(EnumerateVocab *to, std::size_t max_entries) { + enumerate_ = to; + if (enumerate_) { + enumerate_->Add(0, ""); + strings_to_enumerate_.resize(max_entries); + } +} + +WordIndex SortedVocabulary::Insert(const StringPiece &str) { + uint64_t hashed = detail::HashForVocab(str); + if (hashed == kUnknownHash || hashed == kUnknownCapHash) { + saw_unk_ = true; + return 0; + } + *end_ = hashed; + if (enumerate_) { + void *copied = string_backing_.Allocate(str.size()); + memcpy(copied, str.data(), str.size()); + strings_to_enumerate_[end_ - begin_] = StringPiece(static_cast(copied), str.size()); + } + ++end_; + // This is 1 + the offset where it was inserted to make room for unk. + return end_ - begin_; +} + +void SortedVocabulary::FinishedLoading(ProbBackoff *reorder) { + GenericFinished(reorder); +} + +namespace { +#pragma pack(push) +#pragma pack(4) +struct RenumberEntry { + uint64_t hash; + const char *str; + WordIndex old; + bool operator<(const RenumberEntry &other) const { + return hash < other.hash; + } +}; +#pragma pack(pop) +} // namespace + +void SortedVocabulary::ComputeRenumbering(WordIndex types, int from_words, int to_words, std::vector &mapping) { + mapping.clear(); + uint64_t file_size = util::SizeOrThrow(from_words); + util::scoped_memory strings; + util::MapRead(util::POPULATE_OR_READ, from_words, 0, file_size, strings); + const char *const start = static_cast(strings.get()); + UTIL_THROW_IF(memcmp(start, "", 6), FormatLoadException, "Vocab file does not begin with followed by null"); + std::vector entries; + entries.reserve(types - 1); + RenumberEntry entry; + entry.old = 1; + for (entry.str = start + 6 /* skip \0 */; entry.str < start + file_size; ++entry.old) { + StringPiece str(entry.str, strlen(entry.str)); + entry.hash = detail::HashForVocab(str); + entries.push_back(entry); + entry.str += str.size() + 1; + } + UTIL_THROW_IF2(entries.size() != types - 1, "Wrong number of vocab ids. Got " << (entries.size() + 1) << " expected " << types); + std::sort(entries.begin(), entries.end()); + // Write out new vocab file. + { + util::FileStream out(to_words); + out << "" << '\0'; + for (std::vector::const_iterator i = entries.begin(); i != entries.end(); ++i) { + out << i->str << '\0'; + } + } + strings.reset(); + + mapping.resize(types); + mapping[0] = 0; // + for (std::vector::const_iterator i = entries.begin(); i != entries.end(); ++i) { + mapping[i->old] = i + 1 - entries.begin(); + } +} + +void SortedVocabulary::Populated() { + saw_unk_ = true; + SetSpecial(Index(""), Index(""), 0); + bound_ = end_ - begin_ + 1; + *(reinterpret_cast(begin_) - 1) = end_ - begin_; +} + +void SortedVocabulary::LoadedBinary(bool have_words, int fd, EnumerateVocab *to, uint64_t offset) { + end_ = begin_ + *(reinterpret_cast(begin_) - 1); + SetSpecial(Index(""), Index(""), 0); + bound_ = end_ - begin_ + 1; + if (have_words) ReadWords(fd, to, bound_, offset); +} + +template void SortedVocabulary::GenericFinished(T *reorder) { + if (enumerate_) { + if (!strings_to_enumerate_.empty()) { + util::PairedIterator values(reorder + 1, &*strings_to_enumerate_.begin()); + util::JointSort(begin_, end_, values); + } + for (WordIndex i = 0; i < static_cast(end_ - begin_); ++i) { + // strikes again: +1 here. + enumerate_->Add(i + 1, strings_to_enumerate_[i]); + } + strings_to_enumerate_.clear(); + string_backing_.FreeAll(); + } else { + util::JointSort(begin_, end_, reorder + 1); + } + SetSpecial(Index(""), Index(""), 0); + // Save size. Excludes UNK. + *(reinterpret_cast(begin_) - 1) = end_ - begin_; + // Includes UNK. + bound_ = end_ - begin_ + 1; +} + +namespace { +const unsigned int kProbingVocabularyVersion = 0; +} // namespace + +namespace detail { +struct ProbingVocabularyHeader { + // Lowest unused vocab id. This is also the number of words, including . + unsigned int version; + WordIndex bound; +}; +} // namespace detail + +ProbingVocabulary::ProbingVocabulary() : enumerate_(NULL) {} + +uint64_t ProbingVocabulary::Size(uint64_t entries, float probing_multiplier) { + return ALIGN8(sizeof(detail::ProbingVocabularyHeader)) + Lookup::Size(entries, probing_multiplier); +} + +uint64_t ProbingVocabulary::Size(uint64_t entries, const Config &config) { + return Size(entries, config.probing_multiplier); +} + +void ProbingVocabulary::SetupMemory(void *start, std::size_t allocated) { + header_ = static_cast(start); + lookup_ = Lookup(static_cast(start) + ALIGN8(sizeof(detail::ProbingVocabularyHeader)), allocated); + bound_ = 1; + saw_unk_ = false; +} + +void ProbingVocabulary::Relocate(void *new_start) { + header_ = static_cast(new_start); + lookup_.Relocate(static_cast(new_start) + ALIGN8(sizeof(detail::ProbingVocabularyHeader))); +} + +void ProbingVocabulary::ConfigureEnumerate(EnumerateVocab *to, std::size_t /*max_entries*/) { + enumerate_ = to; + if (enumerate_) { + enumerate_->Add(0, ""); + } +} + +WordIndex ProbingVocabulary::Insert(const StringPiece &str) { + uint64_t hashed = detail::HashForVocab(str); + // Prevent unknown from going into the table. + if (hashed == kUnknownHash || hashed == kUnknownCapHash) { + saw_unk_ = true; + return 0; + } else { + if (enumerate_) enumerate_->Add(bound_, str); + lookup_.Insert(ProbingVocabularyEntry::Make(hashed, bound_)); + return bound_++; + } +} + +void ProbingVocabulary::InternalFinishedLoading() { + lookup_.FinishedInserting(); + header_->bound = bound_; + header_->version = kProbingVocabularyVersion; + SetSpecial(Index(""), Index(""), 0); +} + +void ProbingVocabulary::LoadedBinary(bool have_words, int fd, EnumerateVocab *to, uint64_t offset) { + UTIL_THROW_IF(header_->version != kProbingVocabularyVersion, FormatLoadException, "The binary file has probing version " << header_->version << " but the code expects version " << kProbingVocabularyVersion << ". Please rerun build_binary using the same version of the code."); + bound_ = header_->bound; + SetSpecial(Index(""), Index(""), 0); + if (have_words) ReadWords(fd, to, bound_, offset); +} + +void MissingUnknown(const Config &config) noexcept(false) { + switch(config.unknown_missing) { + case SILENT: + return; + case COMPLAIN: + if (config.messages) *config.messages << "The ARPA file is missing . Substituting log10 probability " << config.unknown_missing_logprob << "." << std::endl; + break; + case THROW_UP: + UTIL_THROW(SpecialWordMissingException, "The ARPA file is missing and the model is configured to throw an exception."); + } +} + +void MissingSentenceMarker(const Config &config, const char *str) noexcept(false) { + switch (config.sentence_marker_missing) { + case SILENT: + return; + case COMPLAIN: + if (config.messages) *config.messages << "Missing special word " << str << "; will treat it as ."; + break; + case THROW_UP: + UTIL_THROW(SpecialWordMissingException, "The ARPA file is missing " << str << " and the model is configured to reject these models. Run build_binary -s to disable this check."); + } +} + +} // namespace ngram +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/vocab.hh b/ctcdecode-master/third_party/kenlm/lm/vocab.hh new file mode 100644 index 0000000..2e0fdd4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/vocab.hh @@ -0,0 +1,283 @@ +#ifndef LM_VOCAB_H +#define LM_VOCAB_H + +#include "lm/enumerate_vocab.hh" +#include "lm/lm_exception.hh" +#include "lm/virtual_interface.hh" +#include "util/file_stream.hh" +#include "util/murmur_hash.hh" +#include "util/pool.hh" +#include "util/probing_hash_table.hh" +#include "util/sorted_uniform.hh" +#include "util/string_piece.hh" + +#include +#include +#include + +namespace lm { +struct ProbBackoff; +class EnumerateVocab; + +namespace ngram { +struct Config; + +namespace detail { +uint64_t HashForVocab(const char *str, std::size_t len); +inline uint64_t HashForVocab(const StringPiece &str) { + return HashForVocab(str.data(), str.length()); +} +struct ProbingVocabularyHeader; +} // namespace detail + +// Writes words immediately to a file instead of buffering, because we know +// where in the file to put them. +class ImmediateWriteWordsWrapper : public EnumerateVocab { + public: + ImmediateWriteWordsWrapper(EnumerateVocab *inner, int fd, uint64_t start); + + void Add(WordIndex index, const StringPiece &str) { + stream_ << str << '\0'; + if (inner_) inner_->Add(index, str); + } + + private: + EnumerateVocab *inner_; + + util::FileStream stream_; +}; + +// When the binary size isn't known yet. +class WriteWordsWrapper : public EnumerateVocab { + public: + WriteWordsWrapper(EnumerateVocab *inner); + + void Add(WordIndex index, const StringPiece &str); + + const std::string &Buffer() const { return buffer_; } + void Write(int fd, uint64_t start); + + private: + EnumerateVocab *inner_; + + std::string buffer_; +}; + +// Vocabulary based on sorted uniform find storing only uint64_t values and using their offsets as indices. +class SortedVocabulary : public base::Vocabulary { + public: + SortedVocabulary(); + + WordIndex Index(const StringPiece &str) const { + const uint64_t *found; + if (util::BoundedSortedUniformFind, util::Pivot64>( + util::IdentityAccessor(), + begin_ - 1, 0, + end_, std::numeric_limits::max(), + detail::HashForVocab(str), found)) { + return found - begin_ + 1; // +1 because is 0 and does not appear in the lookup table. + } else { + return 0; + } + } + + // Size for purposes of file writing + static uint64_t Size(uint64_t entries, const Config &config); + + /* Read null-delimited words from file from_words, renumber according to + * hash order, write null-delimited words to to_words, and create a mapping + * from old id to new id. The 0th vocab word must be . + */ + static void ComputeRenumbering(WordIndex types, int from_words, int to_words, std::vector &mapping); + + // Vocab words are [0, Bound()) Only valid after FinishedLoading/LoadedBinary. + WordIndex Bound() const { return bound_; } + + // Everything else is for populating. I'm too lazy to hide and friend these, but you'll only get a const reference anyway. + void SetupMemory(void *start, std::size_t allocated, std::size_t entries, const Config &config); + + void Relocate(void *new_start); + + void ConfigureEnumerate(EnumerateVocab *to, std::size_t max_entries); + + // Insert and FinishedLoading go together. + WordIndex Insert(const StringPiece &str); + // Reorders reorder_vocab so that the IDs are sorted. + void FinishedLoading(ProbBackoff *reorder_vocab); + + // Trie stores the correct counts including in the header. If this was previously sized based on a count exluding , padding with 8 bytes will make it the correct size based on a count including . + std::size_t UnkCountChangePadding() const { return SawUnk() ? 0 : sizeof(uint64_t); } + + bool SawUnk() const { return saw_unk_; } + + void LoadedBinary(bool have_words, int fd, EnumerateVocab *to, uint64_t offset); + + uint64_t *&EndHack() { return end_; } + + void Populated(); + + private: + template void GenericFinished(T *reorder); + + uint64_t *begin_, *end_; + + WordIndex bound_; + + bool saw_unk_; + + EnumerateVocab *enumerate_; + + // Actual strings. Used only when loading from ARPA and enumerate_ != NULL + util::Pool string_backing_; + + std::vector strings_to_enumerate_; +}; + +#pragma pack(push) +#pragma pack(4) +struct ProbingVocabularyEntry { + uint64_t key; + WordIndex value; + + typedef uint64_t Key; + uint64_t GetKey() const { return key; } + void SetKey(uint64_t to) { key = to; } + + static ProbingVocabularyEntry Make(uint64_t key, WordIndex value) { + ProbingVocabularyEntry ret; + ret.key = key; + ret.value = value; + return ret; + } +}; +#pragma pack(pop) + +// Vocabulary storing a map from uint64_t to WordIndex. +class ProbingVocabulary : public base::Vocabulary { + public: + ProbingVocabulary(); + + WordIndex Index(const StringPiece &str) const { + Lookup::ConstIterator i; + return lookup_.Find(detail::HashForVocab(str), i) ? i->value : 0; + } + + static uint64_t Size(uint64_t entries, float probing_multiplier); + // This just unwraps Config to get the probing_multiplier. + static uint64_t Size(uint64_t entries, const Config &config); + + // Vocab words are [0, Bound()). + WordIndex Bound() const { return bound_; } + + // Everything else is for populating. I'm too lazy to hide and friend these, but you'll only get a const reference anyway. + void SetupMemory(void *start, std::size_t allocated); + void SetupMemory(void *start, std::size_t allocated, std::size_t /*entries*/, const Config &/*config*/) { + SetupMemory(start, allocated); + } + + void Relocate(void *new_start); + + void ConfigureEnumerate(EnumerateVocab *to, std::size_t max_entries); + + WordIndex Insert(const StringPiece &str); + + template void FinishedLoading(Weights * /*reorder_vocab*/) { + InternalFinishedLoading(); + } + + std::size_t UnkCountChangePadding() const { return 0; } + + bool SawUnk() const { return saw_unk_; } + + void LoadedBinary(bool have_words, int fd, EnumerateVocab *to, uint64_t offset); + + private: + void InternalFinishedLoading(); + + typedef util::ProbingHashTable Lookup; + + Lookup lookup_; + + WordIndex bound_; + + bool saw_unk_; + + EnumerateVocab *enumerate_; + + detail::ProbingVocabularyHeader *header_; +}; + +void MissingUnknown(const Config &config) noexcept(false); +void MissingSentenceMarker(const Config &config, const char *str) noexcept(false); + +template void CheckSpecials(const Config &config, const Vocab &vocab) noexcept(false) { + if (!vocab.SawUnk()) MissingUnknown(config); + if (vocab.BeginSentence() == vocab.NotFound()) MissingSentenceMarker(config, ""); + if (vocab.EndSentence() == vocab.NotFound()) MissingSentenceMarker(config, ""); +} + +class WriteUniqueWords { + public: + explicit WriteUniqueWords(int fd) : word_list_(fd) {} + + void operator()(const StringPiece &word) { + word_list_ << word << '\0'; + } + + private: + util::FileStream word_list_; +}; + +class NoOpUniqueWords { + public: + NoOpUniqueWords() {} + void operator()(const StringPiece &word) {} +}; + +template class GrowableVocab { + public: + static std::size_t MemUsage(WordIndex content) { + return Lookup::MemUsage(content > 2 ? content : 2); + } + + // Does not take ownership of new_word_construct + template GrowableVocab(WordIndex initial_size, const NewWordConstruct &new_word_construct = NewWordAction()) + : lookup_(initial_size), new_word_(new_word_construct) { + FindOrInsert(""); // Force 0 + FindOrInsert(""); // Force 1 + FindOrInsert(""); // Force 2 + } + + WordIndex Index(const StringPiece &str) const { + Lookup::ConstIterator i; + return lookup_.Find(detail::HashForVocab(str), i) ? i->value : 0; + } + + WordIndex FindOrInsert(const StringPiece &word) { + ProbingVocabularyEntry entry = ProbingVocabularyEntry::Make(util::MurmurHashNative(word.data(), word.size()), Size()); + Lookup::MutableIterator it; + if (!lookup_.FindOrInsert(entry, it)) { + new_word_(word); + UTIL_THROW_IF(Size() >= std::numeric_limits::max(), VocabLoadException, "Too many vocabulary words. Change WordIndex to uint64_t in lm/word_index.hh"); + } + return it->value; + } + + WordIndex Size() const { return lookup_.Size(); } + + bool IsSpecial(WordIndex word) const { + return word <= 2; + } + + private: + typedef util::AutoProbing Lookup; + + Lookup lookup_; + + NewWordAction new_word_; +}; + +} // namespace ngram +} // namespace lm + +#endif // LM_VOCAB_H diff --git a/ctcdecode-master/third_party/kenlm/lm/weights.hh b/ctcdecode-master/third_party/kenlm/lm/weights.hh new file mode 100644 index 0000000..f143127 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/weights.hh @@ -0,0 +1,22 @@ +#ifndef LM_WEIGHTS_H +#define LM_WEIGHTS_H + +// Weights for n-grams. Probability and possibly a backoff. + +namespace lm { +struct Prob { + float prob; +}; +// No inheritance so this will be a POD. +struct ProbBackoff { + float prob; + float backoff; +}; +struct RestWeights { + float prob; + float backoff; + float rest; +}; + +} // namespace lm +#endif // LM_WEIGHTS_H diff --git a/ctcdecode-master/third_party/kenlm/lm/word_index.hh b/ctcdecode-master/third_party/kenlm/lm/word_index.hh new file mode 100644 index 0000000..59b24d7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/word_index.hh @@ -0,0 +1,15 @@ +// Separate header because this is used often. +#ifndef LM_WORD_INDEX_H +#define LM_WORD_INDEX_H + +#include + +namespace lm { +typedef unsigned int WordIndex; +const WordIndex kMaxWordIndex = UINT_MAX; +const WordIndex kUNK = 0; +} // namespace lm + +typedef lm::WordIndex LMWordIndex; + +#endif diff --git a/ctcdecode-master/third_party/kenlm/lm/wrappers/README b/ctcdecode-master/third_party/kenlm/lm/wrappers/README new file mode 100644 index 0000000..56c34c2 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/wrappers/README @@ -0,0 +1,3 @@ +This directory is for wrappers around other people's LMs, presenting an interface similar to KenLM's. You will need to have their LM installed. + +NPLM is a work in progress. diff --git a/ctcdecode-master/third_party/kenlm/lm/wrappers/nplm.cc b/ctcdecode-master/third_party/kenlm/lm/wrappers/nplm.cc new file mode 100644 index 0000000..9bd7c1e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/wrappers/nplm.cc @@ -0,0 +1,116 @@ +#include "lm/wrappers/nplm.hh" +#include "util/exception.hh" +#include "util/file.hh" + +#include +#include + +#include "neuralLM.h" + +namespace lm { +namespace np { + +Vocabulary::Vocabulary(const nplm::vocabulary &vocab) + : base::Vocabulary(vocab.lookup_word(""), vocab.lookup_word(""), vocab.lookup_word("")), + vocab_(vocab), null_word_(vocab.lookup_word("")) {} + +Vocabulary::~Vocabulary() {} + +WordIndex Vocabulary::Index(const std::string &str) const { + return vocab_.lookup_word(str); +} + +class Backend { + public: + Backend(const nplm::neuralLM &from, const std::size_t cache_size) : lm_(from), ngram_(from.get_order()) { + lm_.set_cache(cache_size); + } + + nplm::neuralLM &LM() { return lm_; } + const nplm::neuralLM &LM() const { return lm_; } + + Eigen::Matrix &staging_ngram() { return ngram_; } + + double lookup_from_staging() { return lm_.lookup_ngram(ngram_); } + + int order() const { return lm_.get_order(); } + + private: + nplm::neuralLM lm_; + Eigen::Matrix ngram_; +}; + +bool Model::Recognize(const std::string &name) { + try { + util::scoped_fd file(util::OpenReadOrThrow(name.c_str())); + char magic_check[16]; + util::ReadOrThrow(file.get(), magic_check, sizeof(magic_check)); + const char nnlm_magic[] = "\\config\nversion "; + return !memcmp(magic_check, nnlm_magic, 16); + } catch (const util::Exception &) { + return false; + } +} + +namespace { +nplm::neuralLM *LoadNPLM(const std::string &file) { + util::scoped_ptr ret(new nplm::neuralLM()); + ret->read(file); + return ret.release(); +} +} // namespace + +Model::Model(const std::string &file, std::size_t cache) + : base_instance_(LoadNPLM(file)), vocab_(base_instance_->get_vocabulary()), cache_size_(cache) { + UTIL_THROW_IF(base_instance_->get_order() > NPLM_MAX_ORDER, util::Exception, "This NPLM has order " << (unsigned int)base_instance_->get_order() << " but the KenLM wrapper was compiled with " << NPLM_MAX_ORDER << ". Change the defintion of NPLM_MAX_ORDER and recompile."); + // log10 compatible with backoff models. + base_instance_->set_log_base(10.0); + State begin_sentence, null_context; + std::fill(begin_sentence.words, begin_sentence.words + NPLM_MAX_ORDER - 1, base_instance_->lookup_word("")); + null_word_ = base_instance_->lookup_word(""); + std::fill(null_context.words, null_context.words + NPLM_MAX_ORDER - 1, null_word_); + + Init(begin_sentence, null_context, vocab_, base_instance_->get_order()); +} + +Model::~Model() {} + +FullScoreReturn Model::FullScore(const State &from, const WordIndex new_word, State &out_state) const { + Backend *backend = backend_.get(); + if (!backend) { + backend = new Backend(*base_instance_, cache_size_); + backend_.reset(backend); + } + // State is in natural word order. + FullScoreReturn ret; + for (int i = 0; i < backend->order() - 1; ++i) { + backend->staging_ngram()(i) = from.words[i]; + } + backend->staging_ngram()(backend->order() - 1) = new_word; + ret.prob = backend->lookup_from_staging(); + // Always say full order. + ret.ngram_length = backend->order(); + // Shift everything down by one. + memcpy(out_state.words, from.words + 1, sizeof(WordIndex) * (backend->order() - 2)); + out_state.words[backend->order() - 2] = new_word; + // Fill in trailing words with zeros so state comparison works. + memset(out_state.words + backend->order() - 1, 0, sizeof(WordIndex) * (NPLM_MAX_ORDER - backend->order())); + return ret; +} + +// TODO: optimize with direct call? +FullScoreReturn Model::FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const { + // State is in natural word order. The API here specifies reverse order. + std::size_t state_length = std::min(Order() - 1, context_rend - context_rbegin); + State state; + // Pad with null words. + for (lm::WordIndex *i = state.words; i < state.words + Order() - 1 - state_length; ++i) { + *i = null_word_; + } + // Put new words at the end. + std::reverse_copy(context_rbegin, context_rbegin + state_length, state.words + Order() - 1 - state_length); + return FullScore(state, new_word, out_state); +} + +} // namespace np +} // namespace lm diff --git a/ctcdecode-master/third_party/kenlm/lm/wrappers/nplm.hh b/ctcdecode-master/third_party/kenlm/lm/wrappers/nplm.hh new file mode 100644 index 0000000..82b38fd --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/lm/wrappers/nplm.hh @@ -0,0 +1,85 @@ +#ifndef LM_WRAPPERS_NPLM_H +#define LM_WRAPPERS_NPLM_H + +#include "lm/facade.hh" +#include "lm/max_order.hh" +#include "util/string_piece.hh" + +#include +#include + +/* Wrapper to NPLM "by Ashish Vaswani, with contributions from David Chiang + * and Victoria Fossum." + * http://nlg.isi.edu/software/nplm/ + */ + +namespace nplm { +class vocabulary; +class neuralLM; +} // namespace nplm + +namespace lm { +namespace np { + +class Vocabulary : public base::Vocabulary { + public: + Vocabulary(const nplm::vocabulary &vocab); + + ~Vocabulary(); + + WordIndex Index(const std::string &str) const; + + // TODO: lobby them to support StringPiece + WordIndex Index(const StringPiece &str) const { + return Index(std::string(str.data(), str.size())); + } + + lm::WordIndex NullWord() const { return null_word_; } + + private: + const nplm::vocabulary &vocab_; + + const lm::WordIndex null_word_; +}; + +// Sorry for imposing my limitations on your code. +#define NPLM_MAX_ORDER 7 + +struct State { + WordIndex words[NPLM_MAX_ORDER - 1]; +}; + +class Backend; + +class Model : public lm::base::ModelFacade { + private: + typedef lm::base::ModelFacade P; + + public: + // Does this look like an NPLM? + static bool Recognize(const std::string &file); + + explicit Model(const std::string &file, std::size_t cache_size = 1 << 20); + + ~Model(); + + FullScoreReturn FullScore(const State &from, const WordIndex new_word, State &out_state) const; + + FullScoreReturn FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const; + + private: + boost::scoped_ptr base_instance_; + + mutable boost::thread_specific_ptr backend_; + + Vocabulary vocab_; + + lm::WordIndex null_word_; + + const std::size_t cache_size_; +}; + +} // namespace np +} // namespace lm + +#endif // LM_WRAPPERS_NPLM_H diff --git a/ctcdecode-master/third_party/kenlm/python/_kenlm.pxd b/ctcdecode-master/third_party/kenlm/python/_kenlm.pxd new file mode 100644 index 0000000..57b056f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/python/_kenlm.pxd @@ -0,0 +1,50 @@ +cdef extern from "lm/word_index.hh" namespace "lm": + ctypedef unsigned WordIndex + +cdef extern from "lm/return.hh" namespace "lm": + cdef struct FullScoreReturn: + float prob + unsigned char ngram_length + +cdef extern from "lm/state.hh" namespace "lm::ngram": + cdef cppclass State : + int Compare(const State &other) const + + int hash_value(const State &state) + +cdef extern from "lm/virtual_interface.hh" namespace "lm::base": + cdef cppclass Vocabulary: + WordIndex Index(char*) + WordIndex BeginSentence() + WordIndex EndSentence() + WordIndex NotFound() + + ctypedef Vocabulary const_Vocabulary "const lm::base::Vocabulary" + + cdef cppclass Model: + void BeginSentenceWrite(void *) + void NullContextWrite(void *) + unsigned int Order() + const_Vocabulary& BaseVocabulary() + float BaseScore(void *in_state, WordIndex new_word, void *out_state) + FullScoreReturn BaseFullScore(void *in_state, WordIndex new_word, void *out_state) + +cdef extern from "util/mmap.hh" namespace "util": + cdef enum LoadMethod: + LAZY + POPULATE_OR_LAZY + POPULATE_OR_READ + READ + PARALLEL_READ + +cdef extern from "lm/config.hh" namespace "lm::ngram": + cdef cppclass Config: + Config() + float probing_multiplier + LoadMethod load_method + +cdef extern from "lm/model.hh" namespace "lm::ngram": + cdef Model *LoadVirtual(char *, Config &config) except + + #default constructor + cdef Model *LoadVirtual(char *) except + + diff --git a/ctcdecode-master/third_party/kenlm/python/example.py b/ctcdecode-master/third_party/kenlm/python/example.py new file mode 100644 index 0000000..7cfc153 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/python/example.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python +import os +import kenlm + +LM = os.path.join(os.path.dirname(__file__), '..', 'lm', 'test.arpa') +model = kenlm.Model(LM) +print('{0}-gram model'.format(model.order)) + +sentence = 'language modeling is fun .' +print(sentence) +print(model.score(sentence)) + +# Check that total full score = direct score +def score(s): + return sum(prob for prob, _, _ in model.full_scores(s)) + +assert (abs(score(sentence) - model.score(sentence)) < 1e-3) + +# Show scores and n-gram matches +words = [''] + sentence.split() + [''] +for i, (prob, length, oov) in enumerate(model.full_scores(sentence)): + print('{0} {1}: {2}'.format(prob, length, ' '.join(words[i+2-length:i+2]))) + if oov: + print('\t"{0}" is an OOV'.format(words[i+1])) + +# Find out-of-vocabulary words +for w in words: + if not w in model: + print('"{0}" is an OOV'.format(w)) + +#Stateful query +state = kenlm.State() +state2 = kenlm.State() +#Use as context. If you don't want , use model.NullContextWrite(state). +model.BeginSentenceWrite(state) +accum = 0.0 +accum += model.BaseScore(state, "a", state2) +accum += model.BaseScore(state2, "sentence", state) +#score defaults to bos = True and eos = True. Here we'll check without the end +#of sentence marker. +assert (abs(accum - model.score("a sentence", eos = False)) < 1e-3) +accum += model.BaseScore(state, "", state2) +assert (abs(accum - model.score("a sentence")) < 1e-3) diff --git a/ctcdecode-master/third_party/kenlm/python/kenlm.cpp b/ctcdecode-master/third_party/kenlm/python/kenlm.cpp new file mode 100644 index 0000000..0b4467d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/python/kenlm.cpp @@ -0,0 +1,8469 @@ +/* Generated by Cython 0.25.2 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_25_2" +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef __cplusplus + #error "Cython files generated with the C++ option must be compiled with a C++ compiler." +#endif +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #else + #define CYTHON_INLINE inline + #endif +#endif +template +void __Pyx_call_destructor(T& x) { + x.~T(); +} +template +class __Pyx_FakeReference { + public: + __Pyx_FakeReference() : ptr(NULL) { } + __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } + T *operator->() { return ptr; } + T *operator&() { return ptr; } + operator T&() { return *ptr; } + template bool operator ==(U other) { return *ptr == other; } + template bool operator !=(U other) { return *ptr != other; } + private: + T *ptr; +}; + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__kenlm +#define __PYX_HAVE_API__kenlm +#include "lm/word_index.hh" +#include "lm/return.hh" +#include "lm/state.hh" +#include "lm/virtual_interface.hh" +#include "util/mmap.hh" +#include "lm/config.hh" +#include "lm/model.hh" +#include "ios" +#include "new" +#include "stdexcept" +#include "typeinfo" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + + +static const char *__pyx_f[] = { + "kenlm.pyx", +}; + +/*--- Type declarations ---*/ +struct __pyx_obj_5kenlm_FullScoreReturn; +struct __pyx_obj_5kenlm_State; +struct __pyx_obj_5kenlm_Config; +struct __pyx_obj_5kenlm_Model; +struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores; + +/* "kenlm.pyx":11 + * raise TypeError('Cannot convert %s to string' % type(data)) + * + * cdef class FullScoreReturn: # <<<<<<<<<<<<<< + * """ + * Wrapper around FullScoreReturn. + */ +struct __pyx_obj_5kenlm_FullScoreReturn { + PyObject_HEAD + float log_prob; + int ngram_length; + int oov; +}; + + +/* "kenlm.pyx":44 + * return self.oov + * + * cdef class State: # <<<<<<<<<<<<<< + * """ + * Wrapper around lm::ngram::State so that python code can make incremental queries. + */ +struct __pyx_obj_5kenlm_State { + PyObject_HEAD + lm::ngram::State _c_state; +}; + + +/* "kenlm.pyx":80 + * PARALLEL_READ = _kenlm.PARALLEL_READ + * + * cdef class Config: # <<<<<<<<<<<<<< + * """ + * Wrapper around lm::ngram::Config. + */ +struct __pyx_obj_5kenlm_Config { + PyObject_HEAD + lm::ngram::Config _c_config; +}; + + +/* "kenlm.pyx":96 + * self._c_config.load_method = to + * + * cdef class Model: # <<<<<<<<<<<<<< + * """ + * Wrapper around lm::ngram::Model. + */ +struct __pyx_obj_5kenlm_Model { + PyObject_HEAD + lm::base::Model *model; + PyObject *path; + const lm::base::Vocabulary *vocab; +}; + + +/* "kenlm.pyx":190 + * return 10.0**(-self.score(sentence) / words) + * + * def full_scores(self, sentence, bos = True, eos = True): # <<<<<<<<<<<<<< + * """ + * full_scores(sentence, bos = True, eos = Ture) -> generate full scores (prob, ngram length, oov) + */ +struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores { + PyObject_HEAD + PyObject *__pyx_v_bos; + PyObject *__pyx_v_eos; + lm::ngram::State __pyx_v_out_state; + struct lm::FullScoreReturn __pyx_v_ret; + struct __pyx_obj_5kenlm_Model *__pyx_v_self; + PyObject *__pyx_v_sentence; + lm::ngram::State __pyx_v_state; + float __pyx_v_total; + lm::WordIndex __pyx_v_wid; + PyObject *__pyx_v_word; + PyObject *__pyx_v_words; + PyObject *__pyx_t_0; + Py_ssize_t __pyx_t_1; +}; + + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = PyThreadState_GET(); +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#endif + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* ArgTypeTest.proto */ +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +/* KeywordStringCheck.proto */ +static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); + +/* GetModuleGlobalName.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* PyObjectCallNoArg.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); +#else +#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) +#endif + +/* IncludeStringH.proto */ +#include + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* CalculateMetaclass.proto */ +static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases); + +/* Py3ClassCreate.proto */ +static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname, + PyObject *mkw, PyObject *modname, PyObject *doc); +static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict, + PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass); + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* None.proto */ +#include + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__util_3a__3a_LoadMethod(enum util::LoadMethod value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CppExceptionConversion.proto */ +#ifndef __Pyx_CppExn2PyErr +#include +#include +#include +#include +static void __Pyx_CppExn2PyErr() { + try { + if (PyErr_Occurred()) + ; // let the latest Python exn pass through and ignore the current one + else + throw; + } catch (const std::bad_alloc& exn) { + PyErr_SetString(PyExc_MemoryError, exn.what()); + } catch (const std::bad_cast& exn) { + PyErr_SetString(PyExc_TypeError, exn.what()); + } catch (const std::bad_typeid& exn) { + PyErr_SetString(PyExc_TypeError, exn.what()); + } catch (const std::domain_error& exn) { + PyErr_SetString(PyExc_ValueError, exn.what()); + } catch (const std::invalid_argument& exn) { + PyErr_SetString(PyExc_ValueError, exn.what()); + } catch (const std::ios_base::failure& exn) { + PyErr_SetString(PyExc_IOError, exn.what()); + } catch (const std::out_of_range& exn) { + PyErr_SetString(PyExc_IndexError, exn.what()); + } catch (const std::overflow_error& exn) { + PyErr_SetString(PyExc_OverflowError, exn.what()); + } catch (const std::range_error& exn) { + PyErr_SetString(PyExc_ArithmeticError, exn.what()); + } catch (const std::underflow_error& exn) { + PyErr_SetString(PyExc_ArithmeticError, exn.what()); + } catch (const std::exception& exn) { + PyErr_SetString(PyExc_RuntimeError, exn.what()); + } + catch (...) + { + PyErr_SetString(PyExc_RuntimeError, "Unknown exception"); + } +} +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_char(unsigned char value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE enum util::LoadMethod __Pyx_PyInt_As_enum__util_3a__3a_LoadMethod(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* FetchCommonType.proto */ +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* PyObjectCallMethod1.proto */ +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg); + +/* CoroutineBase.proto */ +typedef PyObject *(*__pyx_coroutine_body_t)(PyObject *, PyObject *); +typedef struct { + PyObject_HEAD + __pyx_coroutine_body_t body; + PyObject *closure; + PyObject *exc_type; + PyObject *exc_value; + PyObject *exc_traceback; + PyObject *gi_weakreflist; + PyObject *classobj; + PyObject *yieldfrom; + PyObject *gi_name; + PyObject *gi_qualname; + PyObject *gi_modulename; + int resume_label; + char is_running; +} __pyx_CoroutineObject; +static __pyx_CoroutineObject *__Pyx__Coroutine_New( + PyTypeObject *type, __pyx_coroutine_body_t body, PyObject *closure, + PyObject *name, PyObject *qualname, PyObject *module_name); +static int __Pyx_Coroutine_clear(PyObject *self); +#if 1 || PY_VERSION_HEX < 0x030300B0 +static int __Pyx_PyGen_FetchStopIterationValue(PyObject **pvalue); +#else +#define __Pyx_PyGen_FetchStopIterationValue(pvalue) PyGen_FetchStopIterationValue(pvalue) +#endif + +/* PatchModuleWithCoroutine.proto */ +static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code); + +/* PatchGeneratorABC.proto */ +static int __Pyx_patch_abc(void); + +/* Generator.proto */ +#define __Pyx_Generator_USED +static PyTypeObject *__pyx_GeneratorType = 0; +#define __Pyx_Generator_CheckExact(obj) (Py_TYPE(obj) == __pyx_GeneratorType) +#define __Pyx_Generator_New(body, closure, name, qualname, module_name)\ + __Pyx__Coroutine_New(__pyx_GeneratorType, body, closure, name, qualname, module_name) +static PyObject *__Pyx_Generator_Next(PyObject *self); +static int __pyx_Generator_init(void); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from '_kenlm' */ + +/* Module declarations from 'kenlm' */ +static PyTypeObject *__pyx_ptype_5kenlm_FullScoreReturn = 0; +static PyTypeObject *__pyx_ptype_5kenlm_State = 0; +static PyTypeObject *__pyx_ptype_5kenlm_Config = 0; +static PyTypeObject *__pyx_ptype_5kenlm_Model = 0; +static PyTypeObject *__pyx_ptype_5kenlm___pyx_scope_struct__full_scores = 0; +static PyObject *__pyx_f_5kenlm_as_str(PyObject *); /*proto*/ +#define __Pyx_MODULE_NAME "kenlm" +int __pyx_module_is_main_kenlm = 0; + +/* Implementation of 'kenlm' */ +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_IOError; +static const char __pyx_k__3[] = "\n"; +static const char __pyx_k__4[] = " "; +static const char __pyx_k_os[] = "os"; +static const char __pyx_k_bos[] = "bos"; +static const char __pyx_k_doc[] = "__doc__"; +static const char __pyx_k_eos[] = "eos"; +static const char __pyx_k_oov[] = "oov"; +static const char __pyx_k_LAZY[] = "LAZY"; +static const char __pyx_k_READ[] = "READ"; +static const char __pyx_k_args[] = "args"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_name[] = "__name__"; +static const char __pyx_k_path[] = "path"; +static const char __pyx_k_send[] = "send"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_utf8[] = "utf8"; +static const char __pyx_k_word[] = "word"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_close[] = "close"; +static const char __pyx_k_kenlm[] = "kenlm"; +static const char __pyx_k_score[] = "score"; +static const char __pyx_k_split[] = "split"; +static const char __pyx_k_throw[] = "throw"; +static const char __pyx_k_config[] = "config"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_module[] = "__module__"; +static const char __pyx_k_0_1_2_3[] = "{0}({1}, {2}, {3})"; +static const char __pyx_k_IOError[] = "IOError"; +static const char __pyx_k_abspath[] = "abspath"; +static const char __pyx_k_prepare[] = "__prepare__"; 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+} + +static PyMethodDef __pyx_methods_5kenlm_FullScoreReturn[] = { + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_5kenlm_FullScoreReturn[] = { + {(char *)"log_prob", __pyx_getprop_5kenlm_15FullScoreReturn_log_prob, 0, (char *)0, 0}, + {(char *)"ngram_length", __pyx_getprop_5kenlm_15FullScoreReturn_ngram_length, 0, (char *)0, 0}, + {(char *)"oov", __pyx_getprop_5kenlm_15FullScoreReturn_oov, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type_5kenlm_FullScoreReturn = { + PyVarObject_HEAD_INIT(0, 0) + "kenlm.FullScoreReturn", /*tp_name*/ + sizeof(struct __pyx_obj_5kenlm_FullScoreReturn), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_5kenlm_FullScoreReturn, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_pw_5kenlm_15FullScoreReturn_3__repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ + "\n Wrapper around FullScoreReturn.\n\n Notes:\n `prob` has been renamed to `log_prob`\n `oov` has been added to flag whether the word is OOV\n ", /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_5kenlm_FullScoreReturn, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_5kenlm_FullScoreReturn, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_5kenlm_FullScoreReturn, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyObject *__pyx_tp_new_5kenlm_State(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_obj_5kenlm_State *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_obj_5kenlm_State *)o); + new((void*)&(p->_c_state)) lm::ngram::State(); + return o; +} + +static void __pyx_tp_dealloc_5kenlm_State(PyObject *o) { + struct __pyx_obj_5kenlm_State *p = (struct __pyx_obj_5kenlm_State *)o; + #if PY_VERSION_HEX >= 0x030400a1 + if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + __Pyx_call_destructor(p->_c_state); + (*Py_TYPE(o)->tp_free)(o); +} + +static PyMethodDef __pyx_methods_5kenlm_State[] = { + {0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type_5kenlm_State = { + PyVarObject_HEAD_INIT(0, 0) + "kenlm.State", /*tp_name*/ + sizeof(struct __pyx_obj_5kenlm_State), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_5kenlm_State, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + __pyx_pw_5kenlm_5State_3__hash__, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ + "\n Wrapper around lm::ngram::State so that python code can make incremental queries.\n\n Notes:\n * rich comparisons \n * hashable\n ", /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + __pyx_pw_5kenlm_5State_1__richcmp__, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_5kenlm_State, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_5kenlm_State, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyObject *__pyx_tp_new_5kenlm_Config(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_obj_5kenlm_Config *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_obj_5kenlm_Config *)o); + new((void*)&(p->_c_config)) lm::ngram::Config(); + return o; +} + +static void __pyx_tp_dealloc_5kenlm_Config(PyObject *o) { + struct __pyx_obj_5kenlm_Config *p = (struct __pyx_obj_5kenlm_Config *)o; + #if PY_VERSION_HEX >= 0x030400a1 + if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + __Pyx_call_destructor(p->_c_config); + (*Py_TYPE(o)->tp_free)(o); +} + +static PyObject *__pyx_getprop_5kenlm_6Config_load_method(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_5kenlm_6Config_11load_method_1__get__(o); +} + +static int __pyx_setprop_5kenlm_6Config_load_method(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { + if (v) { + return __pyx_pw_5kenlm_6Config_11load_method_3__set__(o, v); + } + else { + PyErr_SetString(PyExc_NotImplementedError, "__del__"); + return -1; + } +} + +static PyMethodDef __pyx_methods_5kenlm_Config[] = { + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_5kenlm_Config[] = { + {(char *)"load_method", __pyx_getprop_5kenlm_6Config_load_method, __pyx_setprop_5kenlm_6Config_load_method, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type_5kenlm_Config = { + PyVarObject_HEAD_INIT(0, 0) + "kenlm.Config", /*tp_name*/ + sizeof(struct __pyx_obj_5kenlm_Config), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_5kenlm_Config, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ + "\n Wrapper around lm::ngram::Config.\n Pass this to Model's constructor to set the load_method.\n ", /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_5kenlm_Config, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_5kenlm_Config, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_pw_5kenlm_6Config_1__init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_5kenlm_Config, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyObject *__pyx_tp_new_5kenlm_Model(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_obj_5kenlm_Model *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_obj_5kenlm_Model *)o); + p->path = ((PyObject*)Py_None); Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_5kenlm_Model(PyObject *o) { + struct __pyx_obj_5kenlm_Model *p = (struct __pyx_obj_5kenlm_Model *)o; + #if PY_VERSION_HEX >= 0x030400a1 + if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_pw_5kenlm_5Model_3__dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->path); + (*Py_TYPE(o)->tp_free)(o); +} + +static PyObject *__pyx_getprop_5kenlm_5Model_order(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_5kenlm_5Model_5order_1__get__(o); +} + +static PyObject *__pyx_getprop_5kenlm_5Model_path(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_5kenlm_5Model_4path_1__get__(o); +} + +static int __pyx_setprop_5kenlm_5Model_path(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { + if (v) { + return __pyx_pw_5kenlm_5Model_4path_3__set__(o, v); + } + else { + return __pyx_pw_5kenlm_5Model_4path_5__del__(o); + } +} + +static PyMethodDef __pyx_methods_5kenlm_Model[] = { + {"score", (PyCFunction)__pyx_pw_5kenlm_5Model_5score, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5kenlm_5Model_4score}, + {"perplexity", (PyCFunction)__pyx_pw_5kenlm_5Model_7perplexity, METH_O, __pyx_doc_5kenlm_5Model_6perplexity}, + {"full_scores", (PyCFunction)__pyx_pw_5kenlm_5Model_9full_scores, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5kenlm_5Model_8full_scores}, + {"BeginSentenceWrite", (PyCFunction)__pyx_pw_5kenlm_5Model_12BeginSentenceWrite, METH_O, __pyx_doc_5kenlm_5Model_11BeginSentenceWrite}, + {"NullContextWrite", (PyCFunction)__pyx_pw_5kenlm_5Model_14NullContextWrite, METH_O, __pyx_doc_5kenlm_5Model_13NullContextWrite}, + {"BaseScore", (PyCFunction)__pyx_pw_5kenlm_5Model_16BaseScore, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5kenlm_5Model_15BaseScore}, + {"BaseFullScore", (PyCFunction)__pyx_pw_5kenlm_5Model_18BaseFullScore, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5kenlm_5Model_17BaseFullScore}, + {"__reduce__", (PyCFunction)__pyx_pw_5kenlm_5Model_24__reduce__, METH_NOARGS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_5kenlm_Model[] = { + {(char *)"order", __pyx_getprop_5kenlm_5Model_order, 0, (char *)0, 0}, + {(char *)"path", __pyx_getprop_5kenlm_5Model_path, __pyx_setprop_5kenlm_5Model_path, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_Model = { + 0, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + 0, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + __pyx_pw_5kenlm_5Model_20__contains__, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyTypeObject __pyx_type_5kenlm_Model = { + PyVarObject_HEAD_INIT(0, 0) + "kenlm.Model", /*tp_name*/ + sizeof(struct __pyx_obj_5kenlm_Model), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_5kenlm_Model, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_pw_5kenlm_5Model_22__repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_Model, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ + "\n Wrapper around lm::ngram::Model.\n ", /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_5kenlm_Model, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_5kenlm_Model, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_pw_5kenlm_5Model_1__init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_5kenlm_Model, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores *__pyx_freelist_5kenlm___pyx_scope_struct__full_scores[8]; +static int __pyx_freecount_5kenlm___pyx_scope_struct__full_scores = 0; + +static PyObject *__pyx_tp_new_5kenlm___pyx_scope_struct__full_scores(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores *p; + PyObject *o; + if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5kenlm___pyx_scope_struct__full_scores > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores)))) { + o = (PyObject*)__pyx_freelist_5kenlm___pyx_scope_struct__full_scores[--__pyx_freecount_5kenlm___pyx_scope_struct__full_scores]; + memset(o, 0, sizeof(struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores)); + (void) PyObject_INIT(o, t); + PyObject_GC_Track(o); + } else { + o = (*t->tp_alloc)(t, 0); + if (unlikely(!o)) return 0; + } + p = ((struct __pyx_obj_5kenlm___pyx_scope_struct__full_scores *)o); 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+ goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* RaiseArgTupleInvalid */ + static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ + static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ + static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +#include "frameobject.h" +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = PyThreadState_GET(); + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = f->f_localsplus; + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif // CPython < 3.6 +#endif // CYTHON_FAST_PYCALL + +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); +} +#endif // CYTHON_FAST_PYCCALL + +/* ArgTypeTest */ + static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +/* KeywordStringCheck */ + static CYTHON_INLINE int __Pyx_CheckKeywordStrings( + PyObject *kwdict, + const char* function_name, + int kw_allowed) +{ + PyObject* key = 0; + Py_ssize_t pos = 0; +#if CYTHON_COMPILING_IN_PYPY + if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0)) + goto invalid_keyword; + return 1; +#else + while (PyDict_Next(kwdict, &pos, &key, 0)) { + #if PY_MAJOR_VERSION < 3 + if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) + #endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } + if ((!kw_allowed) && unlikely(key)) + goto invalid_keyword; + return 1; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + return 0; +#endif +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif + return 0; +} + +/* GetModuleGlobalName */ + static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { + PyObject *exc_type = tstate->curexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + return PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { +#endif + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* PyObjectCallNoArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, NULL, 0); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); +} +#endif + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* CalculateMetaclass */ + static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) { + Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases); + for (i=0; i < nbases; i++) { + PyTypeObject *tmptype; + PyObject *tmp = PyTuple_GET_ITEM(bases, i); + tmptype = Py_TYPE(tmp); +#if PY_MAJOR_VERSION < 3 + if (tmptype == &PyClass_Type) + continue; +#endif + if (!metaclass) { + metaclass = tmptype; + continue; + } + if (PyType_IsSubtype(metaclass, tmptype)) + continue; + if (PyType_IsSubtype(tmptype, metaclass)) { + metaclass = tmptype; + continue; + } + PyErr_SetString(PyExc_TypeError, + "metaclass conflict: " + "the metaclass of a derived class " + "must be a (non-strict) subclass " + "of the metaclasses of all its bases"); + return NULL; + } + if (!metaclass) { +#if PY_MAJOR_VERSION < 3 + metaclass = &PyClass_Type; +#else + metaclass = &PyType_Type; +#endif + } + Py_INCREF((PyObject*) metaclass); + return (PyObject*) metaclass; +} + +/* Py3ClassCreate */ + static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, + PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) { + PyObject *ns; + if (metaclass) { + PyObject *prep = __Pyx_PyObject_GetAttrStr(metaclass, __pyx_n_s_prepare); + if (prep) { + PyObject *pargs = PyTuple_Pack(2, name, bases); + if (unlikely(!pargs)) { + Py_DECREF(prep); + return NULL; + } + ns = PyObject_Call(prep, pargs, mkw); + Py_DECREF(prep); + Py_DECREF(pargs); + } else { + if (unlikely(!PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + PyErr_Clear(); + ns = PyDict_New(); + } + } else { + ns = PyDict_New(); + } + if (unlikely(!ns)) + return NULL; + if (unlikely(PyObject_SetItem(ns, __pyx_n_s_module, modname) < 0)) goto bad; + if (unlikely(PyObject_SetItem(ns, __pyx_n_s_qualname, qualname) < 0)) goto bad; + if (unlikely(doc && PyObject_SetItem(ns, __pyx_n_s_doc, doc) < 0)) goto bad; + return ns; +bad: + Py_DECREF(ns); + return NULL; +} +static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, + PyObject *dict, PyObject *mkw, + int calculate_metaclass, int allow_py2_metaclass) { + PyObject *result, *margs; + PyObject *owned_metaclass = NULL; + if (allow_py2_metaclass) { + owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass); + if (owned_metaclass) { + metaclass = owned_metaclass; + } else if (likely(PyErr_ExceptionMatches(PyExc_KeyError))) { + PyErr_Clear(); + } else { + return NULL; + } + } + if (calculate_metaclass && (!metaclass || PyType_Check(metaclass))) { + metaclass = __Pyx_CalculateMetaclass((PyTypeObject*) metaclass, bases); + Py_XDECREF(owned_metaclass); + if (unlikely(!metaclass)) + return NULL; + owned_metaclass = metaclass; + } + margs = PyTuple_Pack(3, name, bases, dict); + if (unlikely(!margs)) { + result = NULL; + } else { + result = PyObject_Call(metaclass, margs, mkw); + Py_DECREF(margs); + } + Py_XDECREF(owned_metaclass); + return result; +} + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__util_3a__3a_LoadMethod(enum util::LoadMethod value) { + const enum util::LoadMethod neg_one = (enum util::LoadMethod) -1, const_zero = (enum util::LoadMethod) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum util::LoadMethod) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum util::LoadMethod) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum util::LoadMethod) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum util::LoadMethod) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum util::LoadMethod) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum util::LoadMethod), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) { + const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(unsigned int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(unsigned int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(unsigned int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(unsigned int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_char(unsigned char value) { + const unsigned char neg_one = (unsigned char) -1, const_zero = (unsigned char) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(unsigned char) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(unsigned char) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned char) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(unsigned char) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned char) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(unsigned char), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE enum util::LoadMethod __Pyx_PyInt_As_enum__util_3a__3a_LoadMethod(PyObject *x) { + const enum util::LoadMethod neg_one = (enum util::LoadMethod) -1, const_zero = (enum util::LoadMethod) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(enum util::LoadMethod) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (enum util::LoadMethod) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (enum util::LoadMethod) 0; + case 1: __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, digit, digits[0]) + case 2: + if (8 * sizeof(enum util::LoadMethod) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) >= 2 * PyLong_SHIFT) { + return (enum util::LoadMethod) (((((enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(enum util::LoadMethod) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) >= 3 * PyLong_SHIFT) { + return (enum util::LoadMethod) (((((((enum util::LoadMethod)digits[2]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(enum util::LoadMethod) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) >= 4 * PyLong_SHIFT) { + return (enum util::LoadMethod) (((((((((enum util::LoadMethod)digits[3]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[2]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (enum util::LoadMethod) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(enum util::LoadMethod) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(enum util::LoadMethod, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum util::LoadMethod) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(enum util::LoadMethod, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (enum util::LoadMethod) 0; + case -1: __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, digit, +digits[0]) + case -2: + if (8 * sizeof(enum util::LoadMethod) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) - 1 > 2 * PyLong_SHIFT) { + return (enum util::LoadMethod) (((enum util::LoadMethod)-1)*(((((enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(enum util::LoadMethod) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) - 1 > 2 * PyLong_SHIFT) { + return (enum util::LoadMethod) ((((((enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(enum util::LoadMethod) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) - 1 > 3 * PyLong_SHIFT) { + return (enum util::LoadMethod) (((enum util::LoadMethod)-1)*(((((((enum util::LoadMethod)digits[2]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(enum util::LoadMethod) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) - 1 > 3 * PyLong_SHIFT) { + return (enum util::LoadMethod) ((((((((enum util::LoadMethod)digits[2]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(enum util::LoadMethod) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) - 1 > 4 * PyLong_SHIFT) { + return (enum util::LoadMethod) (((enum util::LoadMethod)-1)*(((((((((enum util::LoadMethod)digits[3]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[2]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(enum util::LoadMethod) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(enum util::LoadMethod, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(enum util::LoadMethod) - 1 > 4 * PyLong_SHIFT) { + return (enum util::LoadMethod) ((((((((((enum util::LoadMethod)digits[3]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[2]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[1]) << PyLong_SHIFT) | (enum util::LoadMethod)digits[0]))); + } + } + break; + } +#endif + if (sizeof(enum util::LoadMethod) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(enum util::LoadMethod, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum util::LoadMethod) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(enum util::LoadMethod, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + enum util::LoadMethod val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (enum util::LoadMethod) -1; + } + } else { + enum util::LoadMethod val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (enum util::LoadMethod) -1; + val = __Pyx_PyInt_As_enum__util_3a__3a_LoadMethod(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to enum util::LoadMethod"); + return (enum util::LoadMethod) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to enum util::LoadMethod"); + return (enum util::LoadMethod) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* FetchCommonType */ + static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* fake_module; + PyTypeObject* cached_type = NULL; + fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); + if (!fake_module) return NULL; + Py_INCREF(fake_module); + cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); + if (cached_type) { + if (!PyType_Check((PyObject*)cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", + type->tp_name); + goto bad; + } + if (cached_type->tp_basicsize != type->tp_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + type->tp_name); + goto bad; + } + } else { + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; + } +done: + Py_DECREF(fake_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} + +/* SwapException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* PyObjectCallMethod1 */ + static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) { + PyObject *method, *result = NULL; + method = __Pyx_PyObject_GetAttrStr(obj, method_name); + if (unlikely(!method)) goto done; +#if CYTHON_UNPACK_METHODS + if (likely(PyMethod_Check(method))) { + PyObject *self = PyMethod_GET_SELF(method); + if (likely(self)) { + PyObject *args; + PyObject *function = PyMethod_GET_FUNCTION(method); + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(function)) { + PyObject *args[2] = {self, arg}; + result = __Pyx_PyFunction_FastCall(function, args, 2); + goto done; + } + #endif + #if CYTHON_FAST_PYCCALL + if (__Pyx_PyFastCFunction_Check(function)) { + PyObject *args[2] = {self, arg}; + result = __Pyx_PyCFunction_FastCall(function, args, 2); + goto done; + } + #endif + args = PyTuple_New(2); + if (unlikely(!args)) goto done; + Py_INCREF(self); + PyTuple_SET_ITEM(args, 0, self); + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 1, arg); + Py_INCREF(function); + Py_DECREF(method); method = NULL; + result = __Pyx_PyObject_Call(function, args, NULL); + Py_DECREF(args); + Py_DECREF(function); + return result; + } + } +#endif + result = __Pyx_PyObject_CallOneArg(method, arg); +done: + Py_XDECREF(method); + return result; +} + +/* CoroutineBase */ + #include +#include +static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value); +static PyObject *__Pyx_Coroutine_Close(PyObject *self); +static PyObject *__Pyx_Coroutine_Throw(PyObject *gen, PyObject *args); +#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom) +#if 1 || PY_VERSION_HEX < 0x030300B0 +static int __Pyx_PyGen_FetchStopIterationValue(PyObject **pvalue) { + PyObject *et, *ev, *tb; + PyObject *value = NULL; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&et, &ev, &tb); + if (!et) { + Py_XDECREF(tb); + Py_XDECREF(ev); + Py_INCREF(Py_None); + *pvalue = Py_None; + return 0; + } + if (likely(et == PyExc_StopIteration)) { + if (!ev) { + Py_INCREF(Py_None); + value = Py_None; + } +#if PY_VERSION_HEX >= 0x030300A0 + else if (Py_TYPE(ev) == (PyTypeObject*)PyExc_StopIteration) { + value = ((PyStopIterationObject *)ev)->value; + Py_INCREF(value); + Py_DECREF(ev); + } +#endif + else if (unlikely(PyTuple_Check(ev))) { + if (PyTuple_GET_SIZE(ev) >= 1) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + value = PyTuple_GET_ITEM(ev, 0); + Py_INCREF(value); +#else + value = PySequence_ITEM(ev, 0); +#endif + } else { + Py_INCREF(Py_None); + value = Py_None; + } + Py_DECREF(ev); + } + else if (!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) { + value = ev; + } + if (likely(value)) { + Py_XDECREF(tb); + Py_DECREF(et); + *pvalue = value; + return 0; + } + } else if (!PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) { + __Pyx_ErrRestore(et, ev, tb); + return -1; + } + PyErr_NormalizeException(&et, &ev, &tb); + if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) { + __Pyx_ErrRestore(et, ev, tb); + return -1; + } + Py_XDECREF(tb); + Py_DECREF(et); +#if PY_VERSION_HEX >= 0x030300A0 + value = ((PyStopIterationObject *)ev)->value; + Py_INCREF(value); + Py_DECREF(ev); +#else + { + PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args); + Py_DECREF(ev); + if (likely(args)) { + value = PySequence_GetItem(args, 0); + Py_DECREF(args); + } + if (unlikely(!value)) { + __Pyx_ErrRestore(NULL, NULL, NULL); + Py_INCREF(Py_None); + value = Py_None; + } + } +#endif + *pvalue = value; + return 0; +} +#endif +static CYTHON_INLINE +void __Pyx_Coroutine_ExceptionClear(__pyx_CoroutineObject *self) { + PyObject *exc_type = self->exc_type; + PyObject *exc_value = self->exc_value; + PyObject *exc_traceback = self->exc_traceback; + self->exc_type = NULL; + self->exc_value = NULL; + self->exc_traceback = NULL; + Py_XDECREF(exc_type); + Py_XDECREF(exc_value); + Py_XDECREF(exc_traceback); +} +static CYTHON_INLINE +int __Pyx_Coroutine_CheckRunning(__pyx_CoroutineObject *gen) { + if (unlikely(gen->is_running)) { + PyErr_SetString(PyExc_ValueError, + "generator already executing"); + return 1; + } + return 0; +} +static CYTHON_INLINE +PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value) { + PyObject *retval; + __Pyx_PyThreadState_declare + assert(!self->is_running); + if (unlikely(self->resume_label == 0)) { + if (unlikely(value && value != Py_None)) { + PyErr_SetString(PyExc_TypeError, + "can't send non-None value to a " + "just-started generator"); + return NULL; + } + } + if (unlikely(self->resume_label == -1)) { + PyErr_SetNone(PyExc_StopIteration); + return NULL; + } + __Pyx_PyThreadState_assign + if (value) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_PYSTON +#else + if (self->exc_traceback) { + PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback; + PyFrameObject *f = tb->tb_frame; + Py_XINCREF(__pyx_tstate->frame); + assert(f->f_back == NULL); + f->f_back = __pyx_tstate->frame; + } +#endif + __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value, + &self->exc_traceback); + } else { + __Pyx_Coroutine_ExceptionClear(self); + } + self->is_running = 1; + retval = self->body((PyObject *) self, value); + self->is_running = 0; + if (retval) { + __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value, + &self->exc_traceback); +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_PYSTON +#else + if (self->exc_traceback) { + PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback; + PyFrameObject *f = tb->tb_frame; + Py_CLEAR(f->f_back); + } +#endif + } else { + __Pyx_Coroutine_ExceptionClear(self); + } + return retval; +} +static CYTHON_INLINE +PyObject *__Pyx_Coroutine_MethodReturn(PyObject *retval) { + if (unlikely(!retval && !PyErr_Occurred())) { + PyErr_SetNone(PyExc_StopIteration); + } + return retval; +} +static CYTHON_INLINE +PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) { + PyObject *ret; + PyObject *val = NULL; + __Pyx_Coroutine_Undelegate(gen); + __Pyx_PyGen_FetchStopIterationValue(&val); + ret = __Pyx_Coroutine_SendEx(gen, val); + Py_XDECREF(val); + return ret; +} +static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) { + PyObject *retval; + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self; + PyObject *yf = gen->yieldfrom; + if (unlikely(__Pyx_Coroutine_CheckRunning(gen))) + return NULL; + if (yf) { + PyObject *ret; + gen->is_running = 1; + #ifdef __Pyx_Generator_USED + if (__Pyx_Generator_CheckExact(yf)) { + ret = __Pyx_Coroutine_Send(yf, value); + } else + #endif + #ifdef __Pyx_Coroutine_USED + if (__Pyx_Coroutine_CheckExact(yf)) { + ret = __Pyx_Coroutine_Send(yf, value); + } else + #endif + { + if (value == Py_None) + ret = Py_TYPE(yf)->tp_iternext(yf); + else + ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value); + } + gen->is_running = 0; + if (likely(ret)) { + return ret; + } + retval = __Pyx_Coroutine_FinishDelegation(gen); + } else { + retval = __Pyx_Coroutine_SendEx(gen, value); + } + return __Pyx_Coroutine_MethodReturn(retval); +} +static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) { + PyObject *retval = NULL; + int err = 0; + #ifdef __Pyx_Generator_USED + if (__Pyx_Generator_CheckExact(yf)) { + retval = __Pyx_Coroutine_Close(yf); + if (!retval) + return -1; + } else + #endif + #ifdef __Pyx_Coroutine_USED + if (__Pyx_Coroutine_CheckExact(yf)) { + retval = __Pyx_Coroutine_Close(yf); + if (!retval) + return -1; + } else + #endif + { + PyObject *meth; + gen->is_running = 1; + meth = __Pyx_PyObject_GetAttrStr(yf, __pyx_n_s_close); + if (unlikely(!meth)) { + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_WriteUnraisable(yf); + } + PyErr_Clear(); + } else { + retval = PyObject_CallFunction(meth, NULL); + Py_DECREF(meth); + if (!retval) + err = -1; + } + gen->is_running = 0; + } + Py_XDECREF(retval); + return err; +} +static PyObject *__Pyx_Generator_Next(PyObject *self) { + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self; + PyObject *yf = gen->yieldfrom; + if (unlikely(__Pyx_Coroutine_CheckRunning(gen))) + return NULL; + if (yf) { + PyObject *ret; + gen->is_running = 1; + #ifdef __Pyx_Generator_USED + if (__Pyx_Generator_CheckExact(yf)) { + ret = __Pyx_Generator_Next(yf); + } else + #endif + ret = Py_TYPE(yf)->tp_iternext(yf); + gen->is_running = 0; + if (likely(ret)) { + return ret; + } + return __Pyx_Coroutine_FinishDelegation(gen); + } + return __Pyx_Coroutine_SendEx(gen, Py_None); +} +static PyObject *__Pyx_Coroutine_Close(PyObject *self) { + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self; + PyObject *retval, *raised_exception; + PyObject *yf = gen->yieldfrom; + int err = 0; + if (unlikely(__Pyx_Coroutine_CheckRunning(gen))) + return NULL; + if (yf) { + Py_INCREF(yf); + err = __Pyx_Coroutine_CloseIter(gen, yf); + __Pyx_Coroutine_Undelegate(gen); + Py_DECREF(yf); + } + if (err == 0) + PyErr_SetNone(PyExc_GeneratorExit); + retval = __Pyx_Coroutine_SendEx(gen, NULL); + if (retval) { + Py_DECREF(retval); + PyErr_SetString(PyExc_RuntimeError, + "generator ignored GeneratorExit"); + return NULL; + } + raised_exception = PyErr_Occurred(); + if (!raised_exception + || raised_exception == PyExc_StopIteration + || raised_exception == PyExc_GeneratorExit + || PyErr_GivenExceptionMatches(raised_exception, PyExc_GeneratorExit) + || PyErr_GivenExceptionMatches(raised_exception, PyExc_StopIteration)) + { + if (raised_exception) PyErr_Clear(); + Py_INCREF(Py_None); + return Py_None; + } + return NULL; +} +static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) { + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self; + PyObject *typ; + PyObject *tb = NULL; + PyObject *val = NULL; + PyObject *yf = gen->yieldfrom; + if (!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb)) + return NULL; + if (unlikely(__Pyx_Coroutine_CheckRunning(gen))) + return NULL; + if (yf) { + PyObject *ret; + Py_INCREF(yf); + if (PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit)) { + int err = __Pyx_Coroutine_CloseIter(gen, yf); + Py_DECREF(yf); + __Pyx_Coroutine_Undelegate(gen); + if (err < 0) + return __Pyx_Coroutine_MethodReturn(__Pyx_Coroutine_SendEx(gen, NULL)); + goto throw_here; + } + gen->is_running = 1; + #ifdef __Pyx_Generator_USED + if (__Pyx_Generator_CheckExact(yf)) { + ret = __Pyx_Coroutine_Throw(yf, args); + } else + #endif + #ifdef __Pyx_Coroutine_USED + if (__Pyx_Coroutine_CheckExact(yf)) { + ret = __Pyx_Coroutine_Throw(yf, args); + } else + #endif + { + PyObject *meth = __Pyx_PyObject_GetAttrStr(yf, __pyx_n_s_throw); + if (unlikely(!meth)) { + Py_DECREF(yf); + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) { + gen->is_running = 0; + return NULL; + } + PyErr_Clear(); + __Pyx_Coroutine_Undelegate(gen); + gen->is_running = 0; + goto throw_here; + } + ret = PyObject_CallObject(meth, args); + Py_DECREF(meth); + } + gen->is_running = 0; + Py_DECREF(yf); + if (!ret) { + ret = __Pyx_Coroutine_FinishDelegation(gen); + } + return __Pyx_Coroutine_MethodReturn(ret); + } +throw_here: + __Pyx_Raise(typ, val, tb, NULL); + return __Pyx_Coroutine_MethodReturn(__Pyx_Coroutine_SendEx(gen, NULL)); +} +static int __Pyx_Coroutine_traverse(PyObject *self, visitproc visit, void *arg) { + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self; + Py_VISIT(gen->closure); + Py_VISIT(gen->classobj); + Py_VISIT(gen->yieldfrom); + Py_VISIT(gen->exc_type); + Py_VISIT(gen->exc_value); + Py_VISIT(gen->exc_traceback); + return 0; +} +static int __Pyx_Coroutine_clear(PyObject *self) { + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self; + Py_CLEAR(gen->closure); + Py_CLEAR(gen->classobj); + Py_CLEAR(gen->yieldfrom); + Py_CLEAR(gen->exc_type); + Py_CLEAR(gen->exc_value); + Py_CLEAR(gen->exc_traceback); + Py_CLEAR(gen->gi_name); + Py_CLEAR(gen->gi_qualname); + return 0; +} +static void __Pyx_Coroutine_dealloc(PyObject *self) { + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self; + PyObject_GC_UnTrack(gen); + if (gen->gi_weakreflist != NULL) + PyObject_ClearWeakRefs(self); + if (gen->resume_label > 0) { + PyObject_GC_Track(self); +#if PY_VERSION_HEX >= 0x030400a1 + if (PyObject_CallFinalizerFromDealloc(self)) +#else + Py_TYPE(gen)->tp_del(self); + if (self->ob_refcnt > 0) +#endif + { + return; + } + PyObject_GC_UnTrack(self); + } + __Pyx_Coroutine_clear(self); + PyObject_GC_Del(gen); +} +static void __Pyx_Coroutine_del(PyObject *self) { + PyObject *res; + PyObject *error_type, *error_value, *error_traceback; + __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self; + __Pyx_PyThreadState_declare + if (gen->resume_label <= 0) + return ; +#if PY_VERSION_HEX < 0x030400a1 + assert(self->ob_refcnt == 0); + self->ob_refcnt = 1; +#endif + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&error_type, &error_value, &error_traceback); + res = __Pyx_Coroutine_Close(self); + if (res == NULL) + PyErr_WriteUnraisable(self); + else + Py_DECREF(res); + __Pyx_ErrRestore(error_type, error_value, error_traceback); +#if PY_VERSION_HEX < 0x030400a1 + assert(self->ob_refcnt > 0); + if (--self->ob_refcnt == 0) { + return; + } + { + Py_ssize_t refcnt = self->ob_refcnt; + _Py_NewReference(self); + self->ob_refcnt = refcnt; + } +#if CYTHON_COMPILING_IN_CPYTHON + assert(PyType_IS_GC(self->ob_type) && + _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED); + _Py_DEC_REFTOTAL; +#endif +#ifdef COUNT_ALLOCS + --Py_TYPE(self)->tp_frees; + --Py_TYPE(self)->tp_allocs; +#endif +#endif +} +static PyObject * +__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self) +{ + PyObject *name = self->gi_name; + if (unlikely(!name)) name = Py_None; + Py_INCREF(name); + return name; +} +static int +__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + tmp = self->gi_name; + Py_INCREF(value); + self->gi_name = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self) +{ + PyObject *name = self->gi_qualname; + if (unlikely(!name)) name = Py_None; + Py_INCREF(name); + return name; +} +static int +__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + tmp = self->gi_qualname; + Py_INCREF(value); + self->gi_qualname = value; + Py_XDECREF(tmp); + return 0; +} +static __pyx_CoroutineObject *__Pyx__Coroutine_New( + PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *closure, + PyObject *name, PyObject *qualname, PyObject *module_name) { + __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type); + if (gen == NULL) + return NULL; + gen->body = body; + gen->closure = closure; + Py_XINCREF(closure); + gen->is_running = 0; + gen->resume_label = 0; + gen->classobj = NULL; + gen->yieldfrom = NULL; + gen->exc_type = NULL; + gen->exc_value = NULL; + gen->exc_traceback = NULL; + gen->gi_weakreflist = NULL; + Py_XINCREF(qualname); + gen->gi_qualname = qualname; + Py_XINCREF(name); + gen->gi_name = name; + Py_XINCREF(module_name); + gen->gi_modulename = module_name; + PyObject_GC_Track(gen); + return gen; +} + +/* PatchModuleWithCoroutine */ + static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) { +#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) + int result; + PyObject *globals, *result_obj; + globals = PyDict_New(); if (unlikely(!globals)) goto ignore; + result = PyDict_SetItemString(globals, "_cython_coroutine_type", + #ifdef __Pyx_Coroutine_USED + (PyObject*)__pyx_CoroutineType); + #else + Py_None); + #endif + if (unlikely(result < 0)) goto ignore; + result = PyDict_SetItemString(globals, "_cython_generator_type", + #ifdef __Pyx_Generator_USED + (PyObject*)__pyx_GeneratorType); + #else + Py_None); + #endif + if (unlikely(result < 0)) goto ignore; + if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore; + if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore; + result_obj = PyRun_String(py_code, Py_file_input, globals, globals); + if (unlikely(!result_obj)) goto ignore; + Py_DECREF(result_obj); + Py_DECREF(globals); + return module; +ignore: + Py_XDECREF(globals); + PyErr_WriteUnraisable(module); + if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) { + Py_DECREF(module); + module = NULL; + } +#else + py_code++; +#endif + return module; +} + +/* PatchGeneratorABC */ + #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) +static PyObject* __Pyx_patch_abc_module(PyObject *module); +static PyObject* __Pyx_patch_abc_module(PyObject *module) { + module = __Pyx_Coroutine_patch_module( + module, "" +"if _cython_generator_type is not None:\n" +" try: Generator = _module.Generator\n" +" except AttributeError: pass\n" +" else: Generator.register(_cython_generator_type)\n" +"if _cython_coroutine_type is not None:\n" +" try: Coroutine = _module.Coroutine\n" +" except AttributeError: pass\n" +" else: Coroutine.register(_cython_coroutine_type)\n" + ); + return module; +} +#endif +static int __Pyx_patch_abc(void) { +#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) + static int abc_patched = 0; + if (!abc_patched) { + PyObject *module; + module = PyImport_ImportModule((PY_VERSION_HEX >= 0x03030000) ? "collections.abc" : "collections"); + if (!module) { + PyErr_WriteUnraisable(NULL); + if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, + ((PY_VERSION_HEX >= 0x03030000) ? + "Cython module failed to register with collections.abc module" : + "Cython module failed to register with collections module"), 1) < 0)) { + return -1; + } + } else { + module = __Pyx_patch_abc_module(module); + abc_patched = 1; + if (unlikely(!module)) + return -1; + Py_DECREF(module); + } + module = PyImport_ImportModule("backports_abc"); + if (module) { + module = __Pyx_patch_abc_module(module); + Py_XDECREF(module); + } + if (!module) { + PyErr_Clear(); + } + } +#else + if (0) __Pyx_Coroutine_patch_module(NULL, NULL); +#endif + return 0; +} + +/* Generator */ + static PyMethodDef __pyx_Generator_methods[] = { + {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O, + (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")}, + {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS, + (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")}, + {"close", (PyCFunction) __Pyx_Coroutine_Close, METH_NOARGS, + (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")}, + {0, 0, 0, 0} +}; +static PyMemberDef __pyx_Generator_memberlist[] = { + {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL}, + {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY, + (char*) PyDoc_STR("object being iterated by 'yield from', or None")}, + {0, 0, 0, 0, 0} +}; +static PyGetSetDef __pyx_Generator_getsets[] = { + {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name, + (char*) PyDoc_STR("name of the generator"), 0}, + {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname, + (char*) PyDoc_STR("qualified name of the generator"), 0}, + {0, 0, 0, 0, 0} +}; +static PyTypeObject __pyx_GeneratorType_type = { + PyVarObject_HEAD_INIT(0, 0) + "generator", + sizeof(__pyx_CoroutineObject), + 0, + (destructor) __Pyx_Coroutine_dealloc, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE, + 0, + (traverseproc) __Pyx_Coroutine_traverse, + 0, + 0, + offsetof(__pyx_CoroutineObject, gi_weakreflist), + 0, + (iternextfunc) __Pyx_Generator_Next, + __pyx_Generator_methods, + __pyx_Generator_memberlist, + __pyx_Generator_getsets, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#else + __Pyx_Coroutine_del, +#endif + 0, +#if PY_VERSION_HEX >= 0x030400a1 + __Pyx_Coroutine_del, +#endif +}; +static int __pyx_Generator_init(void) { + __pyx_GeneratorType_type.tp_getattro = PyObject_GenericGetAttr; + __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter; + __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type); + if (unlikely(!__pyx_GeneratorType)) { + return -1; + } + return 0; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } + #else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } + #endif +#else + res = PyNumber_Int(x); +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/ctcdecode-master/third_party/kenlm/python/kenlm.pyx b/ctcdecode-master/third_party/kenlm/python/kenlm.pyx new file mode 100644 index 0000000..e1c2453 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/python/kenlm.pyx @@ -0,0 +1,261 @@ +import os +cimport _kenlm + +cdef bytes as_str(data): + if isinstance(data, bytes): + return data + elif isinstance(data, unicode): + return data.encode('utf8') + raise TypeError('Cannot convert %s to string' % type(data)) + +cdef class FullScoreReturn: + """ + Wrapper around FullScoreReturn. + + Notes: + `prob` has been renamed to `log_prob` + `oov` has been added to flag whether the word is OOV + """ + + cdef float log_prob + cdef int ngram_length + cdef bint oov + + def __cinit__(self, log_prob, ngram_length, oov): + self.log_prob = log_prob + self.ngram_length = ngram_length + self.oov = oov + + def __repr__(self): + return '{0}({1}, {2}, {3})'.format(self.__class__.__name__, repr(self.log_prob), repr(self.ngram_length), repr(self.oov)) + + property log_prob: + def __get__(self): + return self.log_prob + + property ngram_length: + def __get__(self): + return self.ngram_length + + property oov: + def __get__(self): + return self.oov + +cdef class State: + """ + Wrapper around lm::ngram::State so that python code can make incremental queries. + + Notes: + * rich comparisons + * hashable + """ + + cdef _kenlm.State _c_state + + def __richcmp__(State qa, State qb, int op): + r = qa._c_state.Compare(qb._c_state) + if op == 0: # < + return r < 0 + elif op == 1: # <= + return r <= 0 + elif op == 2: # == + return r == 0 + elif op == 3: # != + return r != 0 + elif op == 4: # > + return r > 0 + else: # >= + return r >= 0 + + def __hash__(self): + return _kenlm.hash_value(self._c_state) + +class LoadMethod: + LAZY = _kenlm.LAZY + POPULATE_OR_LAZY = _kenlm.POPULATE_OR_LAZY + POPULATE_OR_READ = _kenlm.POPULATE_OR_READ + READ = _kenlm.READ + PARALLEL_READ = _kenlm.PARALLEL_READ + +cdef class Config: + """ + Wrapper around lm::ngram::Config. + Pass this to Model's constructor to set the load_method. + """ + cdef _kenlm.Config _c_config + + def __init__(self): + self._c_config = _kenlm.Config() + + property load_method: + def __get__(self): + return self._c_config.load_method + def __set__(self, to): + self._c_config.load_method = to + +cdef class Model: + """ + Wrapper around lm::ngram::Model. + """ + + cdef _kenlm.Model* model + cdef public bytes path + cdef _kenlm.const_Vocabulary* vocab + + def __init__(self, path, Config config = Config()): + """ + Load the language model. + + :param path: path to an arpa file or a kenlm binary file. + :param config: configuration options (see lm/config.hh for documentation) + """ + self.path = os.path.abspath(as_str(path)) + try: + self.model = _kenlm.LoadVirtual(self.path, config._c_config) + except RuntimeError as exception: + exception_message = str(exception).replace('\n', ' ') + raise IOError('Cannot read model \'{}\' ({})'.format(path, exception_message))\ + from exception + self.vocab = &self.model.BaseVocabulary() + + def __dealloc__(self): + del self.model + + property order: + def __get__(self): + return self.model.Order() + + def score(self, sentence, bos = True, eos = True): + """ + Return the log10 probability of a string. By default, the string is + treated as a sentence. + return log10 p(sentence | ) + + If you do not want to condition on the beginning of sentence, pass + bos = False + Never include as part of the string. That would be predicting the + beginning of sentence. Language models are only supposed to condition + on it as context. + + Similarly, the end of sentence token can be omitted with + eos = False + Since language models explicitly predict , it can be part of the + string. + + Examples: + + #Good: returns log10 p(this is a sentence . | ) + model.score("this is a sentence .") + #Good: same as the above but more explicit + model.score("this is a sentence .", bos = True, eos = True) + + #Bad: never include + model.score(" this is a sentence") + #Bad: never include , even if bos = False. + model.score(" this is a sentence", bos = False) + + #Good: returns log10 p(a fragment) + model.score("a fragment", bos = False, eos = False) + + #Good: returns log10 p(a fragment ) + model.score("a fragment", bos = False, eos = True) + + #Ok, but bad practice: returns log10 p(a fragment ) + #Unlike , the end of sentence token can appear explicitly. + model.score("a fragment ", bos = False, eos = False) + """ + cdef list words = as_str(sentence).split() + cdef _kenlm.State state + if bos: + self.model.BeginSentenceWrite(&state) + else: + self.model.NullContextWrite(&state) + cdef _kenlm.State out_state + cdef float total = 0 + for word in words: + total += self.model.BaseScore(&state, self.vocab.Index(word), &out_state) + state = out_state + if eos: + total += self.model.BaseScore(&state, self.vocab.EndSentence(), &out_state) + return total + + def perplexity(self, sentence): + """ + Compute perplexity of a sentence. + @param sentence One full sentence to score. Do not include or . + """ + words = len(as_str(sentence).split()) + 1 # For + return 10.0**(-self.score(sentence) / words) + + def full_scores(self, sentence, bos = True, eos = True): + """ + full_scores(sentence, bos = True, eos = Ture) -> generate full scores (prob, ngram length, oov) + @param sentence is a string (do not use boundary symbols) + @param bos should kenlm add a bos state + @param eos should kenlm add an eos state + """ + cdef list words = as_str(sentence).split() + cdef _kenlm.State state + if bos: + self.model.BeginSentenceWrite(&state) + else: + self.model.NullContextWrite(&state) + cdef _kenlm.State out_state + cdef _kenlm.FullScoreReturn ret + cdef float total = 0 + cdef _kenlm.WordIndex wid + for word in words: + wid = self.vocab.Index(word) + ret = self.model.BaseFullScore(&state, wid, &out_state) + yield (ret.prob, ret.ngram_length, wid == 0) + state = out_state + if eos: + ret = self.model.BaseFullScore(&state, + self.vocab.EndSentence(), &out_state) + yield (ret.prob, ret.ngram_length, False) + + + def BeginSentenceWrite(self, State state): + """Change the given state to a BOS state.""" + self.model.BeginSentenceWrite(&state._c_state) + + def NullContextWrite(self, State state): + """Change the given state to a NULL state.""" + self.model.NullContextWrite(&state._c_state) + + def BaseScore(self, State in_state, str word, State out_state): + """ + Return p(word|in_state) and update the output state. + Wrapper around model.BaseScore(in_state, Index(word), out_state) + + :param word: the suffix + :param state: the context (defaults to NullContext) + :returns: p(word|state) + """ + cdef float total = self.model.BaseScore(&in_state._c_state, self.vocab.Index(as_str(word)), &out_state._c_state) + return total + + def BaseFullScore(self, State in_state, str word, State out_state): + """ + Wrapper around model.BaseScore(in_state, Index(word), out_state) + + :param word: the suffix + :param state: the context (defaults to NullContext) + :returns: FullScoreReturn(word|state) + """ + cdef _kenlm.WordIndex wid = self.vocab.Index(as_str(word)) + cdef _kenlm.FullScoreReturn ret = self.model.BaseFullScore(&in_state._c_state, wid, &out_state._c_state) + return FullScoreReturn(ret.prob, ret.ngram_length, wid == 0) + + def __contains__(self, word): + cdef bytes w = as_str(word) + return (self.vocab.Index(w) != 0) + + def __repr__(self): + return ''.format(os.path.basename(self.path)) + + def __reduce__(self): + return (Model, (self.path,)) + +class LanguageModel(Model): + """Backwards compatability stub. Use Model.""" diff --git a/ctcdecode-master/third_party/kenlm/setup.py b/ctcdecode-master/third_party/kenlm/setup.py new file mode 100644 index 0000000..9d40c01 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/setup.py @@ -0,0 +1,48 @@ +from setuptools import setup, Extension +import glob +import platform +import os + +#Does gcc compile with this header and library? +def compile_test(header, library): + dummy_path = os.path.join(os.path.dirname(__file__), "dummy") + command = "bash -c \"g++ -include " + header + " -l" + library + " -x c++ - <<<'int main() {}' -o " + dummy_path + " >/dev/null 2>/dev/null && rm " + dummy_path + " 2>/dev/null\"" + return os.system(command) == 0 + + +FILES = glob.glob('util/*.cc') + glob.glob('lm/*.cc') + glob.glob('util/double-conversion/*.cc') +FILES = [fn for fn in FILES if not (fn.endswith('main.cc') or fn.endswith('test.cc'))] + +LIBS = ['stdc++'] +if platform.system() != 'Darwin': + LIBS.append('rt') + +#We don't need -std=c++11 but python seems to be compiled with it now. https://github.com/kpu/kenlm/issues/86 +ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER=6', '-std=c++11'] + +if compile_test('zlib.h', 'z'): + ARGS.append('-DHAVE_ZLIB') + LIBS.append('z') + +if compile_test('bzlib.h', 'bz2'): + ARGS.append('-DHAVE_BZLIB') + LIBS.append('bz2') + +if compile_test('lzma.h', 'lzma'): + ARGS.append('-DHAVE_XZLIB') + LIBS.append('lzma') + +ext_modules = [ + Extension(name='kenlm', + sources=FILES + ['python/kenlm.cpp'], + language='C++', + include_dirs=['.'], + libraries=LIBS, + extra_compile_args=ARGS) +] + +setup( + name='kenlm', + ext_modules=ext_modules, + include_package_data=True, +) diff --git a/ctcdecode-master/third_party/kenlm/util/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/util/CMakeLists.txt new file mode 100644 index 0000000..a148ad4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/CMakeLists.txt @@ -0,0 +1,100 @@ +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +# +# Because we do not set PARENT_SCOPE in the following definition, +# CMake files in the parent directory won't be able to access this variable. +# +set(KENLM_UTIL_SOURCE + bit_packing.cc + ersatz_progress.cc + exception.cc + file.cc + file_piece.cc + float_to_string.cc + integer_to_string.cc + mmap.cc + murmur_hash.cc + parallel_read.cc + pool.cc + read_compressed.cc + scoped.cc + spaces.cc + string_piece.cc + usage.cc + ) + +set(READ_COMPRESSED_FLAGS) +set(READ_COMPRESSED_LIBS) +find_package(ZLIB) +if (ZLIB_FOUND) + set(READ_COMPRESSED_FLAGS "${READ_COMPRESSED_FLAGS} -DHAVE_ZLIB") + set(READ_COMPRESSED_LIBS ${READ_COMPRESSED_LIBS} ${ZLIB_LIBRARIES}) + include_directories(${ZLIB_INCLUDE_DIR}) +endif() + +find_package(BZip2) +if (BZIP2_FOUND) + set(READ_COMPRESSED_FLAGS "${READ_COMPRESSED_FLAGS} -DHAVE_BZLIB") + set(READ_COMPRESSED_LIBS ${READ_COMPRESSED_LIBS} ${BZIP2_LIBRARIES}) + include_directories(${BZIP2_INCLUDE_DIR}) +endif() + +find_package(LibLZMA) +if (LIBLZMA_FOUND) + set(READ_COMPRESSED_FLAGS "${READ_COMPRESSED_FLAGS} -DHAVE_XZLIB") + set(READ_COMPRESSED_LIBS ${READ_COMPRESSED_LIBS} ${LIBLZMA_LIBRARIES}) + include_directories(${LIBLZMA_INCLUDE_DIRS}) +endif() +set_source_files_properties(read_compressed.cc PROPERTIES COMPILE_FLAGS ${READ_COMPRESSED_FLAGS}) +set_source_files_properties(read_compressed_test.cc PROPERTIES COMPILE_FLAGS ${READ_COMPRESSED_FLAGS}) +set_source_files_properties(file_piece_test.cc PROPERTIES COMPILE_FLAGS ${READ_COMPRESSED_FLAGS}) + +# This directory has children that need to be processed +add_subdirectory(double-conversion) +add_subdirectory(stream) + +if (UNIX AND NOT APPLE) + set(TIMER_LINK rt) +else() + set(TIMER_LINK) +endif() + +if (NOT MSVC) + set(THREADS pthread) +endif() + +# Group these objects together for later use. +add_library(kenlm_util ${KENLM_UTIL_DOUBLECONVERSION_SOURCE} ${KENLM_UTIL_STREAM_SOURCE} ${KENLM_UTIL_SOURCE}) +target_link_libraries(kenlm_util ${Boost_LIBRARIES} ${READ_COMPRESSED_LIBS} ${THREADS} ${TIMER_LINK}) + +AddExes(EXES probing_hash_table_benchmark + LIBRARIES kenlm_util ${Boost_LIBRARIES} ${THREADS}) + +# Only compile and run unit tests if tests should be run +if(BUILD_TESTING) + set(KENLM_BOOST_TESTS_LIST + bit_packing_test + integer_to_string_test + joint_sort_test + multi_intersection_test + pcqueue_test + probing_hash_table_test + read_compressed_test + sized_iterator_test + sorted_uniform_test + string_stream_test + tokenize_piece_test + ) + + AddTests(TESTS ${KENLM_BOOST_TESTS_LIST} + LIBRARIES kenlm_util ${Boost_LIBRARIES} ${THREADS}) + + # file_piece_test requires an extra command line parameter + KenLMAddTest(TEST file_piece_test + LIBRARIES kenlm_util ${Boost_LIBRARIES} ${THREADS} + TEST_ARGS ${CMAKE_CURRENT_SOURCE_DIR}/file_piece.cc) +endif() diff --git a/ctcdecode-master/third_party/kenlm/util/bit_packing.cc b/ctcdecode-master/third_party/kenlm/util/bit_packing.cc new file mode 100644 index 0000000..cffd9cf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/bit_packing.cc @@ -0,0 +1,40 @@ +#include "util/bit_packing.hh" +#include "util/exception.hh" + +#include + +namespace util { + +namespace { +template struct StaticCheck {}; +template <> struct StaticCheck { typedef bool StaticAssertionPassed; }; + +// If your float isn't 4 bytes, we're hosed. +typedef StaticCheck::StaticAssertionPassed FloatSize; + +} // namespace + +uint8_t RequiredBits(uint64_t max_value) { + if (!max_value) return 0; + uint8_t ret = 1; + while (max_value >>= 1) ++ret; + return ret; +} + +void BitPackingSanity() { + const FloatEnc neg1 = { -1.0 }, pos1 = { 1.0 }; + if ((neg1.i ^ pos1.i) != 0x80000000) UTIL_THROW(Exception, "Sign bit is not 0x80000000"); + char mem[57+8]; + memset(mem, 0, sizeof(mem)); + const uint64_t test57 = 0x123456789abcdefULL; + for (uint64_t b = 0; b < 57 * 8; b += 57) { + WriteInt57(mem, b, 57, test57); + } + for (uint64_t b = 0; b < 57 * 8; b += 57) { + if (test57 != ReadInt57(mem, b, 57, (1ULL << 57) - 1)) + UTIL_THROW(Exception, "The bit packing routines are failing for your architecture. Please send a bug report with your architecture, operating system, and compiler."); + } + // TODO: more checks. +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/bit_packing.hh b/ctcdecode-master/third_party/kenlm/util/bit_packing.hh new file mode 100644 index 0000000..b24fd9c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/bit_packing.hh @@ -0,0 +1,185 @@ +#ifndef UTIL_BIT_PACKING_H +#define UTIL_BIT_PACKING_H + +/* Bit-level packing routines + * + * WARNING WARNING WARNING: + * The write functions assume that memory is zero initially. This makes them + * faster and is the appropriate case for mmapped language model construction. + * These routines assume that unaligned access to uint64_t is fast. This is + * the case on x86_64. I'm not sure how fast unaligned 64-bit access is on + * x86 but my target audience is large language models for which 64-bit is + * necessary. + * + * Call the BitPackingSanity function to sanity check. Calling once suffices, + * but it may be called multiple times when that's inconvenient. + * + * ARM and MinGW ports contributed by Hideo Okuma and Tomoyuki Yoshimura at + * NICT. + */ + +#include +#ifdef __APPLE__ +#include +#elif __linux__ +#include +#elif !defined(_WIN32) && !defined(_WIN64) +#include +#endif + +#include +#include + +namespace util { + +// Fun fact: __BYTE_ORDER is wrong on Solaris Sparc, but the version without __ is correct. +#if BYTE_ORDER == LITTLE_ENDIAN +inline uint8_t BitPackShift(uint8_t bit, uint8_t /*length*/) { + return bit; +} +#elif BYTE_ORDER == BIG_ENDIAN +inline uint8_t BitPackShift(uint8_t bit, uint8_t length) { + return 64 - length - bit; +} +#else +#error "Bit packing code isn't written for your byte order." +#endif + +inline uint64_t ReadOff(const void *base, uint64_t bit_off) { +#if defined(__arm) || defined(__arm__) + const uint8_t *base_off = reinterpret_cast(base) + (bit_off >> 3); + uint64_t value64; + memcpy(&value64, base_off, sizeof(value64)); + return value64; +#else + return *reinterpret_cast(reinterpret_cast(base) + (bit_off >> 3)); +#endif +} + +/* Pack integers up to 57 bits using their least significant digits. + * The length is specified using mask: + * Assumes mask == (1 << length) - 1 where length <= 57. + */ +inline uint64_t ReadInt57(const void *base, uint64_t bit_off, uint8_t length, uint64_t mask) { + return (ReadOff(base, bit_off) >> BitPackShift(bit_off & 7, length)) & mask; +} +/* Assumes value < (1 << length) and length <= 57. + * Assumes the memory is zero initially. + */ +inline void WriteInt57(void *base, uint64_t bit_off, uint8_t length, uint64_t value) { +#if defined(__arm) || defined(__arm__) + uint8_t *base_off = reinterpret_cast(base) + (bit_off >> 3); + uint64_t value64; + memcpy(&value64, base_off, sizeof(value64)); + value64 |= (value << BitPackShift(bit_off & 7, length)); + memcpy(base_off, &value64, sizeof(value64)); +#else + *reinterpret_cast(reinterpret_cast(base) + (bit_off >> 3)) |= + (value << BitPackShift(bit_off & 7, length)); +#endif +} + +/* Same caveats as above, but for a 25 bit limit. */ +inline uint32_t ReadInt25(const void *base, uint64_t bit_off, uint8_t length, uint32_t mask) { +#if defined(__arm) || defined(__arm__) + const uint8_t *base_off = reinterpret_cast(base) + (bit_off >> 3); + uint32_t value32; + memcpy(&value32, base_off, sizeof(value32)); + return (value32 >> BitPackShift(bit_off & 7, length)) & mask; +#else + return (*reinterpret_cast(reinterpret_cast(base) + (bit_off >> 3)) >> BitPackShift(bit_off & 7, length)) & mask; +#endif +} + +inline void WriteInt25(void *base, uint64_t bit_off, uint8_t length, uint32_t value) { +#if defined(__arm) || defined(__arm__) + uint8_t *base_off = reinterpret_cast(base) + (bit_off >> 3); + uint32_t value32; + memcpy(&value32, base_off, sizeof(value32)); + value32 |= (value << BitPackShift(bit_off & 7, length)); + memcpy(base_off, &value32, sizeof(value32)); +#else + *reinterpret_cast(reinterpret_cast(base) + (bit_off >> 3)) |= + (value << BitPackShift(bit_off & 7, length)); +#endif +} + +typedef union { float f; uint32_t i; } FloatEnc; + +inline float ReadFloat32(const void *base, uint64_t bit_off) { + FloatEnc encoded; + encoded.i = ReadOff(base, bit_off) >> BitPackShift(bit_off & 7, 32); + return encoded.f; +} +inline void WriteFloat32(void *base, uint64_t bit_off, float value) { + FloatEnc encoded; + encoded.f = value; + WriteInt57(base, bit_off, 32, encoded.i); +} + +const uint32_t kSignBit = 0x80000000; + +inline void SetSign(float &to) { + FloatEnc enc; + enc.f = to; + enc.i |= kSignBit; + to = enc.f; +} + +inline void UnsetSign(float &to) { + FloatEnc enc; + enc.f = to; + enc.i &= ~kSignBit; + to = enc.f; +} + +inline float ReadNonPositiveFloat31(const void *base, uint64_t bit_off) { + FloatEnc encoded; + encoded.i = ReadOff(base, bit_off) >> BitPackShift(bit_off & 7, 31); + // Sign bit set means negative. + encoded.i |= kSignBit; + return encoded.f; +} +inline void WriteNonPositiveFloat31(void *base, uint64_t bit_off, float value) { + FloatEnc encoded; + encoded.f = value; + encoded.i &= ~kSignBit; + WriteInt57(base, bit_off, 31, encoded.i); +} + +void BitPackingSanity(); + +// Return bits required to store integers upto max_value. Not the most +// efficient implementation, but this is only called a few times to size tries. +uint8_t RequiredBits(uint64_t max_value); + +struct BitsMask { + static BitsMask ByMax(uint64_t max_value) { + BitsMask ret; + ret.FromMax(max_value); + return ret; + } + static BitsMask ByBits(uint8_t bits) { + BitsMask ret; + ret.bits = bits; + ret.mask = (1ULL << bits) - 1; + return ret; + } + void FromMax(uint64_t max_value) { + bits = RequiredBits(max_value); + mask = (1ULL << bits) - 1; + } + uint8_t bits; + uint64_t mask; +}; + +struct BitAddress { + BitAddress(void *in_base, uint64_t in_offset) : base(in_base), offset(in_offset) {} + + void *base; + uint64_t offset; +}; + +} // namespace util + +#endif // UTIL_BIT_PACKING_H diff --git a/ctcdecode-master/third_party/kenlm/util/bit_packing_test.cc b/ctcdecode-master/third_party/kenlm/util/bit_packing_test.cc new file mode 100644 index 0000000..c4494b6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/bit_packing_test.cc @@ -0,0 +1,59 @@ +#include "util/bit_packing.hh" + +#define BOOST_TEST_MODULE BitPackingTest +#include + +#include + +namespace util { +namespace { + +const uint64_t test57 = 0x123456789abcdefULL; +const uint32_t test25 = 0x1234567; + +BOOST_AUTO_TEST_CASE(ZeroBit57) { + char mem[16]; + memset(mem, 0, sizeof(mem)); + WriteInt57(mem, 0, 57, test57); + BOOST_CHECK_EQUAL(test57, ReadInt57(mem, 0, 57, (1ULL << 57) - 1)); +} + +BOOST_AUTO_TEST_CASE(EachBit57) { + char mem[16]; + for (uint8_t b = 0; b < 8; ++b) { + memset(mem, 0, sizeof(mem)); + WriteInt57(mem, b, 57, test57); + BOOST_CHECK_EQUAL(test57, ReadInt57(mem, b, 57, (1ULL << 57) - 1)); + } +} + +BOOST_AUTO_TEST_CASE(Consecutive57) { + char mem[57+8]; + memset(mem, 0, sizeof(mem)); + for (uint64_t b = 0; b < 57 * 8; b += 57) { + WriteInt57(mem, b, 57, test57); + BOOST_CHECK_EQUAL(test57, ReadInt57(mem, b, 57, (1ULL << 57) - 1)); + } + for (uint64_t b = 0; b < 57 * 8; b += 57) { + BOOST_CHECK_EQUAL(test57, ReadInt57(mem, b, 57, (1ULL << 57) - 1)); + } +} + +BOOST_AUTO_TEST_CASE(Consecutive25) { + char mem[25+8]; + memset(mem, 0, sizeof(mem)); + for (uint64_t b = 0; b < 25 * 8; b += 25) { + WriteInt25(mem, b, 25, test25); + BOOST_CHECK_EQUAL(test25, ReadInt25(mem, b, 25, (1ULL << 25) - 1)); + } + for (uint64_t b = 0; b < 25 * 8; b += 25) { + BOOST_CHECK_EQUAL(test25, ReadInt25(mem, b, 25, (1ULL << 25) - 1)); + } +} + +BOOST_AUTO_TEST_CASE(Sanity) { + BitPackingSanity(); +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/cat_compressed_main.cc b/ctcdecode-master/third_party/kenlm/util/cat_compressed_main.cc new file mode 100644 index 0000000..0c7cda9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/cat_compressed_main.cc @@ -0,0 +1,47 @@ +// Like cat but interprets compressed files. +#include "util/file.hh" +#include "util/read_compressed.hh" + +#include +#include + +namespace { +const std::size_t kBufSize = 16384; +void Copy(util::ReadCompressed &from, int to) { + util::scoped_malloc buffer(util::MallocOrThrow(kBufSize)); + while (std::size_t amount = from.Read(buffer.get(), kBufSize)) { + util::WriteOrThrow(to, buffer.get(), amount); + } +} +} // namespace + +int main(int argc, char *argv[]) { + // Lane Schwartz likes -h and --help + for (int i = 1; i < argc; ++i) { + char *arg = argv[i]; + if (!strcmp(arg, "--")) break; + if (!strcmp(arg, "-h") || !strcmp(arg, "--help")) { + std::cerr << + "A cat implementation that interprets compressed files.\n" + "Usage: " << argv[0] << " [file1] [file2] ...\n" + "If no file is provided, then stdin is read.\n"; + return 1; + } + } + + try { + if (argc == 1) { + util::ReadCompressed in(0); + Copy(in, 1); + } else { + for (int i = 1; i < argc; ++i) { + util::ReadCompressed in(util::OpenReadOrThrow(argv[i])); + Copy(in, 1); + } + } + } catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + return 2; + } + return 0; +} diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/util/double-conversion/CMakeLists.txt new file mode 100644 index 0000000..5f265ae --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/CMakeLists.txt @@ -0,0 +1,27 @@ +# This CMake file was created by Lane Schwartz + +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +# +# In order to allow CMake files in the parent directory +# to see this variable definition, we set PARENT_SCOPE. +# +# In order to set correct paths to these files +# when this variable is referenced by CMake files in the parent directory, +# we prefix all files with ${CMAKE_CURRENT_SOURCE_DIR}. +# +set(KENLM_UTIL_DOUBLECONVERSION_SOURCE + ${CMAKE_CURRENT_SOURCE_DIR}/bignum-dtoa.cc + ${CMAKE_CURRENT_SOURCE_DIR}/bignum.cc + ${CMAKE_CURRENT_SOURCE_DIR}/cached-powers.cc + ${CMAKE_CURRENT_SOURCE_DIR}/diy-fp.cc + ${CMAKE_CURRENT_SOURCE_DIR}/double-conversion.cc + ${CMAKE_CURRENT_SOURCE_DIR}/fast-dtoa.cc + ${CMAKE_CURRENT_SOURCE_DIR}/fixed-dtoa.cc + ${CMAKE_CURRENT_SOURCE_DIR}/strtod.cc + PARENT_SCOPE) + diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/LICENSE b/ctcdecode-master/third_party/kenlm/util/double-conversion/LICENSE new file mode 100644 index 0000000..933718a --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/LICENSE @@ -0,0 +1,26 @@ +Copyright 2006-2011, the V8 project authors. All rights reserved. +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + * Neither the name of Google Inc. nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum-dtoa.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum-dtoa.cc new file mode 100644 index 0000000..f1ad7a5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum-dtoa.cc @@ -0,0 +1,641 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include + +#include "bignum-dtoa.h" + +#include "bignum.h" +#include "ieee.h" + +namespace double_conversion { + +static int NormalizedExponent(uint64_t significand, int exponent) { + ASSERT(significand != 0); + while ((significand & Double::kHiddenBit) == 0) { + significand = significand << 1; + exponent = exponent - 1; + } + return exponent; +} + + +// Forward declarations: +// Returns an estimation of k such that 10^(k-1) <= v < 10^k. +static int EstimatePower(int exponent); +// Computes v / 10^estimated_power exactly, as a ratio of two bignums, numerator +// and denominator. +static void InitialScaledStartValues(uint64_t significand, + int exponent, + bool lower_boundary_is_closer, + int estimated_power, + bool need_boundary_deltas, + Bignum* numerator, + Bignum* denominator, + Bignum* delta_minus, + Bignum* delta_plus); +// Multiplies numerator/denominator so that its values lies in the range 1-10. +// Returns decimal_point s.t. +// v = numerator'/denominator' * 10^(decimal_point-1) +// where numerator' and denominator' are the values of numerator and +// denominator after the call to this function. +static void FixupMultiply10(int estimated_power, bool is_even, + int* decimal_point, + Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus); +// Generates digits from the left to the right and stops when the generated +// digits yield the shortest decimal representation of v. +static void GenerateShortestDigits(Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus, + bool is_even, + Vector buffer, int* length); +// Generates 'requested_digits' after the decimal point. +static void BignumToFixed(int requested_digits, int* decimal_point, + Bignum* numerator, Bignum* denominator, + Vector(buffer), int* length); +// Generates 'count' digits of numerator/denominator. +// Once 'count' digits have been produced rounds the result depending on the +// remainder (remainders of exactly .5 round upwards). Might update the +// decimal_point when rounding up (for example for 0.9999). +static void GenerateCountedDigits(int count, int* decimal_point, + Bignum* numerator, Bignum* denominator, + Vector(buffer), int* length); + + +void BignumDtoa(double v, BignumDtoaMode mode, int requested_digits, + Vector buffer, int* length, int* decimal_point) { + ASSERT(v > 0); + ASSERT(!Double(v).IsSpecial()); + uint64_t significand; + int exponent; + bool lower_boundary_is_closer; + if (mode == BIGNUM_DTOA_SHORTEST_SINGLE) { + float f = static_cast(v); + ASSERT(f == v); + significand = Single(f).Significand(); + exponent = Single(f).Exponent(); + lower_boundary_is_closer = Single(f).LowerBoundaryIsCloser(); + } else { + significand = Double(v).Significand(); + exponent = Double(v).Exponent(); + lower_boundary_is_closer = Double(v).LowerBoundaryIsCloser(); + } + bool need_boundary_deltas = + (mode == BIGNUM_DTOA_SHORTEST || mode == BIGNUM_DTOA_SHORTEST_SINGLE); + + bool is_even = (significand & 1) == 0; + int normalized_exponent = NormalizedExponent(significand, exponent); + // estimated_power might be too low by 1. + int estimated_power = EstimatePower(normalized_exponent); + + // Shortcut for Fixed. + // The requested digits correspond to the digits after the point. If the + // number is much too small, then there is no need in trying to get any + // digits. + if (mode == BIGNUM_DTOA_FIXED && -estimated_power - 1 > requested_digits) { + buffer[0] = '\0'; + *length = 0; + // Set decimal-point to -requested_digits. This is what Gay does. + // Note that it should not have any effect anyways since the string is + // empty. + *decimal_point = -requested_digits; + return; + } + + Bignum numerator; + Bignum denominator; + Bignum delta_minus; + Bignum delta_plus; + // Make sure the bignum can grow large enough. The smallest double equals + // 4e-324. In this case the denominator needs fewer than 324*4 binary digits. + // The maximum double is 1.7976931348623157e308 which needs fewer than + // 308*4 binary digits. + ASSERT(Bignum::kMaxSignificantBits >= 324*4); + InitialScaledStartValues(significand, exponent, lower_boundary_is_closer, + estimated_power, need_boundary_deltas, + &numerator, &denominator, + &delta_minus, &delta_plus); + // We now have v = (numerator / denominator) * 10^estimated_power. + FixupMultiply10(estimated_power, is_even, decimal_point, + &numerator, &denominator, + &delta_minus, &delta_plus); + // We now have v = (numerator / denominator) * 10^(decimal_point-1), and + // 1 <= (numerator + delta_plus) / denominator < 10 + switch (mode) { + case BIGNUM_DTOA_SHORTEST: + case BIGNUM_DTOA_SHORTEST_SINGLE: + GenerateShortestDigits(&numerator, &denominator, + &delta_minus, &delta_plus, + is_even, buffer, length); + break; + case BIGNUM_DTOA_FIXED: + BignumToFixed(requested_digits, decimal_point, + &numerator, &denominator, + buffer, length); + break; + case BIGNUM_DTOA_PRECISION: + GenerateCountedDigits(requested_digits, decimal_point, + &numerator, &denominator, + buffer, length); + break; + default: + UNREACHABLE(); + } + buffer[*length] = '\0'; +} + + +// The procedure starts generating digits from the left to the right and stops +// when the generated digits yield the shortest decimal representation of v. A +// decimal representation of v is a number lying closer to v than to any other +// double, so it converts to v when read. +// +// This is true if d, the decimal representation, is between m- and m+, the +// upper and lower boundaries. d must be strictly between them if !is_even. +// m- := (numerator - delta_minus) / denominator +// m+ := (numerator + delta_plus) / denominator +// +// Precondition: 0 <= (numerator+delta_plus) / denominator < 10. +// If 1 <= (numerator+delta_plus) / denominator < 10 then no leading 0 digit +// will be produced. This should be the standard precondition. +static void GenerateShortestDigits(Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus, + bool is_even, + Vector buffer, int* length) { + // Small optimization: if delta_minus and delta_plus are the same just reuse + // one of the two bignums. + if (Bignum::Equal(*delta_minus, *delta_plus)) { + delta_plus = delta_minus; + } + *length = 0; + for (;;) { + uint16_t digit; + digit = numerator->DivideModuloIntBignum(*denominator); + ASSERT(digit <= 9); // digit is a uint16_t and therefore always positive. + // digit = numerator / denominator (integer division). + // numerator = numerator % denominator. + buffer[(*length)++] = static_cast(digit + '0'); + + // Can we stop already? + // If the remainder of the division is less than the distance to the lower + // boundary we can stop. In this case we simply round down (discarding the + // remainder). + // Similarly we test if we can round up (using the upper boundary). + bool in_delta_room_minus; + bool in_delta_room_plus; + if (is_even) { + in_delta_room_minus = Bignum::LessEqual(*numerator, *delta_minus); + } else { + in_delta_room_minus = Bignum::Less(*numerator, *delta_minus); + } + if (is_even) { + in_delta_room_plus = + Bignum::PlusCompare(*numerator, *delta_plus, *denominator) >= 0; + } else { + in_delta_room_plus = + Bignum::PlusCompare(*numerator, *delta_plus, *denominator) > 0; + } + if (!in_delta_room_minus && !in_delta_room_plus) { + // Prepare for next iteration. + numerator->Times10(); + delta_minus->Times10(); + // We optimized delta_plus to be equal to delta_minus (if they share the + // same value). So don't multiply delta_plus if they point to the same + // object. + if (delta_minus != delta_plus) { + delta_plus->Times10(); + } + } else if (in_delta_room_minus && in_delta_room_plus) { + // Let's see if 2*numerator < denominator. + // If yes, then the next digit would be < 5 and we can round down. + int compare = Bignum::PlusCompare(*numerator, *numerator, *denominator); + if (compare < 0) { + // Remaining digits are less than .5. -> Round down (== do nothing). + } else if (compare > 0) { + // Remaining digits are more than .5 of denominator. -> Round up. + // Note that the last digit could not be a '9' as otherwise the whole + // loop would have stopped earlier. + // We still have an assert here in case the preconditions were not + // satisfied. + ASSERT(buffer[(*length) - 1] != '9'); + buffer[(*length) - 1]++; + } else { + // Halfway case. + // TODO(floitsch): need a way to solve half-way cases. + // For now let's round towards even (since this is what Gay seems to + // do). + + if ((buffer[(*length) - 1] - '0') % 2 == 0) { + // Round down => Do nothing. + } else { + ASSERT(buffer[(*length) - 1] != '9'); + buffer[(*length) - 1]++; + } + } + return; + } else if (in_delta_room_minus) { + // Round down (== do nothing). + return; + } else { // in_delta_room_plus + // Round up. + // Note again that the last digit could not be '9' since this would have + // stopped the loop earlier. + // We still have an ASSERT here, in case the preconditions were not + // satisfied. + ASSERT(buffer[(*length) -1] != '9'); + buffer[(*length) - 1]++; + return; + } + } +} + + +// Let v = numerator / denominator < 10. +// Then we generate 'count' digits of d = x.xxxxx... (without the decimal point) +// from left to right. Once 'count' digits have been produced we decide wether +// to round up or down. Remainders of exactly .5 round upwards. Numbers such +// as 9.999999 propagate a carry all the way, and change the +// exponent (decimal_point), when rounding upwards. +static void GenerateCountedDigits(int count, int* decimal_point, + Bignum* numerator, Bignum* denominator, + Vector buffer, int* length) { + ASSERT(count >= 0); + for (int i = 0; i < count - 1; ++i) { + uint16_t digit; + digit = numerator->DivideModuloIntBignum(*denominator); + ASSERT(digit <= 9); // digit is a uint16_t and therefore always positive. + // digit = numerator / denominator (integer division). + // numerator = numerator % denominator. + buffer[i] = static_cast(digit + '0'); + // Prepare for next iteration. + numerator->Times10(); + } + // Generate the last digit. + uint16_t digit; + digit = numerator->DivideModuloIntBignum(*denominator); + if (Bignum::PlusCompare(*numerator, *numerator, *denominator) >= 0) { + digit++; + } + ASSERT(digit <= 10); + buffer[count - 1] = static_cast(digit + '0'); + // Correct bad digits (in case we had a sequence of '9's). Propagate the + // carry until we hat a non-'9' or til we reach the first digit. + for (int i = count - 1; i > 0; --i) { + if (buffer[i] != '0' + 10) break; + buffer[i] = '0'; + buffer[i - 1]++; + } + if (buffer[0] == '0' + 10) { + // Propagate a carry past the top place. + buffer[0] = '1'; + (*decimal_point)++; + } + *length = count; +} + + +// Generates 'requested_digits' after the decimal point. It might omit +// trailing '0's. If the input number is too small then no digits at all are +// generated (ex.: 2 fixed digits for 0.00001). +// +// Input verifies: 1 <= (numerator + delta) / denominator < 10. +static void BignumToFixed(int requested_digits, int* decimal_point, + Bignum* numerator, Bignum* denominator, + Vector(buffer), int* length) { + // Note that we have to look at more than just the requested_digits, since + // a number could be rounded up. Example: v=0.5 with requested_digits=0. + // Even though the power of v equals 0 we can't just stop here. + if (-(*decimal_point) > requested_digits) { + // The number is definitively too small. + // Ex: 0.001 with requested_digits == 1. + // Set decimal-point to -requested_digits. This is what Gay does. + // Note that it should not have any effect anyways since the string is + // empty. + *decimal_point = -requested_digits; + *length = 0; + return; + } else if (-(*decimal_point) == requested_digits) { + // We only need to verify if the number rounds down or up. + // Ex: 0.04 and 0.06 with requested_digits == 1. + ASSERT(*decimal_point == -requested_digits); + // Initially the fraction lies in range (1, 10]. Multiply the denominator + // by 10 so that we can compare more easily. + denominator->Times10(); + if (Bignum::PlusCompare(*numerator, *numerator, *denominator) >= 0) { + // If the fraction is >= 0.5 then we have to include the rounded + // digit. + buffer[0] = '1'; + *length = 1; + (*decimal_point)++; + } else { + // Note that we caught most of similar cases earlier. + *length = 0; + } + return; + } else { + // The requested digits correspond to the digits after the point. + // The variable 'needed_digits' includes the digits before the point. + int needed_digits = (*decimal_point) + requested_digits; + GenerateCountedDigits(needed_digits, decimal_point, + numerator, denominator, + buffer, length); + } +} + + +// Returns an estimation of k such that 10^(k-1) <= v < 10^k where +// v = f * 2^exponent and 2^52 <= f < 2^53. +// v is hence a normalized double with the given exponent. The output is an +// approximation for the exponent of the decimal approimation .digits * 10^k. +// +// The result might undershoot by 1 in which case 10^k <= v < 10^k+1. +// Note: this property holds for v's upper boundary m+ too. +// 10^k <= m+ < 10^k+1. +// (see explanation below). +// +// Examples: +// EstimatePower(0) => 16 +// EstimatePower(-52) => 0 +// +// Note: e >= 0 => EstimatedPower(e) > 0. No similar claim can be made for e<0. +static int EstimatePower(int exponent) { + // This function estimates log10 of v where v = f*2^e (with e == exponent). + // Note that 10^floor(log10(v)) <= v, but v <= 10^ceil(log10(v)). + // Note that f is bounded by its container size. Let p = 53 (the double's + // significand size). Then 2^(p-1) <= f < 2^p. + // + // Given that log10(v) == log2(v)/log2(10) and e+(len(f)-1) is quite close + // to log2(v) the function is simplified to (e+(len(f)-1)/log2(10)). + // The computed number undershoots by less than 0.631 (when we compute log3 + // and not log10). + // + // Optimization: since we only need an approximated result this computation + // can be performed on 64 bit integers. On x86/x64 architecture the speedup is + // not really measurable, though. + // + // Since we want to avoid overshooting we decrement by 1e10 so that + // floating-point imprecisions don't affect us. + // + // Explanation for v's boundary m+: the computation takes advantage of + // the fact that 2^(p-1) <= f < 2^p. Boundaries still satisfy this requirement + // (even for denormals where the delta can be much more important). + + const double k1Log10 = 0.30102999566398114; // 1/lg(10) + + // For doubles len(f) == 53 (don't forget the hidden bit). + const int kSignificandSize = Double::kSignificandSize; + double estimate = ceil((exponent + kSignificandSize - 1) * k1Log10 - 1e-10); + return static_cast(estimate); +} + + +// See comments for InitialScaledStartValues. +static void InitialScaledStartValuesPositiveExponent( + uint64_t significand, int exponent, + int estimated_power, bool need_boundary_deltas, + Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus) { + // A positive exponent implies a positive power. + ASSERT(estimated_power >= 0); + // Since the estimated_power is positive we simply multiply the denominator + // by 10^estimated_power. + + // numerator = v. + numerator->AssignUInt64(significand); + numerator->ShiftLeft(exponent); + // denominator = 10^estimated_power. + denominator->AssignPowerUInt16(10, estimated_power); + + if (need_boundary_deltas) { + // Introduce a common denominator so that the deltas to the boundaries are + // integers. + denominator->ShiftLeft(1); + numerator->ShiftLeft(1); + // Let v = f * 2^e, then m+ - v = 1/2 * 2^e; With the common + // denominator (of 2) delta_plus equals 2^e. + delta_plus->AssignUInt16(1); + delta_plus->ShiftLeft(exponent); + // Same for delta_minus. The adjustments if f == 2^p-1 are done later. + delta_minus->AssignUInt16(1); + delta_minus->ShiftLeft(exponent); + } +} + + +// See comments for InitialScaledStartValues +static void InitialScaledStartValuesNegativeExponentPositivePower( + uint64_t significand, int exponent, + int estimated_power, bool need_boundary_deltas, + Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus) { + // v = f * 2^e with e < 0, and with estimated_power >= 0. + // This means that e is close to 0 (have a look at how estimated_power is + // computed). + + // numerator = significand + // since v = significand * 2^exponent this is equivalent to + // numerator = v * / 2^-exponent + numerator->AssignUInt64(significand); + // denominator = 10^estimated_power * 2^-exponent (with exponent < 0) + denominator->AssignPowerUInt16(10, estimated_power); + denominator->ShiftLeft(-exponent); + + if (need_boundary_deltas) { + // Introduce a common denominator so that the deltas to the boundaries are + // integers. + denominator->ShiftLeft(1); + numerator->ShiftLeft(1); + // Let v = f * 2^e, then m+ - v = 1/2 * 2^e; With the common + // denominator (of 2) delta_plus equals 2^e. + // Given that the denominator already includes v's exponent the distance + // to the boundaries is simply 1. + delta_plus->AssignUInt16(1); + // Same for delta_minus. The adjustments if f == 2^p-1 are done later. + delta_minus->AssignUInt16(1); + } +} + + +// See comments for InitialScaledStartValues +static void InitialScaledStartValuesNegativeExponentNegativePower( + uint64_t significand, int exponent, + int estimated_power, bool need_boundary_deltas, + Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus) { + // Instead of multiplying the denominator with 10^estimated_power we + // multiply all values (numerator and deltas) by 10^-estimated_power. + + // Use numerator as temporary container for power_ten. + Bignum* power_ten = numerator; + power_ten->AssignPowerUInt16(10, -estimated_power); + + if (need_boundary_deltas) { + // Since power_ten == numerator we must make a copy of 10^estimated_power + // before we complete the computation of the numerator. + // delta_plus = delta_minus = 10^estimated_power + delta_plus->AssignBignum(*power_ten); + delta_minus->AssignBignum(*power_ten); + } + + // numerator = significand * 2 * 10^-estimated_power + // since v = significand * 2^exponent this is equivalent to + // numerator = v * 10^-estimated_power * 2 * 2^-exponent. + // Remember: numerator has been abused as power_ten. So no need to assign it + // to itself. + ASSERT(numerator == power_ten); + numerator->MultiplyByUInt64(significand); + + // denominator = 2 * 2^-exponent with exponent < 0. + denominator->AssignUInt16(1); + denominator->ShiftLeft(-exponent); + + if (need_boundary_deltas) { + // Introduce a common denominator so that the deltas to the boundaries are + // integers. + numerator->ShiftLeft(1); + denominator->ShiftLeft(1); + // With this shift the boundaries have their correct value, since + // delta_plus = 10^-estimated_power, and + // delta_minus = 10^-estimated_power. + // These assignments have been done earlier. + // The adjustments if f == 2^p-1 (lower boundary is closer) are done later. + } +} + + +// Let v = significand * 2^exponent. +// Computes v / 10^estimated_power exactly, as a ratio of two bignums, numerator +// and denominator. The functions GenerateShortestDigits and +// GenerateCountedDigits will then convert this ratio to its decimal +// representation d, with the required accuracy. +// Then d * 10^estimated_power is the representation of v. +// (Note: the fraction and the estimated_power might get adjusted before +// generating the decimal representation.) +// +// The initial start values consist of: +// - a scaled numerator: s.t. numerator/denominator == v / 10^estimated_power. +// - a scaled (common) denominator. +// optionally (used by GenerateShortestDigits to decide if it has the shortest +// decimal converting back to v): +// - v - m-: the distance to the lower boundary. +// - m+ - v: the distance to the upper boundary. +// +// v, m+, m-, and therefore v - m- and m+ - v all share the same denominator. +// +// Let ep == estimated_power, then the returned values will satisfy: +// v / 10^ep = numerator / denominator. +// v's boundarys m- and m+: +// m- / 10^ep == v / 10^ep - delta_minus / denominator +// m+ / 10^ep == v / 10^ep + delta_plus / denominator +// Or in other words: +// m- == v - delta_minus * 10^ep / denominator; +// m+ == v + delta_plus * 10^ep / denominator; +// +// Since 10^(k-1) <= v < 10^k (with k == estimated_power) +// or 10^k <= v < 10^(k+1) +// we then have 0.1 <= numerator/denominator < 1 +// or 1 <= numerator/denominator < 10 +// +// It is then easy to kickstart the digit-generation routine. +// +// The boundary-deltas are only filled if the mode equals BIGNUM_DTOA_SHORTEST +// or BIGNUM_DTOA_SHORTEST_SINGLE. + +static void InitialScaledStartValues(uint64_t significand, + int exponent, + bool lower_boundary_is_closer, + int estimated_power, + bool need_boundary_deltas, + Bignum* numerator, + Bignum* denominator, + Bignum* delta_minus, + Bignum* delta_plus) { + if (exponent >= 0) { + InitialScaledStartValuesPositiveExponent( + significand, exponent, estimated_power, need_boundary_deltas, + numerator, denominator, delta_minus, delta_plus); + } else if (estimated_power >= 0) { + InitialScaledStartValuesNegativeExponentPositivePower( + significand, exponent, estimated_power, need_boundary_deltas, + numerator, denominator, delta_minus, delta_plus); + } else { + InitialScaledStartValuesNegativeExponentNegativePower( + significand, exponent, estimated_power, need_boundary_deltas, + numerator, denominator, delta_minus, delta_plus); + } + + if (need_boundary_deltas && lower_boundary_is_closer) { + // The lower boundary is closer at half the distance of "normal" numbers. + // Increase the common denominator and adapt all but the delta_minus. + denominator->ShiftLeft(1); // *2 + numerator->ShiftLeft(1); // *2 + delta_plus->ShiftLeft(1); // *2 + } +} + + +// This routine multiplies numerator/denominator so that its values lies in the +// range 1-10. That is after a call to this function we have: +// 1 <= (numerator + delta_plus) /denominator < 10. +// Let numerator the input before modification and numerator' the argument +// after modification, then the output-parameter decimal_point is such that +// numerator / denominator * 10^estimated_power == +// numerator' / denominator' * 10^(decimal_point - 1) +// In some cases estimated_power was too low, and this is already the case. We +// then simply adjust the power so that 10^(k-1) <= v < 10^k (with k == +// estimated_power) but do not touch the numerator or denominator. +// Otherwise the routine multiplies the numerator and the deltas by 10. +static void FixupMultiply10(int estimated_power, bool is_even, + int* decimal_point, + Bignum* numerator, Bignum* denominator, + Bignum* delta_minus, Bignum* delta_plus) { + bool in_range; + if (is_even) { + // For IEEE doubles half-way cases (in decimal system numbers ending with 5) + // are rounded to the closest floating-point number with even significand. + in_range = Bignum::PlusCompare(*numerator, *delta_plus, *denominator) >= 0; + } else { + in_range = Bignum::PlusCompare(*numerator, *delta_plus, *denominator) > 0; + } + if (in_range) { + // Since numerator + delta_plus >= denominator we already have + // 1 <= numerator/denominator < 10. Simply update the estimated_power. + *decimal_point = estimated_power + 1; + } else { + *decimal_point = estimated_power; + numerator->Times10(); + if (Bignum::Equal(*delta_minus, *delta_plus)) { + delta_minus->Times10(); + delta_plus->AssignBignum(*delta_minus); + } else { + delta_minus->Times10(); + delta_plus->Times10(); + } + } +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum-dtoa.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum-dtoa.h new file mode 100644 index 0000000..34b9619 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum-dtoa.h @@ -0,0 +1,84 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_BIGNUM_DTOA_H_ +#define DOUBLE_CONVERSION_BIGNUM_DTOA_H_ + +#include "utils.h" + +namespace double_conversion { + +enum BignumDtoaMode { + // Return the shortest correct representation. + // For example the output of 0.299999999999999988897 is (the less accurate but + // correct) 0.3. + BIGNUM_DTOA_SHORTEST, + // Same as BIGNUM_DTOA_SHORTEST but for single-precision floats. + BIGNUM_DTOA_SHORTEST_SINGLE, + // Return a fixed number of digits after the decimal point. + // For instance fixed(0.1, 4) becomes 0.1000 + // If the input number is big, the output will be big. + BIGNUM_DTOA_FIXED, + // Return a fixed number of digits, no matter what the exponent is. + BIGNUM_DTOA_PRECISION +}; + +// Converts the given double 'v' to ascii. +// The result should be interpreted as buffer * 10^(point-length). +// The buffer will be null-terminated. +// +// The input v must be > 0 and different from NaN, and Infinity. +// +// The output depends on the given mode: +// - SHORTEST: produce the least amount of digits for which the internal +// identity requirement is still satisfied. If the digits are printed +// (together with the correct exponent) then reading this number will give +// 'v' again. The buffer will choose the representation that is closest to +// 'v'. If there are two at the same distance, than the number is round up. +// In this mode the 'requested_digits' parameter is ignored. +// - FIXED: produces digits necessary to print a given number with +// 'requested_digits' digits after the decimal point. The produced digits +// might be too short in which case the caller has to fill the gaps with '0's. +// Example: toFixed(0.001, 5) is allowed to return buffer="1", point=-2. +// Halfway cases are rounded up. The call toFixed(0.15, 2) thus returns +// buffer="2", point=0. +// Note: the length of the returned buffer has no meaning wrt the significance +// of its digits. That is, just because it contains '0's does not mean that +// any other digit would not satisfy the internal identity requirement. +// - PRECISION: produces 'requested_digits' where the first digit is not '0'. +// Even though the length of produced digits usually equals +// 'requested_digits', the function is allowed to return fewer digits, in +// which case the caller has to fill the missing digits with '0's. +// Halfway cases are again rounded up. +// 'BignumDtoa' expects the given buffer to be big enough to hold all digits +// and a terminating null-character. +void BignumDtoa(double v, BignumDtoaMode mode, int requested_digits, + Vector buffer, int* length, int* point); + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_BIGNUM_DTOA_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum.cc new file mode 100644 index 0000000..8892de8 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum.cc @@ -0,0 +1,766 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include "bignum.h" +#include "utils.h" + +namespace double_conversion { + +Bignum::Bignum() + : bigits_(bigits_buffer_, kBigitCapacity), used_digits_(0), exponent_(0) { + for (int i = 0; i < kBigitCapacity; ++i) { + bigits_[i] = 0; + } +} + + +template +static int BitSize(S value) { + (void) value; // Mark variable as used. + return 8 * sizeof(value); +} + +// Guaranteed to lie in one Bigit. +void Bignum::AssignUInt16(uint16_t value) { + ASSERT(kBigitSize >= BitSize(value)); + Zero(); + if (value == 0) return; + + EnsureCapacity(1); + bigits_[0] = value; + used_digits_ = 1; +} + + +void Bignum::AssignUInt64(uint64_t value) { + const int kUInt64Size = 64; + + Zero(); + if (value == 0) return; + + int needed_bigits = kUInt64Size / kBigitSize + 1; + EnsureCapacity(needed_bigits); + for (int i = 0; i < needed_bigits; ++i) { + bigits_[i] = value & kBigitMask; + value = value >> kBigitSize; + } + used_digits_ = needed_bigits; + Clamp(); +} + + +void Bignum::AssignBignum(const Bignum& other) { + exponent_ = other.exponent_; + for (int i = 0; i < other.used_digits_; ++i) { + bigits_[i] = other.bigits_[i]; + } + // Clear the excess digits (if there were any). + for (int i = other.used_digits_; i < used_digits_; ++i) { + bigits_[i] = 0; + } + used_digits_ = other.used_digits_; +} + + +static uint64_t ReadUInt64(Vector buffer, + int from, + int digits_to_read) { + uint64_t result = 0; + for (int i = from; i < from + digits_to_read; ++i) { + int digit = buffer[i] - '0'; + ASSERT(0 <= digit && digit <= 9); + result = result * 10 + digit; + } + return result; +} + + +void Bignum::AssignDecimalString(Vector value) { + // 2^64 = 18446744073709551616 > 10^19 + const int kMaxUint64DecimalDigits = 19; + Zero(); + int length = value.length(); + unsigned int pos = 0; + // Let's just say that each digit needs 4 bits. + while (length >= kMaxUint64DecimalDigits) { + uint64_t digits = ReadUInt64(value, pos, kMaxUint64DecimalDigits); + pos += kMaxUint64DecimalDigits; + length -= kMaxUint64DecimalDigits; + MultiplyByPowerOfTen(kMaxUint64DecimalDigits); + AddUInt64(digits); + } + uint64_t digits = ReadUInt64(value, pos, length); + MultiplyByPowerOfTen(length); + AddUInt64(digits); + Clamp(); +} + + +static int HexCharValue(char c) { + if ('0' <= c && c <= '9') return c - '0'; + if ('a' <= c && c <= 'f') return 10 + c - 'a'; + ASSERT('A' <= c && c <= 'F'); + return 10 + c - 'A'; +} + + +void Bignum::AssignHexString(Vector value) { + Zero(); + int length = value.length(); + + int needed_bigits = length * 4 / kBigitSize + 1; + EnsureCapacity(needed_bigits); + int string_index = length - 1; + for (int i = 0; i < needed_bigits - 1; ++i) { + // These bigits are guaranteed to be "full". + Chunk current_bigit = 0; + for (int j = 0; j < kBigitSize / 4; j++) { + current_bigit += HexCharValue(value[string_index--]) << (j * 4); + } + bigits_[i] = current_bigit; + } + used_digits_ = needed_bigits - 1; + + Chunk most_significant_bigit = 0; // Could be = 0; + for (int j = 0; j <= string_index; ++j) { + most_significant_bigit <<= 4; + most_significant_bigit += HexCharValue(value[j]); + } + if (most_significant_bigit != 0) { + bigits_[used_digits_] = most_significant_bigit; + used_digits_++; + } + Clamp(); +} + + +void Bignum::AddUInt64(uint64_t operand) { + if (operand == 0) return; + Bignum other; + other.AssignUInt64(operand); + AddBignum(other); +} + + +void Bignum::AddBignum(const Bignum& other) { + ASSERT(IsClamped()); + ASSERT(other.IsClamped()); + + // If this has a greater exponent than other append zero-bigits to this. + // After this call exponent_ <= other.exponent_. + Align(other); + + // There are two possibilities: + // aaaaaaaaaaa 0000 (where the 0s represent a's exponent) + // bbbbb 00000000 + // ---------------- + // ccccccccccc 0000 + // or + // aaaaaaaaaa 0000 + // bbbbbbbbb 0000000 + // ----------------- + // cccccccccccc 0000 + // In both cases we might need a carry bigit. + + EnsureCapacity(1 + Max(BigitLength(), other.BigitLength()) - exponent_); + Chunk carry = 0; + int bigit_pos = other.exponent_ - exponent_; + ASSERT(bigit_pos >= 0); + for (int i = 0; i < other.used_digits_; ++i) { + Chunk sum = bigits_[bigit_pos] + other.bigits_[i] + carry; + bigits_[bigit_pos] = sum & kBigitMask; + carry = sum >> kBigitSize; + bigit_pos++; + } + + while (carry != 0) { + Chunk sum = bigits_[bigit_pos] + carry; + bigits_[bigit_pos] = sum & kBigitMask; + carry = sum >> kBigitSize; + bigit_pos++; + } + used_digits_ = Max(bigit_pos, used_digits_); + ASSERT(IsClamped()); +} + + +void Bignum::SubtractBignum(const Bignum& other) { + ASSERT(IsClamped()); + ASSERT(other.IsClamped()); + // We require this to be bigger than other. + ASSERT(LessEqual(other, *this)); + + Align(other); + + int offset = other.exponent_ - exponent_; + Chunk borrow = 0; + int i; + for (i = 0; i < other.used_digits_; ++i) { + ASSERT((borrow == 0) || (borrow == 1)); + Chunk difference = bigits_[i + offset] - other.bigits_[i] - borrow; + bigits_[i + offset] = difference & kBigitMask; + borrow = difference >> (kChunkSize - 1); + } + while (borrow != 0) { + Chunk difference = bigits_[i + offset] - borrow; + bigits_[i + offset] = difference & kBigitMask; + borrow = difference >> (kChunkSize - 1); + ++i; + } + Clamp(); +} + + +void Bignum::ShiftLeft(int shift_amount) { + if (used_digits_ == 0) return; + exponent_ += shift_amount / kBigitSize; + int local_shift = shift_amount % kBigitSize; + EnsureCapacity(used_digits_ + 1); + BigitsShiftLeft(local_shift); +} + + +void Bignum::MultiplyByUInt32(uint32_t factor) { + if (factor == 1) return; + if (factor == 0) { + Zero(); + return; + } + if (used_digits_ == 0) return; + + // The product of a bigit with the factor is of size kBigitSize + 32. + // Assert that this number + 1 (for the carry) fits into double chunk. + ASSERT(kDoubleChunkSize >= kBigitSize + 32 + 1); + DoubleChunk carry = 0; + for (int i = 0; i < used_digits_; ++i) { + DoubleChunk product = static_cast(factor) * bigits_[i] + carry; + bigits_[i] = static_cast(product & kBigitMask); + carry = (product >> kBigitSize); + } + while (carry != 0) { + EnsureCapacity(used_digits_ + 1); + bigits_[used_digits_] = carry & kBigitMask; + used_digits_++; + carry >>= kBigitSize; + } +} + + +void Bignum::MultiplyByUInt64(uint64_t factor) { + if (factor == 1) return; + if (factor == 0) { + Zero(); + return; + } + ASSERT(kBigitSize < 32); + uint64_t carry = 0; + uint64_t low = factor & 0xFFFFFFFF; + uint64_t high = factor >> 32; + for (int i = 0; i < used_digits_; ++i) { + uint64_t product_low = low * bigits_[i]; + uint64_t product_high = high * bigits_[i]; + uint64_t tmp = (carry & kBigitMask) + product_low; + bigits_[i] = tmp & kBigitMask; + carry = (carry >> kBigitSize) + (tmp >> kBigitSize) + + (product_high << (32 - kBigitSize)); + } + while (carry != 0) { + EnsureCapacity(used_digits_ + 1); + bigits_[used_digits_] = carry & kBigitMask; + used_digits_++; + carry >>= kBigitSize; + } +} + + +void Bignum::MultiplyByPowerOfTen(int exponent) { + const uint64_t kFive27 = UINT64_2PART_C(0x6765c793, fa10079d); + const uint16_t kFive1 = 5; + const uint16_t kFive2 = kFive1 * 5; + const uint16_t kFive3 = kFive2 * 5; + const uint16_t kFive4 = kFive3 * 5; + const uint16_t kFive5 = kFive4 * 5; + const uint16_t kFive6 = kFive5 * 5; + const uint32_t kFive7 = kFive6 * 5; + const uint32_t kFive8 = kFive7 * 5; + const uint32_t kFive9 = kFive8 * 5; + const uint32_t kFive10 = kFive9 * 5; + const uint32_t kFive11 = kFive10 * 5; + const uint32_t kFive12 = kFive11 * 5; + const uint32_t kFive13 = kFive12 * 5; + const uint32_t kFive1_to_12[] = + { kFive1, kFive2, kFive3, kFive4, kFive5, kFive6, + kFive7, kFive8, kFive9, kFive10, kFive11, kFive12 }; + + ASSERT(exponent >= 0); + if (exponent == 0) return; + if (used_digits_ == 0) return; + + // We shift by exponent at the end just before returning. + int remaining_exponent = exponent; + while (remaining_exponent >= 27) { + MultiplyByUInt64(kFive27); + remaining_exponent -= 27; + } + while (remaining_exponent >= 13) { + MultiplyByUInt32(kFive13); + remaining_exponent -= 13; + } + if (remaining_exponent > 0) { + MultiplyByUInt32(kFive1_to_12[remaining_exponent - 1]); + } + ShiftLeft(exponent); +} + + +void Bignum::Square() { + ASSERT(IsClamped()); + int product_length = 2 * used_digits_; + EnsureCapacity(product_length); + + // Comba multiplication: compute each column separately. + // Example: r = a2a1a0 * b2b1b0. + // r = 1 * a0b0 + + // 10 * (a1b0 + a0b1) + + // 100 * (a2b0 + a1b1 + a0b2) + + // 1000 * (a2b1 + a1b2) + + // 10000 * a2b2 + // + // In the worst case we have to accumulate nb-digits products of digit*digit. + // + // Assert that the additional number of bits in a DoubleChunk are enough to + // sum up used_digits of Bigit*Bigit. + if ((1 << (2 * (kChunkSize - kBigitSize))) <= used_digits_) { + UNIMPLEMENTED(); + } + DoubleChunk accumulator = 0; + // First shift the digits so we don't overwrite them. + int copy_offset = used_digits_; + for (int i = 0; i < used_digits_; ++i) { + bigits_[copy_offset + i] = bigits_[i]; + } + // We have two loops to avoid some 'if's in the loop. + for (int i = 0; i < used_digits_; ++i) { + // Process temporary digit i with power i. + // The sum of the two indices must be equal to i. + int bigit_index1 = i; + int bigit_index2 = 0; + // Sum all of the sub-products. + while (bigit_index1 >= 0) { + Chunk chunk1 = bigits_[copy_offset + bigit_index1]; + Chunk chunk2 = bigits_[copy_offset + bigit_index2]; + accumulator += static_cast(chunk1) * chunk2; + bigit_index1--; + bigit_index2++; + } + bigits_[i] = static_cast(accumulator) & kBigitMask; + accumulator >>= kBigitSize; + } + for (int i = used_digits_; i < product_length; ++i) { + int bigit_index1 = used_digits_ - 1; + int bigit_index2 = i - bigit_index1; + // Invariant: sum of both indices is again equal to i. + // Inner loop runs 0 times on last iteration, emptying accumulator. + while (bigit_index2 < used_digits_) { + Chunk chunk1 = bigits_[copy_offset + bigit_index1]; + Chunk chunk2 = bigits_[copy_offset + bigit_index2]; + accumulator += static_cast(chunk1) * chunk2; + bigit_index1--; + bigit_index2++; + } + // The overwritten bigits_[i] will never be read in further loop iterations, + // because bigit_index1 and bigit_index2 are always greater + // than i - used_digits_. + bigits_[i] = static_cast(accumulator) & kBigitMask; + accumulator >>= kBigitSize; + } + // Since the result was guaranteed to lie inside the number the + // accumulator must be 0 now. + ASSERT(accumulator == 0); + + // Don't forget to update the used_digits and the exponent. + used_digits_ = product_length; + exponent_ *= 2; + Clamp(); +} + + +void Bignum::AssignPowerUInt16(uint16_t base, int power_exponent) { + ASSERT(base != 0); + ASSERT(power_exponent >= 0); + if (power_exponent == 0) { + AssignUInt16(1); + return; + } + Zero(); + int shifts = 0; + // We expect base to be in range 2-32, and most often to be 10. + // It does not make much sense to implement different algorithms for counting + // the bits. + while ((base & 1) == 0) { + base >>= 1; + shifts++; + } + int bit_size = 0; + int tmp_base = base; + while (tmp_base != 0) { + tmp_base >>= 1; + bit_size++; + } + int final_size = bit_size * power_exponent; + // 1 extra bigit for the shifting, and one for rounded final_size. + EnsureCapacity(final_size / kBigitSize + 2); + + // Left to Right exponentiation. + int mask = 1; + while (power_exponent >= mask) mask <<= 1; + + // The mask is now pointing to the bit above the most significant 1-bit of + // power_exponent. + // Get rid of first 1-bit; + mask >>= 2; + uint64_t this_value = base; + + bool delayed_multipliciation = false; + const uint64_t max_32bits = 0xFFFFFFFF; + while (mask != 0 && this_value <= max_32bits) { + this_value = this_value * this_value; + // Verify that there is enough space in this_value to perform the + // multiplication. The first bit_size bits must be 0. + if ((power_exponent & mask) != 0) { + uint64_t base_bits_mask = + ~((static_cast(1) << (64 - bit_size)) - 1); + bool high_bits_zero = (this_value & base_bits_mask) == 0; + if (high_bits_zero) { + this_value *= base; + } else { + delayed_multipliciation = true; + } + } + mask >>= 1; + } + AssignUInt64(this_value); + if (delayed_multipliciation) { + MultiplyByUInt32(base); + } + + // Now do the same thing as a bignum. + while (mask != 0) { + Square(); + if ((power_exponent & mask) != 0) { + MultiplyByUInt32(base); + } + mask >>= 1; + } + + // And finally add the saved shifts. + ShiftLeft(shifts * power_exponent); +} + + +// Precondition: this/other < 16bit. +uint16_t Bignum::DivideModuloIntBignum(const Bignum& other) { + ASSERT(IsClamped()); + ASSERT(other.IsClamped()); + ASSERT(other.used_digits_ > 0); + + // Easy case: if we have less digits than the divisor than the result is 0. + // Note: this handles the case where this == 0, too. + if (BigitLength() < other.BigitLength()) { + return 0; + } + + Align(other); + + uint16_t result = 0; + + // Start by removing multiples of 'other' until both numbers have the same + // number of digits. + while (BigitLength() > other.BigitLength()) { + // This naive approach is extremely inefficient if `this` divided by other + // is big. This function is implemented for doubleToString where + // the result should be small (less than 10). + ASSERT(other.bigits_[other.used_digits_ - 1] >= ((1 << kBigitSize) / 16)); + ASSERT(bigits_[used_digits_ - 1] < 0x10000); + // Remove the multiples of the first digit. + // Example this = 23 and other equals 9. -> Remove 2 multiples. + result += static_cast(bigits_[used_digits_ - 1]); + SubtractTimes(other, bigits_[used_digits_ - 1]); + } + + ASSERT(BigitLength() == other.BigitLength()); + + // Both bignums are at the same length now. + // Since other has more than 0 digits we know that the access to + // bigits_[used_digits_ - 1] is safe. + Chunk this_bigit = bigits_[used_digits_ - 1]; + Chunk other_bigit = other.bigits_[other.used_digits_ - 1]; + + if (other.used_digits_ == 1) { + // Shortcut for easy (and common) case. + int quotient = this_bigit / other_bigit; + bigits_[used_digits_ - 1] = this_bigit - other_bigit * quotient; + ASSERT(quotient < 0x10000); + result += static_cast(quotient); + Clamp(); + return result; + } + + int division_estimate = this_bigit / (other_bigit + 1); + ASSERT(division_estimate < 0x10000); + result += static_cast(division_estimate); + SubtractTimes(other, division_estimate); + + if (other_bigit * (division_estimate + 1) > this_bigit) { + // No need to even try to subtract. Even if other's remaining digits were 0 + // another subtraction would be too much. + return result; + } + + while (LessEqual(other, *this)) { + SubtractBignum(other); + result++; + } + return result; +} + + +template +static int SizeInHexChars(S number) { + ASSERT(number > 0); + int result = 0; + while (number != 0) { + number >>= 4; + result++; + } + return result; +} + + +static char HexCharOfValue(int value) { + ASSERT(0 <= value && value <= 16); + if (value < 10) return static_cast(value + '0'); + return static_cast(value - 10 + 'A'); +} + + +bool Bignum::ToHexString(char* buffer, int buffer_size) const { + ASSERT(IsClamped()); + // Each bigit must be printable as separate hex-character. + ASSERT(kBigitSize % 4 == 0); + const int kHexCharsPerBigit = kBigitSize / 4; + + if (used_digits_ == 0) { + if (buffer_size < 2) return false; + buffer[0] = '0'; + buffer[1] = '\0'; + return true; + } + // We add 1 for the terminating '\0' character. + int needed_chars = (BigitLength() - 1) * kHexCharsPerBigit + + SizeInHexChars(bigits_[used_digits_ - 1]) + 1; + if (needed_chars > buffer_size) return false; + int string_index = needed_chars - 1; + buffer[string_index--] = '\0'; + for (int i = 0; i < exponent_; ++i) { + for (int j = 0; j < kHexCharsPerBigit; ++j) { + buffer[string_index--] = '0'; + } + } + for (int i = 0; i < used_digits_ - 1; ++i) { + Chunk current_bigit = bigits_[i]; + for (int j = 0; j < kHexCharsPerBigit; ++j) { + buffer[string_index--] = HexCharOfValue(current_bigit & 0xF); + current_bigit >>= 4; + } + } + // And finally the last bigit. + Chunk most_significant_bigit = bigits_[used_digits_ - 1]; + while (most_significant_bigit != 0) { + buffer[string_index--] = HexCharOfValue(most_significant_bigit & 0xF); + most_significant_bigit >>= 4; + } + return true; +} + + +Bignum::Chunk Bignum::BigitAt(int index) const { + if (index >= BigitLength()) return 0; + if (index < exponent_) return 0; + return bigits_[index - exponent_]; +} + + +int Bignum::Compare(const Bignum& a, const Bignum& b) { + ASSERT(a.IsClamped()); + ASSERT(b.IsClamped()); + int bigit_length_a = a.BigitLength(); + int bigit_length_b = b.BigitLength(); + if (bigit_length_a < bigit_length_b) return -1; + if (bigit_length_a > bigit_length_b) return +1; + for (int i = bigit_length_a - 1; i >= Min(a.exponent_, b.exponent_); --i) { + Chunk bigit_a = a.BigitAt(i); + Chunk bigit_b = b.BigitAt(i); + if (bigit_a < bigit_b) return -1; + if (bigit_a > bigit_b) return +1; + // Otherwise they are equal up to this digit. Try the next digit. + } + return 0; +} + + +int Bignum::PlusCompare(const Bignum& a, const Bignum& b, const Bignum& c) { + ASSERT(a.IsClamped()); + ASSERT(b.IsClamped()); + ASSERT(c.IsClamped()); + if (a.BigitLength() < b.BigitLength()) { + return PlusCompare(b, a, c); + } + if (a.BigitLength() + 1 < c.BigitLength()) return -1; + if (a.BigitLength() > c.BigitLength()) return +1; + // The exponent encodes 0-bigits. So if there are more 0-digits in 'a' than + // 'b' has digits, then the bigit-length of 'a'+'b' must be equal to the one + // of 'a'. + if (a.exponent_ >= b.BigitLength() && a.BigitLength() < c.BigitLength()) { + return -1; + } + + Chunk borrow = 0; + // Starting at min_exponent all digits are == 0. So no need to compare them. + int min_exponent = Min(Min(a.exponent_, b.exponent_), c.exponent_); + for (int i = c.BigitLength() - 1; i >= min_exponent; --i) { + Chunk chunk_a = a.BigitAt(i); + Chunk chunk_b = b.BigitAt(i); + Chunk chunk_c = c.BigitAt(i); + Chunk sum = chunk_a + chunk_b; + if (sum > chunk_c + borrow) { + return +1; + } else { + borrow = chunk_c + borrow - sum; + if (borrow > 1) return -1; + borrow <<= kBigitSize; + } + } + if (borrow == 0) return 0; + return -1; +} + + +void Bignum::Clamp() { + while (used_digits_ > 0 && bigits_[used_digits_ - 1] == 0) { + used_digits_--; + } + if (used_digits_ == 0) { + // Zero. + exponent_ = 0; + } +} + + +bool Bignum::IsClamped() const { + return used_digits_ == 0 || bigits_[used_digits_ - 1] != 0; +} + + +void Bignum::Zero() { + for (int i = 0; i < used_digits_; ++i) { + bigits_[i] = 0; + } + used_digits_ = 0; + exponent_ = 0; +} + + +void Bignum::Align(const Bignum& other) { + if (exponent_ > other.exponent_) { + // If "X" represents a "hidden" digit (by the exponent) then we are in the + // following case (a == this, b == other): + // a: aaaaaaXXXX or a: aaaaaXXX + // b: bbbbbbX b: bbbbbbbbXX + // We replace some of the hidden digits (X) of a with 0 digits. + // a: aaaaaa000X or a: aaaaa0XX + int zero_digits = exponent_ - other.exponent_; + EnsureCapacity(used_digits_ + zero_digits); + for (int i = used_digits_ - 1; i >= 0; --i) { + bigits_[i + zero_digits] = bigits_[i]; + } + for (int i = 0; i < zero_digits; ++i) { + bigits_[i] = 0; + } + used_digits_ += zero_digits; + exponent_ -= zero_digits; + ASSERT(used_digits_ >= 0); + ASSERT(exponent_ >= 0); + } +} + + +void Bignum::BigitsShiftLeft(int shift_amount) { + ASSERT(shift_amount < kBigitSize); + ASSERT(shift_amount >= 0); + Chunk carry = 0; + for (int i = 0; i < used_digits_; ++i) { + Chunk new_carry = bigits_[i] >> (kBigitSize - shift_amount); + bigits_[i] = ((bigits_[i] << shift_amount) + carry) & kBigitMask; + carry = new_carry; + } + if (carry != 0) { + bigits_[used_digits_] = carry; + used_digits_++; + } +} + + +void Bignum::SubtractTimes(const Bignum& other, int factor) { + ASSERT(exponent_ <= other.exponent_); + if (factor < 3) { + for (int i = 0; i < factor; ++i) { + SubtractBignum(other); + } + return; + } + Chunk borrow = 0; + int exponent_diff = other.exponent_ - exponent_; + for (int i = 0; i < other.used_digits_; ++i) { + DoubleChunk product = static_cast(factor) * other.bigits_[i]; + DoubleChunk remove = borrow + product; + Chunk difference = bigits_[i + exponent_diff] - (remove & kBigitMask); + bigits_[i + exponent_diff] = difference & kBigitMask; + borrow = static_cast((difference >> (kChunkSize - 1)) + + (remove >> kBigitSize)); + } + for (int i = other.used_digits_ + exponent_diff; i < used_digits_; ++i) { + if (borrow == 0) return; + Chunk difference = bigits_[i] - borrow; + bigits_[i] = difference & kBigitMask; + borrow = difference >> (kChunkSize - 1); + } + Clamp(); +} + + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum.h new file mode 100644 index 0000000..c385f22 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/bignum.h @@ -0,0 +1,144 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_BIGNUM_H_ +#define DOUBLE_CONVERSION_BIGNUM_H_ + +#include "utils.h" + +namespace double_conversion { + +class Bignum { + public: + // 3584 = 128 * 28. We can represent 2^3584 > 10^1000 accurately. + // This bignum can encode much bigger numbers, since it contains an + // exponent. + static const int kMaxSignificantBits = 3584; + + Bignum(); + void AssignUInt16(uint16_t value); + void AssignUInt64(uint64_t value); + void AssignBignum(const Bignum& other); + + void AssignDecimalString(Vector value); + void AssignHexString(Vector value); + + void AssignPowerUInt16(uint16_t base, int exponent); + + void AddUInt64(uint64_t operand); + void AddBignum(const Bignum& other); + // Precondition: this >= other. + void SubtractBignum(const Bignum& other); + + void Square(); + void ShiftLeft(int shift_amount); + void MultiplyByUInt32(uint32_t factor); + void MultiplyByUInt64(uint64_t factor); + void MultiplyByPowerOfTen(int exponent); + void Times10() { return MultiplyByUInt32(10); } + // Pseudocode: + // int result = this / other; + // this = this % other; + // In the worst case this function is in O(this/other). + uint16_t DivideModuloIntBignum(const Bignum& other); + + bool ToHexString(char* buffer, int buffer_size) const; + + // Returns + // -1 if a < b, + // 0 if a == b, and + // +1 if a > b. + static int Compare(const Bignum& a, const Bignum& b); + static bool Equal(const Bignum& a, const Bignum& b) { + return Compare(a, b) == 0; + } + static bool LessEqual(const Bignum& a, const Bignum& b) { + return Compare(a, b) <= 0; + } + static bool Less(const Bignum& a, const Bignum& b) { + return Compare(a, b) < 0; + } + // Returns Compare(a + b, c); + static int PlusCompare(const Bignum& a, const Bignum& b, const Bignum& c); + // Returns a + b == c + static bool PlusEqual(const Bignum& a, const Bignum& b, const Bignum& c) { + return PlusCompare(a, b, c) == 0; + } + // Returns a + b <= c + static bool PlusLessEqual(const Bignum& a, const Bignum& b, const Bignum& c) { + return PlusCompare(a, b, c) <= 0; + } + // Returns a + b < c + static bool PlusLess(const Bignum& a, const Bignum& b, const Bignum& c) { + return PlusCompare(a, b, c) < 0; + } + private: + typedef uint32_t Chunk; + typedef uint64_t DoubleChunk; + + static const int kChunkSize = sizeof(Chunk) * 8; + static const int kDoubleChunkSize = sizeof(DoubleChunk) * 8; + // With bigit size of 28 we loose some bits, but a double still fits easily + // into two chunks, and more importantly we can use the Comba multiplication. + static const int kBigitSize = 28; + static const Chunk kBigitMask = (1 << kBigitSize) - 1; + // Every instance allocates kBigitLength chunks on the stack. Bignums cannot + // grow. There are no checks if the stack-allocated space is sufficient. + static const int kBigitCapacity = kMaxSignificantBits / kBigitSize; + + void EnsureCapacity(int size) { + if (size > kBigitCapacity) { + UNREACHABLE(); + } + } + void Align(const Bignum& other); + void Clamp(); + bool IsClamped() const; + void Zero(); + // Requires this to have enough capacity (no tests done). + // Updates used_digits_ if necessary. + // shift_amount must be < kBigitSize. + void BigitsShiftLeft(int shift_amount); + // BigitLength includes the "hidden" digits encoded in the exponent. + int BigitLength() const { return used_digits_ + exponent_; } + Chunk BigitAt(int index) const; + void SubtractTimes(const Bignum& other, int factor); + + Chunk bigits_buffer_[kBigitCapacity]; + // A vector backed by bigits_buffer_. This way accesses to the array are + // checked for out-of-bounds errors. + Vector bigits_; + int used_digits_; + // The Bignum's value equals value(bigits_) * 2^(exponent_ * kBigitSize). + int exponent_; + + DISALLOW_COPY_AND_ASSIGN(Bignum); +}; + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_BIGNUM_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/cached-powers.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/cached-powers.cc new file mode 100644 index 0000000..2b43f06 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/cached-powers.cc @@ -0,0 +1,175 @@ +// Copyright 2006-2008 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include +#include +#include + +#include "utils.h" + +#include "cached-powers.h" + +namespace double_conversion { + +struct CachedPower { + uint64_t significand; + int16_t binary_exponent; + int16_t decimal_exponent; +}; + +static const CachedPower kCachedPowers[] = { + {UINT64_2PART_C(0xfa8fd5a0, 081c0288), -1220, -348}, + {UINT64_2PART_C(0xbaaee17f, a23ebf76), -1193, -340}, + {UINT64_2PART_C(0x8b16fb20, 3055ac76), -1166, -332}, + {UINT64_2PART_C(0xcf42894a, 5dce35ea), -1140, -324}, + {UINT64_2PART_C(0x9a6bb0aa, 55653b2d), -1113, -316}, + {UINT64_2PART_C(0xe61acf03, 3d1a45df), -1087, -308}, + {UINT64_2PART_C(0xab70fe17, c79ac6ca), -1060, -300}, + {UINT64_2PART_C(0xff77b1fc, bebcdc4f), -1034, -292}, + {UINT64_2PART_C(0xbe5691ef, 416bd60c), -1007, -284}, + {UINT64_2PART_C(0x8dd01fad, 907ffc3c), -980, -276}, + {UINT64_2PART_C(0xd3515c28, 31559a83), -954, -268}, + {UINT64_2PART_C(0x9d71ac8f, ada6c9b5), -927, -260}, + {UINT64_2PART_C(0xea9c2277, 23ee8bcb), -901, -252}, + {UINT64_2PART_C(0xaecc4991, 4078536d), -874, -244}, + {UINT64_2PART_C(0x823c1279, 5db6ce57), -847, -236}, + {UINT64_2PART_C(0xc2109436, 4dfb5637), -821, -228}, + {UINT64_2PART_C(0x9096ea6f, 3848984f), -794, -220}, + {UINT64_2PART_C(0xd77485cb, 25823ac7), -768, -212}, + {UINT64_2PART_C(0xa086cfcd, 97bf97f4), -741, -204}, + {UINT64_2PART_C(0xef340a98, 172aace5), -715, -196}, + {UINT64_2PART_C(0xb23867fb, 2a35b28e), -688, -188}, + {UINT64_2PART_C(0x84c8d4df, d2c63f3b), -661, -180}, + {UINT64_2PART_C(0xc5dd4427, 1ad3cdba), -635, -172}, + {UINT64_2PART_C(0x936b9fce, bb25c996), -608, -164}, + {UINT64_2PART_C(0xdbac6c24, 7d62a584), -582, -156}, + {UINT64_2PART_C(0xa3ab6658, 0d5fdaf6), -555, -148}, + {UINT64_2PART_C(0xf3e2f893, dec3f126), -529, -140}, + {UINT64_2PART_C(0xb5b5ada8, aaff80b8), -502, -132}, + {UINT64_2PART_C(0x87625f05, 6c7c4a8b), -475, -124}, + {UINT64_2PART_C(0xc9bcff60, 34c13053), -449, -116}, + {UINT64_2PART_C(0x964e858c, 91ba2655), -422, -108}, + {UINT64_2PART_C(0xdff97724, 70297ebd), -396, -100}, + {UINT64_2PART_C(0xa6dfbd9f, b8e5b88f), -369, -92}, + {UINT64_2PART_C(0xf8a95fcf, 88747d94), -343, -84}, + {UINT64_2PART_C(0xb9447093, 8fa89bcf), -316, -76}, + {UINT64_2PART_C(0x8a08f0f8, bf0f156b), -289, -68}, + {UINT64_2PART_C(0xcdb02555, 653131b6), -263, -60}, + {UINT64_2PART_C(0x993fe2c6, d07b7fac), -236, -52}, + {UINT64_2PART_C(0xe45c10c4, 2a2b3b06), -210, -44}, + {UINT64_2PART_C(0xaa242499, 697392d3), -183, -36}, + {UINT64_2PART_C(0xfd87b5f2, 8300ca0e), -157, -28}, + {UINT64_2PART_C(0xbce50864, 92111aeb), -130, -20}, + {UINT64_2PART_C(0x8cbccc09, 6f5088cc), -103, -12}, + {UINT64_2PART_C(0xd1b71758, e219652c), -77, -4}, + {UINT64_2PART_C(0x9c400000, 00000000), -50, 4}, + {UINT64_2PART_C(0xe8d4a510, 00000000), -24, 12}, + {UINT64_2PART_C(0xad78ebc5, ac620000), 3, 20}, + {UINT64_2PART_C(0x813f3978, f8940984), 30, 28}, + {UINT64_2PART_C(0xc097ce7b, c90715b3), 56, 36}, + {UINT64_2PART_C(0x8f7e32ce, 7bea5c70), 83, 44}, + {UINT64_2PART_C(0xd5d238a4, abe98068), 109, 52}, + {UINT64_2PART_C(0x9f4f2726, 179a2245), 136, 60}, + {UINT64_2PART_C(0xed63a231, d4c4fb27), 162, 68}, + {UINT64_2PART_C(0xb0de6538, 8cc8ada8), 189, 76}, + {UINT64_2PART_C(0x83c7088e, 1aab65db), 216, 84}, + {UINT64_2PART_C(0xc45d1df9, 42711d9a), 242, 92}, + {UINT64_2PART_C(0x924d692c, a61be758), 269, 100}, + {UINT64_2PART_C(0xda01ee64, 1a708dea), 295, 108}, + {UINT64_2PART_C(0xa26da399, 9aef774a), 322, 116}, + {UINT64_2PART_C(0xf209787b, b47d6b85), 348, 124}, + {UINT64_2PART_C(0xb454e4a1, 79dd1877), 375, 132}, + {UINT64_2PART_C(0x865b8692, 5b9bc5c2), 402, 140}, + {UINT64_2PART_C(0xc83553c5, c8965d3d), 428, 148}, + {UINT64_2PART_C(0x952ab45c, fa97a0b3), 455, 156}, + {UINT64_2PART_C(0xde469fbd, 99a05fe3), 481, 164}, + {UINT64_2PART_C(0xa59bc234, db398c25), 508, 172}, + {UINT64_2PART_C(0xf6c69a72, a3989f5c), 534, 180}, + {UINT64_2PART_C(0xb7dcbf53, 54e9bece), 561, 188}, + {UINT64_2PART_C(0x88fcf317, f22241e2), 588, 196}, + {UINT64_2PART_C(0xcc20ce9b, d35c78a5), 614, 204}, + {UINT64_2PART_C(0x98165af3, 7b2153df), 641, 212}, + {UINT64_2PART_C(0xe2a0b5dc, 971f303a), 667, 220}, + {UINT64_2PART_C(0xa8d9d153, 5ce3b396), 694, 228}, + {UINT64_2PART_C(0xfb9b7cd9, a4a7443c), 720, 236}, + {UINT64_2PART_C(0xbb764c4c, a7a44410), 747, 244}, + {UINT64_2PART_C(0x8bab8eef, b6409c1a), 774, 252}, + {UINT64_2PART_C(0xd01fef10, a657842c), 800, 260}, + {UINT64_2PART_C(0x9b10a4e5, e9913129), 827, 268}, + {UINT64_2PART_C(0xe7109bfb, a19c0c9d), 853, 276}, + {UINT64_2PART_C(0xac2820d9, 623bf429), 880, 284}, + {UINT64_2PART_C(0x80444b5e, 7aa7cf85), 907, 292}, + {UINT64_2PART_C(0xbf21e440, 03acdd2d), 933, 300}, + {UINT64_2PART_C(0x8e679c2f, 5e44ff8f), 960, 308}, + {UINT64_2PART_C(0xd433179d, 9c8cb841), 986, 316}, + {UINT64_2PART_C(0x9e19db92, b4e31ba9), 1013, 324}, + {UINT64_2PART_C(0xeb96bf6e, badf77d9), 1039, 332}, + {UINT64_2PART_C(0xaf87023b, 9bf0ee6b), 1066, 340}, +}; + +static const int kCachedPowersOffset = 348; // -1 * the first decimal_exponent. +static const double kD_1_LOG2_10 = 0.30102999566398114; // 1 / lg(10) +// Difference between the decimal exponents in the table above. +const int PowersOfTenCache::kDecimalExponentDistance = 8; +const int PowersOfTenCache::kMinDecimalExponent = -348; +const int PowersOfTenCache::kMaxDecimalExponent = 340; + +void PowersOfTenCache::GetCachedPowerForBinaryExponentRange( + int min_exponent, + int max_exponent, + DiyFp* power, + int* decimal_exponent) { + int kQ = DiyFp::kSignificandSize; + double k = ceil((min_exponent + kQ - 1) * kD_1_LOG2_10); + int foo = kCachedPowersOffset; + int index = + (foo + static_cast(k) - 1) / kDecimalExponentDistance + 1; + ASSERT(0 <= index && index < static_cast(ARRAY_SIZE(kCachedPowers))); + CachedPower cached_power = kCachedPowers[index]; + ASSERT(min_exponent <= cached_power.binary_exponent); + (void) max_exponent; // Mark variable as used. + ASSERT(cached_power.binary_exponent <= max_exponent); + *decimal_exponent = cached_power.decimal_exponent; + *power = DiyFp(cached_power.significand, cached_power.binary_exponent); +} + + +void PowersOfTenCache::GetCachedPowerForDecimalExponent(int requested_exponent, + DiyFp* power, + int* found_exponent) { + ASSERT(kMinDecimalExponent <= requested_exponent); + ASSERT(requested_exponent < kMaxDecimalExponent + kDecimalExponentDistance); + int index = + (requested_exponent + kCachedPowersOffset) / kDecimalExponentDistance; + CachedPower cached_power = kCachedPowers[index]; + *power = DiyFp(cached_power.significand, cached_power.binary_exponent); + *found_exponent = cached_power.decimal_exponent; + ASSERT(*found_exponent <= requested_exponent); + ASSERT(requested_exponent < *found_exponent + kDecimalExponentDistance); +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/cached-powers.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/cached-powers.h new file mode 100644 index 0000000..61a5061 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/cached-powers.h @@ -0,0 +1,64 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_CACHED_POWERS_H_ +#define DOUBLE_CONVERSION_CACHED_POWERS_H_ + +#include "diy-fp.h" + +namespace double_conversion { + +class PowersOfTenCache { + public: + + // Not all powers of ten are cached. The decimal exponent of two neighboring + // cached numbers will differ by kDecimalExponentDistance. + static const int kDecimalExponentDistance; + + static const int kMinDecimalExponent; + static const int kMaxDecimalExponent; + + // Returns a cached power-of-ten with a binary exponent in the range + // [min_exponent; max_exponent] (boundaries included). + static void GetCachedPowerForBinaryExponentRange(int min_exponent, + int max_exponent, + DiyFp* power, + int* decimal_exponent); + + // Returns a cached power of ten x ~= 10^k such that + // k <= decimal_exponent < k + kCachedPowersDecimalDistance. + // The given decimal_exponent must satisfy + // kMinDecimalExponent <= requested_exponent, and + // requested_exponent < kMaxDecimalExponent + kDecimalExponentDistance. + static void GetCachedPowerForDecimalExponent(int requested_exponent, + DiyFp* power, + int* found_exponent); +}; + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_CACHED_POWERS_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/diy-fp.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/diy-fp.cc new file mode 100644 index 0000000..ddd1891 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/diy-fp.cc @@ -0,0 +1,57 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +#include "diy-fp.h" +#include "utils.h" + +namespace double_conversion { + +void DiyFp::Multiply(const DiyFp& other) { + // Simply "emulates" a 128 bit multiplication. + // However: the resulting number only contains 64 bits. The least + // significant 64 bits are only used for rounding the most significant 64 + // bits. + const uint64_t kM32 = 0xFFFFFFFFU; + uint64_t a = f_ >> 32; + uint64_t b = f_ & kM32; + uint64_t c = other.f_ >> 32; + uint64_t d = other.f_ & kM32; + uint64_t ac = a * c; + uint64_t bc = b * c; + uint64_t ad = a * d; + uint64_t bd = b * d; + uint64_t tmp = (bd >> 32) + (ad & kM32) + (bc & kM32); + // By adding 1U << 31 to tmp we round the final result. + // Halfway cases will be round up. + tmp += 1U << 31; + uint64_t result_f = ac + (ad >> 32) + (bc >> 32) + (tmp >> 32); + e_ += other.e_ + 64; + f_ = result_f; +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/diy-fp.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/diy-fp.h new file mode 100644 index 0000000..2edf346 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/diy-fp.h @@ -0,0 +1,118 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_DIY_FP_H_ +#define DOUBLE_CONVERSION_DIY_FP_H_ + +#include "utils.h" + +namespace double_conversion { + +// This "Do It Yourself Floating Point" class implements a floating-point number +// with a uint64 significand and an int exponent. Normalized DiyFp numbers will +// have the most significant bit of the significand set. +// Multiplication and Subtraction do not normalize their results. +// DiyFp are not designed to contain special doubles (NaN and Infinity). +class DiyFp { + public: + static const int kSignificandSize = 64; + + DiyFp() : f_(0), e_(0) {} + DiyFp(uint64_t significand, int exponent) : f_(significand), e_(exponent) {} + + // this = this - other. + // The exponents of both numbers must be the same and the significand of this + // must be bigger than the significand of other. + // The result will not be normalized. + void Subtract(const DiyFp& other) { + ASSERT(e_ == other.e_); + ASSERT(f_ >= other.f_); + f_ -= other.f_; + } + + // Returns a - b. + // The exponents of both numbers must be the same and this must be bigger + // than other. The result will not be normalized. + static DiyFp Minus(const DiyFp& a, const DiyFp& b) { + DiyFp result = a; + result.Subtract(b); + return result; + } + + + // this = this * other. + void Multiply(const DiyFp& other); + + // returns a * b; + static DiyFp Times(const DiyFp& a, const DiyFp& b) { + DiyFp result = a; + result.Multiply(b); + return result; + } + + void Normalize() { + ASSERT(f_ != 0); + uint64_t significand = f_; + int exponent = e_; + + // This method is mainly called for normalizing boundaries. In general + // boundaries need to be shifted by 10 bits. We thus optimize for this case. + const uint64_t k10MSBits = UINT64_2PART_C(0xFFC00000, 00000000); + while ((significand & k10MSBits) == 0) { + significand <<= 10; + exponent -= 10; + } + while ((significand & kUint64MSB) == 0) { + significand <<= 1; + exponent--; + } + f_ = significand; + e_ = exponent; + } + + static DiyFp Normalize(const DiyFp& a) { + DiyFp result = a; + result.Normalize(); + return result; + } + + uint64_t f() const { return f_; } + int e() const { return e_; } + + void set_f(uint64_t new_value) { f_ = new_value; } + void set_e(int new_value) { e_ = new_value; } + + private: + static const uint64_t kUint64MSB = UINT64_2PART_C(0x80000000, 00000000); + + uint64_t f_; + int e_; +}; + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_DIY_FP_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/double-conversion.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/double-conversion.cc new file mode 100644 index 0000000..6f21a01 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/double-conversion.cc @@ -0,0 +1,982 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include +#include + +#include "double-conversion.h" + +#include "bignum-dtoa.h" +#include "fast-dtoa.h" +#include "fixed-dtoa.h" +#include "ieee.h" +#include "strtod.h" +#include "utils.h" + +namespace double_conversion { + +const DoubleToStringConverter& DoubleToStringConverter::EcmaScriptConverter() { + int flags = UNIQUE_ZERO | EMIT_POSITIVE_EXPONENT_SIGN; + static DoubleToStringConverter converter(flags, + "Infinity", + "NaN", + 'e', + -6, 21, + 6, 0); + return converter; +} + + +bool DoubleToStringConverter::HandleSpecialValues( + double value, + StringBuilder* result_builder) const { + Double double_inspect(value); + if (double_inspect.IsInfinite()) { + if (infinity_symbol_ == NULL) return false; + if (value < 0) { + result_builder->AddCharacter('-'); + } + result_builder->AddString(infinity_symbol_); + return true; + } + if (double_inspect.IsNan()) { + if (nan_symbol_ == NULL) return false; + result_builder->AddString(nan_symbol_); + return true; + } + return false; +} + + +void DoubleToStringConverter::CreateExponentialRepresentation( + const char* decimal_digits, + int length, + int exponent, + StringBuilder* result_builder) const { + ASSERT(length != 0); + result_builder->AddCharacter(decimal_digits[0]); + if (length != 1) { + result_builder->AddCharacter('.'); + result_builder->AddSubstring(&decimal_digits[1], length-1); + } + result_builder->AddCharacter(exponent_character_); + if (exponent < 0) { + result_builder->AddCharacter('-'); + exponent = -exponent; + } else { + if ((flags_ & EMIT_POSITIVE_EXPONENT_SIGN) != 0) { + result_builder->AddCharacter('+'); + } + } + if (exponent == 0) { + result_builder->AddCharacter('0'); + return; + } + ASSERT(exponent < 1e4); + const int kMaxExponentLength = 5; + char buffer[kMaxExponentLength + 1]; + buffer[kMaxExponentLength] = '\0'; + int first_char_pos = kMaxExponentLength; + while (exponent > 0) { + buffer[--first_char_pos] = '0' + (exponent % 10); + exponent /= 10; + } + result_builder->AddSubstring(&buffer[first_char_pos], + kMaxExponentLength - first_char_pos); +} + + +void DoubleToStringConverter::CreateDecimalRepresentation( + const char* decimal_digits, + int length, + int decimal_point, + int digits_after_point, + StringBuilder* result_builder) const { + // Create a representation that is padded with zeros if needed. + if (decimal_point <= 0) { + // "0.00000decimal_rep" or "0.000decimal_rep00". + result_builder->AddCharacter('0'); + if (digits_after_point > 0) { + result_builder->AddCharacter('.'); + result_builder->AddPadding('0', -decimal_point); + ASSERT(length <= digits_after_point - (-decimal_point)); + result_builder->AddSubstring(decimal_digits, length); + int remaining_digits = digits_after_point - (-decimal_point) - length; + result_builder->AddPadding('0', remaining_digits); + } + } else if (decimal_point >= length) { + // "decimal_rep0000.00000" or "decimal_rep.0000". + result_builder->AddSubstring(decimal_digits, length); + result_builder->AddPadding('0', decimal_point - length); + if (digits_after_point > 0) { + result_builder->AddCharacter('.'); + result_builder->AddPadding('0', digits_after_point); + } + } else { + // "decima.l_rep000". + ASSERT(digits_after_point > 0); + result_builder->AddSubstring(decimal_digits, decimal_point); + result_builder->AddCharacter('.'); + ASSERT(length - decimal_point <= digits_after_point); + result_builder->AddSubstring(&decimal_digits[decimal_point], + length - decimal_point); + int remaining_digits = digits_after_point - (length - decimal_point); + result_builder->AddPadding('0', remaining_digits); + } + if (digits_after_point == 0) { + if ((flags_ & EMIT_TRAILING_DECIMAL_POINT) != 0) { + result_builder->AddCharacter('.'); + } + if ((flags_ & EMIT_TRAILING_ZERO_AFTER_POINT) != 0) { + result_builder->AddCharacter('0'); + } + } +} + + +bool DoubleToStringConverter::ToShortestIeeeNumber( + double value, + StringBuilder* result_builder, + DoubleToStringConverter::DtoaMode mode) const { + ASSERT(mode == SHORTEST || mode == SHORTEST_SINGLE); + if (Double(value).IsSpecial()) { + return HandleSpecialValues(value, result_builder); + } + + int decimal_point; + bool sign; + const int kDecimalRepCapacity = kBase10MaximalLength + 1; + char decimal_rep[kDecimalRepCapacity]; + int decimal_rep_length; + + DoubleToAscii(value, mode, 0, decimal_rep, kDecimalRepCapacity, + &sign, &decimal_rep_length, &decimal_point); + + bool unique_zero = (flags_ & UNIQUE_ZERO) != 0; + if (sign && (value != 0.0 || !unique_zero)) { + result_builder->AddCharacter('-'); + } + + int exponent = decimal_point - 1; + if ((decimal_in_shortest_low_ <= exponent) && + (exponent < decimal_in_shortest_high_)) { + CreateDecimalRepresentation(decimal_rep, decimal_rep_length, + decimal_point, + Max(0, decimal_rep_length - decimal_point), + result_builder); + } else { + CreateExponentialRepresentation(decimal_rep, decimal_rep_length, exponent, + result_builder); + } + return true; +} + + +bool DoubleToStringConverter::ToFixed(double value, + int requested_digits, + StringBuilder* result_builder) const { + ASSERT(kMaxFixedDigitsBeforePoint == 60); + const double kFirstNonFixed = 1e60; + + if (Double(value).IsSpecial()) { + return HandleSpecialValues(value, result_builder); + } + + if (requested_digits > kMaxFixedDigitsAfterPoint) return false; + if (value >= kFirstNonFixed || value <= -kFirstNonFixed) return false; + + // Find a sufficiently precise decimal representation of n. + int decimal_point; + bool sign; + // Add space for the '\0' byte. + const int kDecimalRepCapacity = + kMaxFixedDigitsBeforePoint + kMaxFixedDigitsAfterPoint + 1; + char decimal_rep[kDecimalRepCapacity]; + int decimal_rep_length; + DoubleToAscii(value, FIXED, requested_digits, + decimal_rep, kDecimalRepCapacity, + &sign, &decimal_rep_length, &decimal_point); + + bool unique_zero = ((flags_ & UNIQUE_ZERO) != 0); + if (sign && (value != 0.0 || !unique_zero)) { + result_builder->AddCharacter('-'); + } + + CreateDecimalRepresentation(decimal_rep, decimal_rep_length, decimal_point, + requested_digits, result_builder); + return true; +} + + +bool DoubleToStringConverter::ToExponential( + double value, + int requested_digits, + StringBuilder* result_builder) const { + if (Double(value).IsSpecial()) { + return HandleSpecialValues(value, result_builder); + } + + if (requested_digits < -1) return false; + if (requested_digits > kMaxExponentialDigits) return false; + + int decimal_point; + bool sign; + // Add space for digit before the decimal point and the '\0' character. + const int kDecimalRepCapacity = kMaxExponentialDigits + 2; + ASSERT(kDecimalRepCapacity > kBase10MaximalLength); + char decimal_rep[kDecimalRepCapacity]; + int decimal_rep_length; + + if (requested_digits == -1) { + DoubleToAscii(value, SHORTEST, 0, + decimal_rep, kDecimalRepCapacity, + &sign, &decimal_rep_length, &decimal_point); + } else { + DoubleToAscii(value, PRECISION, requested_digits + 1, + decimal_rep, kDecimalRepCapacity, + &sign, &decimal_rep_length, &decimal_point); + ASSERT(decimal_rep_length <= requested_digits + 1); + + for (int i = decimal_rep_length; i < requested_digits + 1; ++i) { + decimal_rep[i] = '0'; + } + decimal_rep_length = requested_digits + 1; + } + + bool unique_zero = ((flags_ & UNIQUE_ZERO) != 0); + if (sign && (value != 0.0 || !unique_zero)) { + result_builder->AddCharacter('-'); + } + + int exponent = decimal_point - 1; + CreateExponentialRepresentation(decimal_rep, + decimal_rep_length, + exponent, + result_builder); + return true; +} + + +bool DoubleToStringConverter::ToPrecision(double value, + int precision, + StringBuilder* result_builder) const { + if (Double(value).IsSpecial()) { + return HandleSpecialValues(value, result_builder); + } + + if (precision < kMinPrecisionDigits || precision > kMaxPrecisionDigits) { + return false; + } + + // Find a sufficiently precise decimal representation of n. + int decimal_point; + bool sign; + // Add one for the terminating null character. + const int kDecimalRepCapacity = kMaxPrecisionDigits + 1; + char decimal_rep[kDecimalRepCapacity]; + int decimal_rep_length; + + DoubleToAscii(value, PRECISION, precision, + decimal_rep, kDecimalRepCapacity, + &sign, &decimal_rep_length, &decimal_point); + ASSERT(decimal_rep_length <= precision); + + bool unique_zero = ((flags_ & UNIQUE_ZERO) != 0); + if (sign && (value != 0.0 || !unique_zero)) { + result_builder->AddCharacter('-'); + } + + // The exponent if we print the number as x.xxeyyy. That is with the + // decimal point after the first digit. + int exponent = decimal_point - 1; + + int extra_zero = ((flags_ & EMIT_TRAILING_ZERO_AFTER_POINT) != 0) ? 1 : 0; + if ((-decimal_point + 1 > max_leading_padding_zeroes_in_precision_mode_) || + (decimal_point - precision + extra_zero > + max_trailing_padding_zeroes_in_precision_mode_)) { + // Fill buffer to contain 'precision' digits. + // Usually the buffer is already at the correct length, but 'DoubleToAscii' + // is allowed to return less characters. + for (int i = decimal_rep_length; i < precision; ++i) { + decimal_rep[i] = '0'; + } + + CreateExponentialRepresentation(decimal_rep, + precision, + exponent, + result_builder); + } else { + CreateDecimalRepresentation(decimal_rep, decimal_rep_length, decimal_point, + Max(0, precision - decimal_point), + result_builder); + } + return true; +} + + +static BignumDtoaMode DtoaToBignumDtoaMode( + DoubleToStringConverter::DtoaMode dtoa_mode) { + switch (dtoa_mode) { + case DoubleToStringConverter::SHORTEST: return BIGNUM_DTOA_SHORTEST; + case DoubleToStringConverter::SHORTEST_SINGLE: + return BIGNUM_DTOA_SHORTEST_SINGLE; + case DoubleToStringConverter::FIXED: return BIGNUM_DTOA_FIXED; + case DoubleToStringConverter::PRECISION: return BIGNUM_DTOA_PRECISION; + default: + UNREACHABLE(); + } +} + + +void DoubleToStringConverter::DoubleToAscii(double v, + DtoaMode mode, + int requested_digits, + char* buffer, + int buffer_length, + bool* sign, + int* length, + int* point) { + Vector vector(buffer, buffer_length); + ASSERT(!Double(v).IsSpecial()); + ASSERT(mode == SHORTEST || mode == SHORTEST_SINGLE || requested_digits >= 0); + + if (Double(v).Sign() < 0) { + *sign = true; + v = -v; + } else { + *sign = false; + } + + if (mode == PRECISION && requested_digits == 0) { + vector[0] = '\0'; + *length = 0; + return; + } + + if (v == 0) { + vector[0] = '0'; + vector[1] = '\0'; + *length = 1; + *point = 1; + return; + } + + bool fast_worked; + switch (mode) { + case SHORTEST: + fast_worked = FastDtoa(v, FAST_DTOA_SHORTEST, 0, vector, length, point); + break; + case SHORTEST_SINGLE: + fast_worked = FastDtoa(v, FAST_DTOA_SHORTEST_SINGLE, 0, + vector, length, point); + break; + case FIXED: + fast_worked = FastFixedDtoa(v, requested_digits, vector, length, point); + break; + case PRECISION: + fast_worked = FastDtoa(v, FAST_DTOA_PRECISION, requested_digits, + vector, length, point); + break; + default: + fast_worked = false; + UNREACHABLE(); + } + if (fast_worked) return; + + // If the fast dtoa didn't succeed use the slower bignum version. + BignumDtoaMode bignum_mode = DtoaToBignumDtoaMode(mode); + BignumDtoa(v, bignum_mode, requested_digits, vector, length, point); + vector[*length] = '\0'; +} + + +// Consumes the given substring from the iterator. +// Returns false, if the substring does not match. +template +static bool ConsumeSubString(Iterator* current, + Iterator end, + const char* substring) { + ASSERT(**current == *substring); + for (substring++; *substring != '\0'; substring++) { + ++*current; + if (*current == end || **current != *substring) return false; + } + ++*current; + return true; +} + + +// Maximum number of significant digits in decimal representation. +// The longest possible double in decimal representation is +// (2^53 - 1) * 2 ^ -1074 that is (2 ^ 53 - 1) * 5 ^ 1074 / 10 ^ 1074 +// (768 digits). If we parse a number whose first digits are equal to a +// mean of 2 adjacent doubles (that could have up to 769 digits) the result +// must be rounded to the bigger one unless the tail consists of zeros, so +// we don't need to preserve all the digits. +const int kMaxSignificantDigits = 772; + + +static const char kWhitespaceTable7[] = { 32, 13, 10, 9, 11, 12 }; +static const int kWhitespaceTable7Length = ARRAY_SIZE(kWhitespaceTable7); + + +static const uc16 kWhitespaceTable16[] = { + 160, 8232, 8233, 5760, 6158, 8192, 8193, 8194, 8195, + 8196, 8197, 8198, 8199, 8200, 8201, 8202, 8239, 8287, 12288, 65279 +}; +static const int kWhitespaceTable16Length = ARRAY_SIZE(kWhitespaceTable16); + + +static bool isWhitespace(int x) { + if (x < 128) { + for (int i = 0; i < kWhitespaceTable7Length; i++) { + if (kWhitespaceTable7[i] == x) return true; + } + } else { + for (int i = 0; i < kWhitespaceTable16Length; i++) { + if (kWhitespaceTable16[i] == x) return true; + } + } + return false; +} + + +// Returns true if a nonspace found and false if the end has reached. +template +static inline bool AdvanceToNonspace(Iterator* current, Iterator end) { + while (*current != end) { + if (!isWhitespace(**current)) return true; + ++*current; + } + return false; +} + + +static bool isDigit(int x, int radix) { + return (x >= '0' && x <= '9' && x < '0' + radix) + || (radix > 10 && x >= 'a' && x < 'a' + radix - 10) + || (radix > 10 && x >= 'A' && x < 'A' + radix - 10); +} + + +static double SignedZero(bool sign) { + return sign ? -0.0 : 0.0; +} + + +// Returns true if 'c' is a decimal digit that is valid for the given radix. +// +// The function is small and could be inlined, but VS2012 emitted a warning +// because it constant-propagated the radix and concluded that the last +// condition was always true. By moving it into a separate function the +// compiler wouldn't warn anymore. +#if _MSC_VER +#pragma optimize("",off) +static bool IsDecimalDigitForRadix(int c, int radix) { + return '0' <= c && c <= '9' && (c - '0') < radix; +} +#pragma optimize("",on) +#else +static bool inline IsDecimalDigitForRadix(int c, int radix) { + return '0' <= c && c <= '9' && (c - '0') < radix; +} +#endif +// Returns true if 'c' is a character digit that is valid for the given radix. +// The 'a_character' should be 'a' or 'A'. +// +// The function is small and could be inlined, but VS2012 emitted a warning +// because it constant-propagated the radix and concluded that the first +// condition was always false. By moving it into a separate function the +// compiler wouldn't warn anymore. +static bool IsCharacterDigitForRadix(int c, int radix, char a_character) { + return radix > 10 && c >= a_character && c < a_character + radix - 10; +} + + +// Parsing integers with radix 2, 4, 8, 16, 32. Assumes current != end. +template +static double RadixStringToIeee(Iterator* current, + Iterator end, + bool sign, + bool allow_trailing_junk, + double junk_string_value, + bool read_as_double, + bool* result_is_junk) { + ASSERT(*current != end); + + const int kDoubleSize = Double::kSignificandSize; + const int kSingleSize = Single::kSignificandSize; + const int kSignificandSize = read_as_double? kDoubleSize: kSingleSize; + + *result_is_junk = true; + + // Skip leading 0s. + while (**current == '0') { + ++(*current); + if (*current == end) { + *result_is_junk = false; + return SignedZero(sign); + } + } + + int64_t number = 0; + int exponent = 0; + const int radix = (1 << radix_log_2); + + do { + int digit; + if (IsDecimalDigitForRadix(**current, radix)) { + digit = static_cast(**current) - '0'; + } else if (IsCharacterDigitForRadix(**current, radix, 'a')) { + digit = static_cast(**current) - 'a' + 10; + } else if (IsCharacterDigitForRadix(**current, radix, 'A')) { + digit = static_cast(**current) - 'A' + 10; + } else { + if (allow_trailing_junk || !AdvanceToNonspace(current, end)) { + break; + } else { + return junk_string_value; + } + } + + number = number * radix + digit; + int overflow = static_cast(number >> kSignificandSize); + if (overflow != 0) { + // Overflow occurred. Need to determine which direction to round the + // result. + int overflow_bits_count = 1; + while (overflow > 1) { + overflow_bits_count++; + overflow >>= 1; + } + + int dropped_bits_mask = ((1 << overflow_bits_count) - 1); + int dropped_bits = static_cast(number) & dropped_bits_mask; + number >>= overflow_bits_count; + exponent = overflow_bits_count; + + bool zero_tail = true; + for (;;) { + ++(*current); + if (*current == end || !isDigit(**current, radix)) break; + zero_tail = zero_tail && **current == '0'; + exponent += radix_log_2; + } + + if (!allow_trailing_junk && AdvanceToNonspace(current, end)) { + return junk_string_value; + } + + int middle_value = (1 << (overflow_bits_count - 1)); + if (dropped_bits > middle_value) { + number++; // Rounding up. + } else if (dropped_bits == middle_value) { + // Rounding to even to consistency with decimals: half-way case rounds + // up if significant part is odd and down otherwise. + if ((number & 1) != 0 || !zero_tail) { + number++; // Rounding up. + } + } + + // Rounding up may cause overflow. + if ((number & ((int64_t)1 << kSignificandSize)) != 0) { + exponent++; + number >>= 1; + } + break; + } + ++(*current); + } while (*current != end); + + ASSERT(number < ((int64_t)1 << kSignificandSize)); + ASSERT(static_cast(static_cast(number)) == number); + + *result_is_junk = false; + + if (exponent == 0) { + if (sign) { + if (number == 0) return -0.0; + number = -number; + } + return static_cast(number); + } + + ASSERT(number != 0); + return Double(DiyFp(number, exponent)).value(); +} + + +template +double StringToDoubleConverter::StringToIeee( + Iterator input, + int length, + bool read_as_double, + int* processed_characters_count) const { + Iterator current = input; + Iterator end = input + length; + + *processed_characters_count = 0; + + const bool allow_trailing_junk = (flags_ & ALLOW_TRAILING_JUNK) != 0; + const bool allow_leading_spaces = (flags_ & ALLOW_LEADING_SPACES) != 0; + const bool allow_trailing_spaces = (flags_ & ALLOW_TRAILING_SPACES) != 0; + const bool allow_spaces_after_sign = (flags_ & ALLOW_SPACES_AFTER_SIGN) != 0; + + // To make sure that iterator dereferencing is valid the following + // convention is used: + // 1. Each '++current' statement is followed by check for equality to 'end'. + // 2. If AdvanceToNonspace returned false then current == end. + // 3. If 'current' becomes equal to 'end' the function returns or goes to + // 'parsing_done'. + // 4. 'current' is not dereferenced after the 'parsing_done' label. + // 5. Code before 'parsing_done' may rely on 'current != end'. + if (current == end) return empty_string_value_; + + if (allow_leading_spaces || allow_trailing_spaces) { + if (!AdvanceToNonspace(¤t, end)) { + *processed_characters_count = static_cast(current - input); + return empty_string_value_; + } + if (!allow_leading_spaces && (input != current)) { + // No leading spaces allowed, but AdvanceToNonspace moved forward. + return junk_string_value_; + } + } + + // The longest form of simplified number is: "-.1eXXX\0". + const int kBufferSize = kMaxSignificantDigits + 10; + char buffer[kBufferSize]; // NOLINT: size is known at compile time. + int buffer_pos = 0; + + // Exponent will be adjusted if insignificant digits of the integer part + // or insignificant leading zeros of the fractional part are dropped. + int exponent = 0; + int significant_digits = 0; + int insignificant_digits = 0; + bool nonzero_digit_dropped = false; + + bool sign = false; + + if (*current == '+' || *current == '-') { + sign = (*current == '-'); + ++current; + Iterator next_non_space = current; + // Skip following spaces (if allowed). + if (!AdvanceToNonspace(&next_non_space, end)) return junk_string_value_; + if (!allow_spaces_after_sign && (current != next_non_space)) { + return junk_string_value_; + } + current = next_non_space; + } + + if (infinity_symbol_ != NULL) { + if (*current == infinity_symbol_[0]) { + if (!ConsumeSubString(¤t, end, infinity_symbol_)) { + return junk_string_value_; + } + + if (!(allow_trailing_spaces || allow_trailing_junk) && (current != end)) { + return junk_string_value_; + } + if (!allow_trailing_junk && AdvanceToNonspace(¤t, end)) { + return junk_string_value_; + } + + ASSERT(buffer_pos == 0); + *processed_characters_count = static_cast(current - input); + return sign ? -Double::Infinity() : Double::Infinity(); + } + } + + if (nan_symbol_ != NULL) { + if (*current == nan_symbol_[0]) { + if (!ConsumeSubString(¤t, end, nan_symbol_)) { + return junk_string_value_; + } + + if (!(allow_trailing_spaces || allow_trailing_junk) && (current != end)) { + return junk_string_value_; + } + if (!allow_trailing_junk && AdvanceToNonspace(¤t, end)) { + return junk_string_value_; + } + + ASSERT(buffer_pos == 0); + *processed_characters_count = static_cast(current - input); + return sign ? -Double::NaN() : Double::NaN(); + } + } + + bool leading_zero = false; + if (*current == '0') { + ++current; + if (current == end) { + *processed_characters_count = static_cast(current - input); + return SignedZero(sign); + } + + leading_zero = true; + + // It could be hexadecimal value. + if ((flags_ & ALLOW_HEX) && (*current == 'x' || *current == 'X')) { + ++current; + if (current == end || !isDigit(*current, 16)) { + return junk_string_value_; // "0x". + } + + bool result_is_junk; + double result = RadixStringToIeee<4>(¤t, + end, + sign, + allow_trailing_junk, + junk_string_value_, + read_as_double, + &result_is_junk); + if (!result_is_junk) { + if (allow_trailing_spaces) AdvanceToNonspace(¤t, end); + *processed_characters_count = static_cast(current - input); + } + return result; + } + + // Ignore leading zeros in the integer part. + while (*current == '0') { + ++current; + if (current == end) { + *processed_characters_count = static_cast(current - input); + return SignedZero(sign); + } + } + } + + bool octal = leading_zero && (flags_ & ALLOW_OCTALS) != 0; + + // Copy significant digits of the integer part (if any) to the buffer. + while (*current >= '0' && *current <= '9') { + if (significant_digits < kMaxSignificantDigits) { + ASSERT(buffer_pos < kBufferSize); + buffer[buffer_pos++] = static_cast(*current); + significant_digits++; + // Will later check if it's an octal in the buffer. + } else { + insignificant_digits++; // Move the digit into the exponential part. + nonzero_digit_dropped = nonzero_digit_dropped || *current != '0'; + } + octal = octal && *current < '8'; + ++current; + if (current == end) goto parsing_done; + } + + if (significant_digits == 0) { + octal = false; + } + + if (*current == '.') { + if (octal && !allow_trailing_junk) return junk_string_value_; + if (octal) goto parsing_done; + + ++current; + if (current == end) { + if (significant_digits == 0 && !leading_zero) { + return junk_string_value_; + } else { + goto parsing_done; + } + } + + if (significant_digits == 0) { + // octal = false; + // Integer part consists of 0 or is absent. Significant digits start after + // leading zeros (if any). + while (*current == '0') { + ++current; + if (current == end) { + *processed_characters_count = static_cast(current - input); + return SignedZero(sign); + } + exponent--; // Move this 0 into the exponent. + } + } + + // There is a fractional part. + // We don't emit a '.', but adjust the exponent instead. + while (*current >= '0' && *current <= '9') { + if (significant_digits < kMaxSignificantDigits) { + ASSERT(buffer_pos < kBufferSize); + buffer[buffer_pos++] = static_cast(*current); + significant_digits++; + exponent--; + } else { + // Ignore insignificant digits in the fractional part. + nonzero_digit_dropped = nonzero_digit_dropped || *current != '0'; + } + ++current; + if (current == end) goto parsing_done; + } + } + + if (!leading_zero && exponent == 0 && significant_digits == 0) { + // If leading_zeros is true then the string contains zeros. + // If exponent < 0 then string was [+-]\.0*... + // If significant_digits != 0 the string is not equal to 0. + // Otherwise there are no digits in the string. + return junk_string_value_; + } + + // Parse exponential part. + if (*current == 'e' || *current == 'E') { + if (octal && !allow_trailing_junk) return junk_string_value_; + if (octal) goto parsing_done; + ++current; + if (current == end) { + if (allow_trailing_junk) { + goto parsing_done; + } else { + return junk_string_value_; + } + } + char exponen_sign = '+'; + if (*current == '+' || *current == '-') { + exponen_sign = static_cast(*current); + ++current; + if (current == end) { + if (allow_trailing_junk) { + goto parsing_done; + } else { + return junk_string_value_; + } + } + } + + if (current == end || *current < '0' || *current > '9') { + if (allow_trailing_junk) { + goto parsing_done; + } else { + return junk_string_value_; + } + } + + const int max_exponent = INT_MAX / 2; + ASSERT(-max_exponent / 2 <= exponent && exponent <= max_exponent / 2); + int num = 0; + do { + // Check overflow. + int digit = *current - '0'; + if (num >= max_exponent / 10 + && !(num == max_exponent / 10 && digit <= max_exponent % 10)) { + num = max_exponent; + } else { + num = num * 10 + digit; + } + ++current; + } while (current != end && *current >= '0' && *current <= '9'); + + exponent += (exponen_sign == '-' ? -num : num); + } + + if (!(allow_trailing_spaces || allow_trailing_junk) && (current != end)) { + return junk_string_value_; + } + if (!allow_trailing_junk && AdvanceToNonspace(¤t, end)) { + return junk_string_value_; + } + if (allow_trailing_spaces) { + AdvanceToNonspace(¤t, end); + } + + parsing_done: + exponent += insignificant_digits; + + if (octal) { + double result; + bool result_is_junk; + char* start = buffer; + result = RadixStringToIeee<3>(&start, + buffer + buffer_pos, + sign, + allow_trailing_junk, + junk_string_value_, + read_as_double, + &result_is_junk); + ASSERT(!result_is_junk); + *processed_characters_count = static_cast(current - input); + return result; + } + + if (nonzero_digit_dropped) { + buffer[buffer_pos++] = '1'; + exponent--; + } + + ASSERT(buffer_pos < kBufferSize); + buffer[buffer_pos] = '\0'; + + double converted; + if (read_as_double) { + converted = Strtod(Vector(buffer, buffer_pos), exponent); + } else { + converted = Strtof(Vector(buffer, buffer_pos), exponent); + } + *processed_characters_count = static_cast(current - input); + return sign? -converted: converted; +} + + +double StringToDoubleConverter::StringToDouble( + const char* buffer, + int length, + int* processed_characters_count) const { + return StringToIeee(buffer, length, true, processed_characters_count); +} + + +double StringToDoubleConverter::StringToDouble( + const uc16* buffer, + int length, + int* processed_characters_count) const { + return StringToIeee(buffer, length, true, processed_characters_count); +} + + +float StringToDoubleConverter::StringToFloat( + const char* buffer, + int length, + int* processed_characters_count) const { + return static_cast(StringToIeee(buffer, length, false, + processed_characters_count)); +} + + +float StringToDoubleConverter::StringToFloat( + const uc16* buffer, + int length, + int* processed_characters_count) const { + return static_cast(StringToIeee(buffer, length, false, + processed_characters_count)); +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/double-conversion.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/double-conversion.h new file mode 100644 index 0000000..6bdfa8d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/double-conversion.h @@ -0,0 +1,543 @@ +// Copyright 2012 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_DOUBLE_CONVERSION_H_ +#define DOUBLE_CONVERSION_DOUBLE_CONVERSION_H_ + +#include "utils.h" + +namespace double_conversion { + +class DoubleToStringConverter { + public: + // When calling ToFixed with a double > 10^kMaxFixedDigitsBeforePoint + // or a requested_digits parameter > kMaxFixedDigitsAfterPoint then the + // function returns false. + static const int kMaxFixedDigitsBeforePoint = 60; + static const int kMaxFixedDigitsAfterPoint = 60; + + // When calling ToExponential with a requested_digits + // parameter > kMaxExponentialDigits then the function returns false. + static const int kMaxExponentialDigits = 120; + + // When calling ToPrecision with a requested_digits + // parameter < kMinPrecisionDigits or requested_digits > kMaxPrecisionDigits + // then the function returns false. + static const int kMinPrecisionDigits = 1; + static const int kMaxPrecisionDigits = 120; + + enum Flags { + NO_FLAGS = 0, + EMIT_POSITIVE_EXPONENT_SIGN = 1, + EMIT_TRAILING_DECIMAL_POINT = 2, + EMIT_TRAILING_ZERO_AFTER_POINT = 4, + UNIQUE_ZERO = 8 + }; + + // Flags should be a bit-or combination of the possible Flags-enum. + // - NO_FLAGS: no special flags. + // - EMIT_POSITIVE_EXPONENT_SIGN: when the number is converted into exponent + // form, emits a '+' for positive exponents. Example: 1.2e+2. + // - EMIT_TRAILING_DECIMAL_POINT: when the input number is an integer and is + // converted into decimal format then a trailing decimal point is appended. + // Example: 2345.0 is converted to "2345.". + // - EMIT_TRAILING_ZERO_AFTER_POINT: in addition to a trailing decimal point + // emits a trailing '0'-character. This flag requires the + // EXMIT_TRAILING_DECIMAL_POINT flag. + // Example: 2345.0 is converted to "2345.0". + // - UNIQUE_ZERO: "-0.0" is converted to "0.0". + // + // Infinity symbol and nan_symbol provide the string representation for these + // special values. If the string is NULL and the special value is encountered + // then the conversion functions return false. + // + // The exponent_character is used in exponential representations. It is + // usually 'e' or 'E'. + // + // When converting to the shortest representation the converter will + // represent input numbers in decimal format if they are in the interval + // [10^decimal_in_shortest_low; 10^decimal_in_shortest_high[ + // (lower boundary included, greater boundary excluded). + // Example: with decimal_in_shortest_low = -6 and + // decimal_in_shortest_high = 21: + // ToShortest(0.000001) -> "0.000001" + // ToShortest(0.0000001) -> "1e-7" + // ToShortest(111111111111111111111.0) -> "111111111111111110000" + // ToShortest(100000000000000000000.0) -> "100000000000000000000" + // ToShortest(1111111111111111111111.0) -> "1.1111111111111111e+21" + // + // When converting to precision mode the converter may add + // max_leading_padding_zeroes before returning the number in exponential + // format. + // Example with max_leading_padding_zeroes_in_precision_mode = 6. + // ToPrecision(0.0000012345, 2) -> "0.0000012" + // ToPrecision(0.00000012345, 2) -> "1.2e-7" + // Similarily the converter may add up to + // max_trailing_padding_zeroes_in_precision_mode in precision mode to avoid + // returning an exponential representation. A zero added by the + // EMIT_TRAILING_ZERO_AFTER_POINT flag is counted for this limit. + // Examples for max_trailing_padding_zeroes_in_precision_mode = 1: + // ToPrecision(230.0, 2) -> "230" + // ToPrecision(230.0, 2) -> "230." with EMIT_TRAILING_DECIMAL_POINT. + // ToPrecision(230.0, 2) -> "2.3e2" with EMIT_TRAILING_ZERO_AFTER_POINT. + DoubleToStringConverter(int flags, + const char* infinity_symbol, + const char* nan_symbol, + char exponent_character, + int decimal_in_shortest_low, + int decimal_in_shortest_high, + int max_leading_padding_zeroes_in_precision_mode, + int max_trailing_padding_zeroes_in_precision_mode) + : flags_(flags), + infinity_symbol_(infinity_symbol), + nan_symbol_(nan_symbol), + exponent_character_(exponent_character), + decimal_in_shortest_low_(decimal_in_shortest_low), + decimal_in_shortest_high_(decimal_in_shortest_high), + max_leading_padding_zeroes_in_precision_mode_( + max_leading_padding_zeroes_in_precision_mode), + max_trailing_padding_zeroes_in_precision_mode_( + max_trailing_padding_zeroes_in_precision_mode) { + // When 'trailing zero after the point' is set, then 'trailing point' + // must be set too. + ASSERT(((flags & EMIT_TRAILING_DECIMAL_POINT) != 0) || + !((flags & EMIT_TRAILING_ZERO_AFTER_POINT) != 0)); + } + + // Returns a converter following the EcmaScript specification. + static const DoubleToStringConverter& EcmaScriptConverter(); + + // Computes the shortest string of digits that correctly represent the input + // number. Depending on decimal_in_shortest_low and decimal_in_shortest_high + // (see constructor) it then either returns a decimal representation, or an + // exponential representation. + // Example with decimal_in_shortest_low = -6, + // decimal_in_shortest_high = 21, + // EMIT_POSITIVE_EXPONENT_SIGN activated, and + // EMIT_TRAILING_DECIMAL_POINT deactived: + // ToShortest(0.000001) -> "0.000001" + // ToShortest(0.0000001) -> "1e-7" + // ToShortest(111111111111111111111.0) -> "111111111111111110000" + // ToShortest(100000000000000000000.0) -> "100000000000000000000" + // ToShortest(1111111111111111111111.0) -> "1.1111111111111111e+21" + // + // Note: the conversion may round the output if the returned string + // is accurate enough to uniquely identify the input-number. + // For example the most precise representation of the double 9e59 equals + // "899999999999999918767229449717619953810131273674690656206848", but + // the converter will return the shorter (but still correct) "9e59". + // + // Returns true if the conversion succeeds. The conversion always succeeds + // except when the input value is special and no infinity_symbol or + // nan_symbol has been given to the constructor. + bool ToShortest(double value, StringBuilder* result_builder) const { + return ToShortestIeeeNumber(value, result_builder, SHORTEST); + } + + // Same as ToShortest, but for single-precision floats. + bool ToShortestSingle(float value, StringBuilder* result_builder) const { + return ToShortestIeeeNumber(value, result_builder, SHORTEST_SINGLE); + } + + + // Computes a decimal representation with a fixed number of digits after the + // decimal point. The last emitted digit is rounded. + // + // Examples: + // ToFixed(3.12, 1) -> "3.1" + // ToFixed(3.1415, 3) -> "3.142" + // ToFixed(1234.56789, 4) -> "1234.5679" + // ToFixed(1.23, 5) -> "1.23000" + // ToFixed(0.1, 4) -> "0.1000" + // ToFixed(1e30, 2) -> "1000000000000000019884624838656.00" + // ToFixed(0.1, 30) -> "0.100000000000000005551115123126" + // ToFixed(0.1, 17) -> "0.10000000000000001" + // + // If requested_digits equals 0, then the tail of the result depends on + // the EMIT_TRAILING_DECIMAL_POINT and EMIT_TRAILING_ZERO_AFTER_POINT. + // Examples, for requested_digits == 0, + // let EMIT_TRAILING_DECIMAL_POINT and EMIT_TRAILING_ZERO_AFTER_POINT be + // - false and false: then 123.45 -> 123 + // 0.678 -> 1 + // - true and false: then 123.45 -> 123. + // 0.678 -> 1. + // - true and true: then 123.45 -> 123.0 + // 0.678 -> 1.0 + // + // Returns true if the conversion succeeds. The conversion always succeeds + // except for the following cases: + // - the input value is special and no infinity_symbol or nan_symbol has + // been provided to the constructor, + // - 'value' > 10^kMaxFixedDigitsBeforePoint, or + // - 'requested_digits' > kMaxFixedDigitsAfterPoint. + // The last two conditions imply that the result will never contain more than + // 1 + kMaxFixedDigitsBeforePoint + 1 + kMaxFixedDigitsAfterPoint characters + // (one additional character for the sign, and one for the decimal point). + bool ToFixed(double value, + int requested_digits, + StringBuilder* result_builder) const; + + // Computes a representation in exponential format with requested_digits + // after the decimal point. The last emitted digit is rounded. + // If requested_digits equals -1, then the shortest exponential representation + // is computed. + // + // Examples with EMIT_POSITIVE_EXPONENT_SIGN deactivated, and + // exponent_character set to 'e'. + // ToExponential(3.12, 1) -> "3.1e0" + // ToExponential(5.0, 3) -> "5.000e0" + // ToExponential(0.001, 2) -> "1.00e-3" + // ToExponential(3.1415, -1) -> "3.1415e0" + // ToExponential(3.1415, 4) -> "3.1415e0" + // ToExponential(3.1415, 3) -> "3.142e0" + // ToExponential(123456789000000, 3) -> "1.235e14" + // ToExponential(1000000000000000019884624838656.0, -1) -> "1e30" + // ToExponential(1000000000000000019884624838656.0, 32) -> + // "1.00000000000000001988462483865600e30" + // ToExponential(1234, 0) -> "1e3" + // + // Returns true if the conversion succeeds. The conversion always succeeds + // except for the following cases: + // - the input value is special and no infinity_symbol or nan_symbol has + // been provided to the constructor, + // - 'requested_digits' > kMaxExponentialDigits. + // The last condition implies that the result will never contain more than + // kMaxExponentialDigits + 8 characters (the sign, the digit before the + // decimal point, the decimal point, the exponent character, the + // exponent's sign, and at most 3 exponent digits). + bool ToExponential(double value, + int requested_digits, + StringBuilder* result_builder) const; + + // Computes 'precision' leading digits of the given 'value' and returns them + // either in exponential or decimal format, depending on + // max_{leading|trailing}_padding_zeroes_in_precision_mode (given to the + // constructor). + // The last computed digit is rounded. + // + // Example with max_leading_padding_zeroes_in_precision_mode = 6. + // ToPrecision(0.0000012345, 2) -> "0.0000012" + // ToPrecision(0.00000012345, 2) -> "1.2e-7" + // Similarily the converter may add up to + // max_trailing_padding_zeroes_in_precision_mode in precision mode to avoid + // returning an exponential representation. A zero added by the + // EMIT_TRAILING_ZERO_AFTER_POINT flag is counted for this limit. + // Examples for max_trailing_padding_zeroes_in_precision_mode = 1: + // ToPrecision(230.0, 2) -> "230" + // ToPrecision(230.0, 2) -> "230." with EMIT_TRAILING_DECIMAL_POINT. + // ToPrecision(230.0, 2) -> "2.3e2" with EMIT_TRAILING_ZERO_AFTER_POINT. + // Examples for max_trailing_padding_zeroes_in_precision_mode = 3, and no + // EMIT_TRAILING_ZERO_AFTER_POINT: + // ToPrecision(123450.0, 6) -> "123450" + // ToPrecision(123450.0, 5) -> "123450" + // ToPrecision(123450.0, 4) -> "123500" + // ToPrecision(123450.0, 3) -> "123000" + // ToPrecision(123450.0, 2) -> "1.2e5" + // + // Returns true if the conversion succeeds. The conversion always succeeds + // except for the following cases: + // - the input value is special and no infinity_symbol or nan_symbol has + // been provided to the constructor, + // - precision < kMinPericisionDigits + // - precision > kMaxPrecisionDigits + // The last condition implies that the result will never contain more than + // kMaxPrecisionDigits + 7 characters (the sign, the decimal point, the + // exponent character, the exponent's sign, and at most 3 exponent digits). + bool ToPrecision(double value, + int precision, + StringBuilder* result_builder) const; + + enum DtoaMode { + // Produce the shortest correct representation. + // For example the output of 0.299999999999999988897 is (the less accurate + // but correct) 0.3. + SHORTEST, + // Same as SHORTEST, but for single-precision floats. + SHORTEST_SINGLE, + // Produce a fixed number of digits after the decimal point. + // For instance fixed(0.1, 4) becomes 0.1000 + // If the input number is big, the output will be big. + FIXED, + // Fixed number of digits (independent of the decimal point). + PRECISION + }; + + // The maximal number of digits that are needed to emit a double in base 10. + // A higher precision can be achieved by using more digits, but the shortest + // accurate representation of any double will never use more digits than + // kBase10MaximalLength. + // Note that DoubleToAscii null-terminates its input. So the given buffer + // should be at least kBase10MaximalLength + 1 characters long. + static const int kBase10MaximalLength = 17; + + // Converts the given double 'v' to ascii. 'v' must not be NaN, +Infinity, or + // -Infinity. In SHORTEST_SINGLE-mode this restriction also applies to 'v' + // after it has been casted to a single-precision float. That is, in this + // mode static_cast(v) must not be NaN, +Infinity or -Infinity. + // + // The result should be interpreted as buffer * 10^(point-length). + // + // The output depends on the given mode: + // - SHORTEST: produce the least amount of digits for which the internal + // identity requirement is still satisfied. If the digits are printed + // (together with the correct exponent) then reading this number will give + // 'v' again. The buffer will choose the representation that is closest to + // 'v'. If there are two at the same distance, than the one farther away + // from 0 is chosen (halfway cases - ending with 5 - are rounded up). + // In this mode the 'requested_digits' parameter is ignored. + // - SHORTEST_SINGLE: same as SHORTEST but with single-precision. + // - FIXED: produces digits necessary to print a given number with + // 'requested_digits' digits after the decimal point. The produced digits + // might be too short in which case the caller has to fill the remainder + // with '0's. + // Example: toFixed(0.001, 5) is allowed to return buffer="1", point=-2. + // Halfway cases are rounded towards +/-Infinity (away from 0). The call + // toFixed(0.15, 2) thus returns buffer="2", point=0. + // The returned buffer may contain digits that would be truncated from the + // shortest representation of the input. + // - PRECISION: produces 'requested_digits' where the first digit is not '0'. + // Even though the length of produced digits usually equals + // 'requested_digits', the function is allowed to return fewer digits, in + // which case the caller has to fill the missing digits with '0's. + // Halfway cases are again rounded away from 0. + // DoubleToAscii expects the given buffer to be big enough to hold all + // digits and a terminating null-character. In SHORTEST-mode it expects a + // buffer of at least kBase10MaximalLength + 1. In all other modes the + // requested_digits parameter and the padding-zeroes limit the size of the + // output. Don't forget the decimal point, the exponent character and the + // terminating null-character when computing the maximal output size. + // The given length is only used in debug mode to ensure the buffer is big + // enough. + static void DoubleToAscii(double v, + DtoaMode mode, + int requested_digits, + char* buffer, + int buffer_length, + bool* sign, + int* length, + int* point); + + private: + // Implementation for ToShortest and ToShortestSingle. + bool ToShortestIeeeNumber(double value, + StringBuilder* result_builder, + DtoaMode mode) const; + + // If the value is a special value (NaN or Infinity) constructs the + // corresponding string using the configured infinity/nan-symbol. + // If either of them is NULL or the value is not special then the + // function returns false. + bool HandleSpecialValues(double value, StringBuilder* result_builder) const; + // Constructs an exponential representation (i.e. 1.234e56). + // The given exponent assumes a decimal point after the first decimal digit. + void CreateExponentialRepresentation(const char* decimal_digits, + int length, + int exponent, + StringBuilder* result_builder) const; + // Creates a decimal representation (i.e 1234.5678). + void CreateDecimalRepresentation(const char* decimal_digits, + int length, + int decimal_point, + int digits_after_point, + StringBuilder* result_builder) const; + + const int flags_; + const char* const infinity_symbol_; + const char* const nan_symbol_; + const char exponent_character_; + const int decimal_in_shortest_low_; + const int decimal_in_shortest_high_; + const int max_leading_padding_zeroes_in_precision_mode_; + const int max_trailing_padding_zeroes_in_precision_mode_; + + DISALLOW_IMPLICIT_CONSTRUCTORS(DoubleToStringConverter); +}; + + +class StringToDoubleConverter { + public: + // Enumeration for allowing octals and ignoring junk when converting + // strings to numbers. + enum Flags { + NO_FLAGS = 0, + ALLOW_HEX = 1, + ALLOW_OCTALS = 2, + ALLOW_TRAILING_JUNK = 4, + ALLOW_LEADING_SPACES = 8, + ALLOW_TRAILING_SPACES = 16, + ALLOW_SPACES_AFTER_SIGN = 32 + }; + + // Flags should be a bit-or combination of the possible Flags-enum. + // - NO_FLAGS: no special flags. + // - ALLOW_HEX: recognizes the prefix "0x". Hex numbers may only be integers. + // Ex: StringToDouble("0x1234") -> 4660.0 + // In StringToDouble("0x1234.56") the characters ".56" are trailing + // junk. The result of the call is hence dependent on + // the ALLOW_TRAILING_JUNK flag and/or the junk value. + // With this flag "0x" is a junk-string. Even with ALLOW_TRAILING_JUNK, + // the string will not be parsed as "0" followed by junk. + // + // - ALLOW_OCTALS: recognizes the prefix "0" for octals: + // If a sequence of octal digits starts with '0', then the number is + // read as octal integer. Octal numbers may only be integers. + // Ex: StringToDouble("01234") -> 668.0 + // StringToDouble("012349") -> 12349.0 // Not a sequence of octal + // // digits. + // In StringToDouble("01234.56") the characters ".56" are trailing + // junk. The result of the call is hence dependent on + // the ALLOW_TRAILING_JUNK flag and/or the junk value. + // In StringToDouble("01234e56") the characters "e56" are trailing + // junk, too. + // - ALLOW_TRAILING_JUNK: ignore trailing characters that are not part of + // a double literal. + // - ALLOW_LEADING_SPACES: skip over leading whitespace, including spaces, + // new-lines, and tabs. + // - ALLOW_TRAILING_SPACES: ignore trailing whitespace. + // - ALLOW_SPACES_AFTER_SIGN: ignore whitespace after the sign. + // Ex: StringToDouble("- 123.2") -> -123.2. + // StringToDouble("+ 123.2") -> 123.2 + // + // empty_string_value is returned when an empty string is given as input. + // If ALLOW_LEADING_SPACES or ALLOW_TRAILING_SPACES are set, then a string + // containing only spaces is converted to the 'empty_string_value', too. + // + // junk_string_value is returned when + // a) ALLOW_TRAILING_JUNK is not set, and a junk character (a character not + // part of a double-literal) is found. + // b) ALLOW_TRAILING_JUNK is set, but the string does not start with a + // double literal. + // + // infinity_symbol and nan_symbol are strings that are used to detect + // inputs that represent infinity and NaN. They can be null, in which case + // they are ignored. + // The conversion routine first reads any possible signs. Then it compares the + // following character of the input-string with the first character of + // the infinity, and nan-symbol. If either matches, the function assumes, that + // a match has been found, and expects the following input characters to match + // the remaining characters of the special-value symbol. + // This means that the following restrictions apply to special-value symbols: + // - they must not start with signs ('+', or '-'), + // - they must not have the same first character. + // - they must not start with digits. + // + // Examples: + // flags = ALLOW_HEX | ALLOW_TRAILING_JUNK, + // empty_string_value = 0.0, + // junk_string_value = NaN, + // infinity_symbol = "infinity", + // nan_symbol = "nan": + // StringToDouble("0x1234") -> 4660.0. + // StringToDouble("0x1234K") -> 4660.0. + // StringToDouble("") -> 0.0 // empty_string_value. + // StringToDouble(" ") -> NaN // junk_string_value. + // StringToDouble(" 1") -> NaN // junk_string_value. + // StringToDouble("0x") -> NaN // junk_string_value. + // StringToDouble("-123.45") -> -123.45. + // StringToDouble("--123.45") -> NaN // junk_string_value. + // StringToDouble("123e45") -> 123e45. + // StringToDouble("123E45") -> 123e45. + // StringToDouble("123e+45") -> 123e45. + // StringToDouble("123E-45") -> 123e-45. + // StringToDouble("123e") -> 123.0 // trailing junk ignored. + // StringToDouble("123e-") -> 123.0 // trailing junk ignored. + // StringToDouble("+NaN") -> NaN // NaN string literal. + // StringToDouble("-infinity") -> -inf. // infinity literal. + // StringToDouble("Infinity") -> NaN // junk_string_value. + // + // flags = ALLOW_OCTAL | ALLOW_LEADING_SPACES, + // empty_string_value = 0.0, + // junk_string_value = NaN, + // infinity_symbol = NULL, + // nan_symbol = NULL: + // StringToDouble("0x1234") -> NaN // junk_string_value. + // StringToDouble("01234") -> 668.0. + // StringToDouble("") -> 0.0 // empty_string_value. + // StringToDouble(" ") -> 0.0 // empty_string_value. + // StringToDouble(" 1") -> 1.0 + // StringToDouble("0x") -> NaN // junk_string_value. + // StringToDouble("0123e45") -> NaN // junk_string_value. + // StringToDouble("01239E45") -> 1239e45. + // StringToDouble("-infinity") -> NaN // junk_string_value. + // StringToDouble("NaN") -> NaN // junk_string_value. + StringToDoubleConverter(int flags, + double empty_string_value, + double junk_string_value, + const char* infinity_symbol, + const char* nan_symbol) + : flags_(flags), + empty_string_value_(empty_string_value), + junk_string_value_(junk_string_value), + infinity_symbol_(infinity_symbol), + nan_symbol_(nan_symbol) { + } + + // Performs the conversion. + // The output parameter 'processed_characters_count' is set to the number + // of characters that have been processed to read the number. + // Spaces than are processed with ALLOW_{LEADING|TRAILING}_SPACES are included + // in the 'processed_characters_count'. Trailing junk is never included. + double StringToDouble(const char* buffer, + int length, + int* processed_characters_count) const; + + // Same as StringToDouble above but for 16 bit characters. + double StringToDouble(const uc16* buffer, + int length, + int* processed_characters_count) const; + + // Same as StringToDouble but reads a float. + // Note that this is not equivalent to static_cast(StringToDouble(...)) + // due to potential double-rounding. + float StringToFloat(const char* buffer, + int length, + int* processed_characters_count) const; + + // Same as StringToFloat above but for 16 bit characters. + float StringToFloat(const uc16* buffer, + int length, + int* processed_characters_count) const; + + private: + const int flags_; + const double empty_string_value_; + const double junk_string_value_; + const char* const infinity_symbol_; + const char* const nan_symbol_; + + template + double StringToIeee(Iterator start_pointer, + int length, + bool read_as_double, + int* processed_characters_count) const; + + DISALLOW_IMPLICIT_CONSTRUCTORS(StringToDoubleConverter); +}; + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_DOUBLE_CONVERSION_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/fast-dtoa.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/fast-dtoa.cc new file mode 100644 index 0000000..6135038 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/fast-dtoa.cc @@ -0,0 +1,665 @@ +// Copyright 2012 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include "fast-dtoa.h" + +#include "cached-powers.h" +#include "diy-fp.h" +#include "ieee.h" + +namespace double_conversion { + +// The minimal and maximal target exponent define the range of w's binary +// exponent, where 'w' is the result of multiplying the input by a cached power +// of ten. +// +// A different range might be chosen on a different platform, to optimize digit +// generation, but a smaller range requires more powers of ten to be cached. +static const int kMinimalTargetExponent = -60; +static const int kMaximalTargetExponent = -32; + + +// Adjusts the last digit of the generated number, and screens out generated +// solutions that may be inaccurate. A solution may be inaccurate if it is +// outside the safe interval, or if we cannot prove that it is closer to the +// input than a neighboring representation of the same length. +// +// Input: * buffer containing the digits of too_high / 10^kappa +// * the buffer's length +// * distance_too_high_w == (too_high - w).f() * unit +// * unsafe_interval == (too_high - too_low).f() * unit +// * rest = (too_high - buffer * 10^kappa).f() * unit +// * ten_kappa = 10^kappa * unit +// * unit = the common multiplier +// Output: returns true if the buffer is guaranteed to contain the closest +// representable number to the input. +// Modifies the generated digits in the buffer to approach (round towards) w. +static bool RoundWeed(Vector buffer, + int length, + uint64_t distance_too_high_w, + uint64_t unsafe_interval, + uint64_t rest, + uint64_t ten_kappa, + uint64_t unit) { + uint64_t small_distance = distance_too_high_w - unit; + uint64_t big_distance = distance_too_high_w + unit; + // Let w_low = too_high - big_distance, and + // w_high = too_high - small_distance. + // Note: w_low < w < w_high + // + // The real w (* unit) must lie somewhere inside the interval + // ]w_low; w_high[ (often written as "(w_low; w_high)") + + // Basically the buffer currently contains a number in the unsafe interval + // ]too_low; too_high[ with too_low < w < too_high + // + // too_high - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + // ^v 1 unit ^ ^ ^ ^ + // boundary_high --------------------- . . . . + // ^v 1 unit . . . . + // - - - - - - - - - - - - - - - - - - - + - - + - - - - - - . . + // . . ^ . . + // . big_distance . . . + // . . . . rest + // small_distance . . . . + // v . . . . + // w_high - - - - - - - - - - - - - - - - - - . . . . + // ^v 1 unit . . . . + // w ---------------------------------------- . . . . + // ^v 1 unit v . . . + // w_low - - - - - - - - - - - - - - - - - - - - - . . . + // . . v + // buffer --------------------------------------------------+-------+-------- + // . . + // safe_interval . + // v . + // - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - . + // ^v 1 unit . + // boundary_low ------------------------- unsafe_interval + // ^v 1 unit v + // too_low - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + // + // + // Note that the value of buffer could lie anywhere inside the range too_low + // to too_high. + // + // boundary_low, boundary_high and w are approximations of the real boundaries + // and v (the input number). They are guaranteed to be precise up to one unit. + // In fact the error is guaranteed to be strictly less than one unit. + // + // Anything that lies outside the unsafe interval is guaranteed not to round + // to v when read again. + // Anything that lies inside the safe interval is guaranteed to round to v + // when read again. + // If the number inside the buffer lies inside the unsafe interval but not + // inside the safe interval then we simply do not know and bail out (returning + // false). + // + // Similarly we have to take into account the imprecision of 'w' when finding + // the closest representation of 'w'. If we have two potential + // representations, and one is closer to both w_low and w_high, then we know + // it is closer to the actual value v. + // + // By generating the digits of too_high we got the largest (closest to + // too_high) buffer that is still in the unsafe interval. In the case where + // w_high < buffer < too_high we try to decrement the buffer. + // This way the buffer approaches (rounds towards) w. + // There are 3 conditions that stop the decrementation process: + // 1) the buffer is already below w_high + // 2) decrementing the buffer would make it leave the unsafe interval + // 3) decrementing the buffer would yield a number below w_high and farther + // away than the current number. In other words: + // (buffer{-1} < w_high) && w_high - buffer{-1} > buffer - w_high + // Instead of using the buffer directly we use its distance to too_high. + // Conceptually rest ~= too_high - buffer + // We need to do the following tests in this order to avoid over- and + // underflows. + ASSERT(rest <= unsafe_interval); + while (rest < small_distance && // Negated condition 1 + unsafe_interval - rest >= ten_kappa && // Negated condition 2 + (rest + ten_kappa < small_distance || // buffer{-1} > w_high + small_distance - rest >= rest + ten_kappa - small_distance)) { + buffer[length - 1]--; + rest += ten_kappa; + } + + // We have approached w+ as much as possible. We now test if approaching w- + // would require changing the buffer. If yes, then we have two possible + // representations close to w, but we cannot decide which one is closer. + if (rest < big_distance && + unsafe_interval - rest >= ten_kappa && + (rest + ten_kappa < big_distance || + big_distance - rest > rest + ten_kappa - big_distance)) { + return false; + } + + // Weeding test. + // The safe interval is [too_low + 2 ulp; too_high - 2 ulp] + // Since too_low = too_high - unsafe_interval this is equivalent to + // [too_high - unsafe_interval + 4 ulp; too_high - 2 ulp] + // Conceptually we have: rest ~= too_high - buffer + return (2 * unit <= rest) && (rest <= unsafe_interval - 4 * unit); +} + + +// Rounds the buffer upwards if the result is closer to v by possibly adding +// 1 to the buffer. If the precision of the calculation is not sufficient to +// round correctly, return false. +// The rounding might shift the whole buffer in which case the kappa is +// adjusted. For example "99", kappa = 3 might become "10", kappa = 4. +// +// If 2*rest > ten_kappa then the buffer needs to be round up. +// rest can have an error of +/- 1 unit. This function accounts for the +// imprecision and returns false, if the rounding direction cannot be +// unambiguously determined. +// +// Precondition: rest < ten_kappa. +static bool RoundWeedCounted(Vector buffer, + int length, + uint64_t rest, + uint64_t ten_kappa, + uint64_t unit, + int* kappa) { + ASSERT(rest < ten_kappa); + // The following tests are done in a specific order to avoid overflows. They + // will work correctly with any uint64 values of rest < ten_kappa and unit. + // + // If the unit is too big, then we don't know which way to round. For example + // a unit of 50 means that the real number lies within rest +/- 50. If + // 10^kappa == 40 then there is no way to tell which way to round. + if (unit >= ten_kappa) return false; + // Even if unit is just half the size of 10^kappa we are already completely + // lost. (And after the previous test we know that the expression will not + // over/underflow.) + if (ten_kappa - unit <= unit) return false; + // If 2 * (rest + unit) <= 10^kappa we can safely round down. + if ((ten_kappa - rest > rest) && (ten_kappa - 2 * rest >= 2 * unit)) { + return true; + } + // If 2 * (rest - unit) >= 10^kappa, then we can safely round up. + if ((rest > unit) && (ten_kappa - (rest - unit) <= (rest - unit))) { + // Increment the last digit recursively until we find a non '9' digit. + buffer[length - 1]++; + for (int i = length - 1; i > 0; --i) { + if (buffer[i] != '0' + 10) break; + buffer[i] = '0'; + buffer[i - 1]++; + } + // If the first digit is now '0'+ 10 we had a buffer with all '9's. With the + // exception of the first digit all digits are now '0'. Simply switch the + // first digit to '1' and adjust the kappa. Example: "99" becomes "10" and + // the power (the kappa) is increased. + if (buffer[0] == '0' + 10) { + buffer[0] = '1'; + (*kappa) += 1; + } + return true; + } + return false; +} + +// Returns the biggest power of ten that is less than or equal to the given +// number. We furthermore receive the maximum number of bits 'number' has. +// +// Returns power == 10^(exponent_plus_one-1) such that +// power <= number < power * 10. +// If number_bits == 0 then 0^(0-1) is returned. +// The number of bits must be <= 32. +// Precondition: number < (1 << (number_bits + 1)). + +// Inspired by the method for finding an integer log base 10 from here: +// http://graphics.stanford.edu/~seander/bithacks.html#IntegerLog10 +static unsigned int const kSmallPowersOfTen[] = + {0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, + 1000000000}; + +static void BiggestPowerTen(uint32_t number, + int number_bits, + uint32_t* power, + int* exponent_plus_one) { + ASSERT(number < (1u << (number_bits + 1))); + // 1233/4096 is approximately 1/lg(10). + int exponent_plus_one_guess = ((number_bits + 1) * 1233 >> 12); + // We increment to skip over the first entry in the kPowersOf10 table. + // Note: kPowersOf10[i] == 10^(i-1). + exponent_plus_one_guess++; + // We don't have any guarantees that 2^number_bits <= number. + if (number < kSmallPowersOfTen[exponent_plus_one_guess]) { + exponent_plus_one_guess--; + } + *power = kSmallPowersOfTen[exponent_plus_one_guess]; + *exponent_plus_one = exponent_plus_one_guess; +} + +// Generates the digits of input number w. +// w is a floating-point number (DiyFp), consisting of a significand and an +// exponent. Its exponent is bounded by kMinimalTargetExponent and +// kMaximalTargetExponent. +// Hence -60 <= w.e() <= -32. +// +// Returns false if it fails, in which case the generated digits in the buffer +// should not be used. +// Preconditions: +// * low, w and high are correct up to 1 ulp (unit in the last place). That +// is, their error must be less than a unit of their last digits. +// * low.e() == w.e() == high.e() +// * low < w < high, and taking into account their error: low~ <= high~ +// * kMinimalTargetExponent <= w.e() <= kMaximalTargetExponent +// Postconditions: returns false if procedure fails. +// otherwise: +// * buffer is not null-terminated, but len contains the number of digits. +// * buffer contains the shortest possible decimal digit-sequence +// such that LOW < buffer * 10^kappa < HIGH, where LOW and HIGH are the +// correct values of low and high (without their error). +// * if more than one decimal representation gives the minimal number of +// decimal digits then the one closest to W (where W is the correct value +// of w) is chosen. +// Remark: this procedure takes into account the imprecision of its input +// numbers. If the precision is not enough to guarantee all the postconditions +// then false is returned. This usually happens rarely (~0.5%). +// +// Say, for the sake of example, that +// w.e() == -48, and w.f() == 0x1234567890abcdef +// w's value can be computed by w.f() * 2^w.e() +// We can obtain w's integral digits by simply shifting w.f() by -w.e(). +// -> w's integral part is 0x1234 +// w's fractional part is therefore 0x567890abcdef. +// Printing w's integral part is easy (simply print 0x1234 in decimal). +// In order to print its fraction we repeatedly multiply the fraction by 10 and +// get each digit. Example the first digit after the point would be computed by +// (0x567890abcdef * 10) >> 48. -> 3 +// The whole thing becomes slightly more complicated because we want to stop +// once we have enough digits. That is, once the digits inside the buffer +// represent 'w' we can stop. Everything inside the interval low - high +// represents w. However we have to pay attention to low, high and w's +// imprecision. +static bool DigitGen(DiyFp low, + DiyFp w, + DiyFp high, + Vector buffer, + int* length, + int* kappa) { + ASSERT(low.e() == w.e() && w.e() == high.e()); + ASSERT(low.f() + 1 <= high.f() - 1); + ASSERT(kMinimalTargetExponent <= w.e() && w.e() <= kMaximalTargetExponent); + // low, w and high are imprecise, but by less than one ulp (unit in the last + // place). + // If we remove (resp. add) 1 ulp from low (resp. high) we are certain that + // the new numbers are outside of the interval we want the final + // representation to lie in. + // Inversely adding (resp. removing) 1 ulp from low (resp. high) would yield + // numbers that are certain to lie in the interval. We will use this fact + // later on. + // We will now start by generating the digits within the uncertain + // interval. Later we will weed out representations that lie outside the safe + // interval and thus _might_ lie outside the correct interval. + uint64_t unit = 1; + DiyFp too_low = DiyFp(low.f() - unit, low.e()); + DiyFp too_high = DiyFp(high.f() + unit, high.e()); + // too_low and too_high are guaranteed to lie outside the interval we want the + // generated number in. + DiyFp unsafe_interval = DiyFp::Minus(too_high, too_low); + // We now cut the input number into two parts: the integral digits and the + // fractionals. We will not write any decimal separator though, but adapt + // kappa instead. + // Reminder: we are currently computing the digits (stored inside the buffer) + // such that: too_low < buffer * 10^kappa < too_high + // We use too_high for the digit_generation and stop as soon as possible. + // If we stop early we effectively round down. + DiyFp one = DiyFp(static_cast(1) << -w.e(), w.e()); + // Division by one is a shift. + uint32_t integrals = static_cast(too_high.f() >> -one.e()); + // Modulo by one is an and. + uint64_t fractionals = too_high.f() & (one.f() - 1); + uint32_t divisor; + int divisor_exponent_plus_one; + BiggestPowerTen(integrals, DiyFp::kSignificandSize - (-one.e()), + &divisor, &divisor_exponent_plus_one); + *kappa = divisor_exponent_plus_one; + *length = 0; + // Loop invariant: buffer = too_high / 10^kappa (integer division) + // The invariant holds for the first iteration: kappa has been initialized + // with the divisor exponent + 1. And the divisor is the biggest power of ten + // that is smaller than integrals. + while (*kappa > 0) { + int digit = integrals / divisor; + ASSERT(digit <= 9); + buffer[*length] = static_cast('0' + digit); + (*length)++; + integrals %= divisor; + (*kappa)--; + // Note that kappa now equals the exponent of the divisor and that the + // invariant thus holds again. + uint64_t rest = + (static_cast(integrals) << -one.e()) + fractionals; + // Invariant: too_high = buffer * 10^kappa + DiyFp(rest, one.e()) + // Reminder: unsafe_interval.e() == one.e() + if (rest < unsafe_interval.f()) { + // Rounding down (by not emitting the remaining digits) yields a number + // that lies within the unsafe interval. + return RoundWeed(buffer, *length, DiyFp::Minus(too_high, w).f(), + unsafe_interval.f(), rest, + static_cast(divisor) << -one.e(), unit); + } + divisor /= 10; + } + + // The integrals have been generated. We are at the point of the decimal + // separator. In the following loop we simply multiply the remaining digits by + // 10 and divide by one. We just need to pay attention to multiply associated + // data (like the interval or 'unit'), too. + // Note that the multiplication by 10 does not overflow, because w.e >= -60 + // and thus one.e >= -60. + ASSERT(one.e() >= -60); + ASSERT(fractionals < one.f()); + ASSERT(UINT64_2PART_C(0xFFFFFFFF, FFFFFFFF) / 10 >= one.f()); + for (;;) { + fractionals *= 10; + unit *= 10; + unsafe_interval.set_f(unsafe_interval.f() * 10); + // Integer division by one. + int digit = static_cast(fractionals >> -one.e()); + ASSERT(digit <= 9); + buffer[*length] = static_cast('0' + digit); + (*length)++; + fractionals &= one.f() - 1; // Modulo by one. + (*kappa)--; + if (fractionals < unsafe_interval.f()) { + return RoundWeed(buffer, *length, DiyFp::Minus(too_high, w).f() * unit, + unsafe_interval.f(), fractionals, one.f(), unit); + } + } +} + + + +// Generates (at most) requested_digits digits of input number w. +// w is a floating-point number (DiyFp), consisting of a significand and an +// exponent. Its exponent is bounded by kMinimalTargetExponent and +// kMaximalTargetExponent. +// Hence -60 <= w.e() <= -32. +// +// Returns false if it fails, in which case the generated digits in the buffer +// should not be used. +// Preconditions: +// * w is correct up to 1 ulp (unit in the last place). That +// is, its error must be strictly less than a unit of its last digit. +// * kMinimalTargetExponent <= w.e() <= kMaximalTargetExponent +// +// Postconditions: returns false if procedure fails. +// otherwise: +// * buffer is not null-terminated, but length contains the number of +// digits. +// * the representation in buffer is the most precise representation of +// requested_digits digits. +// * buffer contains at most requested_digits digits of w. If there are less +// than requested_digits digits then some trailing '0's have been removed. +// * kappa is such that +// w = buffer * 10^kappa + eps with |eps| < 10^kappa / 2. +// +// Remark: This procedure takes into account the imprecision of its input +// numbers. If the precision is not enough to guarantee all the postconditions +// then false is returned. This usually happens rarely, but the failure-rate +// increases with higher requested_digits. +static bool DigitGenCounted(DiyFp w, + int requested_digits, + Vector buffer, + int* length, + int* kappa) { + ASSERT(kMinimalTargetExponent <= w.e() && w.e() <= kMaximalTargetExponent); + ASSERT(kMinimalTargetExponent >= -60); + ASSERT(kMaximalTargetExponent <= -32); + // w is assumed to have an error less than 1 unit. Whenever w is scaled we + // also scale its error. + uint64_t w_error = 1; + // We cut the input number into two parts: the integral digits and the + // fractional digits. We don't emit any decimal separator, but adapt kappa + // instead. Example: instead of writing "1.2" we put "12" into the buffer and + // increase kappa by 1. + DiyFp one = DiyFp(static_cast(1) << -w.e(), w.e()); + // Division by one is a shift. + uint32_t integrals = static_cast(w.f() >> -one.e()); + // Modulo by one is an and. + uint64_t fractionals = w.f() & (one.f() - 1); + uint32_t divisor; + int divisor_exponent_plus_one; + BiggestPowerTen(integrals, DiyFp::kSignificandSize - (-one.e()), + &divisor, &divisor_exponent_plus_one); + *kappa = divisor_exponent_plus_one; + *length = 0; + + // Loop invariant: buffer = w / 10^kappa (integer division) + // The invariant holds for the first iteration: kappa has been initialized + // with the divisor exponent + 1. And the divisor is the biggest power of ten + // that is smaller than 'integrals'. + while (*kappa > 0) { + int digit = integrals / divisor; + ASSERT(digit <= 9); + buffer[*length] = static_cast('0' + digit); + (*length)++; + requested_digits--; + integrals %= divisor; + (*kappa)--; + // Note that kappa now equals the exponent of the divisor and that the + // invariant thus holds again. + if (requested_digits == 0) break; + divisor /= 10; + } + + if (requested_digits == 0) { + uint64_t rest = + (static_cast(integrals) << -one.e()) + fractionals; + return RoundWeedCounted(buffer, *length, rest, + static_cast(divisor) << -one.e(), w_error, + kappa); + } + + // The integrals have been generated. We are at the point of the decimal + // separator. In the following loop we simply multiply the remaining digits by + // 10 and divide by one. We just need to pay attention to multiply associated + // data (the 'unit'), too. + // Note that the multiplication by 10 does not overflow, because w.e >= -60 + // and thus one.e >= -60. + ASSERT(one.e() >= -60); + ASSERT(fractionals < one.f()); + ASSERT(UINT64_2PART_C(0xFFFFFFFF, FFFFFFFF) / 10 >= one.f()); + while (requested_digits > 0 && fractionals > w_error) { + fractionals *= 10; + w_error *= 10; + // Integer division by one. + int digit = static_cast(fractionals >> -one.e()); + ASSERT(digit <= 9); + buffer[*length] = static_cast('0' + digit); + (*length)++; + requested_digits--; + fractionals &= one.f() - 1; // Modulo by one. + (*kappa)--; + } + if (requested_digits != 0) return false; + return RoundWeedCounted(buffer, *length, fractionals, one.f(), w_error, + kappa); +} + + +// Provides a decimal representation of v. +// Returns true if it succeeds, otherwise the result cannot be trusted. +// There will be *length digits inside the buffer (not null-terminated). +// If the function returns true then +// v == (double) (buffer * 10^decimal_exponent). +// The digits in the buffer are the shortest representation possible: no +// 0.09999999999999999 instead of 0.1. The shorter representation will even be +// chosen even if the longer one would be closer to v. +// The last digit will be closest to the actual v. That is, even if several +// digits might correctly yield 'v' when read again, the closest will be +// computed. +static bool Grisu3(double v, + FastDtoaMode mode, + Vector buffer, + int* length, + int* decimal_exponent) { + DiyFp w = Double(v).AsNormalizedDiyFp(); + // boundary_minus and boundary_plus are the boundaries between v and its + // closest floating-point neighbors. Any number strictly between + // boundary_minus and boundary_plus will round to v when convert to a double. + // Grisu3 will never output representations that lie exactly on a boundary. + DiyFp boundary_minus, boundary_plus; + if (mode == FAST_DTOA_SHORTEST) { + Double(v).NormalizedBoundaries(&boundary_minus, &boundary_plus); + } else { + ASSERT(mode == FAST_DTOA_SHORTEST_SINGLE); + float single_v = static_cast(v); + Single(single_v).NormalizedBoundaries(&boundary_minus, &boundary_plus); + } + ASSERT(boundary_plus.e() == w.e()); + DiyFp ten_mk; // Cached power of ten: 10^-k + int mk; // -k + int ten_mk_minimal_binary_exponent = + kMinimalTargetExponent - (w.e() + DiyFp::kSignificandSize); + int ten_mk_maximal_binary_exponent = + kMaximalTargetExponent - (w.e() + DiyFp::kSignificandSize); + PowersOfTenCache::GetCachedPowerForBinaryExponentRange( + ten_mk_minimal_binary_exponent, + ten_mk_maximal_binary_exponent, + &ten_mk, &mk); + ASSERT((kMinimalTargetExponent <= w.e() + ten_mk.e() + + DiyFp::kSignificandSize) && + (kMaximalTargetExponent >= w.e() + ten_mk.e() + + DiyFp::kSignificandSize)); + // Note that ten_mk is only an approximation of 10^-k. A DiyFp only contains a + // 64 bit significand and ten_mk is thus only precise up to 64 bits. + + // The DiyFp::Times procedure rounds its result, and ten_mk is approximated + // too. The variable scaled_w (as well as scaled_boundary_minus/plus) are now + // off by a small amount. + // In fact: scaled_w - w*10^k < 1ulp (unit in the last place) of scaled_w. + // In other words: let f = scaled_w.f() and e = scaled_w.e(), then + // (f-1) * 2^e < w*10^k < (f+1) * 2^e + DiyFp scaled_w = DiyFp::Times(w, ten_mk); + ASSERT(scaled_w.e() == + boundary_plus.e() + ten_mk.e() + DiyFp::kSignificandSize); + // In theory it would be possible to avoid some recomputations by computing + // the difference between w and boundary_minus/plus (a power of 2) and to + // compute scaled_boundary_minus/plus by subtracting/adding from + // scaled_w. However the code becomes much less readable and the speed + // enhancements are not terriffic. + DiyFp scaled_boundary_minus = DiyFp::Times(boundary_minus, ten_mk); + DiyFp scaled_boundary_plus = DiyFp::Times(boundary_plus, ten_mk); + + // DigitGen will generate the digits of scaled_w. Therefore we have + // v == (double) (scaled_w * 10^-mk). + // Set decimal_exponent == -mk and pass it to DigitGen. If scaled_w is not an + // integer than it will be updated. For instance if scaled_w == 1.23 then + // the buffer will be filled with "123" und the decimal_exponent will be + // decreased by 2. + int kappa; + bool result = DigitGen(scaled_boundary_minus, scaled_w, scaled_boundary_plus, + buffer, length, &kappa); + *decimal_exponent = -mk + kappa; + return result; +} + + +// The "counted" version of grisu3 (see above) only generates requested_digits +// number of digits. This version does not generate the shortest representation, +// and with enough requested digits 0.1 will at some point print as 0.9999999... +// Grisu3 is too imprecise for real halfway cases (1.5 will not work) and +// therefore the rounding strategy for halfway cases is irrelevant. +static bool Grisu3Counted(double v, + int requested_digits, + Vector buffer, + int* length, + int* decimal_exponent) { + DiyFp w = Double(v).AsNormalizedDiyFp(); + DiyFp ten_mk; // Cached power of ten: 10^-k + int mk; // -k + int ten_mk_minimal_binary_exponent = + kMinimalTargetExponent - (w.e() + DiyFp::kSignificandSize); + int ten_mk_maximal_binary_exponent = + kMaximalTargetExponent - (w.e() + DiyFp::kSignificandSize); + PowersOfTenCache::GetCachedPowerForBinaryExponentRange( + ten_mk_minimal_binary_exponent, + ten_mk_maximal_binary_exponent, + &ten_mk, &mk); + ASSERT((kMinimalTargetExponent <= w.e() + ten_mk.e() + + DiyFp::kSignificandSize) && + (kMaximalTargetExponent >= w.e() + ten_mk.e() + + DiyFp::kSignificandSize)); + // Note that ten_mk is only an approximation of 10^-k. A DiyFp only contains a + // 64 bit significand and ten_mk is thus only precise up to 64 bits. + + // The DiyFp::Times procedure rounds its result, and ten_mk is approximated + // too. The variable scaled_w (as well as scaled_boundary_minus/plus) are now + // off by a small amount. + // In fact: scaled_w - w*10^k < 1ulp (unit in the last place) of scaled_w. + // In other words: let f = scaled_w.f() and e = scaled_w.e(), then + // (f-1) * 2^e < w*10^k < (f+1) * 2^e + DiyFp scaled_w = DiyFp::Times(w, ten_mk); + + // We now have (double) (scaled_w * 10^-mk). + // DigitGen will generate the first requested_digits digits of scaled_w and + // return together with a kappa such that scaled_w ~= buffer * 10^kappa. (It + // will not always be exactly the same since DigitGenCounted only produces a + // limited number of digits.) + int kappa; + bool result = DigitGenCounted(scaled_w, requested_digits, + buffer, length, &kappa); + *decimal_exponent = -mk + kappa; + return result; +} + + +bool FastDtoa(double v, + FastDtoaMode mode, + int requested_digits, + Vector buffer, + int* length, + int* decimal_point) { + ASSERT(v > 0); + ASSERT(!Double(v).IsSpecial()); + + bool result = false; + int decimal_exponent = 0; + switch (mode) { + case FAST_DTOA_SHORTEST: + case FAST_DTOA_SHORTEST_SINGLE: + result = Grisu3(v, mode, buffer, length, &decimal_exponent); + break; + case FAST_DTOA_PRECISION: + result = Grisu3Counted(v, requested_digits, + buffer, length, &decimal_exponent); + break; + default: + UNREACHABLE(); + } + if (result) { + *decimal_point = *length + decimal_exponent; + buffer[*length] = '\0'; + } + return result; +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/fast-dtoa.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/fast-dtoa.h new file mode 100644 index 0000000..5f1e8ee --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/fast-dtoa.h @@ -0,0 +1,88 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_FAST_DTOA_H_ +#define DOUBLE_CONVERSION_FAST_DTOA_H_ + +#include "utils.h" + +namespace double_conversion { + +enum FastDtoaMode { + // Computes the shortest representation of the given input. The returned + // result will be the most accurate number of this length. Longer + // representations might be more accurate. + FAST_DTOA_SHORTEST, + // Same as FAST_DTOA_SHORTEST but for single-precision floats. + FAST_DTOA_SHORTEST_SINGLE, + // Computes a representation where the precision (number of digits) is + // given as input. The precision is independent of the decimal point. + FAST_DTOA_PRECISION +}; + +// FastDtoa will produce at most kFastDtoaMaximalLength digits. This does not +// include the terminating '\0' character. +static const int kFastDtoaMaximalLength = 17; +// Same for single-precision numbers. +static const int kFastDtoaMaximalSingleLength = 9; + +// Provides a decimal representation of v. +// The result should be interpreted as buffer * 10^(point - length). +// +// Precondition: +// * v must be a strictly positive finite double. +// +// Returns true if it succeeds, otherwise the result can not be trusted. +// There will be *length digits inside the buffer followed by a null terminator. +// If the function returns true and mode equals +// - FAST_DTOA_SHORTEST, then +// the parameter requested_digits is ignored. +// The result satisfies +// v == (double) (buffer * 10^(point - length)). +// The digits in the buffer are the shortest representation possible. E.g. +// if 0.099999999999 and 0.1 represent the same double then "1" is returned +// with point = 0. +// The last digit will be closest to the actual v. That is, even if several +// digits might correctly yield 'v' when read again, the buffer will contain +// the one closest to v. +// - FAST_DTOA_PRECISION, then +// the buffer contains requested_digits digits. +// the difference v - (buffer * 10^(point-length)) is closest to zero for +// all possible representations of requested_digits digits. +// If there are two values that are equally close, then FastDtoa returns +// false. +// For both modes the buffer must be large enough to hold the result. +bool FastDtoa(double d, + FastDtoaMode mode, + int requested_digits, + Vector buffer, + int* length, + int* decimal_point); + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_FAST_DTOA_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/fixed-dtoa.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/fixed-dtoa.cc new file mode 100644 index 0000000..0f55a0b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/fixed-dtoa.cc @@ -0,0 +1,405 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include + +#include "fixed-dtoa.h" +#include "ieee.h" + +namespace double_conversion { + +// Represents a 128bit type. This class should be replaced by a native type on +// platforms that support 128bit integers. +class UInt128 { + public: + UInt128() : high_bits_(0), low_bits_(0) { } + UInt128(uint64_t high, uint64_t low) : high_bits_(high), low_bits_(low) { } + + void Multiply(uint32_t multiplicand) { + uint64_t accumulator; + + accumulator = (low_bits_ & kMask32) * multiplicand; + uint32_t part = static_cast(accumulator & kMask32); + accumulator >>= 32; + accumulator = accumulator + (low_bits_ >> 32) * multiplicand; + low_bits_ = (accumulator << 32) + part; + accumulator >>= 32; + accumulator = accumulator + (high_bits_ & kMask32) * multiplicand; + part = static_cast(accumulator & kMask32); + accumulator >>= 32; + accumulator = accumulator + (high_bits_ >> 32) * multiplicand; + high_bits_ = (accumulator << 32) + part; + ASSERT((accumulator >> 32) == 0); + } + + void Shift(int shift_amount) { + ASSERT(-64 <= shift_amount && shift_amount <= 64); + if (shift_amount == 0) { + return; + } else if (shift_amount == -64) { + high_bits_ = low_bits_; + low_bits_ = 0; + } else if (shift_amount == 64) { + low_bits_ = high_bits_; + high_bits_ = 0; + } else if (shift_amount <= 0) { + high_bits_ <<= -shift_amount; + high_bits_ += low_bits_ >> (64 + shift_amount); + low_bits_ <<= -shift_amount; + } else { + low_bits_ >>= shift_amount; + low_bits_ += high_bits_ << (64 - shift_amount); + high_bits_ >>= shift_amount; + } + } + + // Modifies *this to *this MOD (2^power). + // Returns *this DIV (2^power). + int DivModPowerOf2(int power) { + if (power >= 64) { + int result = static_cast(high_bits_ >> (power - 64)); + high_bits_ -= static_cast(result) << (power - 64); + return result; + } else { + uint64_t part_low = low_bits_ >> power; + uint64_t part_high = high_bits_ << (64 - power); + int result = static_cast(part_low + part_high); + high_bits_ = 0; + low_bits_ -= part_low << power; + return result; + } + } + + bool IsZero() const { + return high_bits_ == 0 && low_bits_ == 0; + } + + int BitAt(int position) const { + if (position >= 64) { + return static_cast(high_bits_ >> (position - 64)) & 1; + } else { + return static_cast(low_bits_ >> position) & 1; + } + } + + private: + static const uint64_t kMask32 = 0xFFFFFFFF; + // Value == (high_bits_ << 64) + low_bits_ + uint64_t high_bits_; + uint64_t low_bits_; +}; + + +static const int kDoubleSignificandSize = 53; // Includes the hidden bit. + + +static void FillDigits32FixedLength(uint32_t number, int requested_length, + Vector buffer, int* length) { + for (int i = requested_length - 1; i >= 0; --i) { + buffer[(*length) + i] = '0' + number % 10; + number /= 10; + } + *length += requested_length; +} + + +static void FillDigits32(uint32_t number, Vector buffer, int* length) { + int number_length = 0; + // We fill the digits in reverse order and exchange them afterwards. + while (number != 0) { + int digit = number % 10; + number /= 10; + buffer[(*length) + number_length] = static_cast('0' + digit); + number_length++; + } + // Exchange the digits. + int i = *length; + int j = *length + number_length - 1; + while (i < j) { + char tmp = buffer[i]; + buffer[i] = buffer[j]; + buffer[j] = tmp; + i++; + j--; + } + *length += number_length; +} + + +static void FillDigits64FixedLength(uint64_t number, + Vector buffer, int* length) { + const uint32_t kTen7 = 10000000; + // For efficiency cut the number into 3 uint32_t parts, and print those. + uint32_t part2 = static_cast(number % kTen7); + number /= kTen7; + uint32_t part1 = static_cast(number % kTen7); + uint32_t part0 = static_cast(number / kTen7); + + FillDigits32FixedLength(part0, 3, buffer, length); + FillDigits32FixedLength(part1, 7, buffer, length); + FillDigits32FixedLength(part2, 7, buffer, length); +} + + +static void FillDigits64(uint64_t number, Vector buffer, int* length) { + const uint32_t kTen7 = 10000000; + // For efficiency cut the number into 3 uint32_t parts, and print those. + uint32_t part2 = static_cast(number % kTen7); + number /= kTen7; + uint32_t part1 = static_cast(number % kTen7); + uint32_t part0 = static_cast(number / kTen7); + + if (part0 != 0) { + FillDigits32(part0, buffer, length); + FillDigits32FixedLength(part1, 7, buffer, length); + FillDigits32FixedLength(part2, 7, buffer, length); + } else if (part1 != 0) { + FillDigits32(part1, buffer, length); + FillDigits32FixedLength(part2, 7, buffer, length); + } else { + FillDigits32(part2, buffer, length); + } +} + + +static void RoundUp(Vector buffer, int* length, int* decimal_point) { + // An empty buffer represents 0. + if (*length == 0) { + buffer[0] = '1'; + *decimal_point = 1; + *length = 1; + return; + } + // Round the last digit until we either have a digit that was not '9' or until + // we reached the first digit. + buffer[(*length) - 1]++; + for (int i = (*length) - 1; i > 0; --i) { + if (buffer[i] != '0' + 10) { + return; + } + buffer[i] = '0'; + buffer[i - 1]++; + } + // If the first digit is now '0' + 10, we would need to set it to '0' and add + // a '1' in front. However we reach the first digit only if all following + // digits had been '9' before rounding up. Now all trailing digits are '0' and + // we simply switch the first digit to '1' and update the decimal-point + // (indicating that the point is now one digit to the right). + if (buffer[0] == '0' + 10) { + buffer[0] = '1'; + (*decimal_point)++; + } +} + + +// The given fractionals number represents a fixed-point number with binary +// point at bit (-exponent). +// Preconditions: +// -128 <= exponent <= 0. +// 0 <= fractionals * 2^exponent < 1 +// The buffer holds the result. +// The function will round its result. During the rounding-process digits not +// generated by this function might be updated, and the decimal-point variable +// might be updated. If this function generates the digits 99 and the buffer +// already contained "199" (thus yielding a buffer of "19999") then a +// rounding-up will change the contents of the buffer to "20000". +static void FillFractionals(uint64_t fractionals, int exponent, + int fractional_count, Vector buffer, + int* length, int* decimal_point) { + ASSERT(-128 <= exponent && exponent <= 0); + // 'fractionals' is a fixed-point number, with binary point at bit + // (-exponent). Inside the function the non-converted remainder of fractionals + // is a fixed-point number, with binary point at bit 'point'. + if (-exponent <= 64) { + // One 64 bit number is sufficient. + ASSERT(fractionals >> 56 == 0); + int point = -exponent; + for (int i = 0; i < fractional_count; ++i) { + if (fractionals == 0) break; + // Instead of multiplying by 10 we multiply by 5 and adjust the point + // location. This way the fractionals variable will not overflow. + // Invariant at the beginning of the loop: fractionals < 2^point. + // Initially we have: point <= 64 and fractionals < 2^56 + // After each iteration the point is decremented by one. + // Note that 5^3 = 125 < 128 = 2^7. + // Therefore three iterations of this loop will not overflow fractionals + // (even without the subtraction at the end of the loop body). At this + // time point will satisfy point <= 61 and therefore fractionals < 2^point + // and any further multiplication of fractionals by 5 will not overflow. + fractionals *= 5; + point--; + int digit = static_cast(fractionals >> point); + ASSERT(digit <= 9); + buffer[*length] = static_cast('0' + digit); + (*length)++; + fractionals -= static_cast(digit) << point; + } + // If the first bit after the point is set we have to round up. + ASSERT(fractionals == 0 || point - 1 >= 0); + if ((fractionals != 0) && ((fractionals >> (point - 1)) & 1) == 1) { + RoundUp(buffer, length, decimal_point); + } + } else { // We need 128 bits. + ASSERT(64 < -exponent && -exponent <= 128); + UInt128 fractionals128 = UInt128(fractionals, 0); + fractionals128.Shift(-exponent - 64); + int point = 128; + for (int i = 0; i < fractional_count; ++i) { + if (fractionals128.IsZero()) break; + // As before: instead of multiplying by 10 we multiply by 5 and adjust the + // point location. + // This multiplication will not overflow for the same reasons as before. + fractionals128.Multiply(5); + point--; + int digit = fractionals128.DivModPowerOf2(point); + ASSERT(digit <= 9); + buffer[*length] = static_cast('0' + digit); + (*length)++; + } + if (fractionals128.BitAt(point - 1) == 1) { + RoundUp(buffer, length, decimal_point); + } + } +} + + +// Removes leading and trailing zeros. +// If leading zeros are removed then the decimal point position is adjusted. +static void TrimZeros(Vector buffer, int* length, int* decimal_point) { + while (*length > 0 && buffer[(*length) - 1] == '0') { + (*length)--; + } + int first_non_zero = 0; + while (first_non_zero < *length && buffer[first_non_zero] == '0') { + first_non_zero++; + } + if (first_non_zero != 0) { + for (int i = first_non_zero; i < *length; ++i) { + buffer[i - first_non_zero] = buffer[i]; + } + *length -= first_non_zero; + *decimal_point -= first_non_zero; + } +} + + +bool FastFixedDtoa(double v, + int fractional_count, + Vector buffer, + int* length, + int* decimal_point) { + const uint32_t kMaxUInt32 = 0xFFFFFFFF; + uint64_t significand = Double(v).Significand(); + int exponent = Double(v).Exponent(); + // v = significand * 2^exponent (with significand a 53bit integer). + // If the exponent is larger than 20 (i.e. we may have a 73bit number) then we + // don't know how to compute the representation. 2^73 ~= 9.5*10^21. + // If necessary this limit could probably be increased, but we don't need + // more. + if (exponent > 20) return false; + if (fractional_count > 20) return false; + *length = 0; + // At most kDoubleSignificandSize bits of the significand are non-zero. + // Given a 64 bit integer we have 11 0s followed by 53 potentially non-zero + // bits: 0..11*..0xxx..53*..xx + if (exponent + kDoubleSignificandSize > 64) { + // The exponent must be > 11. + // + // We know that v = significand * 2^exponent. + // And the exponent > 11. + // We simplify the task by dividing v by 10^17. + // The quotient delivers the first digits, and the remainder fits into a 64 + // bit number. + // Dividing by 10^17 is equivalent to dividing by 5^17*2^17. + const uint64_t kFive17 = UINT64_2PART_C(0xB1, A2BC2EC5); // 5^17 + uint64_t divisor = kFive17; + int divisor_power = 17; + uint64_t dividend = significand; + uint32_t quotient; + uint64_t remainder; + // Let v = f * 2^e with f == significand and e == exponent. + // Then need q (quotient) and r (remainder) as follows: + // v = q * 10^17 + r + // f * 2^e = q * 10^17 + r + // f * 2^e = q * 5^17 * 2^17 + r + // If e > 17 then + // f * 2^(e-17) = q * 5^17 + r/2^17 + // else + // f = q * 5^17 * 2^(17-e) + r/2^e + if (exponent > divisor_power) { + // We only allow exponents of up to 20 and therefore (17 - e) <= 3 + dividend <<= exponent - divisor_power; + quotient = static_cast(dividend / divisor); + remainder = (dividend % divisor) << divisor_power; + } else { + divisor <<= divisor_power - exponent; + quotient = static_cast(dividend / divisor); + remainder = (dividend % divisor) << exponent; + } + FillDigits32(quotient, buffer, length); + FillDigits64FixedLength(remainder, buffer, length); + *decimal_point = *length; + } else if (exponent >= 0) { + // 0 <= exponent <= 11 + significand <<= exponent; + FillDigits64(significand, buffer, length); + *decimal_point = *length; + } else if (exponent > -kDoubleSignificandSize) { + // We have to cut the number. + uint64_t integrals = significand >> -exponent; + uint64_t fractionals = significand - (integrals << -exponent); + if (integrals > kMaxUInt32) { + FillDigits64(integrals, buffer, length); + } else { + FillDigits32(static_cast(integrals), buffer, length); + } + *decimal_point = *length; + FillFractionals(fractionals, exponent, fractional_count, + buffer, length, decimal_point); + } else if (exponent < -128) { + // This configuration (with at most 20 digits) means that all digits must be + // 0. + ASSERT(fractional_count <= 20); + buffer[0] = '\0'; + *length = 0; + *decimal_point = -fractional_count; + } else { + *decimal_point = 0; + FillFractionals(significand, exponent, fractional_count, + buffer, length, decimal_point); + } + TrimZeros(buffer, length, decimal_point); + buffer[*length] = '\0'; + if ((*length) == 0) { + // The string is empty and the decimal_point thus has no importance. Mimick + // Gay's dtoa and and set it to -fractional_count. + *decimal_point = -fractional_count; + } + return true; +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/fixed-dtoa.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/fixed-dtoa.h new file mode 100644 index 0000000..3bdd08e --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/fixed-dtoa.h @@ -0,0 +1,56 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_FIXED_DTOA_H_ +#define DOUBLE_CONVERSION_FIXED_DTOA_H_ + +#include "utils.h" + +namespace double_conversion { + +// Produces digits necessary to print a given number with +// 'fractional_count' digits after the decimal point. +// The buffer must be big enough to hold the result plus one terminating null +// character. +// +// The produced digits might be too short in which case the caller has to fill +// the gaps with '0's. +// Example: FastFixedDtoa(0.001, 5, ...) is allowed to return buffer = "1", and +// decimal_point = -2. +// Halfway cases are rounded towards +/-Infinity (away from 0). The call +// FastFixedDtoa(0.15, 2, ...) thus returns buffer = "2", decimal_point = 0. +// The returned buffer may contain digits that would be truncated from the +// shortest representation of the input. +// +// This method only works for some parameters. If it can't handle the input it +// returns false. The output is null-terminated when the function succeeds. +bool FastFixedDtoa(double v, int fractional_count, + Vector buffer, int* length, int* decimal_point); + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_FIXED_DTOA_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/ieee.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/ieee.h new file mode 100644 index 0000000..b14cf4f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/ieee.h @@ -0,0 +1,402 @@ +// Copyright 2012 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_DOUBLE_H_ +#define DOUBLE_CONVERSION_DOUBLE_H_ + +#include "diy-fp.h" + +namespace double_conversion { + +// We assume that doubles and uint64_t have the same endianness. +static uint64_t double_to_uint64(double d) { return BitCast(d); } +static double uint64_to_double(uint64_t d64) { return BitCast(d64); } +static uint32_t float_to_uint32(float f) { return BitCast(f); } +static float uint32_to_float(uint32_t d32) { return BitCast(d32); } + +// Helper functions for doubles. +class Double { + public: + static const uint64_t kSignMask = UINT64_2PART_C(0x80000000, 00000000); + static const uint64_t kExponentMask = UINT64_2PART_C(0x7FF00000, 00000000); + static const uint64_t kSignificandMask = UINT64_2PART_C(0x000FFFFF, FFFFFFFF); + static const uint64_t kHiddenBit = UINT64_2PART_C(0x00100000, 00000000); + static const int kPhysicalSignificandSize = 52; // Excludes the hidden bit. + static const int kSignificandSize = 53; + + Double() : d64_(0) {} + explicit Double(double d) : d64_(double_to_uint64(d)) {} + explicit Double(uint64_t d64) : d64_(d64) {} + explicit Double(DiyFp diy_fp) + : d64_(DiyFpToUint64(diy_fp)) {} + + // The value encoded by this Double must be greater or equal to +0.0. + // It must not be special (infinity, or NaN). + DiyFp AsDiyFp() const { + ASSERT(Sign() > 0); + ASSERT(!IsSpecial()); + return DiyFp(Significand(), Exponent()); + } + + // The value encoded by this Double must be strictly greater than 0. + DiyFp AsNormalizedDiyFp() const { + ASSERT(value() > 0.0); + uint64_t f = Significand(); + int e = Exponent(); + + // The current double could be a denormal. + while ((f & kHiddenBit) == 0) { + f <<= 1; + e--; + } + // Do the final shifts in one go. + f <<= DiyFp::kSignificandSize - kSignificandSize; + e -= DiyFp::kSignificandSize - kSignificandSize; + return DiyFp(f, e); + } + + // Returns the double's bit as uint64. + uint64_t AsUint64() const { + return d64_; + } + + // Returns the next greater double. Returns +infinity on input +infinity. + double NextDouble() const { + if (d64_ == kInfinity) return Double(kInfinity).value(); + if (Sign() < 0 && Significand() == 0) { + // -0.0 + return 0.0; + } + if (Sign() < 0) { + return Double(d64_ - 1).value(); + } else { + return Double(d64_ + 1).value(); + } + } + + double PreviousDouble() const { + if (d64_ == (kInfinity | kSignMask)) return -Infinity(); + if (Sign() < 0) { + return Double(d64_ + 1).value(); + } else { + if (Significand() == 0) return -0.0; + return Double(d64_ - 1).value(); + } + } + + int Exponent() const { + if (IsDenormal()) return kDenormalExponent; + + uint64_t d64 = AsUint64(); + int biased_e = + static_cast((d64 & kExponentMask) >> kPhysicalSignificandSize); + return biased_e - kExponentBias; + } + + uint64_t Significand() const { + uint64_t d64 = AsUint64(); + uint64_t significand = d64 & kSignificandMask; + if (!IsDenormal()) { + return significand + kHiddenBit; + } else { + return significand; + } + } + + // Returns true if the double is a denormal. + bool IsDenormal() const { + uint64_t d64 = AsUint64(); + return (d64 & kExponentMask) == 0; + } + + // We consider denormals not to be special. + // Hence only Infinity and NaN are special. + bool IsSpecial() const { + uint64_t d64 = AsUint64(); + return (d64 & kExponentMask) == kExponentMask; + } + + bool IsNan() const { + uint64_t d64 = AsUint64(); + return ((d64 & kExponentMask) == kExponentMask) && + ((d64 & kSignificandMask) != 0); + } + + bool IsInfinite() const { + uint64_t d64 = AsUint64(); + return ((d64 & kExponentMask) == kExponentMask) && + ((d64 & kSignificandMask) == 0); + } + + int Sign() const { + uint64_t d64 = AsUint64(); + return (d64 & kSignMask) == 0? 1: -1; + } + + // Precondition: the value encoded by this Double must be greater or equal + // than +0.0. + DiyFp UpperBoundary() const { + ASSERT(Sign() > 0); + return DiyFp(Significand() * 2 + 1, Exponent() - 1); + } + + // Computes the two boundaries of this. + // The bigger boundary (m_plus) is normalized. The lower boundary has the same + // exponent as m_plus. + // Precondition: the value encoded by this Double must be greater than 0. + void NormalizedBoundaries(DiyFp* out_m_minus, DiyFp* out_m_plus) const { + ASSERT(value() > 0.0); + DiyFp v = this->AsDiyFp(); + DiyFp m_plus = DiyFp::Normalize(DiyFp((v.f() << 1) + 1, v.e() - 1)); + DiyFp m_minus; + if (LowerBoundaryIsCloser()) { + m_minus = DiyFp((v.f() << 2) - 1, v.e() - 2); + } else { + m_minus = DiyFp((v.f() << 1) - 1, v.e() - 1); + } + m_minus.set_f(m_minus.f() << (m_minus.e() - m_plus.e())); + m_minus.set_e(m_plus.e()); + *out_m_plus = m_plus; + *out_m_minus = m_minus; + } + + bool LowerBoundaryIsCloser() const { + // The boundary is closer if the significand is of the form f == 2^p-1 then + // the lower boundary is closer. + // Think of v = 1000e10 and v- = 9999e9. + // Then the boundary (== (v - v-)/2) is not just at a distance of 1e9 but + // at a distance of 1e8. + // The only exception is for the smallest normal: the largest denormal is + // at the same distance as its successor. + // Note: denormals have the same exponent as the smallest normals. + bool physical_significand_is_zero = ((AsUint64() & kSignificandMask) == 0); + return physical_significand_is_zero && (Exponent() != kDenormalExponent); + } + + double value() const { return uint64_to_double(d64_); } + + // Returns the significand size for a given order of magnitude. + // If v = f*2^e with 2^p-1 <= f <= 2^p then p+e is v's order of magnitude. + // This function returns the number of significant binary digits v will have + // once it's encoded into a double. In almost all cases this is equal to + // kSignificandSize. The only exceptions are denormals. They start with + // leading zeroes and their effective significand-size is hence smaller. + static int SignificandSizeForOrderOfMagnitude(int order) { + if (order >= (kDenormalExponent + kSignificandSize)) { + return kSignificandSize; + } + if (order <= kDenormalExponent) return 0; + return order - kDenormalExponent; + } + + static double Infinity() { + return Double(kInfinity).value(); + } + + static double NaN() { + return Double(kNaN).value(); + } + + private: + static const int kExponentBias = 0x3FF + kPhysicalSignificandSize; + static const int kDenormalExponent = -kExponentBias + 1; + static const int kMaxExponent = 0x7FF - kExponentBias; + static const uint64_t kInfinity = UINT64_2PART_C(0x7FF00000, 00000000); + static const uint64_t kNaN = UINT64_2PART_C(0x7FF80000, 00000000); + + const uint64_t d64_; + + static uint64_t DiyFpToUint64(DiyFp diy_fp) { + uint64_t significand = diy_fp.f(); + int exponent = diy_fp.e(); + while (significand > kHiddenBit + kSignificandMask) { + significand >>= 1; + exponent++; + } + if (exponent >= kMaxExponent) { + return kInfinity; + } + if (exponent < kDenormalExponent) { + return 0; + } + while (exponent > kDenormalExponent && (significand & kHiddenBit) == 0) { + significand <<= 1; + exponent--; + } + uint64_t biased_exponent; + if (exponent == kDenormalExponent && (significand & kHiddenBit) == 0) { + biased_exponent = 0; + } else { + biased_exponent = static_cast(exponent + kExponentBias); + } + return (significand & kSignificandMask) | + (biased_exponent << kPhysicalSignificandSize); + } + + DISALLOW_COPY_AND_ASSIGN(Double); +}; + +class Single { + public: + static const uint32_t kSignMask = 0x80000000; + static const uint32_t kExponentMask = 0x7F800000; + static const uint32_t kSignificandMask = 0x007FFFFF; + static const uint32_t kHiddenBit = 0x00800000; + static const int kPhysicalSignificandSize = 23; // Excludes the hidden bit. + static const int kSignificandSize = 24; + + Single() : d32_(0) {} + explicit Single(float f) : d32_(float_to_uint32(f)) {} + explicit Single(uint32_t d32) : d32_(d32) {} + + // The value encoded by this Single must be greater or equal to +0.0. + // It must not be special (infinity, or NaN). + DiyFp AsDiyFp() const { + ASSERT(Sign() > 0); + ASSERT(!IsSpecial()); + return DiyFp(Significand(), Exponent()); + } + + // Returns the single's bit as uint64. + uint32_t AsUint32() const { + return d32_; + } + + int Exponent() const { + if (IsDenormal()) return kDenormalExponent; + + uint32_t d32 = AsUint32(); + int biased_e = + static_cast((d32 & kExponentMask) >> kPhysicalSignificandSize); + return biased_e - kExponentBias; + } + + uint32_t Significand() const { + uint32_t d32 = AsUint32(); + uint32_t significand = d32 & kSignificandMask; + if (!IsDenormal()) { + return significand + kHiddenBit; + } else { + return significand; + } + } + + // Returns true if the single is a denormal. + bool IsDenormal() const { + uint32_t d32 = AsUint32(); + return (d32 & kExponentMask) == 0; + } + + // We consider denormals not to be special. + // Hence only Infinity and NaN are special. + bool IsSpecial() const { + uint32_t d32 = AsUint32(); + return (d32 & kExponentMask) == kExponentMask; + } + + bool IsNan() const { + uint32_t d32 = AsUint32(); + return ((d32 & kExponentMask) == kExponentMask) && + ((d32 & kSignificandMask) != 0); + } + + bool IsInfinite() const { + uint32_t d32 = AsUint32(); + return ((d32 & kExponentMask) == kExponentMask) && + ((d32 & kSignificandMask) == 0); + } + + int Sign() const { + uint32_t d32 = AsUint32(); + return (d32 & kSignMask) == 0? 1: -1; + } + + // Computes the two boundaries of this. + // The bigger boundary (m_plus) is normalized. The lower boundary has the same + // exponent as m_plus. + // Precondition: the value encoded by this Single must be greater than 0. + void NormalizedBoundaries(DiyFp* out_m_minus, DiyFp* out_m_plus) const { + ASSERT(value() > 0.0); + DiyFp v = this->AsDiyFp(); + DiyFp m_plus = DiyFp::Normalize(DiyFp((v.f() << 1) + 1, v.e() - 1)); + DiyFp m_minus; + if (LowerBoundaryIsCloser()) { + m_minus = DiyFp((v.f() << 2) - 1, v.e() - 2); + } else { + m_minus = DiyFp((v.f() << 1) - 1, v.e() - 1); + } + m_minus.set_f(m_minus.f() << (m_minus.e() - m_plus.e())); + m_minus.set_e(m_plus.e()); + *out_m_plus = m_plus; + *out_m_minus = m_minus; + } + + // Precondition: the value encoded by this Single must be greater or equal + // than +0.0. + DiyFp UpperBoundary() const { + ASSERT(Sign() > 0); + return DiyFp(Significand() * 2 + 1, Exponent() - 1); + } + + bool LowerBoundaryIsCloser() const { + // The boundary is closer if the significand is of the form f == 2^p-1 then + // the lower boundary is closer. + // Think of v = 1000e10 and v- = 9999e9. + // Then the boundary (== (v - v-)/2) is not just at a distance of 1e9 but + // at a distance of 1e8. + // The only exception is for the smallest normal: the largest denormal is + // at the same distance as its successor. + // Note: denormals have the same exponent as the smallest normals. + bool physical_significand_is_zero = ((AsUint32() & kSignificandMask) == 0); + return physical_significand_is_zero && (Exponent() != kDenormalExponent); + } + + float value() const { return uint32_to_float(d32_); } + + static float Infinity() { + return Single(kInfinity).value(); + } + + static float NaN() { + return Single(kNaN).value(); + } + + private: + static const int kExponentBias = 0x7F + kPhysicalSignificandSize; + static const int kDenormalExponent = -kExponentBias + 1; + static const int kMaxExponent = 0xFF - kExponentBias; + static const uint32_t kInfinity = 0x7F800000; + static const uint32_t kNaN = 0x7FC00000; + + const uint32_t d32_; + + DISALLOW_COPY_AND_ASSIGN(Single); +}; + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_DOUBLE_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/strtod.cc b/ctcdecode-master/third_party/kenlm/util/double-conversion/strtod.cc new file mode 100644 index 0000000..17abcbb --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/strtod.cc @@ -0,0 +1,555 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#include +#include + +#include "strtod.h" +#include "bignum.h" +#include "cached-powers.h" +#include "ieee.h" + +namespace double_conversion { + +// 2^53 = 9007199254740992. +// Any integer with at most 15 decimal digits will hence fit into a double +// (which has a 53bit significand) without loss of precision. +static const int kMaxExactDoubleIntegerDecimalDigits = 15; +// 2^64 = 18446744073709551616 > 10^19 +static const int kMaxUint64DecimalDigits = 19; + +// Max double: 1.7976931348623157 x 10^308 +// Min non-zero double: 4.9406564584124654 x 10^-324 +// Any x >= 10^309 is interpreted as +infinity. +// Any x <= 10^-324 is interpreted as 0. +// Note that 2.5e-324 (despite being smaller than the min double) will be read +// as non-zero (equal to the min non-zero double). +static const int kMaxDecimalPower = 309; +static const int kMinDecimalPower = -324; + +// 2^64 = 18446744073709551616 +static const uint64_t kMaxUint64 = UINT64_2PART_C(0xFFFFFFFF, FFFFFFFF); + + +static const double exact_powers_of_ten[] = { + 1.0, // 10^0 + 10.0, + 100.0, + 1000.0, + 10000.0, + 100000.0, + 1000000.0, + 10000000.0, + 100000000.0, + 1000000000.0, + 10000000000.0, // 10^10 + 100000000000.0, + 1000000000000.0, + 10000000000000.0, + 100000000000000.0, + 1000000000000000.0, + 10000000000000000.0, + 100000000000000000.0, + 1000000000000000000.0, + 10000000000000000000.0, + 100000000000000000000.0, // 10^20 + 1000000000000000000000.0, + // 10^22 = 0x21e19e0c9bab2400000 = 0x878678326eac9 * 2^22 + 10000000000000000000000.0 +}; +static const int kExactPowersOfTenSize = ARRAY_SIZE(exact_powers_of_ten); + +// Maximum number of significant digits in the decimal representation. +// In fact the value is 772 (see conversions.cc), but to give us some margin +// we round up to 780. +static const int kMaxSignificantDecimalDigits = 780; + +static Vector TrimLeadingZeros(Vector buffer) { + for (int i = 0; i < buffer.length(); i++) { + if (buffer[i] != '0') { + return buffer.SubVector(i, buffer.length()); + } + } + return Vector(buffer.start(), 0); +} + + +static Vector TrimTrailingZeros(Vector buffer) { + for (int i = buffer.length() - 1; i >= 0; --i) { + if (buffer[i] != '0') { + return buffer.SubVector(0, i + 1); + } + } + return Vector(buffer.start(), 0); +} + + +static void CutToMaxSignificantDigits(Vector buffer, + int exponent, + char* significant_buffer, + int* significant_exponent) { + for (int i = 0; i < kMaxSignificantDecimalDigits - 1; ++i) { + significant_buffer[i] = buffer[i]; + } + // The input buffer has been trimmed. Therefore the last digit must be + // different from '0'. + ASSERT(buffer[buffer.length() - 1] != '0'); + // Set the last digit to be non-zero. This is sufficient to guarantee + // correct rounding. + significant_buffer[kMaxSignificantDecimalDigits - 1] = '1'; + *significant_exponent = + exponent + (buffer.length() - kMaxSignificantDecimalDigits); +} + + +// Trims the buffer and cuts it to at most kMaxSignificantDecimalDigits. +// If possible the input-buffer is reused, but if the buffer needs to be +// modified (due to cutting), then the input needs to be copied into the +// buffer_copy_space. +static void TrimAndCut(Vector buffer, int exponent, + char* buffer_copy_space, int space_size, + Vector* trimmed, int* updated_exponent) { + Vector left_trimmed = TrimLeadingZeros(buffer); + Vector right_trimmed = TrimTrailingZeros(left_trimmed); + exponent += left_trimmed.length() - right_trimmed.length(); + if (right_trimmed.length() > kMaxSignificantDecimalDigits) { + (void) space_size; // Mark variable as used. + ASSERT(space_size >= kMaxSignificantDecimalDigits); + CutToMaxSignificantDigits(right_trimmed, exponent, + buffer_copy_space, updated_exponent); + *trimmed = Vector(buffer_copy_space, + kMaxSignificantDecimalDigits); + } else { + *trimmed = right_trimmed; + *updated_exponent = exponent; + } +} + + +// Reads digits from the buffer and converts them to a uint64. +// Reads in as many digits as fit into a uint64. +// When the string starts with "1844674407370955161" no further digit is read. +// Since 2^64 = 18446744073709551616 it would still be possible read another +// digit if it was less or equal than 6, but this would complicate the code. +static uint64_t ReadUint64(Vector buffer, + int* number_of_read_digits) { + uint64_t result = 0; + int i = 0; + while (i < buffer.length() && result <= (kMaxUint64 / 10 - 1)) { + int digit = buffer[i++] - '0'; + ASSERT(0 <= digit && digit <= 9); + result = 10 * result + digit; + } + *number_of_read_digits = i; + return result; +} + + +// Reads a DiyFp from the buffer. +// The returned DiyFp is not necessarily normalized. +// If remaining_decimals is zero then the returned DiyFp is accurate. +// Otherwise it has been rounded and has error of at most 1/2 ulp. +static void ReadDiyFp(Vector buffer, + DiyFp* result, + int* remaining_decimals) { + int read_digits; + uint64_t significand = ReadUint64(buffer, &read_digits); + if (buffer.length() == read_digits) { + *result = DiyFp(significand, 0); + *remaining_decimals = 0; + } else { + // Round the significand. + if (buffer[read_digits] >= '5') { + significand++; + } + // Compute the binary exponent. + int exponent = 0; + *result = DiyFp(significand, exponent); + *remaining_decimals = buffer.length() - read_digits; + } +} + + +static bool DoubleStrtod(Vector trimmed, + int exponent, + double* result) { +#if !defined(DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS) + // On x86 the floating-point stack can be 64 or 80 bits wide. If it is + // 80 bits wide (as is the case on Linux) then double-rounding occurs and the + // result is not accurate. + // We know that Windows32 uses 64 bits and is therefore accurate. + // Note that the ARM simulator is compiled for 32bits. It therefore exhibits + // the same problem. + return false; +#endif + if (trimmed.length() <= kMaxExactDoubleIntegerDecimalDigits) { + int read_digits; + // The trimmed input fits into a double. + // If the 10^exponent (resp. 10^-exponent) fits into a double too then we + // can compute the result-double simply by multiplying (resp. dividing) the + // two numbers. + // This is possible because IEEE guarantees that floating-point operations + // return the best possible approximation. + if (exponent < 0 && -exponent < kExactPowersOfTenSize) { + // 10^-exponent fits into a double. + *result = static_cast(ReadUint64(trimmed, &read_digits)); + ASSERT(read_digits == trimmed.length()); + *result /= exact_powers_of_ten[-exponent]; + return true; + } + if (0 <= exponent && exponent < kExactPowersOfTenSize) { + // 10^exponent fits into a double. + *result = static_cast(ReadUint64(trimmed, &read_digits)); + ASSERT(read_digits == trimmed.length()); + *result *= exact_powers_of_ten[exponent]; + return true; + } + int remaining_digits = + kMaxExactDoubleIntegerDecimalDigits - trimmed.length(); + if ((0 <= exponent) && + (exponent - remaining_digits < kExactPowersOfTenSize)) { + // The trimmed string was short and we can multiply it with + // 10^remaining_digits. As a result the remaining exponent now fits + // into a double too. + *result = static_cast(ReadUint64(trimmed, &read_digits)); + ASSERT(read_digits == trimmed.length()); + *result *= exact_powers_of_ten[remaining_digits]; + *result *= exact_powers_of_ten[exponent - remaining_digits]; + return true; + } + } + return false; +} + + +// Returns 10^exponent as an exact DiyFp. +// The given exponent must be in the range [1; kDecimalExponentDistance[. +static DiyFp AdjustmentPowerOfTen(int exponent) { + ASSERT(0 < exponent); + ASSERT(exponent < PowersOfTenCache::kDecimalExponentDistance); + // Simply hardcode the remaining powers for the given decimal exponent + // distance. + ASSERT(PowersOfTenCache::kDecimalExponentDistance == 8); + switch (exponent) { + case 1: return DiyFp(UINT64_2PART_C(0xa0000000, 00000000), -60); + case 2: return DiyFp(UINT64_2PART_C(0xc8000000, 00000000), -57); + case 3: return DiyFp(UINT64_2PART_C(0xfa000000, 00000000), -54); + case 4: return DiyFp(UINT64_2PART_C(0x9c400000, 00000000), -50); + case 5: return DiyFp(UINT64_2PART_C(0xc3500000, 00000000), -47); + case 6: return DiyFp(UINT64_2PART_C(0xf4240000, 00000000), -44); + case 7: return DiyFp(UINT64_2PART_C(0x98968000, 00000000), -40); + default: + UNREACHABLE(); + } +} + + +// If the function returns true then the result is the correct double. +// Otherwise it is either the correct double or the double that is just below +// the correct double. +static bool DiyFpStrtod(Vector buffer, + int exponent, + double* result) { + DiyFp input; + int remaining_decimals; + ReadDiyFp(buffer, &input, &remaining_decimals); + // Since we may have dropped some digits the input is not accurate. + // If remaining_decimals is different than 0 than the error is at most + // .5 ulp (unit in the last place). + // We don't want to deal with fractions and therefore keep a common + // denominator. + const int kDenominatorLog = 3; + const int kDenominator = 1 << kDenominatorLog; + // Move the remaining decimals into the exponent. + exponent += remaining_decimals; + uint64_t error = (remaining_decimals == 0 ? 0 : kDenominator / 2); + + int old_e = input.e(); + input.Normalize(); + error <<= old_e - input.e(); + + ASSERT(exponent <= PowersOfTenCache::kMaxDecimalExponent); + if (exponent < PowersOfTenCache::kMinDecimalExponent) { + *result = 0.0; + return true; + } + DiyFp cached_power; + int cached_decimal_exponent; + PowersOfTenCache::GetCachedPowerForDecimalExponent(exponent, + &cached_power, + &cached_decimal_exponent); + + if (cached_decimal_exponent != exponent) { + int adjustment_exponent = exponent - cached_decimal_exponent; + DiyFp adjustment_power = AdjustmentPowerOfTen(adjustment_exponent); + input.Multiply(adjustment_power); + if (kMaxUint64DecimalDigits - buffer.length() >= adjustment_exponent) { + // The product of input with the adjustment power fits into a 64 bit + // integer. + ASSERT(DiyFp::kSignificandSize == 64); + } else { + // The adjustment power is exact. There is hence only an error of 0.5. + error += kDenominator / 2; + } + } + + input.Multiply(cached_power); + // The error introduced by a multiplication of a*b equals + // error_a + error_b + error_a*error_b/2^64 + 0.5 + // Substituting a with 'input' and b with 'cached_power' we have + // error_b = 0.5 (all cached powers have an error of less than 0.5 ulp), + // error_ab = 0 or 1 / kDenominator > error_a*error_b/ 2^64 + int error_b = kDenominator / 2; + int error_ab = (error == 0 ? 0 : 1); // We round up to 1. + int fixed_error = kDenominator / 2; + error += error_b + error_ab + fixed_error; + + old_e = input.e(); + input.Normalize(); + error <<= old_e - input.e(); + + // See if the double's significand changes if we add/subtract the error. + int order_of_magnitude = DiyFp::kSignificandSize + input.e(); + int effective_significand_size = + Double::SignificandSizeForOrderOfMagnitude(order_of_magnitude); + int precision_digits_count = + DiyFp::kSignificandSize - effective_significand_size; + if (precision_digits_count + kDenominatorLog >= DiyFp::kSignificandSize) { + // This can only happen for very small denormals. In this case the + // half-way multiplied by the denominator exceeds the range of an uint64. + // Simply shift everything to the right. + int shift_amount = (precision_digits_count + kDenominatorLog) - + DiyFp::kSignificandSize + 1; + input.set_f(input.f() >> shift_amount); + input.set_e(input.e() + shift_amount); + // We add 1 for the lost precision of error, and kDenominator for + // the lost precision of input.f(). + error = (error >> shift_amount) + 1 + kDenominator; + precision_digits_count -= shift_amount; + } + // We use uint64_ts now. This only works if the DiyFp uses uint64_ts too. + ASSERT(DiyFp::kSignificandSize == 64); + ASSERT(precision_digits_count < 64); + uint64_t one64 = 1; + uint64_t precision_bits_mask = (one64 << precision_digits_count) - 1; + uint64_t precision_bits = input.f() & precision_bits_mask; + uint64_t half_way = one64 << (precision_digits_count - 1); + precision_bits *= kDenominator; + half_way *= kDenominator; + DiyFp rounded_input(input.f() >> precision_digits_count, + input.e() + precision_digits_count); + if (precision_bits >= half_way + error) { + rounded_input.set_f(rounded_input.f() + 1); + } + // If the last_bits are too close to the half-way case than we are too + // inaccurate and round down. In this case we return false so that we can + // fall back to a more precise algorithm. + + *result = Double(rounded_input).value(); + if (half_way - error < precision_bits && precision_bits < half_way + error) { + // Too imprecise. The caller will have to fall back to a slower version. + // However the returned number is guaranteed to be either the correct + // double, or the next-lower double. + return false; + } else { + return true; + } +} + + +// Returns +// - -1 if buffer*10^exponent < diy_fp. +// - 0 if buffer*10^exponent == diy_fp. +// - +1 if buffer*10^exponent > diy_fp. +// Preconditions: +// buffer.length() + exponent <= kMaxDecimalPower + 1 +// buffer.length() + exponent > kMinDecimalPower +// buffer.length() <= kMaxDecimalSignificantDigits +static int CompareBufferWithDiyFp(Vector buffer, + int exponent, + DiyFp diy_fp) { + ASSERT(buffer.length() + exponent <= kMaxDecimalPower + 1); + ASSERT(buffer.length() + exponent > kMinDecimalPower); + ASSERT(buffer.length() <= kMaxSignificantDecimalDigits); + // Make sure that the Bignum will be able to hold all our numbers. + // Our Bignum implementation has a separate field for exponents. Shifts will + // consume at most one bigit (< 64 bits). + // ln(10) == 3.3219... + ASSERT(((kMaxDecimalPower + 1) * 333 / 100) < Bignum::kMaxSignificantBits); + Bignum buffer_bignum; + Bignum diy_fp_bignum; + buffer_bignum.AssignDecimalString(buffer); + diy_fp_bignum.AssignUInt64(diy_fp.f()); + if (exponent >= 0) { + buffer_bignum.MultiplyByPowerOfTen(exponent); + } else { + diy_fp_bignum.MultiplyByPowerOfTen(-exponent); + } + if (diy_fp.e() > 0) { + diy_fp_bignum.ShiftLeft(diy_fp.e()); + } else { + buffer_bignum.ShiftLeft(-diy_fp.e()); + } + return Bignum::Compare(buffer_bignum, diy_fp_bignum); +} + + +// Returns true if the guess is the correct double. +// Returns false, when guess is either correct or the next-lower double. +static bool ComputeGuess(Vector trimmed, int exponent, + double* guess) { + if (trimmed.length() == 0) { + *guess = 0.0; + return true; + } + if (exponent + trimmed.length() - 1 >= kMaxDecimalPower) { + *guess = Double::Infinity(); + return true; + } + if (exponent + trimmed.length() <= kMinDecimalPower) { + *guess = 0.0; + return true; + } + + if (DoubleStrtod(trimmed, exponent, guess) || + DiyFpStrtod(trimmed, exponent, guess)) { + return true; + } + if (*guess == Double::Infinity()) { + return true; + } + return false; +} + +double Strtod(Vector buffer, int exponent) { + char copy_buffer[kMaxSignificantDecimalDigits]; + Vector trimmed; + int updated_exponent; + TrimAndCut(buffer, exponent, copy_buffer, kMaxSignificantDecimalDigits, + &trimmed, &updated_exponent); + exponent = updated_exponent; + + double guess; + bool is_correct = ComputeGuess(trimmed, exponent, &guess); + if (is_correct) return guess; + + DiyFp upper_boundary = Double(guess).UpperBoundary(); + int comparison = CompareBufferWithDiyFp(trimmed, exponent, upper_boundary); + if (comparison < 0) { + return guess; + } else if (comparison > 0) { + return Double(guess).NextDouble(); + } else if ((Double(guess).Significand() & 1) == 0) { + // Round towards even. + return guess; + } else { + return Double(guess).NextDouble(); + } +} + +float Strtof(Vector buffer, int exponent) { + char copy_buffer[kMaxSignificantDecimalDigits]; + Vector trimmed; + int updated_exponent; + TrimAndCut(buffer, exponent, copy_buffer, kMaxSignificantDecimalDigits, + &trimmed, &updated_exponent); + exponent = updated_exponent; + + double double_guess; + bool is_correct = ComputeGuess(trimmed, exponent, &double_guess); + + float float_guess = static_cast(double_guess); + if (float_guess == double_guess) { + // This shortcut triggers for integer values. + return float_guess; + } + + // We must catch double-rounding. Say the double has been rounded up, and is + // now a boundary of a float, and rounds up again. This is why we have to + // look at previous too. + // Example (in decimal numbers): + // input: 12349 + // high-precision (4 digits): 1235 + // low-precision (3 digits): + // when read from input: 123 + // when rounded from high precision: 124. + // To do this we simply look at the neigbors of the correct result and see + // if they would round to the same float. If the guess is not correct we have + // to look at four values (since two different doubles could be the correct + // double). + + double double_next = Double(double_guess).NextDouble(); + double double_previous = Double(double_guess).PreviousDouble(); + + float f1 = static_cast(double_previous); + float f2 = float_guess; + float f3 = static_cast(double_next); + float f4; + if (is_correct) { + f4 = f3; + } else { + double double_next2 = Double(double_next).NextDouble(); + f4 = static_cast(double_next2); + } + (void) f2; // Mark variable as used. + ASSERT(f1 <= f2 && f2 <= f3 && f3 <= f4); + + // If the guess doesn't lie near a single-precision boundary we can simply + // return its float-value. + if (f1 == f4) { + return float_guess; + } + + ASSERT((f1 != f2 && f2 == f3 && f3 == f4) || + (f1 == f2 && f2 != f3 && f3 == f4) || + (f1 == f2 && f2 == f3 && f3 != f4)); + + // guess and next are the two possible canditates (in the same way that + // double_guess was the lower candidate for a double-precision guess). + float guess = f1; + float next = f4; + DiyFp upper_boundary; + if (guess == 0.0f) { + float min_float = 1e-45f; + upper_boundary = Double(static_cast(min_float) / 2).AsDiyFp(); + } else { + upper_boundary = Single(guess).UpperBoundary(); + } + int comparison = CompareBufferWithDiyFp(trimmed, exponent, upper_boundary); + if (comparison < 0) { + return guess; + } else if (comparison > 0) { + return next; + } else if ((Single(guess).Significand() & 1) == 0) { + // Round towards even. + return guess; + } else { + return next; + } +} + +} // namespace double_conversion diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/strtod.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/strtod.h new file mode 100644 index 0000000..ed0293b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/strtod.h @@ -0,0 +1,45 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_STRTOD_H_ +#define DOUBLE_CONVERSION_STRTOD_H_ + +#include "utils.h" + +namespace double_conversion { + +// The buffer must only contain digits in the range [0-9]. It must not +// contain a dot or a sign. It must not start with '0', and must not be empty. +double Strtod(Vector buffer, int exponent); + +// The buffer must only contain digits in the range [0-9]. It must not +// contain a dot or a sign. It must not start with '0', and must not be empty. +float Strtof(Vector buffer, int exponent); + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_STRTOD_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/double-conversion/utils.h b/ctcdecode-master/third_party/kenlm/util/double-conversion/utils.h new file mode 100644 index 0000000..2c745f3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/double-conversion/utils.h @@ -0,0 +1,342 @@ +// Copyright 2010 the V8 project authors. All rights reserved. +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following +// disclaimer in the documentation and/or other materials provided +// with the distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived +// from this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +#ifndef DOUBLE_CONVERSION_UTILS_H_ +#define DOUBLE_CONVERSION_UTILS_H_ + +#include +#include + +#include +#ifndef ASSERT +#define ASSERT(condition) \ + assert(condition); +#endif +#ifndef UNIMPLEMENTED +#define UNIMPLEMENTED() (abort()) +#endif +#ifndef DOUBLE_CONVERSION_NO_RETURN +#ifdef _MSC_VER +#define DOUBLE_CONVERSION_NO_RETURN __declspec(noreturn) +#else +#define DOUBLE_CONVERSION_NO_RETURN __attribute__((noreturn)) +#endif +#endif +#ifndef UNREACHABLE +#ifdef _MSC_VER +void DOUBLE_CONVERSION_NO_RETURN abort_noreturn(); +inline void abort_noreturn() { abort(); } +#define UNREACHABLE() (abort_noreturn()) +#else +#define UNREACHABLE() (abort()) +#endif +#endif + + +// Double operations detection based on target architecture. +// Linux uses a 80bit wide floating point stack on x86. This induces double +// rounding, which in turn leads to wrong results. +// An easy way to test if the floating-point operations are correct is to +// evaluate: 89255.0/1e22. If the floating-point stack is 64 bits wide then +// the result is equal to 89255e-22. +// The best way to test this, is to create a division-function and to compare +// the output of the division with the expected result. (Inlining must be +// disabled.) +// On Linux,x86 89255e-22 != Div_double(89255.0/1e22) +#if defined(_M_X64) || defined(__x86_64__) || \ + defined(__ARMEL__) || defined(__avr32__) || \ + defined(__hppa__) || defined(__ia64__) || \ + defined(__mips__) || \ + defined(__powerpc__) || defined(__ppc__) || defined(__ppc64__) || \ + defined(_POWER) || defined(_ARCH_PPC) || defined(_ARCH_PPC64) || \ + defined(__sparc__) || defined(__sparc) || defined(__s390__) || \ + defined(__SH4__) || defined(__alpha__) || \ + defined(_MIPS_ARCH_MIPS32R2) || \ + defined(__AARCH64EL__) || defined(__aarch64__) || \ + defined(__riscv) +#define DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS 1 +#elif defined(__mc68000__) +#undef DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS +#elif defined(_M_IX86) || defined(__i386__) || defined(__i386) +#if defined(_WIN32) +// Windows uses a 64bit wide floating point stack. +#define DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS 1 +#else +#undef DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS +#endif // _WIN32 +#else +#error Target architecture was not detected as supported by Double-Conversion. +#endif + +#if defined(__GNUC__) +#define DOUBLE_CONVERSION_UNUSED __attribute__((unused)) +#else +#define DOUBLE_CONVERSION_UNUSED +#endif + +#if defined(_WIN32) && !defined(__MINGW32__) + +typedef signed char int8_t; +typedef unsigned char uint8_t; +typedef short int16_t; // NOLINT +typedef unsigned short uint16_t; // NOLINT +typedef int int32_t; +typedef unsigned int uint32_t; +typedef __int64 int64_t; +typedef unsigned __int64 uint64_t; +// intptr_t and friends are defined in crtdefs.h through stdio.h. + +#else + +#include + +#endif + +typedef uint16_t uc16; + +// The following macro works on both 32 and 64-bit platforms. +// Usage: instead of writing 0x1234567890123456 +// write UINT64_2PART_C(0x12345678,90123456); +#define UINT64_2PART_C(a, b) (((static_cast(a) << 32) + 0x##b##u)) + + +// The expression ARRAY_SIZE(a) is a compile-time constant of type +// size_t which represents the number of elements of the given +// array. You should only use ARRAY_SIZE on statically allocated +// arrays. +#ifndef ARRAY_SIZE +#define ARRAY_SIZE(a) \ + ((sizeof(a) / sizeof(*(a))) / \ + static_cast(!(sizeof(a) % sizeof(*(a))))) +#endif + +// A macro to disallow the evil copy constructor and operator= functions +// This should be used in the private: declarations for a class +#ifndef DISALLOW_COPY_AND_ASSIGN +#define DISALLOW_COPY_AND_ASSIGN(TypeName) \ + TypeName(const TypeName&); \ + void operator=(const TypeName&) +#endif + +// A macro to disallow all the implicit constructors, namely the +// default constructor, copy constructor and operator= functions. +// +// This should be used in the private: declarations for a class +// that wants to prevent anyone from instantiating it. This is +// especially useful for classes containing only static methods. +#ifndef DISALLOW_IMPLICIT_CONSTRUCTORS +#define DISALLOW_IMPLICIT_CONSTRUCTORS(TypeName) \ + TypeName(); \ + DISALLOW_COPY_AND_ASSIGN(TypeName) +#endif + +namespace double_conversion { + +static const int kCharSize = sizeof(char); + +// Returns the maximum of the two parameters. +template +static T Max(T a, T b) { + return a < b ? b : a; +} + + +// Returns the minimum of the two parameters. +template +static T Min(T a, T b) { + return a < b ? a : b; +} + + +inline int StrLength(const char* string) { + size_t length = strlen(string); + ASSERT(length == static_cast(static_cast(length))); + return static_cast(length); +} + +// This is a simplified version of V8's Vector class. +template +class Vector { + public: + Vector() : start_(NULL), length_(0) {} + Vector(T* data, int len) : start_(data), length_(len) { + ASSERT(len == 0 || (len > 0 && data != NULL)); + } + + // Returns a vector using the same backing storage as this one, + // spanning from and including 'from', to but not including 'to'. + Vector SubVector(int from, int to) { + ASSERT(to <= length_); + ASSERT(from < to); + ASSERT(0 <= from); + return Vector(start() + from, to - from); + } + + // Returns the length of the vector. + int length() const { return length_; } + + // Returns whether or not the vector is empty. + bool is_empty() const { return length_ == 0; } + + // Returns the pointer to the start of the data in the vector. + T* start() const { return start_; } + + // Access individual vector elements - checks bounds in debug mode. + T& operator[](int index) const { + ASSERT(0 <= index && index < length_); + return start_[index]; + } + + T& first() { return start_[0]; } + + T& last() { return start_[length_ - 1]; } + + private: + T* start_; + int length_; +}; + + +// Helper class for building result strings in a character buffer. The +// purpose of the class is to use safe operations that checks the +// buffer bounds on all operations in debug mode. +class StringBuilder { + public: + StringBuilder(char* buffer, int buffer_size) + : buffer_(buffer, buffer_size), position_(0) { } + + ~StringBuilder() { if (!is_finalized()) Finalize(); } + + int size() const { return buffer_.length(); } + + // Get the current position in the builder. + int position() const { + ASSERT(!is_finalized()); + return position_; + } + + // Reset the position. + void Reset() { position_ = 0; } + + // Add a single character to the builder. It is not allowed to add + // 0-characters; use the Finalize() method to terminate the string + // instead. + void AddCharacter(char c) { + ASSERT(c != '\0'); + ASSERT(!is_finalized() && position_ < buffer_.length()); + buffer_[position_++] = c; + } + + // Add an entire string to the builder. Uses strlen() internally to + // compute the length of the input string. + void AddString(const char* s) { + AddSubstring(s, StrLength(s)); + } + + // Add the first 'n' characters of the given string 's' to the + // builder. The input string must have enough characters. + void AddSubstring(const char* s, int n) { + ASSERT(!is_finalized() && position_ + n < buffer_.length()); + ASSERT(static_cast(n) <= strlen(s)); + memmove(&buffer_[position_], s, n * kCharSize); + position_ += n; + } + + + // Add character padding to the builder. If count is non-positive, + // nothing is added to the builder. + void AddPadding(char c, int count) { + for (int i = 0; i < count; i++) { + AddCharacter(c); + } + } + + // Finalize the string by 0-terminating it and returning the buffer. + char* Finalize() { + ASSERT(!is_finalized() && position_ < buffer_.length()); + buffer_[position_] = '\0'; + // Make sure nobody managed to add a 0-character to the + // buffer while building the string. + ASSERT(strlen(buffer_.start()) == static_cast(position_)); + position_ = -1; + ASSERT(is_finalized()); + return buffer_.start(); + } + + private: + Vector buffer_; + int position_; + + bool is_finalized() const { return position_ < 0; } + + DISALLOW_IMPLICIT_CONSTRUCTORS(StringBuilder); +}; + +// The type-based aliasing rule allows the compiler to assume that pointers of +// different types (for some definition of different) never alias each other. +// Thus the following code does not work: +// +// float f = foo(); +// int fbits = *(int*)(&f); +// +// The compiler 'knows' that the int pointer can't refer to f since the types +// don't match, so the compiler may cache f in a register, leaving random data +// in fbits. Using C++ style casts makes no difference, however a pointer to +// char data is assumed to alias any other pointer. This is the 'memcpy +// exception'. +// +// Bit_cast uses the memcpy exception to move the bits from a variable of one +// type of a variable of another type. Of course the end result is likely to +// be implementation dependent. Most compilers (gcc-4.2 and MSVC 2005) +// will completely optimize BitCast away. +// +// There is an additional use for BitCast. +// Recent gccs will warn when they see casts that may result in breakage due to +// the type-based aliasing rule. If you have checked that there is no breakage +// you can use BitCast to cast one pointer type to another. This confuses gcc +// enough that it can no longer see that you have cast one pointer type to +// another thus avoiding the warning. +template +inline Dest BitCast(const Source& source) { + // Compile time assertion: sizeof(Dest) == sizeof(Source) + // A compile error here means your Dest and Source have different sizes. + DOUBLE_CONVERSION_UNUSED + typedef char VerifySizesAreEqual[sizeof(Dest) == sizeof(Source) ? 1 : -1]; + + Dest dest; + memmove(&dest, &source, sizeof(dest)); + return dest; +} + +template +inline Dest BitCast(Source* source) { + return BitCast(reinterpret_cast(source)); +} + +} // namespace double_conversion + +#endif // DOUBLE_CONVERSION_UTILS_H_ diff --git a/ctcdecode-master/third_party/kenlm/util/ersatz_progress.cc b/ctcdecode-master/third_party/kenlm/util/ersatz_progress.cc new file mode 100644 index 0000000..55c82e7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/ersatz_progress.cc @@ -0,0 +1,47 @@ +#include "util/ersatz_progress.hh" + +#include +#include +#include +#include + +namespace util { + +namespace { const unsigned char kWidth = 100; } + +const char kProgressBanner[] = "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n"; + +ErsatzProgress::ErsatzProgress() : current_(0), next_(std::numeric_limits::max()), complete_(next_), out_(NULL) {} + +ErsatzProgress::~ErsatzProgress() { + if (out_) Finished(); +} + +ErsatzProgress::ErsatzProgress(uint64_t complete, std::ostream *to, const std::string &message) + : current_(0), next_(complete / kWidth), complete_(complete), stones_written_(0), out_(to) { + if (!out_) { + next_ = std::numeric_limits::max(); + return; + } + if (!message.empty()) *out_ << message << '\n'; + *out_ << kProgressBanner; +} + +void ErsatzProgress::Milestone() { + if (!out_) { current_ = 0; return; } + if (!complete_) return; + unsigned char stone = std::min(static_cast(kWidth), (current_ * kWidth) / complete_); + + for (; stones_written_ < stone; ++stones_written_) { + (*out_) << '*'; + } + if (stone == kWidth) { + (*out_) << std::endl; + next_ = std::numeric_limits::max(); + out_ = NULL; + } else { + next_ = std::max(next_, ((stone + 1) * complete_ + kWidth - 1) / kWidth); + } +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/ersatz_progress.hh b/ctcdecode-master/third_party/kenlm/util/ersatz_progress.hh new file mode 100644 index 0000000..11cb043 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/ersatz_progress.hh @@ -0,0 +1,64 @@ +#ifndef UTIL_ERSATZ_PROGRESS_H +#define UTIL_ERSATZ_PROGRESS_H + +#include +#include +#include + +// Ersatz version of boost::progress so core language model doesn't depend on +// boost. Also adds option to print nothing. + +namespace util { + +extern const char kProgressBanner[]; + +class ErsatzProgress { + public: + // No output. + ErsatzProgress(); + + // Null means no output. The null value is useful for passing along the ostream pointer from another caller. + explicit ErsatzProgress(uint64_t complete, std::ostream *to = &std::cerr, const std::string &message = ""); + +#if __cplusplus >= 201103L + ErsatzProgress(ErsatzProgress &&from) noexcept : current_(from.current_), next_(from.next_), complete_(from.complete_), stones_written_(from.stones_written_), out_(from.out_) { + from.out_ = nullptr; + from.next_ = (uint64_t)-1; + } +#endif + + ~ErsatzProgress(); + + ErsatzProgress &operator++() { + if (++current_ >= next_) Milestone(); + return *this; + } + + ErsatzProgress &operator+=(uint64_t amount) { + if ((current_ += amount) >= next_) Milestone(); + return *this; + } + + void Set(uint64_t to) { + if ((current_ = to) >= next_) Milestone(); + } + + void Finished() { + Set(complete_); + } + + private: + void Milestone(); + + uint64_t current_, next_, complete_; + unsigned char stones_written_; + std::ostream *out_; + + // noncopyable + ErsatzProgress(const ErsatzProgress &other); + ErsatzProgress &operator=(const ErsatzProgress &other); +}; + +} // namespace util + +#endif // UTIL_ERSATZ_PROGRESS_H diff --git a/ctcdecode-master/third_party/kenlm/util/exception.cc b/ctcdecode-master/third_party/kenlm/util/exception.cc new file mode 100644 index 0000000..01ff9a6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/exception.cc @@ -0,0 +1,104 @@ +#include "util/exception.hh" + +#ifdef __GXX_RTTI +#include +#endif + +#include +#include + +#if defined(_WIN32) || defined(_WIN64) +#include +#include +#endif + +namespace util { + +Exception::Exception() throw() {} +Exception::~Exception() throw() {} + +void Exception::SetLocation(const char *file, unsigned int line, const char *func, const char *child_name, const char *condition) { + /* The child class might have set some text, but we want this to come first. + * Another option would be passing this information to the constructor, but + * then child classes would have to accept constructor arguments and pass + * them down. + */ + std::string old_text; + what_.swap(old_text); + what_ << file << ':' << line; + if (func) what_ << " in " << func << " threw "; + if (child_name) { + what_ << child_name; + } else { +#ifdef __GXX_RTTI + what_ << typeid(this).name(); +#else + what_ << "an exception"; +#endif + } + if (condition) { + what_ << " because `" << condition << '\''; + } + what_ << ".\n"; + what_ << old_text; +} + +namespace { + +#ifdef __GNUC__ +const char *HandleStrerror(int ret, const char *buf) __attribute__ ((unused)); +const char *HandleStrerror(const char *ret, const char * /*buf*/) __attribute__ ((unused)); +#endif +// At least one of these functions will not be called. +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wunused-function" +#endif +// The XOPEN version. +const char *HandleStrerror(int ret, const char *buf) { + if (!ret) return buf; + return NULL; +} + +// The GNU version. +const char *HandleStrerror(const char *ret, const char * /*buf*/) { + return ret; +} +#ifdef __clang__ +#pragma clang diagnostic pop +#endif +} // namespace + +ErrnoException::ErrnoException() throw() : errno_(errno) { + char buf[200]; + buf[0] = 0; +#if defined(sun) || defined(_WIN32) || defined(_WIN64) + const char *add = strerror(errno); +#else + const char *add = HandleStrerror(strerror_r(errno, buf, 200), buf); +#endif + + if (add) { + *this << add << ' '; + } +} + +ErrnoException::~ErrnoException() throw() {} + +OverflowException::OverflowException() throw() {} +OverflowException::~OverflowException() throw() {} + +#if defined(_WIN32) || defined(_WIN64) +WindowsException::WindowsException() throw() { + unsigned int last_error = GetLastError(); + char error_msg[256] = ""; + if (!FormatMessageA(FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS, NULL, last_error, LANG_NEUTRAL, error_msg, sizeof(error_msg), NULL)) { + *this << "Windows error " << GetLastError() << " while formatting Windows error " << last_error << ". "; + } else { + *this << "Windows error " << last_error << ": " << error_msg; + } +} +WindowsException::~WindowsException() throw() {} +#endif + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/exception.hh b/ctcdecode-master/third_party/kenlm/util/exception.hh new file mode 100644 index 0000000..03543a9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/exception.hh @@ -0,0 +1,159 @@ +#ifndef UTIL_EXCEPTION_H +#define UTIL_EXCEPTION_H + +#include "util/string_stream.hh" + +#include +#include +#include +#include + +namespace util { + +template typename Except::template ExceptionTag::Identity operator<<(Except &e, const Data &data); + +class Exception : public std::exception { + public: + Exception() throw(); + virtual ~Exception() throw(); + + const char *what() const throw() { return what_.str().c_str(); } + + // For use by the UTIL_THROW macros. + void SetLocation( + const char *file, + unsigned int line, + const char *func, + const char *child_name, + const char *condition); + + private: + template friend typename Except::template ExceptionTag::Identity operator<<(Except &e, const Data &data); + + // This helps restrict operator<< defined below. + template struct ExceptionTag { + typedef T Identity; + }; + + StringStream what_; +}; + +/* This implements the normal operator<< for Exception and all its children. + * SFINAE means it only applies to Exception. Think of this as an ersatz + * boost::enable_if. + */ +template typename Except::template ExceptionTag::Identity operator<<(Except &e, const Data &data) { + e.what_ << data; + return e; +} + +#ifdef __GNUC__ +#define UTIL_FUNC_NAME __PRETTY_FUNCTION__ +#else +#ifdef _WIN32 +#define UTIL_FUNC_NAME __FUNCTION__ +#else +#define UTIL_FUNC_NAME NULL +#endif +#endif + +/* Create an instance of Exception, add the message Modify, and throw it. + * Modify is appended to the what() message and can contain << for ostream + * operations. + * + * do .. while kludge to swallow trailing ; character + * http://gcc.gnu.org/onlinedocs/cpp/Swallowing-the-Semicolon.html . + * Arg can be a constructor argument to the exception. + */ +#define UTIL_THROW_BACKEND(Condition, Exception, Arg, Modify) do { \ + Exception UTIL_e Arg; \ + UTIL_e.SetLocation(__FILE__, __LINE__, UTIL_FUNC_NAME, #Exception, Condition); \ + UTIL_e << Modify; \ + throw UTIL_e; \ +} while (0) + +#define UTIL_THROW_ARG(Exception, Arg, Modify) \ + UTIL_THROW_BACKEND(NULL, Exception, Arg, Modify) + +#define UTIL_THROW(Exception, Modify) \ + UTIL_THROW_BACKEND(NULL, Exception, , Modify); + +#define UTIL_THROW2(Modify) \ + UTIL_THROW_BACKEND(NULL, util::Exception, , Modify); + +#if __GNUC__ >= 3 +#define UTIL_UNLIKELY(x) __builtin_expect (!!(x), 0) +#else +#define UTIL_UNLIKELY(x) (x) +#endif + +#if __GNUC__ >= 3 +#define UTIL_LIKELY(x) __builtin_expect (!!(x), 1) +#else +#define UTIL_LIKELY(x) (x) +#endif + +#define UTIL_THROW_IF_ARG(Condition, Exception, Arg, Modify) do { \ + if (UTIL_UNLIKELY(Condition)) { \ + UTIL_THROW_BACKEND(#Condition, Exception, Arg, Modify); \ + } \ +} while (0) + +#define UTIL_THROW_IF(Condition, Exception, Modify) \ + UTIL_THROW_IF_ARG(Condition, Exception, , Modify) + +#define UTIL_THROW_IF2(Condition, Modify) \ + UTIL_THROW_IF_ARG(Condition, util::Exception, , Modify) + +// Exception that records errno and adds it to the message. +class ErrnoException : public Exception { + public: + ErrnoException() throw(); + + virtual ~ErrnoException() throw(); + + int Error() const throw() { return errno_; } + + private: + int errno_; +}; + +// file wasn't there, or couldn't be open for some reason +class FileOpenException : public Exception { + public: + FileOpenException() throw() {} + ~FileOpenException() throw() {} +}; + +// Utilities for overflow checking. +class OverflowException : public Exception { + public: + OverflowException() throw(); + ~OverflowException() throw(); +}; + +template inline std::size_t CheckOverflowInternal(uint64_t value) { + UTIL_THROW_IF(value > static_cast(std::numeric_limits::max()), OverflowException, "Integer overflow detected. This model is too big for 32-bit code."); + return value; +} + +template <> inline std::size_t CheckOverflowInternal<8>(uint64_t value) { + return value; +} + +inline std::size_t CheckOverflow(uint64_t value) { + return CheckOverflowInternal(value); +} + +#if defined(_WIN32) || defined(_WIN64) +/* Thrown for Windows specific operations. */ +class WindowsException : public Exception { + public: + WindowsException() throw(); + ~WindowsException() throw(); +}; +#endif + +} // namespace util + +#endif // UTIL_EXCEPTION_H diff --git a/ctcdecode-master/third_party/kenlm/util/fake_ostream.hh b/ctcdecode-master/third_party/kenlm/util/fake_ostream.hh new file mode 100644 index 0000000..203edf4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/fake_ostream.hh @@ -0,0 +1,111 @@ +#ifndef UTIL_FAKE_OSTREAM_H +#define UTIL_FAKE_OSTREAM_H + +#include "util/float_to_string.hh" +#include "util/integer_to_string.hh" +#include "util/string_piece.hh" + +#include +#include + +#include + +namespace util { + +/* Like std::ostream but without being incredibly slow. + * Supports most of the built-in types except for long double. + * + * The FakeOStream class is intended to be inherited from. The inherting class + * should provide: + * public: + * Derived &flush(); + * Derived &write(const void *data, std::size_t length); + * + * private: or protected: + * friend class FakeOStream; + * char *Ensure(std::size_t amount); + * void AdvanceTo(char *to); + * + * The Ensure function makes enough space for an in-place write and returns + * where to write. The AdvanceTo function happens after the write, saying how + * much was actually written. + * + * Precondition: + * amount <= kToStringMaxBytes for in-place writes. + */ +template class FakeOStream { + public: + FakeOStream() {} + + // This also covers std::string and char* + Derived &operator<<(StringPiece str) { + return C().write(str.data(), str.size()); + } + + // Handle integers by size and signedness. + private: + template struct EnableIfKludge { + typedef Derived type; + }; + template ::is_signed, bool IsInteger = std::numeric_limits::is_integer> struct Coerce {}; + + template struct Coerce { typedef uint16_t To; }; + template struct Coerce { typedef uint32_t To; }; + template struct Coerce { typedef uint64_t To; }; + + template struct Coerce { typedef int16_t To; }; + template struct Coerce { typedef int32_t To; }; + template struct Coerce { typedef int64_t To; }; + public: + template typename EnableIfKludge::To>::type &operator<<(const From value) { + return CallToString(static_cast::To>(value)); + } + + // Character types that get copied as bytes instead of displayed as integers. + Derived &operator<<(char val) { return put(val); } + Derived &operator<<(signed char val) { return put(static_cast(val)); } + Derived &operator<<(unsigned char val) { return put(static_cast(val)); } + + Derived &operator<<(bool val) { return put(val + '0'); } + // enums will fall back to int but are not caught by the template. + Derived &operator<<(int val) { return CallToString(static_cast::To>(val)); } + + Derived &operator<<(float val) { return CallToString(val); } + Derived &operator<<(double val) { return CallToString(val); } + + // This is here to catch all the other pointer types. + Derived &operator<<(const void *value) { return CallToString(value); } + // This is here because the above line also catches const char*. + Derived &operator<<(const char *value) { return *this << StringPiece(value); } + Derived &operator<<(char *value) { return *this << StringPiece(value); } + + Derived &put(char val) { + char *c = C().Ensure(1); + *c = val; + C().AdvanceTo(++c); + return C(); + } + + char widen(char val) const { return val; } + + private: + // References to derived class for convenience. + Derived &C() { + return *static_cast(this); + } + + const Derived &C() const { + return *static_cast(this); + } + + // This is separate to prevent an infinite loop if the compiler considers + // types the same (i.e. gcc std::size_t and uint64_t or uint32_t). + template Derived &CallToString(const T value) { + C().AdvanceTo(ToString(value, C().Ensure(ToStringBuf::kBytes))); + return C(); + } +}; + +} // namespace + +#endif // UTIL_FAKE_OSTREAM_H diff --git a/ctcdecode-master/third_party/kenlm/util/file.cc b/ctcdecode-master/third_party/kenlm/util/file.cc new file mode 100644 index 0000000..7ccc2d2 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/file.cc @@ -0,0 +1,621 @@ +#define _LARGEFILE64_SOURCE +#define _FILE_OFFSET_BITS 64 + +#include "util/file.hh" + +#include "util/exception.hh" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#if defined(__MINGW32__) +#include +#include +#warning "The file functions on MinGW have not been tested for file sizes above 2^31 - 1. Please read https://stackoverflow.com/questions/12539488/determine-64-bit-file-size-in-c-on-mingw-32-bit and fix" +#elif defined(_WIN32) || defined(_WIN64) +#include +#include +#else +#include +#endif + +namespace util { + +scoped_fd::~scoped_fd() { + if (fd_ != -1 && close(fd_)) { + std::cerr << "Could not close file " << fd_ << std::endl; + std::abort(); + } +} + +void scoped_FILE_closer::Close(std::FILE *file) { + if (file && std::fclose(file)) { + std::cerr << "Could not close file " << file << std::endl; + std::abort(); + } +} + +// Note that ErrnoException records errno before NameFromFD is called. +FDException::FDException(int fd) throw() : fd_(fd), name_guess_(NameFromFD(fd)) { + *this << "in " << name_guess_ << ' '; +} + +FDException::~FDException() throw() {} + +EndOfFileException::EndOfFileException() throw() { + *this << "End of file"; +} +EndOfFileException::~EndOfFileException() throw() {} + +bool InputFileIsStdin(StringPiece path) { + return path == "-" || path == "/dev/stdin"; +} + +bool OutputFileIsStdout(StringPiece path) { + return path == "-" || path == "/dev/stdout"; +} + +int OpenReadOrThrow(const char *name) { + int ret; +#if defined(_WIN32) || defined(_WIN64) + UTIL_THROW_IF(-1 == (ret = _open(name, _O_BINARY | _O_RDONLY)), ErrnoException, "while opening " << name); +#else + UTIL_THROW_IF(-1 == (ret = open(name, O_RDONLY)), ErrnoException, "while opening " << name); +#endif + return ret; +} + +int CreateOrThrow(const char *name) { + int ret; +#if defined(_WIN32) || defined(_WIN64) + UTIL_THROW_IF(-1 == (ret = _open(name, _O_CREAT | _O_TRUNC | _O_RDWR | _O_BINARY, _S_IREAD | _S_IWRITE)), ErrnoException, "while creating " << name); +#else + UTIL_THROW_IF(-1 == (ret = open(name, O_CREAT | O_TRUNC | O_RDWR, S_IRUSR | S_IWUSR | S_IRGRP | S_IROTH)), ErrnoException, "while creating " << name); +#endif + return ret; +} + +uint64_t SizeFile(int fd) { +#if defined __MINGW32__ + struct stat sb; + // Does this handle 64-bit? + int ret = fstat(fd, &sb); + if (ret == -1 || (!sb.st_size && !S_ISREG(sb.st_mode))) return kBadSize; + return sb.st_size; +#elif defined(_WIN32) || defined(_WIN64) + __int64 ret = _filelengthi64(fd); + return (ret == -1) ? kBadSize : ret; +#else // Not windows. + +#ifdef OS_ANDROID + struct stat64 sb; + int ret = fstat64(fd, &sb); +#else + struct stat sb; + int ret = fstat(fd, &sb); +#endif + if (ret == -1 || (!sb.st_size && !S_ISREG(sb.st_mode))) return kBadSize; + return sb.st_size; +#endif +} + +uint64_t SizeOrThrow(int fd) { + uint64_t ret = SizeFile(fd); + UTIL_THROW_IF_ARG(ret == kBadSize, FDException, (fd), "Failed to size"); + return ret; +} + +void ResizeOrThrow(int fd, uint64_t to) { +#if defined __MINGW32__ + // Does this handle 64-bit? + int ret = ftruncate +#elif defined(_WIN32) || defined(_WIN64) + errno_t ret = _chsize_s +#elif defined(OS_ANDROID) + int ret = ftruncate64 +#else + int ret = ftruncate +#endif + (fd, to); + UTIL_THROW_IF_ARG(ret, FDException, (fd), "while resizing to " << to << " bytes"); +} + +void HolePunch(int fd, uint64_t offset, uint64_t size) { +#if defined(__linux__) && defined(FALLOC_FL_PUNCH_HOLE) && defined(FALLOC_FL_KEEP_SIZE) + UTIL_THROW_IF_ARG(-1 == fallocate(fd, FALLOC_FL_PUNCH_HOLE | FALLOC_FL_KEEP_SIZE, offset, size), FDException, (fd), "in punching a hole at " << offset << " for " << size << " bytes."); +#else + UTIL_THROW(UnsupportedOSException, "fallocate hole punching requires Linux and glibc >= 2.18"); +#endif +} + +namespace { +std::size_t GuardLarge(std::size_t size) { + // The following operating systems have broken read/write/pread/pwrite that + // only supports up to 2^31. + // OS X man pages claim to support 64-bit, but Kareem M. Darwish had problems + // building with larger files, so APPLE is also here. +#if defined(_WIN32) || defined(_WIN64) || defined(__APPLE__) || defined(OS_ANDROID) || defined(__MINGW32__) + return size < INT_MAX ? size : INT_MAX; +#else + return size; +#endif +} +} + +#if defined(_WIN32) || defined(_WIN64) +namespace { +const std::size_t kMaxDWORD = static_cast(4294967295UL); +} // namespace +#endif + +std::size_t PartialRead(int fd, void *to, std::size_t amount) { +#if defined(_WIN32) || defined(_WIN64) + DWORD ret; + HANDLE file_handle = reinterpret_cast(_get_osfhandle(fd)); + DWORD larger_size = static_cast(std::min(kMaxDWORD, amount)); + DWORD smaller_size = 28672; // Received reports that 31346 worked but higher values did not. This rounds down to the nearest multiple of 4096, the page size. + if (!ReadFile(file_handle, to, larger_size, &ret, NULL)) + { + DWORD last_error = GetLastError(); + if (last_error != ERROR_NOT_ENOUGH_MEMORY || !ReadFile(file_handle, to, smaller_size, &ret, NULL)) { + UTIL_THROW(WindowsException, "Windows error in ReadFile."); + } + } +#else + errno = 0; + ssize_t ret; + do { + ret = read(fd, to, GuardLarge(amount)); + } while (ret == -1 && errno == EINTR); + UTIL_THROW_IF_ARG(ret < 0, FDException, (fd), "while reading " << amount << " bytes"); +#endif + return static_cast(ret); +} + +void ReadOrThrow(int fd, void *to_void, std::size_t amount) { + uint8_t *to = static_cast(to_void); + while (amount) { + std::size_t ret = PartialRead(fd, to, amount); + UTIL_THROW_IF(ret == 0, EndOfFileException, " in " << NameFromFD(fd) << " but there should be " << amount << " more bytes to read."); + amount -= ret; + to += ret; + } +} + +std::size_t ReadOrEOF(int fd, void *to_void, std::size_t amount) { + uint8_t *to = static_cast(to_void); + std::size_t remaining = amount; + while (remaining) { + std::size_t ret = PartialRead(fd, to, remaining); + if (!ret) return amount - remaining; + remaining -= ret; + to += ret; + } + return amount; +} + +void WriteOrThrow(int fd, const void *data_void, std::size_t size) { + const uint8_t *data = static_cast(data_void); + while (size) { +#if defined(_WIN32) || defined(_WIN64) + int ret; +#else + ssize_t ret; +#endif + errno = 0; + do { + ret = +#if defined(_WIN32) || defined(_WIN64) + _write +#else + write +#endif + (fd, data, GuardLarge(size)); + } while (ret == -1 && errno == EINTR); + UTIL_THROW_IF_ARG(ret < 1, FDException, (fd), "while writing " << size << " bytes"); + data += ret; + size -= ret; + } +} + +void WriteOrThrow(FILE *to, const void *data, std::size_t size) { + if (!size) return; + UTIL_THROW_IF(1 != std::fwrite(data, size, 1, to), ErrnoException, "Short write; requested size " << size); +} + +void ErsatzPRead(int fd, void *to_void, std::size_t size, uint64_t off) { + uint8_t *to = static_cast(to_void); + while (size) { +#if defined(_WIN32) || defined(_WIN64) + /* BROKEN: changes file pointer. Even if you save it and change it back, it won't be safe to use concurrently with write() or read() which lmplz does. */ + // size_t might be 64-bit. DWORD is always 32. + DWORD reading = static_cast(std::min(kMaxDWORD, size)); + DWORD ret; + OVERLAPPED overlapped; + memset(&overlapped, 0, sizeof(OVERLAPPED)); + overlapped.Offset = static_cast(off); + overlapped.OffsetHigh = static_cast(off >> 32); + UTIL_THROW_IF(!ReadFile((HANDLE)_get_osfhandle(fd), to, reading, &ret, &overlapped), WindowsException, "ReadFile failed for offset " << off); +#else + ssize_t ret; + errno = 0; + ret = +#ifdef OS_ANDROID + pread64 +#else + pread +#endif + (fd, to, GuardLarge(size), off); + if (ret <= 0) { + if (ret == -1 && errno == EINTR) continue; + UTIL_THROW_IF(ret == 0, EndOfFileException, " for reading " << size << " bytes at " << off << " from " << NameFromFD(fd)); + UTIL_THROW_ARG(FDException, (fd), "while reading " << size << " bytes at offset " << off); + } +#endif + size -= ret; + off += ret; + to += ret; + } +} + +void ErsatzPWrite(int fd, const void *from_void, std::size_t size, uint64_t off) { + const uint8_t *from = static_cast(from_void); + while(size) { +#if defined(_WIN32) || defined(_WIN64) + /* Changes file pointer. Even if you save it and change it back, it won't be safe to use concurrently with write() or read() */ + // size_t might be 64-bit. DWORD is always 32. + DWORD writing = static_cast(std::min(kMaxDWORD, size)); + DWORD ret; + OVERLAPPED overlapped; + memset(&overlapped, 0, sizeof(OVERLAPPED)); + overlapped.Offset = static_cast(off); + overlapped.OffsetHigh = static_cast(off >> 32); + UTIL_THROW_IF(!WriteFile((HANDLE)_get_osfhandle(fd), from, writing, &ret, &overlapped), Exception, "WriteFile failed for offset " << off); +#else + ssize_t ret; + errno = 0; + ret = +#ifdef OS_ANDROID + pwrite64 +#else + pwrite +#endif + (fd, from, GuardLarge(size), off); + if (ret <= 0) { + if (ret == -1 && errno == EINTR) continue; + UTIL_THROW_IF(ret == 0, EndOfFileException, " for writing " << size << " bytes at " << off << " from " << NameFromFD(fd)); + UTIL_THROW_ARG(FDException, (fd), "while writing " << size << " bytes at offset " << off); + } +#endif + size -= ret; + off += ret; + from += ret; + } +} + + +void FSyncOrThrow(int fd) { +// Apparently windows doesn't have fsync? +#if !defined(_WIN32) && !defined(_WIN64) + UTIL_THROW_IF_ARG(-1 == fsync(fd), FDException, (fd), "while syncing"); +#endif +} + +namespace { + +// Static assert for 64-bit off_t size. +#if !defined(_WIN32) && !defined(_WIN64) && !defined(OS_ANDROID) +template struct CheckOffT; +template <> struct CheckOffT<8> { + struct True {}; +}; +// If there's a compiler error on the next line, then off_t isn't 64 bit. And +// that makes me a sad panda. +typedef CheckOffT::True IgnoredType; +#endif + +// Can't we all just get along? +uint64_t InternalSeek(int fd, int64_t off, int whence) { +#if defined __MINGW32__ + // Does this handle 64-bit? + typedef off_t Offset; + Offset ret = lseek(fd, off, whence); +#elif defined(_WIN32) || defined(_WIN64) + typedef __int64 Offset; + Offset ret = _lseeki64(fd, off, whence); +#elif defined(OS_ANDROID) + typedef off64_t Offset; + Offset ret = lseek64(fd, off, whence); +#else + typedef off_t Offset; + Offset ret = lseek(fd, off, whence); +#endif + UTIL_THROW_IF_ARG((Offset)-1 == ret, FDException, (fd), "while seeking to " << off << " whence " << whence); + return (uint64_t)ret; +} +} // namespace + +uint64_t SeekOrThrow(int fd, uint64_t off) { + return InternalSeek(fd, off, SEEK_SET); +} + +uint64_t AdvanceOrThrow(int fd, int64_t off) { + return InternalSeek(fd, off, SEEK_CUR); +} + +uint64_t SeekEnd(int fd) { + return InternalSeek(fd, 0, SEEK_END); +} + +std::FILE *FDOpenOrThrow(scoped_fd &file) { + std::FILE *ret = fdopen(file.get(), "r+b"); + UTIL_THROW_IF_ARG(!ret, FDException, (file.get()), "Could not fdopen for write"); + file.release(); + return ret; +} + +std::FILE *FDOpenReadOrThrow(scoped_fd &file) { + std::FILE *ret = fdopen(file.get(), "rb"); + UTIL_THROW_IF_ARG(!ret, FDException, (file.get()), "Could not fdopen for read"); + file.release(); + return ret; +} + +// Sigh. Windows temporary file creation is full of race conditions. +#if defined(_WIN32) || defined(_WIN64) +/* mkstemp extracted from libc/sysdeps/posix/tempname.c. Copyright + (C) 1991-1999, 2000, 2001, 2006 Free Software Foundation, Inc. + + The GNU C Library is free software; you can redistribute it and/or + modify it under the terms of the GNU Lesser General Public + License as published by the Free Software Foundation; either + version 2.1 of the License, or (at your option) any later version. */ + +/* This has been modified from the original version to rename the function and + * set the Windows temporary flag. */ + +static const char letters[] = +"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; + +/* Generate a temporary file name based on TMPL. TMPL must match the + rules for mk[s]temp (i.e. end in "XXXXXX"). The name constructed + does not exist at the time of the call to mkstemp. TMPL is + overwritten with the result. */ +int +mkstemp_and_unlink(char *tmpl) +{ + int len; + char *XXXXXX; + static unsigned long long value; + unsigned long long random_time_bits; + unsigned int count; + int fd = -1; + int save_errno = errno; + + /* A lower bound on the number of temporary files to attempt to + generate. The maximum total number of temporary file names that + can exist for a given template is 62**6. It should never be + necessary to try all these combinations. Instead if a reasonable + number of names is tried (we define reasonable as 62**3) fail to + give the system administrator the chance to remove the problems. */ +#define ATTEMPTS_MIN (62 * 62 * 62) + + /* The number of times to attempt to generate a temporary file. To + conform to POSIX, this must be no smaller than TMP_MAX. */ +#if ATTEMPTS_MIN < TMP_MAX + unsigned int attempts = TMP_MAX; +#else + unsigned int attempts = ATTEMPTS_MIN; +#endif + + len = strlen (tmpl); + if (len < 6 || strcmp (&tmpl[len - 6], "XXXXXX")) + { + errno = EINVAL; + return -1; + } + +/* This is where the Xs start. */ + XXXXXX = &tmpl[len - 6]; + + /* Get some more or less random data. */ + { + SYSTEMTIME stNow; + FILETIME ftNow; + + // get system time + GetSystemTime(&stNow); + stNow.wMilliseconds = 500; + if (!SystemTimeToFileTime(&stNow, &ftNow)) + { + errno = -1; + return -1; + } + + random_time_bits = (((unsigned long long)ftNow.dwHighDateTime << 32) + | (unsigned long long)ftNow.dwLowDateTime); + } + value += random_time_bits ^ (unsigned long long)GetCurrentThreadId (); + + for (count = 0; count < attempts; value += 7777, ++count) + { + unsigned long long v = value; + + /* Fill in the random bits. */ + XXXXXX[0] = letters[v % 62]; + v /= 62; + XXXXXX[1] = letters[v % 62]; + v /= 62; + XXXXXX[2] = letters[v % 62]; + v /= 62; + XXXXXX[3] = letters[v % 62]; + v /= 62; + XXXXXX[4] = letters[v % 62]; + v /= 62; + XXXXXX[5] = letters[v % 62]; + + /* Modified for windows and to unlink */ + // fd = open (tmpl, O_RDWR | O_CREAT | O_EXCL, _S_IREAD | _S_IWRITE); + int flags = _O_RDWR | _O_CREAT | _O_EXCL | _O_BINARY; + flags |= _O_TEMPORARY; + fd = _open (tmpl, flags, _S_IREAD | _S_IWRITE); + if (fd >= 0) + { + errno = save_errno; + return fd; + } + else if (errno != EEXIST) + return -1; + } + + /* We got out of the loop because we ran out of combinations to try. */ + errno = EEXIST; + return -1; +} +#else +int +mkstemp_and_unlink(char *tmpl) { + int ret = mkstemp(tmpl); + if (ret != -1) { + UTIL_THROW_IF(unlink(tmpl), ErrnoException, "while deleting " << tmpl); + } + return ret; +} +#endif + +// If it's a directory, add a /. This lets users say -T /tmp without creating +// /tmpAAAAAA +void NormalizeTempPrefix(std::string &base) { + if (base.empty()) return; + if (base[base.size() - 1] == '/') return; + struct stat sb; + // It's fine for it to not exist. + if (-1 == stat(base.c_str(), &sb)) return; + if ( +#if defined(_WIN32) || defined(_WIN64) + sb.st_mode & _S_IFDIR +#else + S_ISDIR(sb.st_mode) +#endif + ) base += '/'; +} + +int MakeTemp(const StringPiece &base) { + std::string name(base.data(), base.size()); + name += "XXXXXX"; + name.push_back(0); + int ret; + UTIL_THROW_IF(-1 == (ret = mkstemp_and_unlink(&name[0])), ErrnoException, "while making a temporary based on " << base); + return ret; +} + +std::FILE *FMakeTemp(const StringPiece &base) { + util::scoped_fd file(MakeTemp(base)); + return FDOpenOrThrow(file); +} + +std::string DefaultTempDirectory() { +#if defined(_WIN32) || defined(_WIN64) + char dir_buffer[1000]; + if (GetTempPath(1000, dir_buffer) == 0) + throw std::runtime_error("Could not read temporary directory."); + std::string ret(dir_buffer); + NormalizeTempPrefix(ret); + return ret; +#else + // POSIX says to try these environment variables, in this order: + const char *const vars[] = {"TMPDIR", "TMP", "TEMPDIR", "TEMP", 0}; + for (int i=0; vars[i]; ++i) { + char *val = +#if defined(_GNU_SOURCE) +#if __GLIBC_PREREQ(2,17) + secure_getenv +#else // __GLIBC_PREREQ + getenv +#endif // __GLIBC_PREREQ +#else // _GNU_SOURCE + getenv +#endif + (vars[i]); + // Environment variable is set and nonempty. Use it. + if (val && *val) { + std::string ret(val); + NormalizeTempPrefix(ret); + return ret; + } + } + // No environment variables set. Default to /tmp. + return "/tmp/"; +#endif +} + +int DupOrThrow(int fd) { + int ret = dup(fd); + UTIL_THROW_IF_ARG(ret == -1, FDException, (fd), "in duplicating the file descriptor"); + return ret; +} + +namespace { +// Try to name things but be willing to fail too. +bool TryName(int fd, std::string &out) { +#if defined(_WIN32) || defined(_WIN64) + return false; +#else + std::string name("/proc/self/fd/"); + std::ostringstream convert; + convert << fd; + name += convert.str(); + + struct stat sb; + if (-1 == lstat(name.c_str(), &sb)) + return false; + out.resize(sb.st_size + 1); + // lstat gave us a size, but I've seen it grow, possibly due to symlinks on top of symlinks. + while (true) { + ssize_t ret = readlink(name.c_str(), &out[0], out.size()); + if (-1 == ret) + return false; + if ((size_t)ret < out.size()) { + out.resize(ret); + break; + } + // Exponential growth. + out.resize(out.size() * 2); + } + // Don't use the non-file names. + if (!out.empty() && out[0] != '/') + return false; + return true; +#endif +} +} // namespace + +std::string NameFromFD(int fd) { + std::string ret; + if (TryName(fd, ret)) return ret; + switch (fd) { + case 0: return "stdin"; + case 1: return "stdout"; + case 2: return "stderr"; + } + ret = "fd "; + std::ostringstream convert; + convert << fd; + ret += convert.str(); + return ret; +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/file.hh b/ctcdecode-master/third_party/kenlm/util/file.hh new file mode 100644 index 0000000..4a50e73 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/file.hh @@ -0,0 +1,169 @@ +#ifndef UTIL_FILE_H +#define UTIL_FILE_H + +#include "util/exception.hh" +#include "util/scoped.hh" +#include "util/string_piece.hh" + +#include +#include +#include +#include + +namespace util { + +class scoped_fd { + public: + scoped_fd() : fd_(-1) {} + + explicit scoped_fd(int fd) : fd_(fd) {} + + ~scoped_fd(); + +#if __cplusplus >= 201103L + scoped_fd(scoped_fd &&from) noexcept : fd_(from.fd_) { + from.fd_ = -1; + } +#endif + + void reset(int to = -1) { + scoped_fd other(fd_); + fd_ = to; + } + + int get() const { return fd_; } + + int operator*() const { return fd_; } + + int release() { + int ret = fd_; + fd_ = -1; + return ret; + } + + private: + int fd_; + + scoped_fd(const scoped_fd &); + scoped_fd &operator=(const scoped_fd &); +}; + +struct scoped_FILE_closer { + static void Close(std::FILE *file); +}; +typedef scoped scoped_FILE; + +/* Thrown for any operation where the fd is known. */ +class FDException : public ErrnoException { + public: + explicit FDException(int fd) throw(); + + virtual ~FDException() throw(); + + // This may no longer be valid if the exception was thrown past open. + int FD() const { return fd_; } + + // Guess from NameFromFD. + const std::string &NameGuess() const { return name_guess_; } + + private: + int fd_; + + std::string name_guess_; +}; + +// End of file reached. +class EndOfFileException : public Exception { + public: + EndOfFileException() throw(); + ~EndOfFileException() throw(); +}; + +class UnsupportedOSException : public Exception {}; + +// Open for read only. +int OpenReadOrThrow(const char *name); +// Create file if it doesn't exist, truncate if it does. Opened for write. +int CreateOrThrow(const char *name); + +/** Does the given input file path denote standard input? + * + * Returns true if, and only if, path is either "-" or "/dev/stdin". + * + * Opening standard input as a file may need some special treatment for + * portability. There's a convention that a dash ("-") in place of an input + * file path denotes standard input, but opening "/dev/stdin" may need to be + * special as well. + */ +bool InputPathIsStdin(StringPiece path); + +/** Does the given output file path denote standard output? + * + * Returns true if, and only if, path is either "-" or "/dev/stdout". + * + * Opening standard output as a file may need some special treatment for + * portability. There's a convention that a dash ("-") in place of an output + * file path denotes standard output, but opening "/dev/stdout" may need to be + * special as well. + */ +bool OutputPathIsStdout(StringPiece path); + +// Return value for SizeFile when it can't size properly. +const uint64_t kBadSize = (uint64_t)-1; +uint64_t SizeFile(int fd); +uint64_t SizeOrThrow(int fd); + +void ResizeOrThrow(int fd, uint64_t to); + +// It bothers me that fallocate has offset before size while pread has size +// before offset. But best to follow the call. +void HolePunch(int fd, uint64_t offset, uint64_t size); + +std::size_t PartialRead(int fd, void *to, std::size_t size); +void ReadOrThrow(int fd, void *to, std::size_t size); +std::size_t ReadOrEOF(int fd, void *to_void, std::size_t size); + +void WriteOrThrow(int fd, const void *data_void, std::size_t size); +void WriteOrThrow(FILE *to, const void *data, std::size_t size); + +/* These call pread/pwrite in a loop. However, on Windows they call ReadFile/ + * WriteFile which changes the file pointer. So it's safe to call ErsatzPRead + * and ErsatzPWrite concurrently (or any combination thereof). But it changes + * the file pointer on windows, so it's not safe to call concurrently with + * anything that uses the implicit file pointer e.g. the Read/Write functions + * above. + */ +void ErsatzPRead(int fd, void *to, std::size_t size, uint64_t off); +void ErsatzPWrite(int fd, const void *data_void, std::size_t size, uint64_t off); + +void FSyncOrThrow(int fd); + +// Seeking: returns offset +uint64_t SeekOrThrow(int fd, uint64_t off); +uint64_t AdvanceOrThrow(int fd, int64_t off); +uint64_t SeekEnd(int fd); + +std::FILE *FDOpenOrThrow(scoped_fd &file); +std::FILE *FDOpenReadOrThrow(scoped_fd &file); + +// Temporary files +// Append a / if base is a directory. +void NormalizeTempPrefix(std::string &base); +int MakeTemp(const StringPiece &prefix); +std::FILE *FMakeTemp(const StringPiece &prefix); + +// Where should we put temporary files? Handles all the windows/POSIX defaults fun. +std::string DefaultTempDirectory(); + +// dup an fd. +int DupOrThrow(int fd); + +/* Attempt get file name from fd. This won't always work (i.e. on Windows or + * a pipe). The file might have been renamed. It's intended for diagnostics + * and logging only. + */ +std::string NameFromFD(int fd); + +} // namespace util + +#endif // UTIL_FILE_H diff --git a/ctcdecode-master/third_party/kenlm/util/file_piece.cc b/ctcdecode-master/third_party/kenlm/util/file_piece.cc new file mode 100644 index 0000000..182cce9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/file_piece.cc @@ -0,0 +1,367 @@ +#include "util/file_piece.hh" + +#include "util/double-conversion/double-conversion.h" +#include "util/exception.hh" +#include "util/file.hh" +#include "util/mmap.hh" + +#if defined(_WIN32) || defined(_WIN64) +#include +#else +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#if defined(_WIN32) || defined(_WIN64) +#include +#endif + +namespace util { + +namespace { const uint64_t kPageSize = SizePage(); } + +ParseNumberException::ParseNumberException(StringPiece value) throw() { + *this << "Could not parse \"" << value << "\" into a "; +} + +LineIterator &LineIterator::operator++() { + if (!backing_->ReadLineOrEOF(line_, delim_)) + backing_ = NULL; + return *this; +} + +FilePiece::FilePiece(const char *name, std::ostream *show_progress, std::size_t min_buffer) : + file_(OpenReadOrThrow(name)), total_size_(SizeFile(file_.get())), + progress_(total_size_, total_size_ == kBadSize ? NULL : show_progress, std::string("Reading ") + name) { + Initialize(name, show_progress, min_buffer); +} + +namespace { +std::string NamePossiblyFind(int fd, const char *name) { + if (name) return name; + return NameFromFD(fd); +} +} // namespace + +FilePiece::FilePiece(int fd, const char *name, std::ostream *show_progress, std::size_t min_buffer) : + file_(fd), total_size_(SizeFile(file_.get())), + progress_(total_size_, total_size_ == kBadSize ? NULL : show_progress, std::string("Reading ") + NamePossiblyFind(fd, name)) { + Initialize(NamePossiblyFind(fd, name).c_str(), show_progress, min_buffer); +} + +FilePiece::FilePiece(std::istream &stream, const char *name, std::size_t min_buffer) : + total_size_(kBadSize) { + InitializeNoRead("istream", min_buffer); + + fallback_to_read_ = true; + HugeMalloc(default_map_size_, false, data_); + position_ = data_.begin(); + position_end_ = position_; + + fell_back_.Reset(stream); +} + +StringPiece FilePiece::ReadLine(char delim, bool strip_cr) { + std::size_t skip = 0; + while (true) { + const char *i = std::find(position_ + skip, position_end_, delim); + if (UTIL_LIKELY(i != position_end_)) { + // End of line. + // Take 1 byte off the end if it's an unwanted carriage return. + const std::size_t subtract_cr = ( + (strip_cr && i > position_ && *(i - 1) == '\r') ? + 1 : 0); + StringPiece ret(position_, i - position_ - subtract_cr); + position_ = i + 1; + return ret; + } + if (at_end_) { + if (position_ == position_end_) { + Shift(); + } + return Consume(position_end_); + } + skip = position_end_ - position_; + Shift(); + } +} + +bool FilePiece::ReadLineOrEOF(StringPiece &to, char delim, bool strip_cr) { + try { + to = ReadLine(delim, strip_cr); + } catch (const util::EndOfFileException &e) { return false; } + return true; +} + +float FilePiece::ReadFloat() { + return ReadNumber(); +} +double FilePiece::ReadDouble() { + return ReadNumber(); +} +long int FilePiece::ReadLong() { + return ReadNumber(); +} +unsigned long int FilePiece::ReadULong() { + return ReadNumber(); +} + +// Factored out so that istream can call this. +void FilePiece::InitializeNoRead(const char *name, std::size_t min_buffer) { + file_name_ = name; + + default_map_size_ = kPageSize * std::max((min_buffer / kPageSize + 1), 2); + position_ = NULL; + position_end_ = NULL; + mapped_offset_ = 0; + at_end_ = false; +} + +void FilePiece::Initialize(const char *name, std::ostream *show_progress, std::size_t min_buffer) { + InitializeNoRead(name, min_buffer); + uint64_t current_offset; + bool valid_current_offset; + try { + current_offset = AdvanceOrThrow(file_.get(), 0); + valid_current_offset = true; + } catch (const FDException &) { + current_offset = 0; + valid_current_offset = false; + } + + // So the assertion in TransitionToRead passes + fallback_to_read_ = false; + if (total_size_ == kBadSize || !valid_current_offset) { + if (show_progress) + *show_progress << "File " << name << " isn't normal. Using slower read() instead of mmap(). No progress bar." << std::endl; + TransitionToRead(); + } else { + mapped_offset_ = current_offset; + } + Shift(); + // gzip detect. + if ((position_end_ >= position_ + ReadCompressed::kMagicSize) && ReadCompressed::DetectCompressedMagic(position_)) { + if (!fallback_to_read_) { + at_end_ = false; + TransitionToRead(); + } + } +} + +namespace { + +static const double_conversion::StringToDoubleConverter kConverter( + double_conversion::StringToDoubleConverter::ALLOW_TRAILING_JUNK | double_conversion::StringToDoubleConverter::ALLOW_LEADING_SPACES, + std::numeric_limits::quiet_NaN(), + std::numeric_limits::quiet_NaN(), + "inf", + "NaN"); + +StringPiece FirstToken(StringPiece str) { + const char *i; + for (i = str.data(); i != str.data() + str.size(); ++i) { + if (kSpaces[(unsigned char)*i]) break; + } + return StringPiece(str.data(), i - str.data()); +} + +// std::isnan is technically C++11 not C++98. But in practice this is a problem for visual studio. +template inline int CrossPlatformIsNaN(T value) { +#if defined(_WIN32) || defined(_WIN64) + return isnan(value); +#else + return std::isnan(value); +#endif +} + +const char *ParseNumber(StringPiece str, float &out) { + int count; + out = kConverter.StringToFloat(str.data(), str.size(), &count); + UTIL_THROW_IF_ARG(CrossPlatformIsNaN(out) && str != "NaN" && str != "nan", ParseNumberException, (FirstToken(str)), "float"); + return str.data() + count; +} +const char *ParseNumber(StringPiece str, double &out) { + int count; + out = kConverter.StringToDouble(str.data(), str.size(), &count); + UTIL_THROW_IF_ARG(CrossPlatformIsNaN(out) && str != "NaN" && str != "nan", ParseNumberException, (FirstToken(str)), "double"); + return str.data() + count; +} +const char *ParseNumber(StringPiece str, long int &out) { + char *end; + errno = 0; + out = strtol(str.data(), &end, 10); + UTIL_THROW_IF_ARG(errno || (end == str.data()), ParseNumberException, (FirstToken(str)), "long int"); + return end; +} +const char *ParseNumber(StringPiece str, unsigned long int &out) { + char *end; + errno = 0; + out = strtoul(str.data(), &end, 10); + UTIL_THROW_IF_ARG(errno || (end == str.data()), ParseNumberException, (FirstToken(str)), "unsigned long int"); + return end; +} +} // namespace + +template T FilePiece::ReadNumber() { + SkipSpaces(); + while (last_space_ < position_) { + if (UTIL_UNLIKELY(at_end_)) { + // Hallucinate a null off the end of the file. + std::string buffer(position_, position_end_); + T ret; + // Has to be null-terminated. + const char *begin = buffer.c_str(); + const char *end = ParseNumber(StringPiece(begin, buffer.size()), ret); + position_ += end - begin; + return ret; + } + Shift(); + } + T ret; + position_ = ParseNumber(StringPiece(position_, last_space_ - position_), ret); + return ret; +} + +const char *FilePiece::FindDelimiterOrEOF(const bool *delim) { + std::size_t skip = 0; + while (true) { + for (const char *i = position_ + skip; i < position_end_; ++i) { + if (delim[static_cast(*i)]) return i; + } + if (at_end_) { + if (position_ == position_end_) Shift(); + return position_end_; + } + skip = position_end_ - position_; + Shift(); + } +} + +void FilePiece::Shift() { + if (at_end_) { + progress_.Finished(); + throw EndOfFileException(); + } + uint64_t desired_begin = position_ - data_.begin() + mapped_offset_; + + if (!fallback_to_read_) MMapShift(desired_begin); + // Notice an mmap failure might set the fallback. + if (fallback_to_read_) ReadShift(); + + for (last_space_ = position_end_ - 1; last_space_ >= position_; --last_space_) { + if (kSpaces[static_cast(*last_space_)]) break; + } +} + +void FilePiece::UpdateProgress() { + if (!fallback_to_read_) + progress_.Set(position_ - data_.begin() + mapped_offset_); +} + +void FilePiece::MMapShift(uint64_t desired_begin) { + // Use mmap. + uint64_t ignore = desired_begin % kPageSize; + // Duplicate request for Shift means give more data. + if (position_ == data_.begin() + ignore && position_) { + default_map_size_ *= 2; + } + // Local version so that in case of failure it doesn't overwrite the class variable. + uint64_t mapped_offset = desired_begin - ignore; + + uint64_t mapped_size; + if (default_map_size_ >= static_cast(total_size_ - mapped_offset)) { + at_end_ = true; + mapped_size = total_size_ - mapped_offset; + } else { + mapped_size = default_map_size_; + } + + // Forcibly clear the existing mmap first. + data_.reset(); + try { + MapRead(POPULATE_OR_LAZY, *file_, mapped_offset, mapped_size, data_); + } catch (const util::ErrnoException &e) { + if (desired_begin) { + SeekOrThrow(*file_, desired_begin); + } + // The mmap was scheduled to end the file, but now we're going to read it. + at_end_ = false; + TransitionToRead(); + return; + } + mapped_offset_ = mapped_offset; + position_ = data_.begin() + ignore; + position_end_ = data_.begin() + mapped_size; + + progress_.Set(desired_begin); +} + +void FilePiece::TransitionToRead() { + assert(!fallback_to_read_); + fallback_to_read_ = true; + data_.reset(); + HugeMalloc(default_map_size_, false, data_); + position_ = data_.begin(); + position_end_ = position_; + + try { + fell_back_.Reset(file_.release()); + } catch (util::Exception &e) { + e << " in file " << file_name_; + throw; + } +} + +void FilePiece::ReadShift() { + assert(fallback_to_read_); + // Bytes [data_.begin(), position_) have been consumed. + // Bytes [position_, position_end_) have been read into the buffer. + + // Start at the beginning of the buffer if there's nothing useful in it. + if (position_ == position_end_) { + mapped_offset_ += (position_end_ - data_.begin()); + position_ = data_.begin(); + position_end_ = position_; + } + + std::size_t already_read = position_end_ - data_.begin(); + + if (already_read == default_map_size_) { + if (position_ == data_.begin()) { + // Buffer too small. + std::size_t valid_length = position_end_ - position_; + default_map_size_ *= 2; + HugeRealloc(default_map_size_, false, data_); + position_ = data_.begin(); + position_end_ = position_ + valid_length; + } else { + std::size_t moving = position_end_ - position_; + memmove(data_.get(), position_, moving); + position_ = data_.begin(); + position_end_ = position_ + moving; + already_read = moving; + } + } + + std::size_t read_return = fell_back_.Read(static_cast(data_.get()) + already_read, default_map_size_ - already_read); + progress_.Set(fell_back_.RawAmount()); + + if (read_return == 0) { + at_end_ = true; + } + position_end_ += read_return; +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/file_piece.hh b/ctcdecode-master/third_party/kenlm/util/file_piece.hh new file mode 100644 index 0000000..67b2884 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/file_piece.hh @@ -0,0 +1,217 @@ +#ifndef UTIL_FILE_PIECE_H +#define UTIL_FILE_PIECE_H + +#include "util/ersatz_progress.hh" +#include "util/exception.hh" +#include "util/file.hh" +#include "util/mmap.hh" +#include "util/read_compressed.hh" +#include "util/spaces.hh" +#include "util/string_piece.hh" + +#include +#include +#include +#include +#include + +namespace util { + +class ParseNumberException : public Exception { + public: + explicit ParseNumberException(StringPiece value) throw(); + ~ParseNumberException() throw() {} +}; + +class FilePiece; + +// Input Iterator over lines. This allows +// for (StringPiece l : FilePiece("file")) +// in C++11. +// NB: not multipass. +class LineIterator { + public: + LineIterator() : backing_(NULL) {} + + explicit LineIterator(FilePiece &f, char delim = '\n') : backing_(&f), delim_(delim) { + ++*this; + } + + LineIterator &operator++(); + + bool operator==(const LineIterator &other) const { + return backing_ == other.backing_; + } + + bool operator!=(const LineIterator &other) const { + return backing_ != other.backing_; + } + + operator bool() const { return backing_ != NULL; } + + StringPiece operator*() const { return line_; } + const StringPiece *operator->() const { return &line_; } + + private: + FilePiece *backing_; + StringPiece line_; + char delim_; +}; + +// Memory backing the returned StringPiece may vanish on the next call. +class FilePiece { + public: + // 1 MB default. + explicit FilePiece(const char *file, std::ostream *show_progress = NULL, std::size_t min_buffer = 1048576); + // Takes ownership of fd. name is used for messages. + explicit FilePiece(int fd, const char *name = NULL, std::ostream *show_progress = NULL, std::size_t min_buffer = 1048576); + + /* Read from an istream. Don't use this if you can avoid it. Raw fd IO is + * much faster. But sometimes you just have an istream like Boost's HTTP + * server and want to parse it the same way. + * name is just used for messages and FileName(). + */ + explicit FilePiece(std::istream &stream, const char *name = NULL, std::size_t min_buffer = 1048576); + + LineIterator begin() { + return LineIterator(*this); + } + + LineIterator end() { + return LineIterator(); + } + + char get() { + if (position_ == position_end_) { + Shift(); + if (at_end_) throw EndOfFileException(); + } + return *(position_++); + } + + // Leaves the delimiter, if any, to be returned by get(). Delimiters defined by isspace(). + StringPiece ReadDelimited(const bool *delim = kSpaces) { + SkipSpaces(delim); + return Consume(FindDelimiterOrEOF(delim)); + } + + /// Read word until the line or file ends. + bool ReadWordSameLine(StringPiece &to, const bool *delim = kSpaces) { + assert(delim[static_cast('\n')]); + // Skip non-enter spaces. + for (; ; ++position_) { + if (position_ == position_end_) { + try { + Shift(); + } catch (const util::EndOfFileException &e) { return false; } + // And break out at end of file. + if (position_ == position_end_) return false; + } + if (!delim[static_cast(*position_)]) break; + if (*position_ == '\n') return false; + } + // We can't be at the end of file because there's at least one character open. + to = Consume(FindDelimiterOrEOF(delim)); + return true; + } + + /** Read a line of text from the file. + * + * Unlike ReadDelimited, this includes leading spaces and consumes the + * delimiter. It is similar to getline in that way. + * + * If strip_cr is true, any trailing carriate return (as would be found on + * a file written on Windows) will be left out of the returned line. + * + * Throws EndOfFileException if the end of the file is encountered. If the + * file does not end in a newline, this could mean that the last line is + * never read. + */ + StringPiece ReadLine(char delim = '\n', bool strip_cr = true); + + /** Read a line of text from the file, or return false on EOF. + * + * This is like ReadLine, except it returns false where ReadLine throws + * EndOfFileException. Like ReadLine it may not read the last line in the + * file if the file does not end in a newline. + * + * If strip_cr is true, any trailing carriate return (as would be found on + * a file written on Windows) will be left out of the returned line. + */ + bool ReadLineOrEOF(StringPiece &to, char delim = '\n', bool strip_cr = true); + + float ReadFloat(); + double ReadDouble(); + long int ReadLong(); + unsigned long int ReadULong(); + + // Skip spaces defined by isspace. + void SkipSpaces(const bool *delim = kSpaces) { + assert(position_ <= position_end_); + for (; ; ++position_) { + if (position_ == position_end_) { + Shift(); + // And break out at end of file. + if (position_ == position_end_) return; + } + assert(position_ < position_end_); + if (!delim[static_cast(*position_)]) return; + } + } + + uint64_t Offset() const { + return position_ - data_.begin() + mapped_offset_; + } + + const std::string &FileName() const { return file_name_; } + + // Force a progress update. + void UpdateProgress(); + + private: + void InitializeNoRead(const char *name, std::size_t min_buffer); + // Calls InitializeNoRead, so don't call both. + void Initialize(const char *name, std::ostream *show_progress, std::size_t min_buffer); + + template T ReadNumber(); + + StringPiece Consume(const char *to) { + assert(to >= position_); + StringPiece ret(position_, to - position_); + position_ = to; + return ret; + } + + const char *FindDelimiterOrEOF(const bool *delim = kSpaces); + + void Shift(); + // Backends to Shift(). + void MMapShift(uint64_t desired_begin); + + void TransitionToRead(); + void ReadShift(); + + const char *position_, *last_space_, *position_end_; + + scoped_fd file_; + const uint64_t total_size_; + + std::size_t default_map_size_; + uint64_t mapped_offset_; + + // Order matters: file_ should always be destroyed after this. + scoped_memory data_; + + bool at_end_; + bool fallback_to_read_; + + ErsatzProgress progress_; + + std::string file_name_; + + ReadCompressed fell_back_; +}; + +} // namespace util + +#endif // UTIL_FILE_PIECE_H diff --git a/ctcdecode-master/third_party/kenlm/util/file_piece_test.cc b/ctcdecode-master/third_party/kenlm/util/file_piece_test.cc new file mode 100644 index 0000000..952eb59 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/file_piece_test.cc @@ -0,0 +1,172 @@ +// Tests might fail if you have creative characters in your path. Sue me. +#include "util/file_piece.hh" + +#include "util/file_stream.hh" +#include "util/file.hh" +#include "util/scoped.hh" + +#define BOOST_TEST_MODULE FilePieceTest +#include +#include +#include +#include +#include +#include + +namespace util { +namespace { + +std::string FileLocation() { + if (boost::unit_test::framework::master_test_suite().argc < 2) { + return "file_piece.cc"; + } + std::string ret(boost::unit_test::framework::master_test_suite().argv[1]); + return ret; +} + +/* istream */ +BOOST_AUTO_TEST_CASE(IStream) { + std::fstream ref(FileLocation().c_str(), std::ios::in); + std::fstream backing(FileLocation().c_str(), std::ios::in); + FilePiece test(backing); + std::string ref_line; + while (getline(ref, ref_line)) { + StringPiece test_line(test.ReadLine()); + BOOST_CHECK_EQUAL(ref_line, test_line); + } + BOOST_CHECK_THROW(test.get(), EndOfFileException); + BOOST_CHECK_THROW(test.get(), EndOfFileException); +} + +/* mmap implementation */ +BOOST_AUTO_TEST_CASE(MMapReadLine) { + std::fstream ref(FileLocation().c_str(), std::ios::in); + FilePiece test(FileLocation().c_str(), NULL, 1); + std::string ref_line; + while (getline(ref, ref_line)) { + StringPiece test_line(test.ReadLine()); + // I submitted a bug report to ICU: http://bugs.icu-project.org/trac/ticket/7924 + if (!test_line.empty() || !ref_line.empty()) { + BOOST_CHECK_EQUAL(ref_line, test_line); + } + } + BOOST_CHECK_THROW(test.get(), EndOfFileException); +} + +/* mmap with seek beforehand */ +BOOST_AUTO_TEST_CASE(MMapSeek) { + std::fstream ref(FileLocation().c_str(), std::ios::in); + ref.seekg(10); + scoped_fd file(util::OpenReadOrThrow(FileLocation().c_str())); + SeekOrThrow(file.get(), 10); + FilePiece test(file.release()); + std::string ref_line; + while (getline(ref, ref_line)) { + StringPiece test_line(test.ReadLine()); + // I submitted a bug report to ICU: http://bugs.icu-project.org/trac/ticket/7924 + if (!test_line.empty() || !ref_line.empty()) { + BOOST_CHECK_EQUAL(ref_line, test_line); + } + } + BOOST_CHECK_THROW(test.get(), EndOfFileException); +} + +#if !defined(_WIN32) && !defined(_WIN64) && !defined(__APPLE__) +/* Apple isn't happy with the popen, fileno, dup. And I don't want to + * reimplement popen. This is an issue with the test. + */ +/* read() implementation */ +BOOST_AUTO_TEST_CASE(StreamReadLine) { + std::fstream ref(FileLocation().c_str(), std::ios::in); + + std::string popen_args = "cat \""; + popen_args += FileLocation(); + popen_args += '"'; + + FILE *catter = popen(popen_args.c_str(), "r"); + BOOST_REQUIRE(catter); + + FilePiece test(dup(fileno(catter)), "file_piece.cc", NULL, 1); + std::string ref_line; + while (getline(ref, ref_line)) { + StringPiece test_line(test.ReadLine()); + // I submitted a bug report to ICU: http://bugs.icu-project.org/trac/ticket/7924 + if (!test_line.empty() || !ref_line.empty()) { + BOOST_CHECK_EQUAL(ref_line, test_line); + } + } + BOOST_CHECK_THROW(test.get(), EndOfFileException); + BOOST_REQUIRE(!pclose(catter)); +} +#endif + +#ifdef HAVE_ZLIB + +// gzip file +BOOST_AUTO_TEST_CASE(PlainZipReadLine) { + std::string location(FileLocation()); + std::fstream ref(location.c_str(), std::ios::in); + + std::string command("gzip <\""); + command += location + "\" >\"" + location + "\".gz"; + + BOOST_REQUIRE_EQUAL(0, system(command.c_str())); + FilePiece test((location + ".gz").c_str(), NULL, 1); + unlink((location + ".gz").c_str()); + std::string ref_line; + while (getline(ref, ref_line)) { + StringPiece test_line(test.ReadLine()); + // I submitted a bug report to ICU: http://bugs.icu-project.org/trac/ticket/7924 + if (!test_line.empty() || !ref_line.empty()) { + BOOST_CHECK_EQUAL(ref_line, test_line); + } + } + BOOST_CHECK_THROW(test.get(), EndOfFileException); +} + +// gzip stream. Apple doesn't like popen, fileno, dup. This is an issue with +// the test. +#if !defined __APPLE__ && !defined __MINGW32__ +BOOST_AUTO_TEST_CASE(StreamZipReadLine) { + std::fstream ref(FileLocation().c_str(), std::ios::in); + + std::string command("gzip <\""); + command += FileLocation() + "\""; + + FILE * catter = popen(command.c_str(), "r"); + BOOST_REQUIRE(catter); + + FilePiece test(dup(fileno(catter)), "file_piece.cc.gz", NULL, 1); + std::string ref_line; + while (getline(ref, ref_line)) { + StringPiece test_line(test.ReadLine()); + // I submitted a bug report to ICU: http://bugs.icu-project.org/trac/ticket/7924 + if (!test_line.empty() || !ref_line.empty()) { + BOOST_CHECK_EQUAL(ref_line, test_line); + } + } + BOOST_CHECK_THROW(test.get(), EndOfFileException); + BOOST_REQUIRE(!pclose(catter)); +} +#endif // __APPLE__ + +#endif // HAVE_ZLIB + +BOOST_AUTO_TEST_CASE(Numbers) { + scoped_fd file(MakeTemp(FileLocation())); + const float floating = 3.2; + { + util::FileStream writing(file.get()); + writing << "94389483984398493890287 " << floating << " 5"; + } + SeekOrThrow(file.get(), 0); + util::FilePiece f(file.release()); + BOOST_CHECK_THROW(f.ReadULong(), ParseNumberException); + BOOST_CHECK_EQUAL("94389483984398493890287", f.ReadDelimited()); + // Yes, exactly equal. Isn't double-conversion wonderful? + BOOST_CHECK_EQUAL(floating, f.ReadFloat()); + BOOST_CHECK_EQUAL(5, f.ReadULong()); +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/file_stream.hh b/ctcdecode-master/third_party/kenlm/util/file_stream.hh new file mode 100644 index 0000000..8b97171 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/file_stream.hh @@ -0,0 +1,97 @@ +/* Like std::ofstream but without being incredibly slow. Backed by a raw fd. + * Supports most of the built-in types except for long double. + */ +#ifndef UTIL_FILE_STREAM_H +#define UTIL_FILE_STREAM_H + +#include "util/fake_ostream.hh" +#include "util/file.hh" +#include "util/scoped.hh" + +#include +#include + +#include + +namespace util { + +class FileStream : public FakeOStream { + public: + explicit FileStream(int out = -1, std::size_t buffer_size = 8192) + : buf_(util::MallocOrThrow(std::max(buffer_size, kToStringMaxBytes))), + current_(static_cast(buf_.get())), + end_(current_ + std::max(buffer_size, kToStringMaxBytes)), + fd_(out) {} + +#if __cplusplus >= 201103L + FileStream(FileStream &&from) noexcept : buf_(from.buf_.release()), current_(from.current_), end_(from.end_), fd_(from.fd_) { + from.end_ = reinterpret_cast(from.buf_.get()); + from.current_ = from.end_; + } +#endif + + ~FileStream() { + flush(); + } + + void SetFD(int to) { + flush(); + fd_ = to; + } + + FileStream &flush() { + if (current_ != buf_.get()) { + util::WriteOrThrow(fd_, buf_.get(), current_ - (char*)buf_.get()); + current_ = static_cast(buf_.get()); + } + return *this; + } + + // For writes of arbitrary size. + FileStream &write(const void *data, std::size_t length) { + if (UTIL_LIKELY(current_ + length <= end_)) { + std::memcpy(current_, data, length); + current_ += length; + return *this; + } + flush(); + if (current_ + length <= end_) { + std::memcpy(current_, data, length); + current_ += length; + } else { + util::WriteOrThrow(fd_, data, length); + } + return *this; + } + + FileStream &seekp(uint64_t to) { + flush(); + util::SeekOrThrow(fd_, to); + return *this; + } + + protected: + friend class FakeOStream; + // For writes directly to buffer guaranteed to have amount < buffer size. + char *Ensure(std::size_t amount) { + if (UTIL_UNLIKELY(current_ + amount > end_)) { + flush(); + assert(current_ + amount <= end_); + } + return current_; + } + + void AdvanceTo(char *to) { + current_ = to; + assert(current_ <= end_); + } + + private: + util::scoped_malloc buf_; + char *current_, *end_; + int fd_; +}; + +} // namespace + +#endif diff --git a/ctcdecode-master/third_party/kenlm/util/fixed_array.hh b/ctcdecode-master/third_party/kenlm/util/fixed_array.hh new file mode 100644 index 0000000..c67e8ed --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/fixed_array.hh @@ -0,0 +1,176 @@ +#ifndef UTIL_FIXED_ARRAY_H +#define UTIL_FIXED_ARRAY_H + +#include "util/scoped.hh" + +#include + +#include +#include + +namespace util { + +/** + * Defines an array with fixed maximum size. + * + * Ever want an array of things but they don't have a default constructor or + * are non-copyable? FixedArray allows constructing one at a time. + */ +template class FixedArray { + public: + /** Initialize with a given size bound but do not construct the objects. */ + explicit FixedArray(std::size_t limit) { + Init(limit); + } + + /** + * Constructs an instance, but does not initialize it. + * + * Any objects constructed in this manner must be subsequently @ref FixedArray::Init() "initialized" prior to use. + * + * @see FixedArray::Init() + */ + FixedArray() + : newed_end_(NULL) +#ifndef NDEBUG + , allocated_end_(NULL) +#endif + {} + + /** + * Initialize with a given size bound but do not construct the objects. + * + * This method is responsible for allocating memory. + * Objects stored in this array will be constructed in a location within this allocated memory. + */ + void Init(std::size_t count) { + assert(!block_.get()); + block_.reset(malloc(sizeof(T) * count)); + if (!block_.get()) throw std::bad_alloc(); + newed_end_ = begin(); +#ifndef NDEBUG + allocated_end_ = begin() + count; +#endif + } + + /** + * Constructs a copy of the provided array. + * + * @param from Array whose elements should be copied into this newly-constructed data structure. + */ + FixedArray(const FixedArray &from) { + std::size_t size = from.newed_end_ - static_cast(from.block_.get()); + Init(size); + for (std::size_t i = 0; i < size; ++i) { + push_back(from[i]); + } + } + + /** + * Frees the memory held by this object. + */ + ~FixedArray() { clear(); } + + /** Gets a pointer to the first object currently stored in this data structure. */ + T *begin() { return static_cast(block_.get()); } + + /** Gets a const pointer to the last object currently stored in this data structure. */ + const T *begin() const { return static_cast(block_.get()); } + + /** Gets a pointer to the last object currently stored in this data structure. */ + T *end() { return newed_end_; } + + /** Gets a const pointer to the last object currently stored in this data structure. */ + const T *end() const { return newed_end_; } + + /** Gets a reference to the last object currently stored in this data structure. */ + T &back() { return *(end() - 1); } + + /** Gets a const reference to the last object currently stored in this data structure. */ + const T &back() const { return *(end() - 1); } + + /** Gets the number of objects currently stored in this data structure. */ + std::size_t size() const { return end() - begin(); } + + /** Returns true if there are no objects currently stored in this data structure. */ + bool empty() const { return begin() == end(); } + + /** + * Gets a reference to the object with index i currently stored in this data structure. + * + * @param i Index of the object to reference + */ + T &operator[](std::size_t i) { + assert(i < size()); + return begin()[i]; + } + + /** + * Gets a const reference to the object with index i currently stored in this data structure. + * + * @param i Index of the object to reference + */ + const T &operator[](std::size_t i) const { + assert(i < size()); + return begin()[i]; + } + + /** + * Constructs a new object using the provided parameter, + * and stores it in this data structure. + * + * The memory backing the constructed object is managed by this data structure. + * I miss C++11 variadic templates. + */ + void push_back() { + new (end()) T(); + Constructed(); + } + template void push_back(const C &c) { + new (end()) T(c); + Constructed(); + } + template void push_back(C &c) { + new (end()) T(c); + Constructed(); + } + template void push_back(const C &c, const D &d) { + new (end()) T(c, d); + Constructed(); + } + + void pop_back() { + back().~T(); + --newed_end_; + } + + /** + * Removes all elements from this array. + */ + void clear() { + while (newed_end_ != begin()) + pop_back(); + } + + protected: + // Always call Constructed after successful completion of new. + void Constructed() { + ++newed_end_; +#ifndef NDEBUG + assert(newed_end_ <= allocated_end_); +#endif + } + + private: + util::scoped_malloc block_; + + T *newed_end_; + +#ifndef NDEBUG + T *allocated_end_; +#endif +}; + +} // namespace util + +#endif // UTIL_FIXED_ARRAY_H diff --git a/ctcdecode-master/third_party/kenlm/util/float_to_string.cc b/ctcdecode-master/third_party/kenlm/util/float_to_string.cc new file mode 100644 index 0000000..1e16d6f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/float_to_string.cc @@ -0,0 +1,23 @@ +#include "util/float_to_string.hh" + +#include "util/double-conversion/double-conversion.h" +#include "util/double-conversion/utils.h" + +namespace util { +namespace { +const double_conversion::DoubleToStringConverter kConverter(double_conversion::DoubleToStringConverter::NO_FLAGS, "inf", "NaN", 'e', -6, 21, 6, 0); +} // namespace + +char *ToString(double value, char *to) { + double_conversion::StringBuilder builder(to, ToStringBuf::kBytes); + kConverter.ToShortest(value, &builder); + return &to[builder.position()]; +} + +char *ToString(float value, char *to) { + double_conversion::StringBuilder builder(to, ToStringBuf::kBytes); + kConverter.ToShortestSingle(value, &builder); + return &to[builder.position()]; +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/float_to_string.hh b/ctcdecode-master/third_party/kenlm/util/float_to_string.hh new file mode 100644 index 0000000..9305327 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/float_to_string.hh @@ -0,0 +1,25 @@ +#ifndef UTIL_FLOAT_TO_STRING_H +#define UTIL_FLOAT_TO_STRING_H + +// Just for ToStringBuf +#include "util/integer_to_string.hh" + +namespace util { + +template <> struct ToStringBuf { + // DoubleToStringConverter::kBase10MaximalLength + 1 for null paranoia. + static const unsigned kBytes = 19; +}; + +// Single wasn't documented in double conversion, so be conservative and +// say the same as double. +template <> struct ToStringBuf { + static const unsigned kBytes = 19; +}; + +char *ToString(double value, char *to); +char *ToString(float value, char *to); + +} // namespace util + +#endif // UTIL_FLOAT_TO_STRING_H diff --git a/ctcdecode-master/third_party/kenlm/util/getopt.c b/ctcdecode-master/third_party/kenlm/util/getopt.c new file mode 100644 index 0000000..50eef42 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/getopt.c @@ -0,0 +1,78 @@ +/* +POSIX getopt for Windows + +AT&T Public License + +Code given out at the 1985 UNIFORUM conference in Dallas. +*/ + +#ifndef __GNUC__ + +#include "getopt.hh" +#include +#include + +#define NULL 0 +#define EOF (-1) +#define ERR(s, c) if(opterr){\ + char errbuf[2];\ + errbuf[0] = c; errbuf[1] = '\n';\ + fputs(argv[0], stderr);\ + fputs(s, stderr);\ + fputc(c, stderr);} + //(void) write(2, argv[0], (unsigned)strlen(argv[0]));\ + //(void) write(2, s, (unsigned)strlen(s));\ + //(void) write(2, errbuf, 2);} + +int opterr = 1; +int optind = 1; +int optopt; +char *optarg; + +int +getopt(argc, argv, opts) +int argc; +char **argv, *opts; +{ + static int sp = 1; + register int c; + register char *cp; + + if(sp == 1) + if(optind >= argc || + argv[optind][0] != '-' || argv[optind][1] == '\0') + return(EOF); + else if(strcmp(argv[optind], "--") == NULL) { + optind++; + return(EOF); + } + optopt = c = argv[optind][sp]; + if(c == ':' || (cp=strchr(opts, c)) == NULL) { + ERR(": illegal option -- ", c); + if(argv[optind][++sp] == '\0') { + optind++; + sp = 1; + } + return('?'); + } + if(*++cp == ':') { + if(argv[optind][sp+1] != '\0') + optarg = &argv[optind++][sp+1]; + else if(++optind >= argc) { + ERR(": option requires an argument -- ", c); + sp = 1; + return('?'); + } else + optarg = argv[optind++]; + sp = 1; + } else { + if(argv[optind][++sp] == '\0') { + sp = 1; + optind++; + } + optarg = NULL; + } + return(c); +} + +#endif /* __GNUC__ */ diff --git a/ctcdecode-master/third_party/kenlm/util/getopt.hh b/ctcdecode-master/third_party/kenlm/util/getopt.hh new file mode 100644 index 0000000..9b0792b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/getopt.hh @@ -0,0 +1,33 @@ +/* +POSIX getopt for Windows + +AT&T Public License + +Code given out at the 1985 UNIFORUM conference in Dallas. +*/ + +#ifdef __GNUC__ +#include +#endif +#ifndef __GNUC__ + +#ifndef UTIL_GETOPT_H +#define UTIL_GETOPT_H + +#ifdef __cplusplus +extern "C" { +#endif + +extern int opterr; +extern int optind; +extern int optopt; +extern char *optarg; +extern int getopt(int argc, char **argv, char *opts); + +#ifdef __cplusplus +} +#endif + +#endif /* UTIL_GETOPT_H */ +#endif /* __GNUC__ */ + diff --git a/ctcdecode-master/third_party/kenlm/util/have.hh b/ctcdecode-master/third_party/kenlm/util/have.hh new file mode 100644 index 0000000..dc3f633 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/have.hh @@ -0,0 +1,13 @@ +/* Optional packages. You might want to integrate this with your build system e.g. config.h from ./configure. */ +#ifndef UTIL_HAVE_H +#define UTIL_HAVE_H + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#ifndef HAVE_ICU +//#define HAVE_ICU +#endif + +#endif // UTIL_HAVE_H diff --git a/ctcdecode-master/third_party/kenlm/util/integer_to_string.cc b/ctcdecode-master/third_party/kenlm/util/integer_to_string.cc new file mode 100644 index 0000000..19fd794 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/integer_to_string.cc @@ -0,0 +1,667 @@ +#include +/* Fast integer to string conversion. +Source: https://github.com/miloyip/itoa-benchmark +Local modifications: +1. Return end of buffer instead of null terminating +2. Collapse to single file +3. Namespace +4. Remove test hook +5. Non-x86 support from the branch_lut code +6. Rename functions +7. Require __SSE2__ on i386 + +Copyright (C) 2014 Milo Yip + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. + +Which is based on: http://0x80.pl/snippets/asm/sse-utoa.c + + SSE: conversion integers to decimal representation + + Author: Wojciech MuÅ‚a + e-mail: wojciech_mula@poczta.onet.pl + www: http://0x80.pl/ + + License: BSD + + initial release 2011-10-21 + $Id$ +*/ + +#include "util/integer_to_string.hh" +#include +#include + +namespace util { + +namespace { +const char gDigitsLut[200] = { + '0','0','0','1','0','2','0','3','0','4','0','5','0','6','0','7','0','8','0','9', + '1','0','1','1','1','2','1','3','1','4','1','5','1','6','1','7','1','8','1','9', + '2','0','2','1','2','2','2','3','2','4','2','5','2','6','2','7','2','8','2','9', + '3','0','3','1','3','2','3','3','3','4','3','5','3','6','3','7','3','8','3','9', + '4','0','4','1','4','2','4','3','4','4','4','5','4','6','4','7','4','8','4','9', + '5','0','5','1','5','2','5','3','5','4','5','5','5','6','5','7','5','8','5','9', + '6','0','6','1','6','2','6','3','6','4','6','5','6','6','6','7','6','8','6','9', + '7','0','7','1','7','2','7','3','7','4','7','5','7','6','7','7','7','8','7','9', + '8','0','8','1','8','2','8','3','8','4','8','5','8','6','8','7','8','8','8','9', + '9','0','9','1','9','2','9','3','9','4','9','5','9','6','9','7','9','8','9','9' +}; +} // namespace + +// SSE2 implementation according to http://0x80.pl/articles/sse-itoa.html +// Modifications: (1) fix incorrect digits (2) accept all ranges (3) write to user provided buffer. + +#if defined(__amd64) || defined(_M_X64) || (defined(__SSE2__) && (defined(_M_IX86) || defined(i386))) + +#include + +#ifdef _MSC_VER +#include "intrin.h" +#endif + +#ifdef _MSC_VER +#define ALIGN_PRE __declspec(align(16)) +#define ALIGN_SUF +#else +#define ALIGN_PRE +#define ALIGN_SUF __attribute__ ((aligned(16))) +#endif + +namespace { + +static const uint32_t kDiv10000 = 0xd1b71759; +ALIGN_PRE static const uint32_t kDiv10000Vector[4] ALIGN_SUF = { kDiv10000, kDiv10000, kDiv10000, kDiv10000 }; +ALIGN_PRE static const uint32_t k10000Vector[4] ALIGN_SUF = { 10000, 10000, 10000, 10000 }; +ALIGN_PRE static const uint16_t kDivPowersVector[8] ALIGN_SUF = { 8389, 5243, 13108, 32768, 8389, 5243, 13108, 32768 }; // 10^3, 10^2, 10^1, 10^0 +ALIGN_PRE static const uint16_t kShiftPowersVector[8] ALIGN_SUF = { + 1 << (16 - (23 + 2 - 16)), + 1 << (16 - (19 + 2 - 16)), + 1 << (16 - 1 - 2), + 1 << (15), + 1 << (16 - (23 + 2 - 16)), + 1 << (16 - (19 + 2 - 16)), + 1 << (16 - 1 - 2), + 1 << (15) +}; +ALIGN_PRE static const uint16_t k10Vector[8] ALIGN_SUF = { 10, 10, 10, 10, 10, 10, 10, 10 }; +ALIGN_PRE static const char kAsciiZero[16] ALIGN_SUF = { '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0' }; + +inline __m128i Convert8DigitsSSE2(uint32_t value) { + assert(value <= 99999999); + + // abcd, efgh = abcdefgh divmod 10000 + const __m128i abcdefgh = _mm_cvtsi32_si128(value); + const __m128i abcd = _mm_srli_epi64(_mm_mul_epu32(abcdefgh, reinterpret_cast(kDiv10000Vector)[0]), 45); + const __m128i efgh = _mm_sub_epi32(abcdefgh, _mm_mul_epu32(abcd, reinterpret_cast(k10000Vector)[0])); + + // v1 = [ abcd, efgh, 0, 0, 0, 0, 0, 0 ] + const __m128i v1 = _mm_unpacklo_epi16(abcd, efgh); + + // v1a = v1 * 4 = [ abcd * 4, efgh * 4, 0, 0, 0, 0, 0, 0 ] + const __m128i v1a = _mm_slli_epi64(v1, 2); + + // v2 = [ abcd * 4, abcd * 4, abcd * 4, abcd * 4, efgh * 4, efgh * 4, efgh * 4, efgh * 4 ] + const __m128i v2a = _mm_unpacklo_epi16(v1a, v1a); + const __m128i v2 = _mm_unpacklo_epi32(v2a, v2a); + + // v4 = v2 div 10^3, 10^2, 10^1, 10^0 = [ a, ab, abc, abcd, e, ef, efg, efgh ] + const __m128i v3 = _mm_mulhi_epu16(v2, reinterpret_cast(kDivPowersVector)[0]); + const __m128i v4 = _mm_mulhi_epu16(v3, reinterpret_cast(kShiftPowersVector)[0]); + + // v5 = v4 * 10 = [ a0, ab0, abc0, abcd0, e0, ef0, efg0, efgh0 ] + const __m128i v5 = _mm_mullo_epi16(v4, reinterpret_cast(k10Vector)[0]); + + // v6 = v5 << 16 = [ 0, a0, ab0, abc0, 0, e0, ef0, efg0 ] + const __m128i v6 = _mm_slli_epi64(v5, 16); + + // v7 = v4 - v6 = { a, b, c, d, e, f, g, h } + const __m128i v7 = _mm_sub_epi16(v4, v6); + + return v7; +} + +inline __m128i ShiftDigits_SSE2(__m128i a, unsigned digit) { + assert(digit <= 8); + switch (digit) { + case 0: return a; + case 1: return _mm_srli_si128(a, 1); + case 2: return _mm_srli_si128(a, 2); + case 3: return _mm_srli_si128(a, 3); + case 4: return _mm_srli_si128(a, 4); + case 5: return _mm_srli_si128(a, 5); + case 6: return _mm_srli_si128(a, 6); + case 7: return _mm_srli_si128(a, 7); + case 8: return _mm_srli_si128(a, 8); + } + return a; // should not execute here. +} + +} // namespace + +// Original name: u32toa_sse2 +char *ToString(uint32_t value, char* buffer) { + if (value < 10000) { + const uint32_t d1 = (value / 100) << 1; + const uint32_t d2 = (value % 100) << 1; + + if (value >= 1000) + *buffer++ = gDigitsLut[d1]; + if (value >= 100) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 10) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + //*buffer++ = '\0'; + return buffer; + } + else if (value < 100000000) { + // Experiment shows that this case SSE2 is slower +#if 0 + const __m128i a = Convert8DigitsSSE2(value); + + // Convert to bytes, add '0' + const __m128i va = _mm_add_epi8(_mm_packus_epi16(a, _mm_setzero_si128()), reinterpret_cast(kAsciiZero)[0]); + + // Count number of digit + const unsigned mask = _mm_movemask_epi8(_mm_cmpeq_epi8(va, reinterpret_cast(kAsciiZero)[0])); + unsigned long digit; +#ifdef _MSC_VER + _BitScanForward(&digit, ~mask | 0x8000); +#else + digit = __builtin_ctz(~mask | 0x8000); +#endif + + // Shift digits to the beginning + __m128i result = ShiftDigits_SSE2(va, digit); + //__m128i result = _mm_srl_epi64(va, _mm_cvtsi32_si128(digit * 8)); + _mm_storel_epi64(reinterpret_cast<__m128i*>(buffer), result); + buffer[8 - digit] = '\0'; +#else + // value = bbbbcccc + const uint32_t b = value / 10000; + const uint32_t c = value % 10000; + + const uint32_t d1 = (b / 100) << 1; + const uint32_t d2 = (b % 100) << 1; + + const uint32_t d3 = (c / 100) << 1; + const uint32_t d4 = (c % 100) << 1; + + if (value >= 10000000) + *buffer++ = gDigitsLut[d1]; + if (value >= 1000000) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 100000) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + + *buffer++ = gDigitsLut[d3]; + *buffer++ = gDigitsLut[d3 + 1]; + *buffer++ = gDigitsLut[d4]; + *buffer++ = gDigitsLut[d4 + 1]; +// *buffer++ = '\0'; + return buffer; +#endif + } + else { + // value = aabbbbbbbb in decimal + + const uint32_t a = value / 100000000; // 1 to 42 + value %= 100000000; + + if (a >= 10) { + const unsigned i = a << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + } + else + *buffer++ = '0' + static_cast(a); + + const __m128i b = Convert8DigitsSSE2(value); + const __m128i ba = _mm_add_epi8(_mm_packus_epi16(_mm_setzero_si128(), b), reinterpret_cast(kAsciiZero)[0]); + const __m128i result = _mm_srli_si128(ba, 8); + _mm_storel_epi64(reinterpret_cast<__m128i*>(buffer), result); +// buffer[8] = '\0'; + return buffer + 8; + } +} + +// Original name: u64toa_sse2 +char *ToString(uint64_t value, char* buffer) { + if (value < 100000000) { + uint32_t v = static_cast(value); + if (v < 10000) { + const uint32_t d1 = (v / 100) << 1; + const uint32_t d2 = (v % 100) << 1; + + if (v >= 1000) + *buffer++ = gDigitsLut[d1]; + if (v >= 100) + *buffer++ = gDigitsLut[d1 + 1]; + if (v >= 10) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + //*buffer++ = '\0'; + return buffer; + } + else { + // Experiment shows that this case SSE2 is slower +#if 0 + const __m128i a = Convert8DigitsSSE2(v); + + // Convert to bytes, add '0' + const __m128i va = _mm_add_epi8(_mm_packus_epi16(a, _mm_setzero_si128()), reinterpret_cast(kAsciiZero)[0]); + + // Count number of digit + const unsigned mask = _mm_movemask_epi8(_mm_cmpeq_epi8(va, reinterpret_cast(kAsciiZero)[0])); + unsigned long digit; +#ifdef _MSC_VER + _BitScanForward(&digit, ~mask | 0x8000); +#else + digit = __builtin_ctz(~mask | 0x8000); +#endif + + // Shift digits to the beginning + __m128i result = ShiftDigits_SSE2(va, digit); + _mm_storel_epi64(reinterpret_cast<__m128i*>(buffer), result); + buffer[8 - digit] = '\0'; +#else + // value = bbbbcccc + const uint32_t b = v / 10000; + const uint32_t c = v % 10000; + + const uint32_t d1 = (b / 100) << 1; + const uint32_t d2 = (b % 100) << 1; + + const uint32_t d3 = (c / 100) << 1; + const uint32_t d4 = (c % 100) << 1; + + if (value >= 10000000) + *buffer++ = gDigitsLut[d1]; + if (value >= 1000000) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 100000) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + + *buffer++ = gDigitsLut[d3]; + *buffer++ = gDigitsLut[d3 + 1]; + *buffer++ = gDigitsLut[d4]; + *buffer++ = gDigitsLut[d4 + 1]; + //*buffer++ = '\0'; + return buffer; +#endif + } + } + else if (value < 10000000000000000) { + const uint32_t v0 = static_cast(value / 100000000); + const uint32_t v1 = static_cast(value % 100000000); + + const __m128i a0 = Convert8DigitsSSE2(v0); + const __m128i a1 = Convert8DigitsSSE2(v1); + + // Convert to bytes, add '0' + const __m128i va = _mm_add_epi8(_mm_packus_epi16(a0, a1), reinterpret_cast(kAsciiZero)[0]); + + // Count number of digit + const unsigned mask = _mm_movemask_epi8(_mm_cmpeq_epi8(va, reinterpret_cast(kAsciiZero)[0])); +#ifdef _MSC_VER + unsigned long digit; + _BitScanForward(&digit, ~mask | 0x8000); +#else + unsigned digit = __builtin_ctz(~mask | 0x8000); +#endif + + // Shift digits to the beginning + __m128i result = ShiftDigits_SSE2(va, digit); + _mm_storeu_si128(reinterpret_cast<__m128i*>(buffer), result); +// buffer[16 - digit] = '\0'; + return &buffer[16 - digit]; + } + else { + const uint32_t a = static_cast(value / 10000000000000000); // 1 to 1844 + value %= 10000000000000000; + + if (a < 10) + *buffer++ = '0' + static_cast(a); + else if (a < 100) { + const uint32_t i = a << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + } + else if (a < 1000) { + *buffer++ = '0' + static_cast(a / 100); + + const uint32_t i = (a % 100) << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + } + else { + const uint32_t i = (a / 100) << 1; + const uint32_t j = (a % 100) << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + *buffer++ = gDigitsLut[j]; + *buffer++ = gDigitsLut[j + 1]; + } + + const uint32_t v0 = static_cast(value / 100000000); + const uint32_t v1 = static_cast(value % 100000000); + + const __m128i a0 = Convert8DigitsSSE2(v0); + const __m128i a1 = Convert8DigitsSSE2(v1); + + // Convert to bytes, add '0' + const __m128i va = _mm_add_epi8(_mm_packus_epi16(a0, a1), reinterpret_cast(kAsciiZero)[0]); + _mm_storeu_si128(reinterpret_cast<__m128i*>(buffer), va); +// buffer[16] = '\0'; + return &buffer[16]; + } +} + +#else // Generic Non-x86 case + +// Orignal name: u32toa_branchlut +char *ToString(uint32_t value, char* buffer) { + if (value < 10000) { + const uint32_t d1 = (value / 100) << 1; + const uint32_t d2 = (value % 100) << 1; + + if (value >= 1000) + *buffer++ = gDigitsLut[d1]; + if (value >= 100) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 10) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + } + else if (value < 100000000) { + // value = bbbbcccc + const uint32_t b = value / 10000; + const uint32_t c = value % 10000; + + const uint32_t d1 = (b / 100) << 1; + const uint32_t d2 = (b % 100) << 1; + + const uint32_t d3 = (c / 100) << 1; + const uint32_t d4 = (c % 100) << 1; + + if (value >= 10000000) + *buffer++ = gDigitsLut[d1]; + if (value >= 1000000) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 100000) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + + *buffer++ = gDigitsLut[d3]; + *buffer++ = gDigitsLut[d3 + 1]; + *buffer++ = gDigitsLut[d4]; + *buffer++ = gDigitsLut[d4 + 1]; + } + else { + // value = aabbbbcccc in decimal + + const uint32_t a = value / 100000000; // 1 to 42 + value %= 100000000; + + if (a >= 10) { + const unsigned i = a << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + } + else + *buffer++ = '0' + static_cast(a); + + const uint32_t b = value / 10000; // 0 to 9999 + const uint32_t c = value % 10000; // 0 to 9999 + + const uint32_t d1 = (b / 100) << 1; + const uint32_t d2 = (b % 100) << 1; + + const uint32_t d3 = (c / 100) << 1; + const uint32_t d4 = (c % 100) << 1; + + *buffer++ = gDigitsLut[d1]; + *buffer++ = gDigitsLut[d1 + 1]; + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + *buffer++ = gDigitsLut[d3]; + *buffer++ = gDigitsLut[d3 + 1]; + *buffer++ = gDigitsLut[d4]; + *buffer++ = gDigitsLut[d4 + 1]; + } + return buffer; //*buffer++ = '\0'; +} + +// Original name: u64toa_branchlut +char *ToString(uint64_t value, char* buffer) { + if (value < 100000000) { + uint32_t v = static_cast(value); + if (v < 10000) { + const uint32_t d1 = (v / 100) << 1; + const uint32_t d2 = (v % 100) << 1; + + if (v >= 1000) + *buffer++ = gDigitsLut[d1]; + if (v >= 100) + *buffer++ = gDigitsLut[d1 + 1]; + if (v >= 10) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + } + else { + // value = bbbbcccc + const uint32_t b = v / 10000; + const uint32_t c = v % 10000; + + const uint32_t d1 = (b / 100) << 1; + const uint32_t d2 = (b % 100) << 1; + + const uint32_t d3 = (c / 100) << 1; + const uint32_t d4 = (c % 100) << 1; + + if (value >= 10000000) + *buffer++ = gDigitsLut[d1]; + if (value >= 1000000) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 100000) + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + + *buffer++ = gDigitsLut[d3]; + *buffer++ = gDigitsLut[d3 + 1]; + *buffer++ = gDigitsLut[d4]; + *buffer++ = gDigitsLut[d4 + 1]; + } + } + else if (value < 10000000000000000) { + const uint32_t v0 = static_cast(value / 100000000); + const uint32_t v1 = static_cast(value % 100000000); + + const uint32_t b0 = v0 / 10000; + const uint32_t c0 = v0 % 10000; + + const uint32_t d1 = (b0 / 100) << 1; + const uint32_t d2 = (b0 % 100) << 1; + + const uint32_t d3 = (c0 / 100) << 1; + const uint32_t d4 = (c0 % 100) << 1; + + const uint32_t b1 = v1 / 10000; + const uint32_t c1 = v1 % 10000; + + const uint32_t d5 = (b1 / 100) << 1; + const uint32_t d6 = (b1 % 100) << 1; + + const uint32_t d7 = (c1 / 100) << 1; + const uint32_t d8 = (c1 % 100) << 1; + + if (value >= 1000000000000000) + *buffer++ = gDigitsLut[d1]; + if (value >= 100000000000000) + *buffer++ = gDigitsLut[d1 + 1]; + if (value >= 10000000000000) + *buffer++ = gDigitsLut[d2]; + if (value >= 1000000000000) + *buffer++ = gDigitsLut[d2 + 1]; + if (value >= 100000000000) + *buffer++ = gDigitsLut[d3]; + if (value >= 10000000000) + *buffer++ = gDigitsLut[d3 + 1]; + if (value >= 1000000000) + *buffer++ = gDigitsLut[d4]; + if (value >= 100000000) + *buffer++ = gDigitsLut[d4 + 1]; + + *buffer++ = gDigitsLut[d5]; + *buffer++ = gDigitsLut[d5 + 1]; + *buffer++ = gDigitsLut[d6]; + *buffer++ = gDigitsLut[d6 + 1]; + *buffer++ = gDigitsLut[d7]; + *buffer++ = gDigitsLut[d7 + 1]; + *buffer++ = gDigitsLut[d8]; + *buffer++ = gDigitsLut[d8 + 1]; + } + else { + const uint32_t a = static_cast(value / 10000000000000000); // 1 to 1844 + value %= 10000000000000000; + + if (a < 10) + *buffer++ = '0' + static_cast(a); + else if (a < 100) { + const uint32_t i = a << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + } + else if (a < 1000) { + *buffer++ = '0' + static_cast(a / 100); + + const uint32_t i = (a % 100) << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + } + else { + const uint32_t i = (a / 100) << 1; + const uint32_t j = (a % 100) << 1; + *buffer++ = gDigitsLut[i]; + *buffer++ = gDigitsLut[i + 1]; + *buffer++ = gDigitsLut[j]; + *buffer++ = gDigitsLut[j + 1]; + } + + const uint32_t v0 = static_cast(value / 100000000); + const uint32_t v1 = static_cast(value % 100000000); + + const uint32_t b0 = v0 / 10000; + const uint32_t c0 = v0 % 10000; + + const uint32_t d1 = (b0 / 100) << 1; + const uint32_t d2 = (b0 % 100) << 1; + + const uint32_t d3 = (c0 / 100) << 1; + const uint32_t d4 = (c0 % 100) << 1; + + const uint32_t b1 = v1 / 10000; + const uint32_t c1 = v1 % 10000; + + const uint32_t d5 = (b1 / 100) << 1; + const uint32_t d6 = (b1 % 100) << 1; + + const uint32_t d7 = (c1 / 100) << 1; + const uint32_t d8 = (c1 % 100) << 1; + + *buffer++ = gDigitsLut[d1]; + *buffer++ = gDigitsLut[d1 + 1]; + *buffer++ = gDigitsLut[d2]; + *buffer++ = gDigitsLut[d2 + 1]; + *buffer++ = gDigitsLut[d3]; + *buffer++ = gDigitsLut[d3 + 1]; + *buffer++ = gDigitsLut[d4]; + *buffer++ = gDigitsLut[d4 + 1]; + *buffer++ = gDigitsLut[d5]; + *buffer++ = gDigitsLut[d5 + 1]; + *buffer++ = gDigitsLut[d6]; + *buffer++ = gDigitsLut[d6 + 1]; + *buffer++ = gDigitsLut[d7]; + *buffer++ = gDigitsLut[d7 + 1]; + *buffer++ = gDigitsLut[d8]; + *buffer++ = gDigitsLut[d8 + 1]; + } + return buffer; +} + +#endif // End of architecture if statement. + +// Signed wrappers. The negation is done on the unsigned version because +// doing so has defined behavior for INT_MIN. +char *ToString(int32_t value, char *to) { + uint32_t un = static_cast(value); + if (value < 0) { + *to++ = '-'; + un = -un; + } + return ToString(un, to); +} + +char *ToString(int64_t value, char *to) { + uint64_t un = static_cast(value); + if (value < 0) { + *to++ = '-'; + un = -un; + } + return ToString(un, to); +} + +// No optimization for this case yet. +char *ToString(int16_t value, char *to) { + return ToString((int32_t)value, to); +} +char *ToString(uint16_t value, char *to) { + return ToString((uint32_t)value, to); +} + +// void * to string. This hasn't been optimized at all really. +namespace { +const char kHexDigits[] = "0123456789abcdef"; +} // namespace + +char *ToString(const void *v, char *to) { + *to++ = '0'; + *to++ = 'x'; + + // Fun fact: gcc/clang boost::lexical_cast on Linux do just "0" while clang on OS X does "0x0" + // I happen to prefer 0x0. + if (!v) { + *to++ = '0'; + return to; + } + + uintptr_t value = reinterpret_cast(v); + uint8_t shift = sizeof(void*) * 8 - 4; + for (; !(value >> shift); shift -= 4) {} + for (; ; shift -= 4) { + *to++ = kHexDigits[(value >> shift) & 0xf]; + if (!shift) break; + } + return to; +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/integer_to_string.hh b/ctcdecode-master/third_party/kenlm/util/integer_to_string.hh new file mode 100644 index 0000000..9ac25bd --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/integer_to_string.hh @@ -0,0 +1,66 @@ +#ifndef UTIL_INTEGER_TO_STRING_H +#define UTIL_INTEGER_TO_STRING_H +#include +#include + +namespace util { + +/* These functions convert integers to strings and return the end pointer. + */ +char *ToString(uint32_t value, char *to); +char *ToString(uint64_t value, char *to); + +// Implemented as wrappers to above +char *ToString(int32_t value, char *to); +char *ToString(int64_t value, char *to); + +// Calls the 32-bit versions for now. +char *ToString(uint16_t value, char *to); +char *ToString(int16_t value, char *to); + +char *ToString(const void *value, char *to); + +inline char *ToString(bool value, char *to) { + *to++ = '0' + value; + return to; +} + +// How many bytes to reserve in the buffer for these strings: +// g++ 4.9.1 doesn't work with this: +// static const std::size_t kBytes = 5; +// So use enum. +template struct ToStringBuf; +template <> struct ToStringBuf { + enum { kBytes = 1 }; +}; +template <> struct ToStringBuf { + enum { kBytes = 5 }; +}; +template <> struct ToStringBuf { + enum { kBytes = 6 }; +}; +template <> struct ToStringBuf { + enum { kBytes = 10 }; +}; +template <> struct ToStringBuf { + enum { kBytes = 11 }; +}; +template <> struct ToStringBuf { + enum { kBytes = 20 }; +}; +template <> struct ToStringBuf { + // Not a typo. 2^63 has 19 digits. + enum { kBytes = 20 }; +}; + +template <> struct ToStringBuf { + // Either 18 on 64-bit or 10 on 32-bit. + enum { kBytes = sizeof(const void*) * 2 + 2 }; +}; + +// Maximum over this and float. +enum { kToStringMaxBytes = 20 }; + +} // namespace util + +#endif // UTIL_INTEGER_TO_STRING_H diff --git a/ctcdecode-master/third_party/kenlm/util/integer_to_string_test.cc b/ctcdecode-master/third_party/kenlm/util/integer_to_string_test.cc new file mode 100644 index 0000000..136c88f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/integer_to_string_test.cc @@ -0,0 +1,81 @@ +#define BOOST_LEXICAL_CAST_ASSUME_C_LOCALE +#include "util/integer_to_string.hh" +#include "util/string_piece.hh" + +#define BOOST_TEST_MODULE IntegerToStringTest +#include +#include + +#include + +namespace util { +namespace { + +template void TestValue(const T value) { + char buf[ToStringBuf::kBytes]; + StringPiece result(buf, ToString(value, buf) - buf); + BOOST_REQUIRE_GE(static_cast(ToStringBuf::kBytes), result.size()); + if (value) { + BOOST_CHECK_EQUAL(boost::lexical_cast(value), result); + } else { + // Platforms can do void * as 0x0 or 0. + BOOST_CHECK(result == "0x0" || result == "0"); + } +} + +template void TestCorners() { + TestValue(std::numeric_limits::min()); + TestValue(std::numeric_limits::max()); + TestValue((T)0); + TestValue((T)-1); + TestValue((T)1); +} + +BOOST_AUTO_TEST_CASE(Corners) { + TestCorners(); + TestCorners(); + TestCorners(); + TestCorners(); + TestCorners(); + TestCorners(); + TestCorners(); +} + +template void TestAll() { + for (T i = std::numeric_limits::min(); i < std::numeric_limits::max(); ++i) { + TestValue(i); + } + TestValue(std::numeric_limits::max()); +} + +BOOST_AUTO_TEST_CASE(Short) { + TestAll(); + TestAll(); +} + +template void Test10s() { + for (T i = 1; i < std::numeric_limits::max() / 10; i *= 10) { + TestValue(i); + TestValue(i - 1); + TestValue(i + 1); + } +} + +BOOST_AUTO_TEST_CASE(Tens) { + Test10s(); + Test10s(); + Test10s(); + Test10s(); +} + +BOOST_AUTO_TEST_CASE(Pointers) { + for (uintptr_t i = 1; i < std::numeric_limits::max() / 10; i *= 10) { + TestValue((const void*)i); + } + for (uintptr_t i = 0; i < 256; ++i) { + TestValue((const void*)i); + TestValue((const void*)(i + 0xf00)); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/joint_sort.hh b/ctcdecode-master/third_party/kenlm/util/joint_sort.hh new file mode 100644 index 0000000..de4b554 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/joint_sort.hh @@ -0,0 +1,146 @@ +#ifndef UTIL_JOINT_SORT_H +#define UTIL_JOINT_SORT_H + +/* A terrifying amount of C++ to coax std::sort into soring one range while + * also permuting another range the same way. + */ + +#include "util/proxy_iterator.hh" + +#include +#include + +namespace util { + +namespace detail { + +template class JointProxy; + +template class JointIter { + public: + JointIter() {} + + JointIter(const KeyIter &key_iter, const ValueIter &value_iter) : key_(key_iter), value_(value_iter) {} + + bool operator==(const JointIter &other) const { return key_ == other.key_; } + + bool operator<(const JointIter &other) const { return (key_ < other.key_); } + + std::ptrdiff_t operator-(const JointIter &other) const { return key_ - other.key_; } + + JointIter &operator+=(std::ptrdiff_t amount) { + key_ += amount; + value_ += amount; + return *this; + } + + friend void swap(JointIter &first, JointIter &second) { + using std::swap; + swap(first.key_, second.key_); + swap(first.value_, second.value_); + } + + void DeepSwap(JointIter &other) { + using std::swap; + swap(*key_, *other.key_); + swap(*value_, *other.value_); + } + + private: + friend class JointProxy; + KeyIter key_; + ValueIter value_; +}; + +template class JointProxy { + private: + typedef JointIter InnerIterator; + + public: + typedef struct { + typename std::iterator_traits::value_type key; + typename std::iterator_traits::value_type value; + const typename std::iterator_traits::value_type &GetKey() const { return key; } + } value_type; + + JointProxy(const KeyIter &key_iter, const ValueIter &value_iter) : inner_(key_iter, value_iter) {} + JointProxy(const JointProxy &other) : inner_(other.inner_) {} + + operator value_type() const { + value_type ret; + ret.key = *inner_.key_; + ret.value = *inner_.value_; + return ret; + } + + JointProxy &operator=(const JointProxy &other) { + *inner_.key_ = *other.inner_.key_; + *inner_.value_ = *other.inner_.value_; + return *this; + } + + JointProxy &operator=(const value_type &other) { + *inner_.key_ = other.key; + *inner_.value_ = other.value; + return *this; + } + + typename std::iterator_traits::reference GetKey() const { + return *(inner_.key_); + } + + friend void swap(JointProxy first, JointProxy second) { + first.Inner().DeepSwap(second.Inner()); + } + + private: + friend class ProxyIterator >; + + InnerIterator &Inner() { return inner_; } + const InnerIterator &Inner() const { return inner_; } + InnerIterator inner_; +}; + +template class LessWrapper : public std::binary_function { + public: + explicit LessWrapper(const Less &less) : less_(less) {} + + bool operator()(const Proxy &left, const Proxy &right) const { + return less_(left.GetKey(), right.GetKey()); + } + bool operator()(const Proxy &left, const typename Proxy::value_type &right) const { + return less_(left.GetKey(), right.GetKey()); + } + bool operator()(const typename Proxy::value_type &left, const Proxy &right) const { + return less_(left.GetKey(), right.GetKey()); + } + bool operator()(const typename Proxy::value_type &left, const typename Proxy::value_type &right) const { + return less_(left.GetKey(), right.GetKey()); + } + + private: + const Less less_; +}; + +} // namespace detail + +template class PairedIterator : public ProxyIterator > { + public: + PairedIterator(const KeyIter &key, const ValueIter &value) : + ProxyIterator >(detail::JointProxy(key, value)) {} +}; + +template void JointSort(const KeyIter &key_begin, const KeyIter &key_end, const ValueIter &value_begin, const Less &less) { + ProxyIterator > full_begin(detail::JointProxy(key_begin, value_begin)); + detail::LessWrapper, Less> less_wrap(less); + std::sort(full_begin, full_begin + (key_end - key_begin), less_wrap); +} + + +template void JointSort(const KeyIter &key_begin, const KeyIter &key_end, const ValueIter &value_begin) { + JointSort(key_begin, key_end, value_begin, std::less::value_type>()); +} + +} // namespace util + +#endif // UTIL_JOINT_SORT_H diff --git a/ctcdecode-master/third_party/kenlm/util/joint_sort_test.cc b/ctcdecode-master/third_party/kenlm/util/joint_sort_test.cc new file mode 100644 index 0000000..b24c602 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/joint_sort_test.cc @@ -0,0 +1,62 @@ +#include "util/joint_sort.hh" + +#define BOOST_TEST_MODULE JointSortTest +#include + +namespace util { namespace { + +BOOST_AUTO_TEST_CASE(just_flip) { + char keys[2]; + int values[2]; + keys[0] = 1; values[0] = 327; + keys[1] = 0; values[1] = 87897; + JointSort(keys + 0, keys + 2, values + 0); + BOOST_CHECK_EQUAL(0, keys[0]); + BOOST_CHECK_EQUAL(87897, values[0]); + BOOST_CHECK_EQUAL(1, keys[1]); + BOOST_CHECK_EQUAL(327, values[1]); +} + +BOOST_AUTO_TEST_CASE(three) { + char keys[3]; + int values[3]; + keys[0] = 1; values[0] = 327; + keys[1] = 2; values[1] = 87897; + keys[2] = 0; values[2] = 10; + JointSort(keys + 0, keys + 3, values + 0); + BOOST_CHECK_EQUAL(0, keys[0]); + BOOST_CHECK_EQUAL(1, keys[1]); + BOOST_CHECK_EQUAL(2, keys[2]); +} + +BOOST_AUTO_TEST_CASE(char_int) { + char keys[4]; + int values[4]; + keys[0] = 3; values[0] = 327; + keys[1] = 1; values[1] = 87897; + keys[2] = 2; values[2] = 10; + keys[3] = 0; values[3] = 24347; + JointSort(keys + 0, keys + 4, values + 0); + BOOST_CHECK_EQUAL(0, keys[0]); + BOOST_CHECK_EQUAL(24347, values[0]); + BOOST_CHECK_EQUAL(1, keys[1]); + BOOST_CHECK_EQUAL(87897, values[1]); + BOOST_CHECK_EQUAL(2, keys[2]); + BOOST_CHECK_EQUAL(10, values[2]); + BOOST_CHECK_EQUAL(3, keys[3]); + BOOST_CHECK_EQUAL(327, values[3]); +} + +BOOST_AUTO_TEST_CASE(swap_proxy) { + char keys[2] = {0, 1}; + int values[2] = {2, 3}; + detail::JointProxy first(keys, values); + detail::JointProxy second(keys + 1, values + 1); + swap(first, second); + BOOST_CHECK_EQUAL(1, keys[0]); + BOOST_CHECK_EQUAL(0, keys[1]); + BOOST_CHECK_EQUAL(3, values[0]); + BOOST_CHECK_EQUAL(2, values[1]); +} + +}} // namespace anonymous util diff --git a/ctcdecode-master/third_party/kenlm/util/mmap.cc b/ctcdecode-master/third_party/kenlm/util/mmap.cc new file mode 100644 index 0000000..4da5a97 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/mmap.cc @@ -0,0 +1,404 @@ +/* Memory mapping wrappers. + * ARM and MinGW ports contributed by Hideo Okuma and Tomoyuki Yoshimura at + * NICT. + */ +#include "util/mmap.hh" + +#include "util/exception.hh" +#include "util/file.hh" +#include "util/parallel_read.hh" +#include "util/scoped.hh" + +#include + +#include +#include +#include +#include +#include + +#if defined(_WIN32) || defined(_WIN64) +#include +#include +#else +#include +#include +#endif + +namespace util { + +std::size_t SizePage() { +#if defined(_WIN32) || defined(_WIN64) + SYSTEM_INFO si; + GetSystemInfo(&si); + return si.dwAllocationGranularity; +#else + return sysconf(_SC_PAGE_SIZE); +#endif +} + +scoped_mmap::~scoped_mmap() { + if (data_ != (void*)-1) { + try { + // Thanks Denis Filimonov for pointing out NFS likes msync first. + SyncOrThrow(data_, size_); + UnmapOrThrow(data_, size_); + } catch (const util::ErrnoException &e) { + std::cerr << e.what(); + abort(); + } + } +} + +namespace { +template T RoundUpPow2(T value, T mult) { + return ((value - 1) & ~(mult - 1)) + mult; +} +} // namespace + +scoped_memory::scoped_memory(std::size_t size, bool zeroed) : data_(NULL), size_(0), source_(NONE_ALLOCATED) { + HugeMalloc(size, zeroed, *this); +} + +void scoped_memory::reset(void *data, std::size_t size, Alloc source) { + switch(source_) { + case MMAP_ROUND_UP_ALLOCATED: + scoped_mmap(data_, RoundUpPow2(size_, (std::size_t)SizePage())); + break; + case MMAP_ALLOCATED: + scoped_mmap(data_, size_); + break; + case MALLOC_ALLOCATED: + free(data_); + break; + case NONE_ALLOCATED: + break; + } + data_ = data; + size_ = size; + source_ = source; +} + +/*void scoped_memory::call_realloc(std::size_t size) { + assert(source_ == MALLOC_ALLOCATED || source_ == NONE_ALLOCATED); + void *new_data = realloc(data_, size); + if (!new_data) { + reset(); + } else { + data_ = new_data; + size_ = size; + source_ = MALLOC_ALLOCATED; + } +}*/ + +const int kFileFlags = +#if defined(_WIN32) || defined(_WIN64) + 0 // MapOrThrow ignores flags on windows +#elif defined(MAP_FILE) + MAP_FILE | MAP_SHARED +#else + MAP_SHARED +#endif + ; + +void *MapOrThrow(std::size_t size, bool for_write, int flags, bool prefault, int fd, uint64_t offset) { +#ifdef MAP_POPULATE // Linux specific + if (prefault) { + flags |= MAP_POPULATE; + } +#endif +#if defined(_WIN32) || defined(_WIN64) + int protectC = for_write ? PAGE_READWRITE : PAGE_READONLY; + int protectM = for_write ? FILE_MAP_WRITE : FILE_MAP_READ; + uint64_t total_size = size + offset; + HANDLE hMapping = CreateFileMapping((HANDLE)_get_osfhandle(fd), NULL, protectC, total_size >> 32, static_cast(total_size), NULL); + UTIL_THROW_IF(!hMapping, ErrnoException, "CreateFileMapping failed"); + LPVOID ret = MapViewOfFile(hMapping, protectM, offset >> 32, offset, size); + CloseHandle(hMapping); + UTIL_THROW_IF(!ret, ErrnoException, "MapViewOfFile failed"); +#else + int protect = for_write ? (PROT_READ | PROT_WRITE) : PROT_READ; + void *ret; + UTIL_THROW_IF((ret = mmap(NULL, size, protect, flags, fd, offset)) == MAP_FAILED, ErrnoException, "mmap failed for size " << size << " at offset " << offset); +# ifdef MADV_HUGEPAGE + /* We like huge pages but it's fine if we can't have them. Note that huge + * pages are not supported for file-backed mmap on linux. + */ + madvise(ret, size, MADV_HUGEPAGE); +# endif +#endif + return ret; +} + +void SyncOrThrow(void *start, size_t length) { +#if defined(_WIN32) || defined(_WIN64) + UTIL_THROW_IF(!::FlushViewOfFile(start, length), ErrnoException, "Failed to sync mmap"); +#else + UTIL_THROW_IF(length && msync(start, length, MS_SYNC), ErrnoException, "Failed to sync mmap"); +#endif +} + +void UnmapOrThrow(void *start, size_t length) { +#if defined(_WIN32) || defined(_WIN64) + UTIL_THROW_IF(!::UnmapViewOfFile(start), ErrnoException, "Failed to unmap a file"); +#else + UTIL_THROW_IF(munmap(start, length), ErrnoException, "munmap failed"); +#endif +} + +// Linux huge pages. +#ifdef __linux__ + +namespace { + +bool AnonymousMap(std::size_t size, int flags, bool populate, util::scoped_memory &to) { + if (populate) flags |= MAP_POPULATE; + void *ret = mmap(NULL, size, PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANONYMOUS | flags, -1, 0); + if (ret == MAP_FAILED) return false; + to.reset(ret, size, scoped_memory::MMAP_ALLOCATED); + return true; +} + +bool TryHuge(std::size_t size, uint8_t alignment_bits, bool populate, util::scoped_memory &to) { + // Don't bother with these cases. + if (size < (1ULL << alignment_bits) || (1ULL << alignment_bits) < SizePage()) + return false; + + // First try: Linux >= 3.8 with manually configured hugetlb pages available. +#ifdef MAP_HUGE_SHIFT + if (AnonymousMap(size, MAP_HUGETLB | (alignment_bits << MAP_HUGE_SHIFT), populate, to)) + return true; +#endif + + // Second try: manually configured hugetlb pages exist, but kernel too old to + // pick size or not available. This might pick the wrong size huge pages, + // but the sysadmin must have made them available in the first place. + if (AnonymousMap(size, MAP_HUGETLB, populate, to)) + return true; + + // Third try: align to a multiple of the huge page size by overallocating. + // I feel bad about doing this, but it's also how posix_memalign is + // implemented. And the memory is virtual. + + // Round up requested size to multiple of page size. This will allow the pages after to be munmapped. + std::size_t size_up = RoundUpPow2(size, SizePage()); + + std::size_t ask = size_up + (1 << alignment_bits) - SizePage(); + // Don't populate because this is asking for more than we will use. + scoped_mmap larger(mmap(NULL, ask, PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANONYMOUS, -1, 0), ask); + if (larger.get() == MAP_FAILED) return false; + + // Throw out pages before the alignment point. + uintptr_t base = reinterpret_cast(larger.get()); + // Round up to next multiple of alignment. + uintptr_t rounded_up = RoundUpPow2(base, static_cast(1) << alignment_bits); + if (base != rounded_up) { + // If this throws an exception (which it shouldn't) then we want to unmap the whole thing by keeping it in larger. + UnmapOrThrow(larger.get(), rounded_up - base); + larger.steal(); + larger.reset(reinterpret_cast(rounded_up), ask - (rounded_up - base)); + } + + // Throw out pages after the requested size. + assert(larger.size() >= size_up); + if (larger.size() > size_up) { + // This is where we assume size_up is a multiple of page size. + UnmapOrThrow(static_cast(larger.get()) + size_up, larger.size() - size_up); + larger.reset(larger.steal(), size_up); + } +#ifdef MADV_HUGEPAGE + madvise(larger.get(), size_up, MADV_HUGEPAGE); +#endif + to.reset(larger.steal(), size, scoped_memory::MMAP_ROUND_UP_ALLOCATED); + return true; +} + +} // namespace + +#endif + +void HugeMalloc(std::size_t size, bool zeroed, scoped_memory &to) { + to.reset(); +#ifdef __linux__ + // TODO: architectures/page sizes other than 2^21 and 2^30. + // Attempt 1 GB pages. + // If the user asked for zeroed memory, assume they want it populated. + if (size >= (1ULL << 30) && TryHuge(size, 30, zeroed, to)) + return; + // Attempt 2 MB pages. + if (size >= (1ULL << 21) && TryHuge(size, 21, zeroed, to)) + return; +#endif // __linux__ + // Non-linux will always do this, as will small allocations on Linux. + to.reset(zeroed ? calloc(1, size) : malloc(size), size, scoped_memory::MALLOC_ALLOCATED); + UTIL_THROW_IF(!to.get(), ErrnoException, "Failed to allocate " << size << " bytes"); +} + +#ifdef __linux__ +const std::size_t kTransitionHuge = std::max(1ULL << 21, SizePage()); +#endif // __linux__ + +void HugeRealloc(std::size_t to, bool zero_new, scoped_memory &mem) { + if (!to) { + mem.reset(); + return; + } +#ifdef __linux__ + std::size_t from_size = mem.size(); +#endif // __linux__ + switch (mem.source()) { + case scoped_memory::NONE_ALLOCATED: + HugeMalloc(to, zero_new, mem); + return; +#ifdef __linux__ + case scoped_memory::MMAP_ROUND_UP_ALLOCATED: + // for mremap's benefit. + from_size = RoundUpPow2(from_size, SizePage()); + case scoped_memory::MMAP_ALLOCATED: + // Downsizing below barrier? + if (to <= SizePage()) { + scoped_malloc replacement(malloc(to)); + memcpy(replacement.get(), mem.get(), std::min(to, mem.size())); + if (zero_new && to > mem.size()) + memset(static_cast(replacement.get()) + mem.size(), 0, to - mem.size()); + mem.reset(replacement.release(), to, scoped_memory::MALLOC_ALLOCATED); + } else { + void *new_addr = mremap(mem.get(), from_size, to, MREMAP_MAYMOVE); + UTIL_THROW_IF(!new_addr, ErrnoException, "Failed to mremap from " << from_size << " to " << to); + mem.steal(); + mem.reset(new_addr, to, scoped_memory::MMAP_ALLOCATED); + } + return; +#endif // __linux__ + case scoped_memory::MALLOC_ALLOCATED: +#ifdef __linux__ + // Transition larger allocations to huge pages, but don't keep trying if we're still malloc allocated. + if (to >= kTransitionHuge && mem.size() < kTransitionHuge) { + scoped_memory replacement; + HugeMalloc(to, zero_new, replacement); + memcpy(replacement.get(), mem.get(), mem.size()); + // This can't throw. + mem.reset(replacement.get(), replacement.size(), replacement.source()); + replacement.steal(); + return; + } +#endif // __linux__ + { + void *new_addr = std::realloc(mem.get(), to); + UTIL_THROW_IF(!new_addr, ErrnoException, "realloc to " << to << " bytes failed."); + if (zero_new && to > mem.size()) + memset(static_cast(new_addr) + mem.size(), 0, to - mem.size()); + mem.steal(); + mem.reset(new_addr, to, scoped_memory::MALLOC_ALLOCATED); + } + return; + default: + UTIL_THROW(Exception, "HugeRealloc called with type " << mem.source()); + } +} + +void MapRead(LoadMethod method, int fd, uint64_t offset, std::size_t size, scoped_memory &out) { + switch (method) { + case LAZY: + out.reset(MapOrThrow(size, false, kFileFlags, false, fd, offset), size, scoped_memory::MMAP_ALLOCATED); + break; + case POPULATE_OR_LAZY: +#ifdef MAP_POPULATE + case POPULATE_OR_READ: +#endif + out.reset(MapOrThrow(size, false, kFileFlags, true, fd, offset), size, scoped_memory::MMAP_ALLOCATED); + break; +#ifndef MAP_POPULATE + case POPULATE_OR_READ: +#endif + case READ: + HugeMalloc(size, false, out); + SeekOrThrow(fd, offset); + ReadOrThrow(fd, out.get(), size); + break; + case PARALLEL_READ: + HugeMalloc(size, false, out); + ParallelRead(fd, out.get(), size, offset); + break; + } +} + +void *MapZeroedWrite(int fd, std::size_t size) { + ResizeOrThrow(fd, 0); + ResizeOrThrow(fd, size); + return MapOrThrow(size, true, kFileFlags, false, fd, 0); +} + +void *MapZeroedWrite(const char *name, std::size_t size, scoped_fd &file) { + file.reset(CreateOrThrow(name)); + try { + return MapZeroedWrite(file.get(), size); + } catch (ErrnoException &e) { + e << " in file " << name; + throw; + } +} + +Rolling::Rolling(const Rolling ©_from, uint64_t increase) { + *this = copy_from; + IncreaseBase(increase); +} + +Rolling &Rolling::operator=(const Rolling ©_from) { + fd_ = copy_from.fd_; + file_begin_ = copy_from.file_begin_; + file_end_ = copy_from.file_end_; + for_write_ = copy_from.for_write_; + block_ = copy_from.block_; + read_bound_ = copy_from.read_bound_; + + current_begin_ = 0; + if (copy_from.IsPassthrough()) { + current_end_ = copy_from.current_end_; + ptr_ = copy_from.ptr_; + } else { + // Force call on next mmap. + current_end_ = 0; + ptr_ = NULL; + } + return *this; +} + +Rolling::Rolling(int fd, bool for_write, std::size_t block, std::size_t read_bound, uint64_t offset, uint64_t amount) { + current_begin_ = 0; + current_end_ = 0; + fd_ = fd; + file_begin_ = offset; + file_end_ = offset + amount; + for_write_ = for_write; + block_ = block; + read_bound_ = read_bound; +} + +void *Rolling::ExtractNonRolling(scoped_memory &out, uint64_t index, std::size_t size) { + out.reset(); + if (IsPassthrough()) return static_cast(get()) + index; + uint64_t offset = index + file_begin_; + // Round down to multiple of page size. + uint64_t cruft = offset % static_cast(SizePage()); + std::size_t map_size = static_cast(size + cruft); + out.reset(MapOrThrow(map_size, for_write_, kFileFlags, true, fd_, offset - cruft), map_size, scoped_memory::MMAP_ALLOCATED); + return static_cast(out.get()) + static_cast(cruft); +} + +void Rolling::Roll(uint64_t index) { + assert(!IsPassthrough()); + std::size_t amount; + if (file_end_ - (index + file_begin_) > static_cast(block_)) { + amount = block_; + current_end_ = index + amount - read_bound_; + } else { + amount = file_end_ - (index + file_begin_); + current_end_ = index + amount; + } + ptr_ = static_cast(ExtractNonRolling(mem_, index, amount)) - index; + + current_begin_ = index; +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/mmap.hh b/ctcdecode-master/third_party/kenlm/util/mmap.hh new file mode 100644 index 0000000..21bca1d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/mmap.hh @@ -0,0 +1,236 @@ +#ifndef UTIL_MMAP_H +#define UTIL_MMAP_H +// Utilities for mmaped files. + +#include +#include + +#include +#include + +namespace util { + +class scoped_fd; + +std::size_t SizePage(); + +// (void*)-1 is MAP_FAILED; this is done to avoid including the mmap header here. +class scoped_mmap { + public: + scoped_mmap() : data_((void*)-1), size_(0) {} + scoped_mmap(void *data, std::size_t size) : data_(data), size_(size) {} + ~scoped_mmap(); + + void *get() const { return data_; } + + const char *begin() const { return reinterpret_cast(data_); } + char *begin() { return reinterpret_cast(data_); } + const char *end() const { return reinterpret_cast(data_) + size_; } + char *end() { return reinterpret_cast(data_) + size_; } + std::size_t size() const { return size_; } + + void reset(void *data, std::size_t size) { + scoped_mmap other(data_, size_); + data_ = data; + size_ = size; + } + + void reset() { + reset((void*)-1, 0); + } + + void *steal() { + void *ret = data_; + data_ = (void*)-1; + size_ = 0; + return ret; + } + + private: + void *data_; + std::size_t size_; + + scoped_mmap(const scoped_mmap &); + scoped_mmap &operator=(const scoped_mmap &); +}; + +/* For when the memory might come from mmap or malloc. Uses NULL and 0 for + * blanks even though mmap signals errors with (void*)-1). + */ +class scoped_memory { + public: + typedef enum { + MMAP_ROUND_UP_ALLOCATED, // The size was rounded up to a multiple of page size. Do the same before munmap. + MMAP_ALLOCATED, // munmap + MALLOC_ALLOCATED, // free + NONE_ALLOCATED // nothing to free (though there can be something here if it's owned by somebody else). + } Alloc; + + scoped_memory(void *data, std::size_t size, Alloc source) + : data_(data), size_(size), source_(source) {} + + scoped_memory() : data_(NULL), size_(0), source_(NONE_ALLOCATED) {} + + // Calls HugeMalloc + scoped_memory(std::size_t to, bool zero_new); + +#if __cplusplus >= 201103L + scoped_memory(scoped_memory &&from) noexcept + : data_(from.data_), size_(from.size_), source_(from.source_) { + from.steal(); + } +#endif + + ~scoped_memory() { reset(); } + + void *get() const { return data_; } + + const char *begin() const { return reinterpret_cast(data_); } + char *begin() { return reinterpret_cast(data_); } + const char *end() const { return reinterpret_cast(data_) + size_; } + char *end() { return reinterpret_cast(data_) + size_; } + std::size_t size() const { return size_; } + + Alloc source() const { return source_; } + + void reset() { reset(NULL, 0, NONE_ALLOCATED); } + + void reset(void *data, std::size_t size, Alloc from); + + void *steal() { + void *ret = data_; + data_ = NULL; + size_ = 0; + source_ = NONE_ALLOCATED; + return ret; + } + + private: + void *data_; + std::size_t size_; + + Alloc source_; + + scoped_memory(const scoped_memory &); + scoped_memory &operator=(const scoped_memory &); +}; + +extern const int kFileFlags; + +// Cross-platform, error-checking wrapper for mmap(). +void *MapOrThrow(std::size_t size, bool for_write, int flags, bool prefault, int fd, uint64_t offset = 0); + +// msync wrapper +void SyncOrThrow(void *start, size_t length); + +// Cross-platform, error-checking wrapper for munmap(). +void UnmapOrThrow(void *start, size_t length); + +// Allocate memory, promising that all/vast majority of it will be used. Tries +// hard to use huge pages on Linux. +// If you want zeroed memory, pass zeroed = true. +void HugeMalloc(std::size_t size, bool zeroed, scoped_memory &to); + +// Reallocates memory ala realloc but with option to zero the new memory. +// On Linux, the memory can come from anonymous mmap or malloc/calloc. +// On non-Linux, only malloc/calloc is supported. +// +// To summarize, any memory from HugeMalloc or HugeRealloc can be resized with +// this. +void HugeRealloc(std::size_t size, bool new_zeroed, scoped_memory &mem); + +enum LoadMethod { + // mmap with no prepopulate + LAZY, + // On linux, pass MAP_POPULATE to mmap. + POPULATE_OR_LAZY, + // Populate on Linux. malloc and read on non-Linux. + POPULATE_OR_READ, + // malloc and read. + READ, + // malloc and read in parallel (recommended for Lustre) + PARALLEL_READ, +}; + +void MapRead(LoadMethod method, int fd, uint64_t offset, std::size_t size, scoped_memory &out); + +// Open file name with mmap of size bytes, all of which are initially zero. +void *MapZeroedWrite(int fd, std::size_t size); +void *MapZeroedWrite(const char *name, std::size_t size, scoped_fd &file); + +// Forward rolling memory map with no overlap. +class Rolling { + public: + Rolling() {} + + explicit Rolling(void *data) { Init(data); } + + Rolling(const Rolling ©_from, uint64_t increase = 0); + Rolling &operator=(const Rolling ©_from); + + // For an actual rolling mmap. + explicit Rolling(int fd, bool for_write, std::size_t block, std::size_t read_bound, uint64_t offset, uint64_t amount); + + // For a static mapping + void Init(void *data) { + ptr_ = data; + current_end_ = std::numeric_limits::max(); + current_begin_ = 0; + // Mark as a pass-through. + fd_ = -1; + } + + void IncreaseBase(uint64_t by) { + file_begin_ += by; + ptr_ = static_cast(ptr_) + by; + if (!IsPassthrough()) current_end_ = 0; + } + + void DecreaseBase(uint64_t by) { + file_begin_ -= by; + ptr_ = static_cast(ptr_) - by; + if (!IsPassthrough()) current_end_ = 0; + } + + void *ExtractNonRolling(scoped_memory &out, uint64_t index, std::size_t size); + + // Returns base pointer + void *get() const { return ptr_; } + + // Returns base pointer. + void *CheckedBase(uint64_t index) { + if (index >= current_end_ || index < current_begin_) { + Roll(index); + } + return ptr_; + } + + // Returns indexed pointer. + void *CheckedIndex(uint64_t index) { + return static_cast(CheckedBase(index)) + index; + } + + private: + void Roll(uint64_t index); + + // True if this is just a thin wrapper on a pointer. + bool IsPassthrough() const { return fd_ == -1; } + + void *ptr_; + uint64_t current_begin_; + uint64_t current_end_; + + scoped_memory mem_; + + int fd_; + uint64_t file_begin_; + uint64_t file_end_; + + bool for_write_; + std::size_t block_; + std::size_t read_bound_; +}; + +} // namespace util + +#endif // UTIL_MMAP_H diff --git a/ctcdecode-master/third_party/kenlm/util/multi_intersection.hh b/ctcdecode-master/third_party/kenlm/util/multi_intersection.hh new file mode 100644 index 0000000..7395460 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/multi_intersection.hh @@ -0,0 +1,80 @@ +#ifndef UTIL_MULTI_INTERSECTION_H +#define UTIL_MULTI_INTERSECTION_H + +#include +#include + +#include +#include +#include + +namespace util { + +namespace detail { +template struct RangeLessBySize : public std::binary_function { + bool operator()(const Range &left, const Range &right) const { + return left.size() < right.size(); + } +}; + +/* Takes sets specified by their iterators and a boost::optional containing + * the lowest intersection if any. Each set must be sorted in increasing + * order. sets is changed to truncate the beginning of each sequence to the + * location of the match or an empty set. Precondition: sets is not empty + * since the intersection over null is the universe and this function does not + * know the universe. + */ +template boost::optional::value_type> FirstIntersectionSorted(std::vector > &sets, const Less &less = std::less::value_type>()) { + typedef std::vector > Sets; + typedef typename std::iterator_traits::value_type Value; + + assert(!sets.empty()); + + if (sets.front().empty()) return boost::optional(); + // Possibly suboptimal to copy for general Value; makes unsigned int go slightly faster. + Value highest(sets.front().front()); + for (typename Sets::iterator i(sets.begin()); i != sets.end(); ) { + i->advance_begin(std::lower_bound(i->begin(), i->end(), highest, less) - i->begin()); + if (i->empty()) return boost::optional(); + if (less(highest, i->front())) { + highest = i->front(); + // start over + i = sets.begin(); + } else { + ++i; + } + } + return boost::optional(highest); +} + +} // namespace detail + +template boost::optional::value_type> FirstIntersection(std::vector > &sets, const Less less) { + assert(!sets.empty()); + + std::sort(sets.begin(), sets.end(), detail::RangeLessBySize >()); + return detail::FirstIntersectionSorted(sets, less); +} + +template boost::optional::value_type> FirstIntersection(std::vector > &sets) { + return FirstIntersection(sets, std::less::value_type>()); +} + +template void AllIntersection(std::vector > &sets, Output &out, const Less less) { + typedef typename std::iterator_traits::value_type Value; + assert(!sets.empty()); + + std::sort(sets.begin(), sets.end(), detail::RangeLessBySize >()); + boost::optional ret; + for (boost::optional ret; (ret = detail::FirstIntersectionSorted(sets, less)); sets.front().advance_begin(1)) { + out(*ret); + } +} + +template void AllIntersection(std::vector > &sets, Output &out) { + AllIntersection(sets, out, std::less::value_type>()); +} + +} // namespace util + +#endif // UTIL_MULTI_INTERSECTION_H diff --git a/ctcdecode-master/third_party/kenlm/util/multi_intersection_test.cc b/ctcdecode-master/third_party/kenlm/util/multi_intersection_test.cc new file mode 100644 index 0000000..ee5af7d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/multi_intersection_test.cc @@ -0,0 +1,63 @@ +#include "util/multi_intersection.hh" + +#define BOOST_TEST_MODULE MultiIntersectionTest +#include + +namespace util { +namespace { + +BOOST_AUTO_TEST_CASE(Empty) { + std::vector > sets; + + sets.push_back(boost::iterator_range(static_cast(NULL), static_cast(NULL))); + BOOST_CHECK(!FirstIntersection(sets)); +} + +BOOST_AUTO_TEST_CASE(Single) { + std::vector nums; + nums.push_back(1); + nums.push_back(4); + nums.push_back(100); + std::vector::const_iterator> > sets; + sets.push_back(nums); + + boost::optional ret(FirstIntersection(sets)); + + BOOST_REQUIRE(ret); + BOOST_CHECK_EQUAL(static_cast(1), *ret); +} + +template boost::iterator_range RangeFromArray(const T (&arr)[len]) { + return boost::iterator_range(arr, arr + len); +} + +BOOST_AUTO_TEST_CASE(MultiNone) { + unsigned int nums0[] = {1, 3, 4, 22}; + unsigned int nums1[] = {2, 5, 12}; + unsigned int nums2[] = {4, 17}; + + std::vector > sets; + sets.push_back(RangeFromArray(nums0)); + sets.push_back(RangeFromArray(nums1)); + sets.push_back(RangeFromArray(nums2)); + + BOOST_CHECK(!FirstIntersection(sets)); +} + +BOOST_AUTO_TEST_CASE(MultiOne) { + unsigned int nums0[] = {1, 3, 4, 17, 22}; + unsigned int nums1[] = {2, 5, 12, 17}; + unsigned int nums2[] = {4, 17}; + + std::vector > sets; + sets.push_back(RangeFromArray(nums0)); + sets.push_back(RangeFromArray(nums1)); + sets.push_back(RangeFromArray(nums2)); + + boost::optional ret(FirstIntersection(sets)); + BOOST_REQUIRE(ret); + BOOST_CHECK_EQUAL(static_cast(17), *ret); +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/murmur_hash.cc b/ctcdecode-master/third_party/kenlm/util/murmur_hash.cc new file mode 100644 index 0000000..bf32498 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/murmur_hash.cc @@ -0,0 +1,175 @@ +/* Downloaded from http://sites.google.com/site/murmurhash/ which says "All + * code is released to the public domain. For business purposes, Murmurhash is + * under the MIT license." + * This is modified from the original: + * ULL tag on 0xc6a4a7935bd1e995 so this will compile on 32-bit. + * length changed to unsigned int. + * placed in namespace util + * add MurmurHashNative + * default option = 0 for seed + * ARM port from NICT + */ + +#include "util/murmur_hash.hh" +#include + +namespace util { + +//----------------------------------------------------------------------------- +// MurmurHash2, 64-bit versions, by Austin Appleby + +// The same caveats as 32-bit MurmurHash2 apply here - beware of alignment +// and endian-ness issues if used across multiple platforms. + +// 64-bit hash for 64-bit platforms + +uint64_t MurmurHash64A ( const void * key, std::size_t len, uint64_t seed ) +{ + const uint64_t m = 0xc6a4a7935bd1e995ULL; + const int r = 47; + + uint64_t h = seed ^ (len * m); + +#if defined(__arm) || defined(__arm__) + const size_t ksize = sizeof(uint64_t); + const unsigned char * data = (const unsigned char *)key; + const unsigned char * end = data + (std::size_t)(len/8) * ksize; +#else + const uint64_t * data = (const uint64_t *)key; + const uint64_t * end = data + (len/8); +#endif + + while(data != end) + { +#if defined(__arm) || defined(__arm__) + uint64_t k; + memcpy(&k, data, ksize); + data += ksize; +#else + uint64_t k = *data++; +#endif + + k *= m; + k ^= k >> r; + k *= m; + + h ^= k; + h *= m; + } + + const unsigned char * data2 = (const unsigned char*)data; + + switch(len & 7) + { + case 7: h ^= uint64_t(data2[6]) << 48; + case 6: h ^= uint64_t(data2[5]) << 40; + case 5: h ^= uint64_t(data2[4]) << 32; + case 4: h ^= uint64_t(data2[3]) << 24; + case 3: h ^= uint64_t(data2[2]) << 16; + case 2: h ^= uint64_t(data2[1]) << 8; + case 1: h ^= uint64_t(data2[0]); + h *= m; + }; + + h ^= h >> r; + h *= m; + h ^= h >> r; + + return h; +} + + +// 64-bit hash for 32-bit platforms + +uint64_t MurmurHash64B ( const void * key, std::size_t len, uint64_t seed ) +{ + const unsigned int m = 0x5bd1e995; + const int r = 24; + + unsigned int h1 = seed ^ len; + unsigned int h2 = 0; + +#if defined(__arm) || defined(__arm__) + size_t ksize = sizeof(unsigned int); + const unsigned char * data = (const unsigned char *)key; +#else + const unsigned int * data = (const unsigned int *)key; +#endif + + unsigned int k1, k2; + while(len >= 8) + { +#if defined(__arm) || defined(__arm__) + memcpy(&k1, data, ksize); + data += ksize; + memcpy(&k2, data, ksize); + data += ksize; +#else + k1 = *data++; + k2 = *data++; +#endif + + k1 *= m; k1 ^= k1 >> r; k1 *= m; + h1 *= m; h1 ^= k1; + len -= 4; + + k2 *= m; k2 ^= k2 >> r; k2 *= m; + h2 *= m; h2 ^= k2; + len -= 4; + } + + if(len >= 4) + { +#if defined(__arm) || defined(__arm__) + memcpy(&k1, data, ksize); + data += ksize; +#else + k1 = *data++; +#endif + k1 *= m; k1 ^= k1 >> r; k1 *= m; + h1 *= m; h1 ^= k1; + len -= 4; + } + + switch(len) + { + case 3: h2 ^= ((unsigned char*)data)[2] << 16; + case 2: h2 ^= ((unsigned char*)data)[1] << 8; + case 1: h2 ^= ((unsigned char*)data)[0]; + h2 *= m; + }; + + h1 ^= h2 >> 18; h1 *= m; + h2 ^= h1 >> 22; h2 *= m; + h1 ^= h2 >> 17; h1 *= m; + h2 ^= h1 >> 19; h2 *= m; + + uint64_t h = h1; + + h = (h << 32) | h2; + + return h; +} + +// Trick to test for 64-bit architecture at compile time. +namespace { +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wunused-function" +#endif +template inline uint64_t MurmurHashNativeBackend(const void * key, std::size_t len, uint64_t seed) { + return MurmurHash64A(key, len, seed); +} +template <> inline uint64_t MurmurHashNativeBackend<4>(const void * key, std::size_t len, uint64_t seed) { + return MurmurHash64B(key, len, seed); +} +#ifdef __clang__ +#pragma clang diagnostic pop +#endif +} // namespace + +uint64_t MurmurHashNative(const void * key, std::size_t len, uint64_t seed) { + return MurmurHashNativeBackend(key, len, seed); +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/murmur_hash.hh b/ctcdecode-master/third_party/kenlm/util/murmur_hash.hh new file mode 100644 index 0000000..f17157c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/murmur_hash.hh @@ -0,0 +1,18 @@ +#ifndef UTIL_MURMUR_HASH_H +#define UTIL_MURMUR_HASH_H +#include +#include + +namespace util { + +// 64-bit machine version +uint64_t MurmurHash64A(const void * key, std::size_t len, uint64_t seed = 0); +// 32-bit machine version (not the same function as above) +uint64_t MurmurHash64B(const void * key, std::size_t len, uint64_t seed = 0); +// Use the version for this arch. Because the values differ across +// architectures, really only use it for in-memory structures. +uint64_t MurmurHashNative(const void * key, std::size_t len, uint64_t seed = 0); + +} // namespace util + +#endif // UTIL_MURMUR_HASH_H diff --git a/ctcdecode-master/third_party/kenlm/util/parallel_read.cc b/ctcdecode-master/third_party/kenlm/util/parallel_read.cc new file mode 100644 index 0000000..6435eb8 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/parallel_read.cc @@ -0,0 +1,69 @@ +#include "util/parallel_read.hh" + +#include "util/file.hh" + +#ifdef WITH_THREADS +#include "util/thread_pool.hh" + +namespace util { +namespace { + +class Reader { + public: + explicit Reader(int fd) : fd_(fd) {} + + struct Request { + void *to; + std::size_t size; + uint64_t offset; + + bool operator==(const Request &other) const { + return (to == other.to) && (size == other.size) && (offset == other.offset); + } + }; + + void operator()(const Request &request) { + util::ErsatzPRead(fd_, request.to, request.size, request.offset); + } + + private: + int fd_; +}; + +} // namespace + +void ParallelRead(int fd, void *to, std::size_t amount, uint64_t offset) { + Reader::Request poison; + poison.to = NULL; + poison.size = 0; + poison.offset = 0; + unsigned threads = boost::thread::hardware_concurrency(); + if (!threads) threads = 2; + ThreadPool pool(2 /* don't need much of a queue */, threads, fd, poison); + const std::size_t kBatch = 1ULL << 25; // 32 MB + Reader::Request request; + request.to = to; + request.size = kBatch; + request.offset = offset; + for (; amount > kBatch; amount -= kBatch) { + pool.Produce(request); + request.to = reinterpret_cast(request.to) + kBatch; + request.offset += kBatch; + } + request.size = amount; + if (request.size) { + pool.Produce(request); + } +} + +} // namespace util + +#else // WITH_THREADS + +namespace util { +void ParallelRead(int fd, void *to, std::size_t amount, uint64_t offset) { + util::ErsatzPRead(fd, to, amount, offset); +} +} // namespace util + +#endif diff --git a/ctcdecode-master/third_party/kenlm/util/parallel_read.hh b/ctcdecode-master/third_party/kenlm/util/parallel_read.hh new file mode 100644 index 0000000..1e96e79 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/parallel_read.hh @@ -0,0 +1,16 @@ +#ifndef UTIL_PARALLEL_READ__ +#define UTIL_PARALLEL_READ__ + +/* Read pieces of a file in parallel. This has a very specific use case: + * reading files from Lustre is CPU bound so multiple threads actually + * increases throughput. Speed matters when an LM takes a terabyte. + */ + +#include +#include + +namespace util { +void ParallelRead(int fd, void *to, std::size_t amount, uint64_t offset); +} // namespace util + +#endif // UTIL_PARALLEL_READ__ diff --git a/ctcdecode-master/third_party/kenlm/util/pcqueue.hh b/ctcdecode-master/third_party/kenlm/util/pcqueue.hh new file mode 100644 index 0000000..05c868f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/pcqueue.hh @@ -0,0 +1,156 @@ +#ifndef UTIL_PCQUEUE_H +#define UTIL_PCQUEUE_H + +#include "util/exception.hh" + +#include +#include +#include +#include + +#include + +#ifdef __APPLE__ +#include +#include +#include +#include +#endif // __APPLE__ + +namespace util { + +/* OS X Maverick and Boost interprocess were doing "Function not implemented." + * So this is my own wrapper around the mach kernel APIs. + */ +#ifdef __APPLE__ + +#define MACH_CALL(call) UTIL_THROW_IF(KERN_SUCCESS != (call), Exception, "Mach call failure") + +class Semaphore { + public: + explicit Semaphore(int value) : task_(mach_task_self()) { + MACH_CALL(semaphore_create(task_, &back_, SYNC_POLICY_FIFO, value)); + } + + ~Semaphore() { + MACH_CALL(semaphore_destroy(task_, back_)); + } + + void wait() { + MACH_CALL(semaphore_wait(back_)); + } + + void post() { + MACH_CALL(semaphore_signal(back_)); + } + + private: + semaphore_t back_; + task_t task_; +}; + +inline void WaitSemaphore(Semaphore &semaphore) { + semaphore.wait(); +} + +#else +typedef boost::interprocess::interprocess_semaphore Semaphore; + +inline void WaitSemaphore (Semaphore &on) { + while (1) { + try { + on.wait(); + break; + } + catch (boost::interprocess::interprocess_exception &e) { + if (e.get_native_error() != EINTR) { + throw; + } + } + } +} + +#endif // __APPLE__ + +/** + * Producer consumer queue safe for multiple producers and multiple consumers. + * T must be default constructable and have operator=. + * The value is copied twice for Consume(T &out) or three times for Consume(), + * so larger objects should be passed via pointer. + * Strong exception guarantee if operator= throws. Undefined if semaphores throw. + */ +template class PCQueue : boost::noncopyable { + public: + explicit PCQueue(size_t size) + : empty_(size), used_(0), + storage_(new T[size]), + end_(storage_.get() + size), + produce_at_(storage_.get()), + consume_at_(storage_.get()) {} + + // Add a value to the queue. + void Produce(const T &val) { + WaitSemaphore(empty_); + { + boost::unique_lock produce_lock(produce_at_mutex_); + try { + *produce_at_ = val; + } + catch (...) { + empty_.post(); + throw; + } + if (++produce_at_ == end_) produce_at_ = storage_.get(); + } + used_.post(); + } + + // Consume a value, assigning it to out. + T& Consume(T &out) { + WaitSemaphore(used_); + { + boost::unique_lock consume_lock(consume_at_mutex_); + try { + out = *consume_at_; + } + catch (...) { + used_.post(); + throw; + } + if (++consume_at_ == end_) consume_at_ = storage_.get(); + } + empty_.post(); + return out; + } + + // Convenience version of Consume that copies the value to return. + // The other version is faster. + T Consume() { + T ret; + Consume(ret); + return ret; + } + + private: + // Number of empty spaces in storage_. + Semaphore empty_; + // Number of occupied spaces in storage_. + Semaphore used_; + + boost::scoped_array storage_; + + T *const end_; + + // Index for next write in storage_. + T *produce_at_; + boost::mutex produce_at_mutex_; + + // Index for next read from storage_. + T *consume_at_; + boost::mutex consume_at_mutex_; + +}; + +} // namespace util + +#endif // UTIL_PCQUEUE_H diff --git a/ctcdecode-master/third_party/kenlm/util/pcqueue_test.cc b/ctcdecode-master/third_party/kenlm/util/pcqueue_test.cc new file mode 100644 index 0000000..22ed2c6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/pcqueue_test.cc @@ -0,0 +1,20 @@ +#include "util/pcqueue.hh" + +#define BOOST_TEST_MODULE PCQueueTest +#include + +namespace util { +namespace { + +BOOST_AUTO_TEST_CASE(SingleThread) { + PCQueue queue(10); + for (int i = 0; i < 10; ++i) { + queue.Produce(i); + } + for (int i = 0; i < 10; ++i) { + BOOST_CHECK_EQUAL(i, queue.Consume()); + } +} + +} +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/pool.cc b/ctcdecode-master/third_party/kenlm/util/pool.cc new file mode 100644 index 0000000..246417c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/pool.cc @@ -0,0 +1,38 @@ +#include "util/pool.hh" + +#include "util/scoped.hh" + +#include + +#include + +namespace util { + +Pool::Pool() { + current_ = NULL; + current_end_ = NULL; +} + +Pool::~Pool() { + FreeAll(); +} + +void Pool::FreeAll() { + for (std::vector::const_iterator i(free_list_.begin()); i != free_list_.end(); ++i) { + free(*i); + } + free_list_.clear(); + current_ = NULL; + current_end_ = NULL; +} + +void *Pool::More(std::size_t size) { + std::size_t amount = std::max(static_cast(32) << free_list_.size(), size); + uint8_t *ret = static_cast(MallocOrThrow(amount)); + free_list_.push_back(ret); + current_ = ret + size; + current_end_ = ret + amount; + return ret; +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/pool.hh b/ctcdecode-master/third_party/kenlm/util/pool.hh new file mode 100644 index 0000000..d72400b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/pool.hh @@ -0,0 +1,119 @@ +#ifndef UTIL_POOL_H +#define UTIL_POOL_H + +#include +#include +#include + +#include + +namespace util { + +/* Very simple pool. It can only allocate memory. And all of the memory it + * allocates must be freed at the same time. + */ +class Pool { + public: + Pool(); + + ~Pool(); + + void *Allocate(std::size_t size) { + void *ret = current_; + current_ += size; + if (current_ > current_end_) { + ret = More(size); + } +#ifdef DEBUG + base_check_ = ret; +#endif + return ret; + } + + /** Extend (or contract) the most recent allocation. + * @param base The base pointer of the allocation. This must must have been + * returned by the MOST RECENT call to Allocate or Continue. + * @param additional Change in the size. + * + * In most cases, more memory from the same page is used, in which case + * base is unchanged and the function returns false. + * If the page runs out, a new page is created and the memory (from base) + * is copied. The function returns true. + * + * @return Whether the base had to be changed due to allocating a page. + */ + bool Continue(void *&base, std::ptrdiff_t additional) { +#ifdef DEBUG + assert(base == base_check_); +#endif + current_ += additional; + if (current_ > current_end_) { + std::size_t new_total = current_ - static_cast(base); + void *new_base = More(new_total); + std::memcpy(new_base, base, new_total - additional); + base = new_base; +#ifdef DEBUG + base_check_ = base; +#endif + return true; + } + return false; + } + + void FreeAll(); + + private: + void *More(std::size_t size); + + std::vector free_list_; + + uint8_t *current_, *current_end_; + +#ifdef DEBUG + // For debugging, check that Continue came from the most recent call. + void *base_check_; +#endif // DEBUG + + // no copying + Pool(const Pool &); + Pool &operator=(const Pool &); +}; + +/** + * Pool designed to allow limited freeing. + * Keeps a linked list of free elements in the free spaces. + * Will not reduce in size until FreeAll is called. + */ +class FreePool { + public: + explicit FreePool(std::size_t element_size) + : free_list_(NULL), element_size_(element_size) {} + + void *Allocate() { + if (free_list_) { + void *ret = free_list_; + free_list_ = *reinterpret_cast(free_list_); + return ret; + } else { + return backing_.Allocate(element_size_); + } + } + + void Free(void *ptr) { + *reinterpret_cast(ptr) = free_list_; + free_list_ = ptr; + } + + std::size_t ElementSize() const { return element_size_; } + + private: + void *free_list_; + + Pool backing_; + + const std::size_t element_size_; +}; + +} // namespace util + +#endif // UTIL_POOL_H diff --git a/ctcdecode-master/third_party/kenlm/util/probing_hash_table.hh b/ctcdecode-master/third_party/kenlm/util/probing_hash_table.hh new file mode 100644 index 0000000..1d45b61 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/probing_hash_table.hh @@ -0,0 +1,421 @@ +#ifndef UTIL_PROBING_HASH_TABLE_H +#define UTIL_PROBING_HASH_TABLE_H + +#include "util/exception.hh" +#include "util/mmap.hh" + +#include +#include +#include +#include + +#include +#include + +namespace util { + +/* Thrown when table grows too large */ +class ProbingSizeException : public Exception { + public: + ProbingSizeException() throw() {} + ~ProbingSizeException() throw() {} +}; + +// std::identity is an SGI extension :-( +struct IdentityHash { + template T operator()(T arg) const { return arg; } +}; + +class DivMod { + public: + explicit DivMod(std::size_t buckets) : buckets_(buckets) {} + + static uint64_t RoundBuckets(uint64_t from) { + return from; + } + + template It Ideal(It begin, uint64_t hash) const { + return begin + (hash % buckets_); + } + + template void Next(BaseIt begin, BaseIt end, OutIt &it) const { + if (++it == end) it = begin; + } + + void Double() { + buckets_ *= 2; + } + + private: + std::size_t buckets_; +}; + +class Power2Mod { + public: + explicit Power2Mod(std::size_t buckets) { + UTIL_THROW_IF(!buckets || (((buckets - 1) & buckets)), ProbingSizeException, "Size " << buckets << " is not a power of 2."); + mask_ = buckets - 1; + } + + // Round up to next power of 2. + static uint64_t RoundBuckets(uint64_t from) { + --from; + from |= from >> 1; + from |= from >> 2; + from |= from >> 4; + from |= from >> 8; + from |= from >> 16; + from |= from >> 32; + return from + 1; + } + + template It Ideal(It begin, uint64_t hash) const { + return begin + (hash & mask_); + } + + template void Next(BaseIt begin, BaseIt /*end*/, OutIt &it) const { + it = begin + ((it - begin + 1) & mask_); + } + + void Double() { + mask_ = (mask_ << 1) | 1; + } + + private: + std::size_t mask_; +}; + +template class AutoProbing; + +/* Non-standard hash table + * Buckets must be set at the beginning and must be greater than maximum number + * of elements, else it throws ProbingSizeException. + * Memory management and initialization is externalized to make it easier to + * serialize these to disk and load them quickly. + * Uses linear probing to find value. + * Only insert and lookup operations. + */ +template , class ModT = DivMod> class ProbingHashTable { + public: + typedef EntryT Entry; + typedef typename Entry::Key Key; + typedef const Entry *ConstIterator; + typedef Entry *MutableIterator; + typedef HashT Hash; + typedef EqualT Equal; + typedef ModT Mod; + + static uint64_t Size(uint64_t entries, float multiplier) { + uint64_t buckets = Mod::RoundBuckets(std::max(entries + 1, static_cast(multiplier * static_cast(entries)))); + return buckets * sizeof(Entry); + } + + // Must be assigned to later. + ProbingHashTable() : mod_(1), entries_(0) +#ifdef DEBUG + , initialized_(false) +#endif + {} + + ProbingHashTable(void *start, std::size_t allocated, const Key &invalid = Key(), const Hash &hash_func = Hash(), const Equal &equal_func = Equal()) + : begin_(reinterpret_cast(start)), + end_(begin_ + allocated / sizeof(Entry)), + buckets_(end_ - begin_), + invalid_(invalid), + hash_(hash_func), + equal_(equal_func), + mod_(end_ - begin_), + entries_(0) +#ifdef DEBUG + , initialized_(true) +#endif + {} + + void Relocate(void *new_base) { + begin_ = reinterpret_cast(new_base); + end_ = begin_ + buckets_; + } + + MutableIterator Ideal(const Key key) { + return mod_.Ideal(begin_, hash_(key)); + } + ConstIterator Ideal(const Key key) const { + return mod_.Ideal(begin_, hash_(key)); + } + + template MutableIterator Insert(const T &t) { +#ifdef DEBUG + assert(initialized_); +#endif + UTIL_THROW_IF(++entries_ >= buckets_, ProbingSizeException, "Hash table with " << buckets_ << " buckets is full."); + return UncheckedInsert(t); + } + + // Return true if the value was found (and not inserted). This is consistent with Find but the opposite of hash_map! + template bool FindOrInsert(const T &t, MutableIterator &out) { +#ifdef DEBUG + assert(initialized_); +#endif + for (MutableIterator i = Ideal(t.GetKey());;mod_.Next(begin_, end_, i)) { + Key got(i->GetKey()); + if (equal_(got, t.GetKey())) { out = i; return true; } + if (equal_(got, invalid_)) { + UTIL_THROW_IF(++entries_ >= buckets_, ProbingSizeException, "Hash table with " << buckets_ << " buckets is full."); + *i = t; + out = i; + return false; + } + } + } + + void FinishedInserting() {} + + // Don't change anything related to GetKey, + template bool UnsafeMutableFind(const Key key, MutableIterator &out) { +#ifdef DEBUG + assert(initialized_); +#endif + for (MutableIterator i(Ideal(key));; mod_.Next(begin_, end_, i)) { + Key got(i->GetKey()); + if (equal_(got, key)) { out = i; return true; } + if (equal_(got, invalid_)) return false; + } + } + + // Like UnsafeMutableFind, but the key must be there. + template MutableIterator UnsafeMutableMustFind(const Key key) { + for (MutableIterator i(Ideal(key));; mod_.Next(begin_, end_, i)) { + Key got(i->GetKey()); + if (equal_(got, key)) { return i; } + assert(!equal_(got, invalid_)); + } + } + + // Iterator is both input and output. + template bool FindFromIdeal(const Key key, ConstIterator &i) const { +#ifdef DEBUG + assert(initialized_); +#endif + for (;; mod_.Next(begin_, end_, i)) { + Key got(i->GetKey()); + if (equal_(got, key)) return true; + if (equal_(got, invalid_)) return false; + } + } + + template bool Find(const Key key, ConstIterator &out) const { + out = Ideal(key); + return FindFromIdeal(key, out); + } + + // Like Find but we're sure it must be there. + template ConstIterator MustFind(const Key key) const { + for (ConstIterator i(Ideal(key));; mod_.Next(begin_, end_, i)) { + Key got(i->GetKey()); + if (equal_(got, key)) { return i; } + assert(!equal_(got, invalid_)); + } + } + + void Clear() { + Entry invalid; + invalid.SetKey(invalid_); + std::fill(begin_, end_, invalid); + entries_ = 0; + } + + // Return number of entries assuming no serialization went on. + std::size_t SizeNoSerialization() const { + return entries_; + } + + // Return memory size expected by Double. + std::size_t DoubleTo() const { + return buckets_ * 2 * sizeof(Entry); + } + + // Inform the table that it has double the amount of memory. + // Pass clear_new = false if you are sure the new memory is initialized + // properly (to invalid_) i.e. by mremap. + void Double(void *new_base, bool clear_new = true) { + begin_ = static_cast(new_base); + MutableIterator old_end = begin_ + buckets_; + buckets_ *= 2; + end_ = begin_ + buckets_; + mod_.Double(); + if (clear_new) { + Entry invalid; + invalid.SetKey(invalid_); + std::fill(old_end, end_, invalid); + } + std::vector rolled_over; + // Move roll-over entries to a buffer because they might not roll over anymore. This should be small. + for (MutableIterator i = begin_; i != old_end && !equal_(i->GetKey(), invalid_); ++i) { + rolled_over.push_back(*i); + i->SetKey(invalid_); + } + /* Re-insert everything. Entries might go backwards to take over a + * recently opened gap, stay, move to new territory, or wrap around. If + * an entry wraps around, it might go to a pointer greater than i (which + * can happen at the beginning) and it will be revisited to possibly fill + * in a gap created later. + */ + Entry temp; + for (MutableIterator i = begin_; i != old_end; ++i) { + if (!equal_(i->GetKey(), invalid_)) { + temp = *i; + i->SetKey(invalid_); + UncheckedInsert(temp); + } + } + // Put the roll-over entries back in. + for (typename std::vector::const_iterator i(rolled_over.begin()); i != rolled_over.end(); ++i) { + UncheckedInsert(*i); + } + } + + // Mostly for tests, check consistency of every entry. + void CheckConsistency() { + MutableIterator last; + for (last = end_ - 1; last >= begin_ && !equal_(last->GetKey(), invalid_); --last) {} + UTIL_THROW_IF(last == begin_, ProbingSizeException, "Completely full"); + MutableIterator i; + // Beginning can be wrap-arounds. + for (i = begin_; !equal_(i->GetKey(), invalid_); ++i) { + MutableIterator ideal = Ideal(i->GetKey()); + UTIL_THROW_IF(ideal > i && ideal <= last, Exception, "Inconsistency at position " << (i - begin_) << " should be at " << (ideal - begin_)); + } + MutableIterator pre_gap = i; + for (; i != end_; ++i) { + if (equal_(i->GetKey(), invalid_)) { + pre_gap = i; + continue; + } + MutableIterator ideal = Ideal(i->GetKey()); + UTIL_THROW_IF(ideal > i || ideal <= pre_gap, Exception, "Inconsistency at position " << (i - begin_) << " with ideal " << (ideal - begin_)); + } + } + + ConstIterator RawBegin() const { + return begin_; + } + ConstIterator RawEnd() const { + return end_; + } + + private: + friend class AutoProbing; + + template MutableIterator UncheckedInsert(const T &t) { + for (MutableIterator i(Ideal(t.GetKey()));; mod_.Next(begin_, end_, i)) { + if (equal_(i->GetKey(), invalid_)) { *i = t; return i; } + } + } + + MutableIterator begin_; + MutableIterator end_; + std::size_t buckets_; + Key invalid_; + Hash hash_; + Equal equal_; + Mod mod_; + + std::size_t entries_; +#ifdef DEBUG + bool initialized_; +#endif +}; + +// Resizable linear probing hash table. This owns the memory. +template > class AutoProbing { + private: + typedef ProbingHashTable Backend; + public: + static std::size_t MemUsage(std::size_t size, float multiplier = 1.5) { + return Backend::Size(size, multiplier); + } + + typedef EntryT Entry; + typedef typename Entry::Key Key; + typedef const Entry *ConstIterator; + typedef Entry *MutableIterator; + typedef HashT Hash; + typedef EqualT Equal; + + AutoProbing(std::size_t initial_size = 5, const Key &invalid = Key(), const Hash &hash_func = Hash(), const Equal &equal_func = Equal()) : + allocated_(Backend::Size(initial_size, 1.2)), mem_(allocated_, KeyIsRawZero(invalid)), backend_(mem_.get(), allocated_, invalid, hash_func, equal_func) { + threshold_ = std::min(backend_.buckets_ - 1, backend_.buckets_ * 0.9); + if (!KeyIsRawZero(invalid)) { + Clear(); + } + } + + // Assumes that the key is unique. Multiple insertions won't cause a failure, just inconsistent lookup. + template MutableIterator Insert(const T &t) { + ++backend_.entries_; + DoubleIfNeeded(); + return backend_.UncheckedInsert(t); + } + + template bool FindOrInsert(const T &t, MutableIterator &out) { + DoubleIfNeeded(); + return backend_.FindOrInsert(t, out); + } + + template bool UnsafeMutableFind(const Key key, MutableIterator &out) { + return backend_.UnsafeMutableFind(key, out); + } + + template MutableIterator UnsafeMutableMustFind(const Key key) { + return backend_.UnsafeMutableMustFind(key); + } + + template bool Find(const Key key, ConstIterator &out) const { + return backend_.Find(key, out); + } + + template ConstIterator MustFind(const Key key) const { + return backend_.MustFind(key); + } + + std::size_t Size() const { + return backend_.SizeNoSerialization(); + } + + void Clear() { + backend_.Clear(); + } + + ConstIterator RawBegin() const { + return backend_.RawBegin(); + } + ConstIterator RawEnd() const { + return backend_.RawEnd(); + } + + private: + void DoubleIfNeeded() { + if (UTIL_LIKELY(Size() < threshold_)) + return; + HugeRealloc(backend_.DoubleTo(), KeyIsRawZero(backend_.invalid_), mem_); + allocated_ = backend_.DoubleTo(); + backend_.Double(mem_.get(), !KeyIsRawZero(backend_.invalid_)); + threshold_ = std::min(backend_.buckets_ - 1, backend_.buckets_ * 0.9); + } + + bool KeyIsRawZero(const Key &key) { + for (const uint8_t *i = reinterpret_cast(&key); i < reinterpret_cast(&key) + sizeof(Key); ++i) { + if (*i) return false; + } + return true; + } + + std::size_t allocated_; + util::scoped_memory mem_; + Backend backend_; + std::size_t threshold_; +}; + +} // namespace util + +#endif // UTIL_PROBING_HASH_TABLE_H diff --git a/ctcdecode-master/third_party/kenlm/util/probing_hash_table_benchmark_main.cc b/ctcdecode-master/third_party/kenlm/util/probing_hash_table_benchmark_main.cc new file mode 100644 index 0000000..b65aec0 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/probing_hash_table_benchmark_main.cc @@ -0,0 +1,316 @@ +#include "util/file.hh" +#include "util/probing_hash_table.hh" +#include "util/mmap.hh" +#include "util/usage.hh" +#include "util/thread_pool.hh" +#include +#include + +#ifdef WIN32 +#include +#else +#include +#endif + +#include + +#include + +namespace util { +namespace { + +struct Entry { + typedef uint64_t Key; + Key key; + Key GetKey() const { return key; } +}; + +// I don't care if this doesn't run on Windows. Empirically /dev/urandom was faster than boost::random's Mersenne Twister. +class URandom { + public: + URandom() : + it_(buf_ + 1024), end_(buf_ + 1024), + file_(util::OpenReadOrThrow("/dev/urandom")) {} + + uint64_t Get() { + if (it_ == end_) { + it_ = buf_; + util::ReadOrThrow(file_.get(), buf_, sizeof(buf_)); + it_ = buf_; + } + return *it_++; + } + + void Batch(uint64_t *begin, uint64_t *end) { + util::ReadOrThrow(file_.get(), begin, (end - begin) * sizeof(uint64_t)); + } + + private: + uint64_t buf_[1024]; + uint64_t *it_, *end_; + + util::scoped_fd file_; +}; + +struct PrefetchEntry { + uint64_t key; + const Entry *pointer; +}; + +template class PrefetchQueue { + public: + typedef TableT Table; + + explicit PrefetchQueue(Table &table) : table_(table), cur_(0), twiddle_(false) { + for (PrefetchEntry *i = entries_; i != entries_ + PrefetchSize; ++i) + i->pointer = NULL; + } + + void Add(uint64_t key) { + if (Cur().pointer) { + twiddle_ ^= table_.FindFromIdeal(Cur().key, Cur().pointer); + } + Cur().key = key; + Cur().pointer = table_.Ideal(key); + __builtin_prefetch(Cur().pointer, 0, 0); + Next(); + } + + bool Drain() { + if (Cur().pointer) { + for (PrefetchEntry *i = &Cur(); i < entries_ + PrefetchSize; ++i) { + twiddle_ ^= table_.FindFromIdeal(i->key, i->pointer); + } + } + for (PrefetchEntry *i = entries_; i < &Cur(); ++i) { + twiddle_ ^= table_.FindFromIdeal(i->key, i->pointer); + } + return twiddle_; + } + + private: + PrefetchEntry &Cur() { return entries_[cur_]; } + void Next() { + ++cur_; + cur_ = cur_ % PrefetchSize; + } + + Table &table_; + PrefetchEntry entries_[PrefetchSize]; + std::size_t cur_; + + bool twiddle_; + + PrefetchQueue(const PrefetchQueue&); + void operator=(const PrefetchQueue&); +}; + +template class Immediate { + public: + typedef TableT Table; + + explicit Immediate(Table &table) : table_(table), twiddle_(false) {} + + void Add(uint64_t key) { + typename Table::ConstIterator it; + twiddle_ ^= table_.Find(key, it); + } + + bool Drain() const { return twiddle_; } + + private: + Table &table_; + bool twiddle_; +}; + +std::size_t Size(uint64_t entries, float multiplier = 1.5) { + typedef util::ProbingHashTable, Power2Mod> Table; + // Always round up to power of 2 for fair comparison. + return Power2Mod::RoundBuckets(Table::Size(entries, multiplier) / sizeof(Entry)) * sizeof(Entry); +} + +template bool Test(URandom &rn, uint64_t entries, const uint64_t *const queries_begin, const uint64_t *const queries_end, bool ordinary_malloc, float multiplier = 1.5) { + std::size_t size = Size(entries, multiplier); + scoped_memory backing; + if (ordinary_malloc) { + backing.reset(util::CallocOrThrow(size), size, scoped_memory::MALLOC_ALLOCATED); + } else { + util::HugeMalloc(size, true, backing); + } + typename Queue::Table table(backing.get(), size); + + double start = CPUTime(); + for (uint64_t i = 0; i < entries; ++i) { + Entry entry; + entry.key = rn.Get(); + table.Insert(entry); + } + double inserted = CPUTime() - start; + double before_lookup = CPUTime(); + Queue queue(table); + for (const uint64_t *i = queries_begin; i != queries_end; ++i) { + queue.Add(*i); + } + bool meaningless = queue.Drain(); + std::cout << ' ' << (inserted / static_cast(entries)) << ' ' << (CPUTime() - before_lookup) / static_cast(queries_end - queries_begin) << std::flush; + return meaningless; +} + +bool TestRun(uint64_t lookups = 20000000, float multiplier = 1.5) { + URandom rn; + util::scoped_memory queries; + HugeMalloc(lookups * sizeof(uint64_t), true, queries); + rn.Batch(static_cast(queries.get()), static_cast(queries.get()) + lookups); + uint64_t physical_mem_limit = util::GuessPhysicalMemory() / 2; + bool meaningless = true; + for (uint64_t i = 4; Size(i / multiplier) < physical_mem_limit; i *= 4) { + std::cout << static_cast(i / multiplier) << ' ' << Size(i / multiplier); + typedef util::ProbingHashTable, Power2Mod> Table; + typedef util::ProbingHashTable, DivMod> TableDiv; + const uint64_t *const queries_begin = static_cast(queries.get()); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, true, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, true, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, true, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, false, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, false, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, false, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, false, multiplier); + meaningless ^= util::Test >(rn, i / multiplier, queries_begin, queries_begin + lookups, false, multiplier); + std::cout << std::endl; + } + return meaningless; +} + +template +struct ParallelTestRequest{ + ParallelTestRequest() : queries_begin_(NULL), queries_end_(NULL), table_(NULL) {} + ParallelTestRequest(const uint64_t *queries_begin, const uint64_t *queries_end, Table *table) : + queries_begin_(queries_begin), + queries_end_(queries_end), + table_(table) {} + bool operator==(const ParallelTestRequest &rhs) const { + return this->queries_begin_ == rhs.queries_begin_ && this->queries_end_ == rhs.queries_end_; + } + const uint64_t *queries_begin_; + const uint64_t *queries_end_; + Table * table_; +}; + +template +struct ParallelTestConstruct{ + ParallelTestConstruct(boost::mutex& lock, const uint64_t* const burn_begin, const uint64_t* const burn_end, TableT* table) : lock_(lock), burn_begin_(burn_begin), burn_end_(burn_end), table_(table){} + boost::mutex& lock_; + const uint64_t* const burn_begin_; + const uint64_t* const burn_end_; + TableT* table_; +}; + +template +struct ParallelTestHandler{ + typedef ParallelTestRequest Request; + explicit ParallelTestHandler(const ParallelTestConstruct& construct) : lock_(construct.lock_), totalTime_(0.0), nRequests_(0), nQueries_(0), error_(false), twiddle_(false){ + //perform initial burn + for(const uint64_t* i = construct.burn_begin_; i < construct.burn_end_; i++){ + typename Queue::Table::ConstIterator it; + twiddle_ ^= construct.table_->Find(*i, it); + } + } + void operator()(Request request){ + if (error_) return; + Queue queue(*request.table_); + double start = ThreadTime(); + if(start < 0.0){ + error_ = true; + return; + } + for(const uint64_t *i = request.queries_begin_; i != request.queries_end_; ++i){ + queue.Add(*i); + } + twiddle_ ^= queue.Drain(); + double end = ThreadTime(); + if(end < 0.0){ + error_ = true; + return; + } + totalTime_ += end - start; + nQueries_ += request.queries_end_ - request.queries_begin_; + ++nRequests_; + } + virtual ~ParallelTestHandler() { + boost::unique_lock produce_lock(lock_); + if (error_){ + std::cout << "Error "; + } + else { + std::cout << nRequests_ << ' ' << ' ' << nQueries_ << ' ' << totalTime_ << std::endl; + } + std::cerr << "Meaningless " << twiddle_ << std::endl; + } + private: + boost::mutex &lock_; + double totalTime_; + std::size_t nRequests_; + std::size_t nQueries_; + bool error_; + bool twiddle_; +}; + +template +void ParallelTest(typename Queue::Table* table, const uint64_t *const queries_begin, + const uint64_t *const queries_end, std::size_t num_threads, + std::size_t tasks_per_thread, std::size_t burn){ + boost::mutex lock; + ParallelTestConstruct construct(lock, queries_begin, queries_begin + burn, table); + ParallelTestRequest poison(NULL, NULL, NULL); + { + util::ThreadPool > pool(num_threads, num_threads, construct, poison); + const uint64_t queries_per_thread =(static_cast(queries_end-queries_begin-burn)/num_threads)/tasks_per_thread; + for (const uint64_t *i = queries_begin+burn; i + queries_per_thread <= queries_end; i += queries_per_thread){ + ParallelTestRequest request(i, i+queries_per_thread, table); + pool.Produce(request); + } + } // pool gets deallocated and all jobs finish + std::cout << std::endl; +} + +void ParallelTestRun(std::size_t tasks_per_thread = 1, std::size_t burn = 4000, uint64_t lookups = 20000000, float multiplier = 1.5) { + URandom rn; + util::scoped_memory queries; + HugeMalloc((lookups + burn)* sizeof(uint64_t), true, queries); + rn.Batch(static_cast(queries.get()), static_cast(queries.get()) + lookups + burn); + const uint64_t *const queries_begin = static_cast(queries.get()); + const uint64_t *const queries_end = queries_begin + lookups + burn; + typedef util::ProbingHashTable, Power2Mod> Table; + uint64_t physical_mem_limit = util::GuessPhysicalMemory() / 2; + for (uint64_t i = 4; Size(i / multiplier, multiplier) < physical_mem_limit; i *= 4) { + std::size_t entries = static_cast(i / multiplier); + std::size_t size = Size(i/multiplier, multiplier); + scoped_memory backing; + util::HugeMalloc(size, true, backing); + Table table(backing.get(), size); + for (uint64_t j = 0; j < entries; ++j) { + Entry entry; + entry.key = rn.Get(); + table.Insert(entry); + } + for(std::size_t num_threads = 1; num_threads <= 16; num_threads*=2){ + std::cout << entries << ' ' << size << ' ' << num_threads << ' ' << std::endl; + util::ParallelTest >(&table, queries_begin, queries_end, num_threads, tasks_per_thread, burn); + util::ParallelTest >(&table, queries_begin, queries_end, num_threads, tasks_per_thread, burn); + util::ParallelTest >(&table, queries_begin, queries_end, num_threads, tasks_per_thread, burn); + util::ParallelTest >(&table, queries_begin, queries_end, num_threads, tasks_per_thread, burn); + util::ParallelTest >(&table, queries_begin, queries_end, num_threads, tasks_per_thread, burn); + } + } +} + +} // namespace +} // namespace util + +int main() { + //bool meaningless = false; + std::cout << "#CPU time\n"; + //meaningless ^= util::TestRun(); + util::ParallelTestRun(10, 4000); + //std::cerr << "Meaningless: " << meaningless << '\n'; +} diff --git a/ctcdecode-master/third_party/kenlm/util/probing_hash_table_test.cc b/ctcdecode-master/third_party/kenlm/util/probing_hash_table_test.cc new file mode 100644 index 0000000..6ed5414 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/probing_hash_table_test.cc @@ -0,0 +1,102 @@ +#include "util/probing_hash_table.hh" + +#include "util/murmur_hash.hh" +#include "util/scoped.hh" + +#define BOOST_TEST_MODULE ProbingHashTableTest +#include +#include +#include +#include +#include +#include +#include + +namespace util { +namespace { + +struct Entry { + unsigned char key; + typedef unsigned char Key; + + unsigned char GetKey() const { + return key; + } + + void SetKey(unsigned char to) { + key = to; + } + + uint64_t GetValue() const { + return value; + } + + uint64_t value; +}; + +typedef ProbingHashTable > Table; + +BOOST_AUTO_TEST_CASE(simple) { + size_t size = Table::Size(10, 1.2); + boost::scoped_array mem(new char[size]); + memset(mem.get(), 0, size); + + Table table(mem.get(), size); + const Entry *i = NULL; + BOOST_CHECK(!table.Find(2, i)); + Entry to_ins; + to_ins.key = 3; + to_ins.value = 328920; + table.Insert(to_ins); + BOOST_REQUIRE(table.Find(3, i)); + BOOST_CHECK_EQUAL(3, i->GetKey()); + BOOST_CHECK_EQUAL(static_cast(328920), i->GetValue()); + BOOST_CHECK(!table.Find(2, i)); +} + +struct Entry64 { + uint64_t key; + typedef uint64_t Key; + + Entry64() {} + + explicit Entry64(uint64_t key_in) { + key = key_in; + } + + Key GetKey() const { return key; } + void SetKey(uint64_t to) { key = to; } +}; + +struct MurmurHashEntry64 { + std::size_t operator()(uint64_t value) const { + return util::MurmurHash64A(&value, 8); + } +}; + +typedef ProbingHashTable Table64; + +BOOST_AUTO_TEST_CASE(Double) { + for (std::size_t initial = 19; initial < 30; ++initial) { + size_t size = Table64::Size(initial, 1.2); + scoped_malloc mem(MallocOrThrow(size)); + Table64 table(mem.get(), size, std::numeric_limits::max()); + table.Clear(); + for (uint64_t i = 0; i < 19; ++i) { + table.Insert(Entry64(i)); + } + table.CheckConsistency(); + mem.call_realloc(table.DoubleTo()); + table.Double(mem.get()); + table.CheckConsistency(); + for (uint64_t i = 20; i < 40 ; ++i) { + table.Insert(Entry64(i)); + } + mem.call_realloc(table.DoubleTo()); + table.Double(mem.get()); + table.CheckConsistency(); + } +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/proxy_iterator.hh b/ctcdecode-master/third_party/kenlm/util/proxy_iterator.hh new file mode 100644 index 0000000..8aa697b --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/proxy_iterator.hh @@ -0,0 +1,101 @@ +#ifndef UTIL_PROXY_ITERATOR_H +#define UTIL_PROXY_ITERATOR_H + +#include +#include + +/* This is a RandomAccessIterator that uses a proxy to access the underlying + * data. Useful for packing data at bit offsets but still using STL + * algorithms. + * + * Normally I would use boost::iterator_facade but some people are too lazy to + * install boost and still want to use my language model. It's amazing how + * many operators an iterator has. + * + * The Proxy needs to provide: + * class InnerIterator; + * InnerIterator &Inner(); + * const InnerIterator &Inner() const; + * + * InnerIterator has to implement: + * operator==(InnerIterator) + * operator<(InnerIterator) + * operator+=(std::ptrdiff_t) + * operator-(InnerIterator) + * and of course whatever Proxy needs to dereference it. + * + * It's also a good idea to specialize std::swap for Proxy. + */ + +namespace util { +template class ProxyIterator { + private: + // Self. + typedef ProxyIterator S; + typedef typename Proxy::InnerIterator InnerIterator; + + public: + typedef std::random_access_iterator_tag iterator_category; + typedef typename Proxy::value_type value_type; + typedef std::ptrdiff_t difference_type; + typedef Proxy reference; + typedef ProxyIterator * pointer; + + ProxyIterator() {} + + // For cast from non const to const. + template ProxyIterator(const ProxyIterator &in) : p_(*in) {} + explicit ProxyIterator(const Proxy &p) : p_(p) {} + +/* // p_'s swap does value swapping, but here we want iterator swapping + friend inline void swap(ProxyIterator &first, ProxyIterator &second) { + swap(first.I(), second.I()); + }*/ + + // p_'s operator= does value copying, but here we want iterator copying. + S &operator=(const S &other) { + I() = other.I(); + return *this; + } + + bool operator==(const S &other) const { return I() == other.I(); } + bool operator!=(const S &other) const { return !(*this == other); } + bool operator<(const S &other) const { return I() < other.I(); } + bool operator>(const S &other) const { return other < *this; } + bool operator<=(const S &other) const { return !(*this > other); } + bool operator>=(const S &other) const { return !(*this < other); } + + S &operator++() { return *this += 1; } + S operator++(int) { S ret(*this); ++*this; return ret; } + S &operator+=(std::ptrdiff_t amount) { I() += amount; return *this; } + S operator+(std::ptrdiff_t amount) const { S ret(*this); ret += amount; return ret; } + + S &operator--() { return *this -= 1; } + S operator--(int) { S ret(*this); --*this; return ret; } + S &operator-=(std::ptrdiff_t amount) { I() += (-amount); return *this; } + S operator-(std::ptrdiff_t amount) const { S ret(*this); ret -= amount; return ret; } + + std::ptrdiff_t operator-(const S &other) const { return I() - other.I(); } + + Proxy operator*() { return p_; } + const Proxy operator*() const { return p_; } + Proxy *operator->() { return &p_; } + const Proxy *operator->() const { return &p_; } + Proxy operator[](std::ptrdiff_t amount) const { return *(*this + amount); } + + const InnerIterator &Inner() { return p_.Inner(); } + + private: + InnerIterator &I() { return p_.Inner(); } + const InnerIterator &I() const { return p_.Inner(); } + + Proxy p_; +}; + +template ProxyIterator operator+(std::ptrdiff_t amount, const ProxyIterator &it) { + return it + amount; +} + +} // namespace util + +#endif // UTIL_PROXY_ITERATOR_H diff --git a/ctcdecode-master/third_party/kenlm/util/read_compressed.cc b/ctcdecode-master/third_party/kenlm/util/read_compressed.cc new file mode 100644 index 0000000..1548030 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/read_compressed.cc @@ -0,0 +1,438 @@ +#include "util/read_compressed.hh" + +#include "util/file.hh" +#include "util/have.hh" +#include "util/scoped.hh" + +#include +#include + +#include +#include +#include +#include + +#ifdef HAVE_ZLIB +#include +#endif + +#ifdef HAVE_BZLIB +#include +#endif + +#ifdef HAVE_XZLIB +#include +#endif + +namespace util { + +CompressedException::CompressedException() throw() {} +CompressedException::~CompressedException() throw() {} + +GZException::GZException() throw() {} +GZException::~GZException() throw() {} + +BZException::BZException() throw() {} +BZException::~BZException() throw() {} + +XZException::XZException() throw() {} +XZException::~XZException() throw() {} + +void ReadBase::ReplaceThis(ReadBase *with, ReadCompressed &thunk) { + thunk.internal_.reset(with); +} + +ReadBase *ReadBase::Current(ReadCompressed &thunk) { return thunk.internal_.get(); } + +uint64_t &ReadBase::ReadCount(ReadCompressed &thunk) { + return thunk.raw_amount_; +} + +namespace { + +ReadBase *ReadFactory(int fd, uint64_t &raw_amount, const void *already_data, std::size_t already_size, bool require_compressed); + +// Completed file that other classes can thunk to. +class Complete : public ReadBase { + public: + std::size_t Read(void *, std::size_t, ReadCompressed &) { + return 0; + } +}; + +class Uncompressed : public ReadBase { + public: + explicit Uncompressed(int fd) : fd_(fd) {} + + std::size_t Read(void *to, std::size_t amount, ReadCompressed &thunk) { + std::size_t got = PartialRead(fd_.get(), to, amount); + ReadCount(thunk) += got; + return got; + } + + private: + scoped_fd fd_; +}; + +class UncompressedWithHeader : public ReadBase { + public: + UncompressedWithHeader(int fd, const void *already_data, std::size_t already_size) : fd_(fd) { + assert(already_size); + buf_.reset(malloc(already_size)); + if (!buf_.get()) throw std::bad_alloc(); + memcpy(buf_.get(), already_data, already_size); + remain_ = static_cast(buf_.get()); + end_ = remain_ + already_size; + } + + std::size_t Read(void *to, std::size_t amount, ReadCompressed &thunk) { + assert(buf_.get()); + assert(remain_ != end_); + std::size_t sending = std::min(amount, end_ - remain_); + memcpy(to, remain_, sending); + remain_ += sending; + if (remain_ == end_) { + ReplaceThis(new Uncompressed(fd_.release()), thunk); + } + return sending; + } + + private: + scoped_malloc buf_; + uint8_t *remain_; + uint8_t *end_; + + scoped_fd fd_; +}; + +static const std::size_t kInputBuffer = 16384; + +template class StreamCompressed : public ReadBase { + public: + StreamCompressed(int fd, const void *already_data, std::size_t already_size) + : file_(fd), + in_buffer_(MallocOrThrow(kInputBuffer)), + back_(memcpy(in_buffer_.get(), already_data, already_size), already_size) {} + + std::size_t Read(void *to, std::size_t amount, ReadCompressed &thunk) { + if (amount == 0) return 0; + back_.SetOutput(to, amount); + do { + if (!back_.Stream().avail_in) ReadInput(thunk); + if (!back_.Process()) { + // reached end, at least for the compressed portion. + std::size_t ret = static_cast(static_cast(back_.Stream().next_out)) - static_cast(to); + ReplaceThis(ReadFactory(file_.release(), ReadCount(thunk), back_.Stream().next_in, back_.Stream().avail_in, true), thunk); + if (ret) return ret; + // We did not read anything this round, so clients might think EOF. Transfer responsibility to the next reader. + return Current(thunk)->Read(to, amount, thunk); + } + } while (back_.Stream().next_out == to); + return static_cast(static_cast(back_.Stream().next_out)) - static_cast(to); + } + + private: + void ReadInput(ReadCompressed &thunk) { + assert(!back_.Stream().avail_in); + std::size_t got = ReadOrEOF(file_.get(), in_buffer_.get(), kInputBuffer); + back_.SetInput(in_buffer_.get(), got); + ReadCount(thunk) += got; + } + + scoped_fd file_; + scoped_malloc in_buffer_; + + Compression back_; +}; + +#ifdef HAVE_ZLIB +class GZip { + public: + GZip(const void *base, std::size_t amount) { + SetInput(base, amount); + stream_.zalloc = Z_NULL; + stream_.zfree = Z_NULL; + stream_.opaque = Z_NULL; + stream_.msg = NULL; + // 32 for zlib and gzip decoding with automatic header detection. + // 15 for maximum window size. + UTIL_THROW_IF(Z_OK != inflateInit2(&stream_, 32 + 15), GZException, "Failed to initialize zlib."); + } + + ~GZip() { + if (Z_OK != inflateEnd(&stream_)) { + std::cerr << "zlib could not close properly." << std::endl; + abort(); + } + } + + void SetOutput(void *to, std::size_t amount) { + stream_.next_out = static_cast(to); + stream_.avail_out = std::min(std::numeric_limits::max(), amount); + } + + void SetInput(const void *base, std::size_t amount) { + assert(amount < static_cast(std::numeric_limits::max())); + stream_.next_in = const_cast(static_cast(base)); + stream_.avail_in = amount; + } + + const z_stream &Stream() const { return stream_; } + + bool Process() { + int result = inflate(&stream_, 0); + switch (result) { + case Z_OK: + return true; + case Z_STREAM_END: + return false; + case Z_ERRNO: + UTIL_THROW(ErrnoException, "zlib error"); + default: + UTIL_THROW(GZException, "zlib encountered " << (stream_.msg ? stream_.msg : "an error ") << " code " << result); + } + } + + private: + z_stream stream_; +}; +#endif // HAVE_ZLIB + +#ifdef HAVE_BZLIB +class BZip { + public: + BZip(const void *base, std::size_t amount) { + memset(&stream_, 0, sizeof(stream_)); + SetInput(base, amount); + HandleError(BZ2_bzDecompressInit(&stream_, 0, 0)); + } + + ~BZip() { + try { + HandleError(BZ2_bzDecompressEnd(&stream_)); + } catch (const std::exception &e) { + std::cerr << e.what() << std::endl; + abort(); + } + } + + bool Process() { + int ret = BZ2_bzDecompress(&stream_); + if (ret == BZ_STREAM_END) return false; + HandleError(ret); + return true; + } + + void SetOutput(void *base, std::size_t amount) { + stream_.next_out = static_cast(base); + stream_.avail_out = std::min(std::numeric_limits::max(), amount); + } + + void SetInput(const void *base, std::size_t amount) { + stream_.next_in = const_cast(static_cast(base)); + stream_.avail_in = amount; + } + + const bz_stream &Stream() const { return stream_; } + + private: + void HandleError(int value) { + switch(value) { + case BZ_OK: + return; + case BZ_CONFIG_ERROR: + UTIL_THROW(BZException, "bzip2 seems to be miscompiled."); + case BZ_PARAM_ERROR: + UTIL_THROW(BZException, "bzip2 Parameter error"); + case BZ_DATA_ERROR: + UTIL_THROW(BZException, "bzip2 detected a corrupt file"); + case BZ_DATA_ERROR_MAGIC: + UTIL_THROW(BZException, "bzip2 detected bad magic bytes. Perhaps this was not a bzip2 file after all?"); + case BZ_MEM_ERROR: + throw std::bad_alloc(); + default: + UTIL_THROW(BZException, "Unknown bzip2 error code " << value); + } + } + + bz_stream stream_; +}; +#endif // HAVE_BZLIB + +#ifdef HAVE_XZLIB +class XZip { + public: + XZip(const void *base, std::size_t amount) + : stream_(), action_(LZMA_RUN) { + memset(&stream_, 0, sizeof(stream_)); + SetInput(base, amount); + HandleError(lzma_stream_decoder(&stream_, UINT64_MAX, 0)); + } + + ~XZip() { + lzma_end(&stream_); + } + + void SetOutput(void *base, std::size_t amount) { + stream_.next_out = static_cast(base); + stream_.avail_out = amount; + } + + void SetInput(const void *base, std::size_t amount) { + stream_.next_in = static_cast(base); + stream_.avail_in = amount; + if (!amount) action_ = LZMA_FINISH; + } + + const lzma_stream &Stream() const { return stream_; } + + bool Process() { + lzma_ret status = lzma_code(&stream_, action_); + if (status == LZMA_STREAM_END) return false; + HandleError(status); + return true; + } + + private: + void HandleError(lzma_ret value) { + switch (value) { + case LZMA_OK: + return; + case LZMA_MEM_ERROR: + throw std::bad_alloc(); + case LZMA_FORMAT_ERROR: + UTIL_THROW(XZException, "xzlib says file format not recognized"); + case LZMA_OPTIONS_ERROR: + UTIL_THROW(XZException, "xzlib says unsupported compression options"); + case LZMA_DATA_ERROR: + UTIL_THROW(XZException, "xzlib says this file is corrupt"); + case LZMA_BUF_ERROR: + UTIL_THROW(XZException, "xzlib says unexpected end of input"); + default: + UTIL_THROW(XZException, "unrecognized xzlib error " << value); + } + } + + lzma_stream stream_; + lzma_action action_; +}; +#endif // HAVE_XZLIB + +class IStreamReader : public ReadBase { + public: + explicit IStreamReader(std::istream &stream) : stream_(stream) {} + + std::size_t Read(void *to, std::size_t amount, ReadCompressed &thunk) { + if (!stream_.read(static_cast(to), amount)) { + UTIL_THROW_IF(!stream_.eof(), ErrnoException, "istream error"); + amount = stream_.gcount(); + } + ReadCount(thunk) += amount; + return amount; + } + + private: + std::istream &stream_; +}; + +enum MagicResult { + UTIL_UNKNOWN, UTIL_GZIP, UTIL_BZIP, UTIL_XZIP +}; + +MagicResult DetectMagic(const void *from_void, std::size_t length) { + const uint8_t *header = static_cast(from_void); + if (length >= 2 && header[0] == 0x1f && header[1] == 0x8b) { + return UTIL_GZIP; + } + const uint8_t kBZMagic[3] = {'B', 'Z', 'h'}; + if (length >= sizeof(kBZMagic) && !memcmp(header, kBZMagic, sizeof(kBZMagic))) { + return UTIL_BZIP; + } + const uint8_t kXZMagic[6] = { 0xFD, '7', 'z', 'X', 'Z', 0x00 }; + if (length >= sizeof(kXZMagic) && !memcmp(header, kXZMagic, sizeof(kXZMagic))) { + return UTIL_XZIP; + } + return UTIL_UNKNOWN; +} + +ReadBase *ReadFactory(int fd, uint64_t &raw_amount, const void *already_data, const std::size_t already_size, bool require_compressed) { + scoped_fd hold(fd); + std::string header(reinterpret_cast(already_data), already_size); + if (header.size() < ReadCompressed::kMagicSize) { + std::size_t original = header.size(); + header.resize(ReadCompressed::kMagicSize); + std::size_t got = ReadOrEOF(fd, &header[original], ReadCompressed::kMagicSize - original); + raw_amount += got; + header.resize(original + got); + } + if (header.empty()) { + return new Complete(); + } + switch (DetectMagic(&header[0], header.size())) { + case UTIL_GZIP: +#ifdef HAVE_ZLIB + return new StreamCompressed(hold.release(), header.data(), header.size()); +#else + UTIL_THROW(CompressedException, "This looks like a gzip file but gzip support was not compiled in."); +#endif + case UTIL_BZIP: +#ifdef HAVE_BZLIB + return new StreamCompressed(hold.release(), &header[0], header.size()); +#else + UTIL_THROW(CompressedException, "This looks like a bzip file (it begins with BZh), but bzip support was not compiled in."); +#endif + case UTIL_XZIP: +#ifdef HAVE_XZLIB + return new StreamCompressed(hold.release(), header.data(), header.size()); +#else + UTIL_THROW(CompressedException, "This looks like an xz file, but xz support was not compiled in."); +#endif + default: + UTIL_THROW_IF(require_compressed, CompressedException, "Uncompressed data detected after a compresssed file. This could be supported but usually indicates an error."); + return new UncompressedWithHeader(hold.release(), header.data(), header.size()); + } +} + +} // namespace + +bool ReadCompressed::DetectCompressedMagic(const void *from_void) { + return DetectMagic(from_void, kMagicSize) != UTIL_UNKNOWN; +} + +ReadCompressed::ReadCompressed(int fd) { + Reset(fd); +} + +ReadCompressed::ReadCompressed(std::istream &in) { + Reset(in); +} + +ReadCompressed::ReadCompressed() {} + +void ReadCompressed::Reset(int fd) { + raw_amount_ = 0; + internal_.reset(); + internal_.reset(ReadFactory(fd, raw_amount_, NULL, 0, false)); +} + +void ReadCompressed::Reset(std::istream &in) { + internal_.reset(); + internal_.reset(new IStreamReader(in)); +} + +std::size_t ReadCompressed::Read(void *to, std::size_t amount) { + return internal_->Read(to, amount, *this); +} + +std::size_t ReadCompressed::ReadOrEOF(void *const to_in, std::size_t amount) { + uint8_t *to = reinterpret_cast(to_in); + while (amount) { + std::size_t got = Read(to, amount); + if (!got) break; + to += got; + amount -= got; + } + return to - reinterpret_cast(to_in); +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/read_compressed.hh b/ctcdecode-master/third_party/kenlm/util/read_compressed.hh new file mode 100644 index 0000000..abc6f04 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/read_compressed.hh @@ -0,0 +1,92 @@ +#ifndef UTIL_READ_COMPRESSED_H +#define UTIL_READ_COMPRESSED_H + +#include "util/exception.hh" +#include "util/scoped.hh" + +#include +#include + +namespace util { + +class CompressedException : public Exception { + public: + CompressedException() throw(); + virtual ~CompressedException() throw(); +}; + +class GZException : public CompressedException { + public: + GZException() throw(); + ~GZException() throw(); +}; + +class BZException : public CompressedException { + public: + BZException() throw(); + ~BZException() throw(); +}; + +class XZException : public CompressedException { + public: + XZException() throw(); + ~XZException() throw(); +}; + +class ReadCompressed; + +class ReadBase { + public: + virtual ~ReadBase() {} + + virtual std::size_t Read(void *to, std::size_t amount, ReadCompressed &thunk) = 0; + + protected: + static void ReplaceThis(ReadBase *with, ReadCompressed &thunk); + + ReadBase *Current(ReadCompressed &thunk); + + static uint64_t &ReadCount(ReadCompressed &thunk); +}; + +class ReadCompressed { + public: + static const std::size_t kMagicSize = 6; + // Must have at least kMagicSize bytes. + static bool DetectCompressedMagic(const void *from); + + // Takes ownership of fd. + explicit ReadCompressed(int fd); + + // Try to avoid using this. Use the fd instead. + // There is no decompression support for istreams. + explicit ReadCompressed(std::istream &in); + + // Must call Reset later. + ReadCompressed(); + + // Takes ownership of fd. + void Reset(int fd); + + // Same advice as the constructor. + void Reset(std::istream &in); + + std::size_t Read(void *to, std::size_t amount); + + // Repeatedly call read to fill a buffer unless EOF is hit. + // Return number of bytes read. + std::size_t ReadOrEOF(void *const to, std::size_t amount); + + uint64_t RawAmount() const { return raw_amount_; } + + private: + friend class ReadBase; + + scoped_ptr internal_; + + uint64_t raw_amount_; +}; + +} // namespace util + +#endif // UTIL_READ_COMPRESSED_H diff --git a/ctcdecode-master/third_party/kenlm/util/read_compressed_test.cc b/ctcdecode-master/third_party/kenlm/util/read_compressed_test.cc new file mode 100644 index 0000000..a983ca7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/read_compressed_test.cc @@ -0,0 +1,130 @@ +#include "util/read_compressed.hh" + +#include "util/file.hh" +#include "util/have.hh" + +#define BOOST_TEST_MODULE ReadCompressedTest +#include +#include + +#include +#include +#include + +#if defined __MINGW32__ +#include +#include + +#if !defined mkstemp +// TODO insecure +int mkstemp(char * stemplate) +{ + char *filename = mktemp(stemplate); + if (filename == NULL) + return -1; + return open(filename, O_RDWR | O_CREAT, 0600); +} +#endif + +#endif // defined + +namespace util { +namespace { + +void ReadLoop(ReadCompressed &reader, void *to_void, std::size_t amount) { + uint8_t *to = static_cast(to_void); + while (amount) { + std::size_t ret = reader.Read(to, amount); + BOOST_REQUIRE(ret); + to += ret; + amount -= ret; + } +} + +const uint32_t kSize4 = 100000 / 4; + +std::string WriteRandom() { + char name[] = "tempXXXXXX"; + scoped_fd original(mkstemp(name)); + BOOST_REQUIRE(original.get() > 0); + for (uint32_t i = 0; i < kSize4; ++i) { + WriteOrThrow(original.get(), &i, sizeof(uint32_t)); + } + return name; +} + +void VerifyRead(ReadCompressed &reader) { + for (uint32_t i = 0; i < kSize4; ++i) { + uint32_t got; + ReadLoop(reader, &got, sizeof(uint32_t)); + BOOST_CHECK_EQUAL(i, got); + } + + char ignored; + BOOST_CHECK_EQUAL((std::size_t)0, reader.Read(&ignored, 1)); + // Test double EOF call. + BOOST_CHECK_EQUAL((std::size_t)0, reader.Read(&ignored, 1)); +} + +void TestRandom(const char *compressor) { + std::string name(WriteRandom()); + + char gzname[] = "tempXXXXXX"; + scoped_fd gzipped(mkstemp(gzname)); + + std::string command(compressor); +#ifdef __CYGWIN__ + command += ".exe"; +#endif + command += " <\""; + command += name; + command += "\" >\""; + command += gzname; + command += "\""; + BOOST_REQUIRE_EQUAL(0, system(command.c_str())); + + BOOST_CHECK_EQUAL(0, unlink(name.c_str())); + BOOST_CHECK_EQUAL(0, unlink(gzname)); + + ReadCompressed reader(gzipped.release()); + VerifyRead(reader); +} + +BOOST_AUTO_TEST_CASE(Uncompressed) { + TestRandom("cat"); +} + +#ifdef HAVE_ZLIB +BOOST_AUTO_TEST_CASE(ReadGZ) { + TestRandom("gzip"); +} +#endif // HAVE_ZLIB + +#ifdef HAVE_BZLIB +BOOST_AUTO_TEST_CASE(ReadBZ) { + TestRandom("bzip2"); +} +#endif // HAVE_BZLIB + +#ifdef HAVE_XZLIB +BOOST_AUTO_TEST_CASE(ReadXZ) { + TestRandom("xz"); +} +#endif + +#ifdef HAVE_ZLIB +BOOST_AUTO_TEST_CASE(AppendGZ) { +} +#endif + +BOOST_AUTO_TEST_CASE(IStream) { + std::string name(WriteRandom()); + std::fstream stream(name.c_str(), std::ios::in); + BOOST_CHECK_EQUAL(0, unlink(name.c_str())); + ReadCompressed reader; + reader.Reset(stream); + VerifyRead(reader); +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/scoped.cc b/ctcdecode-master/third_party/kenlm/util/scoped.cc new file mode 100644 index 0000000..817aa24 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/scoped.cc @@ -0,0 +1,43 @@ +#include "util/scoped.hh" + +#include +#if !defined(_WIN32) && !defined(_WIN64) +#include +#endif + +namespace util { + +// TODO: if we're really under memory pressure, don't allocate memory to +// display the error. +MallocException::MallocException(std::size_t requested) throw() { + *this << "for " << requested << " bytes "; +} + +MallocException::~MallocException() throw() {} + +namespace { +void *InspectAddr(void *addr, std::size_t requested, const char *func_name) { + UTIL_THROW_IF_ARG(!addr && requested, MallocException, (requested), "in " << func_name); + return addr; +} +} // namespace + +void *MallocOrThrow(std::size_t requested) { + return InspectAddr(std::malloc(requested), requested, "malloc"); +} + +void *CallocOrThrow(std::size_t requested) { + return InspectAddr(std::calloc(requested, 1), requested, "calloc"); +} + +void scoped_malloc::call_realloc(std::size_t requested) { + p_ = InspectAddr(std::realloc(p_, requested), requested, "realloc"); +} + +void AdviseHugePages(const void *addr, std::size_t size) { +#if MADV_HUGEPAGE + madvise((void*)addr, size, MADV_HUGEPAGE); +#endif +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/scoped.hh b/ctcdecode-master/third_party/kenlm/util/scoped.hh new file mode 100644 index 0000000..23782b6 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/scoped.hh @@ -0,0 +1,125 @@ +#ifndef UTIL_SCOPED_H +#define UTIL_SCOPED_H +/* Other scoped objects in the style of scoped_ptr. */ + +#include "util/exception.hh" +#include +#include + +namespace util { + +class MallocException : public ErrnoException { + public: + explicit MallocException(std::size_t requested) throw(); + ~MallocException() throw(); +}; + +void *MallocOrThrow(std::size_t requested); +void *CallocOrThrow(std::size_t requested); + +/* Unfortunately, defining the operator* for void * makes the compiler complain. + * So scoped is specialized to void. This includes the functionality common to + * both, namely everything except reference. + */ +template class scoped_base { + public: + explicit scoped_base(T *p = NULL) : p_(p) {} + + ~scoped_base() { Closer::Close(p_); } + +#if __cplusplus >= 201103L + scoped_base(scoped_base &&from) noexcept : p_(from.p_) { + from.p_ = nullptr; + } +#endif + + void reset(T *p = NULL) { + scoped_base other(p_); + p_ = p; + } + + T *get() { return p_; } + const T *get() const { return p_; } + + T *operator->() { return p_; } + const T *operator->() const { return p_; } + + T *release() { + T *ret = p_; + p_ = NULL; + return ret; + } + + protected: + T *p_; + +#if __cplusplus >= 201103L + public: + scoped_base(const scoped_base &) = delete; + scoped_base &operator=(const scoped_base &) = delete; +#else + private: + scoped_base(const scoped_base &); + scoped_base &operator=(const scoped_base &); +#endif +}; + +template class scoped : public scoped_base { + public: + explicit scoped(T *p = NULL) : scoped_base(p) {} + + T &operator*() { return *scoped_base::p_; } + const T&operator*() const { return *scoped_base::p_; } +}; + +template class scoped : public scoped_base { + public: + explicit scoped(void *p = NULL) : scoped_base(p) {} +}; + +/* Closer for c functions like std::free and cmph cleanup functions */ +template struct scoped_c_forward { + static void Close(T *p) { clean(p); } +}; +// Call a C function to delete stuff +template class scoped_c : public scoped > { + public: + explicit scoped_c(T *p = NULL) : scoped >(p) {} +}; + +class scoped_malloc : public scoped_c { + public: + explicit scoped_malloc(void *p = NULL) : scoped_c(p) {} + + explicit scoped_malloc(std::size_t size) : scoped_c(MallocOrThrow(size)) {} + + void call_realloc(std::size_t to); +}; + +/* scoped_array using delete[] */ +struct scoped_delete_array_forward { + template static void Close(T *p) { delete [] p; } +}; +// Hat tip to boost. +template class scoped_array : public scoped { + public: + explicit scoped_array(T *p = NULL) : scoped(p) {} + + T &operator[](std::size_t idx) { return scoped::p_[idx]; } + const T &operator[](std::size_t idx) const { return scoped::p_[idx]; } +}; + +/* scoped_ptr using delete. If only there were a template typedef. */ +struct scoped_delete_forward { + template static void Close(T *p) { delete p; } +}; +template class scoped_ptr : public scoped { + public: + explicit scoped_ptr(T *p = NULL) : scoped(p) {} +}; + +void AdviseHugePages(const void *addr, std::size_t size); + +} // namespace util + +#endif // UTIL_SCOPED_H diff --git a/ctcdecode-master/third_party/kenlm/util/sized_iterator.hh b/ctcdecode-master/third_party/kenlm/util/sized_iterator.hh new file mode 100644 index 0000000..e9fd7bf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/sized_iterator.hh @@ -0,0 +1,215 @@ +#ifndef UTIL_SIZED_ITERATOR_H +#define UTIL_SIZED_ITERATOR_H + +#include "util/pool.hh" +#include "util/proxy_iterator.hh" + +#include +#include +#include + +#include +#include + +#include + +namespace util { + +class SizedInnerIterator { + public: + SizedInnerIterator() {} + + SizedInnerIterator(void *ptr, std::size_t size) : ptr_(static_cast(ptr)), size_(size) {} + + bool operator==(const SizedInnerIterator &other) const { + return ptr_ == other.ptr_; + } + bool operator<(const SizedInnerIterator &other) const { + return ptr_ < other.ptr_; + } + SizedInnerIterator &operator+=(std::ptrdiff_t amount) { + ptr_ += amount * size_; + return *this; + } + std::ptrdiff_t operator-(const SizedInnerIterator &other) const { + return (ptr_ - other.ptr_) / size_; + } + + const void *Data() const { return ptr_; } + void *Data() { return ptr_; } + std::size_t EntrySize() const { return size_; } + + friend void swap(SizedInnerIterator &first, SizedInnerIterator &second); + + private: + uint8_t *ptr_; + std::size_t size_; +}; + +inline void swap(SizedInnerIterator &first, SizedInnerIterator &second) { + using std::swap; + swap(first.ptr_, second.ptr_); + swap(first.size_, second.size_); +} + +class ValueBlock { + public: + explicit ValueBlock(const void *from, FreePool &pool) + : ptr_(std::memcpy(pool.Allocate(), from, pool.ElementSize())), + pool_(pool) {} + + ValueBlock(const ValueBlock &from) + : ptr_(std::memcpy(from.pool_.Allocate(), from.ptr_, from.pool_.ElementSize())), + pool_(from.pool_) {} + + ValueBlock &operator=(const ValueBlock &from) { + std::memcpy(ptr_, from.ptr_, pool_.ElementSize()); + return *this; + } + + ~ValueBlock() { pool_.Free(ptr_); } + + const void *Data() const { return ptr_; } + void *Data() { return ptr_; } + + private: + void *ptr_; + FreePool &pool_; +}; + +class SizedProxy { + public: + SizedProxy() {} + + SizedProxy(void *ptr, FreePool &pool) : inner_(ptr, pool.ElementSize()), pool_(&pool) {} + + operator ValueBlock() const { + return ValueBlock(inner_.Data(), *pool_); + } + + SizedProxy &operator=(const SizedProxy &from) { + memcpy(inner_.Data(), from.inner_.Data(), inner_.EntrySize()); + return *this; + } + + SizedProxy &operator=(const ValueBlock &from) { + memcpy(inner_.Data(), from.Data(), inner_.EntrySize()); + return *this; + } + + const void *Data() const { return inner_.Data(); } + void *Data() { return inner_.Data(); } + + friend void swap(SizedProxy first, SizedProxy second); + + private: + friend class util::ProxyIterator; + + typedef ValueBlock value_type; + + typedef SizedInnerIterator InnerIterator; + + InnerIterator &Inner() { return inner_; } + const InnerIterator &Inner() const { return inner_; } + + InnerIterator inner_; + + FreePool *pool_; +}; + +inline void swap(SizedProxy first, SizedProxy second) { + std::swap_ranges( + static_cast(first.inner_.Data()), + static_cast(first.inner_.Data()) + first.inner_.EntrySize(), + static_cast(second.inner_.Data())); +} + +typedef ProxyIterator SizedIterator; + +// Useful wrapper for a comparison function i.e. sort. +template class SizedCompare : public std::binary_function { + public: + explicit SizedCompare(const Delegate &delegate = Delegate()) : delegate_(delegate) {} + + bool operator()(const Proxy &first, const Proxy &second) const { + return delegate_(first.Data(), second.Data()); + } + bool operator()(const Proxy &first, const ValueBlock &second) const { + return delegate_(first.Data(), second.Data()); + } + bool operator()(const ValueBlock &first, const Proxy &second) const { + return delegate_(first.Data(), second.Data()); + } + bool operator()(const ValueBlock &first, const ValueBlock &second) const { + return delegate_(first.Data(), second.Data()); + } + + const Delegate &GetDelegate() const { return delegate_; } + + private: + const Delegate delegate_; +}; + +template class JustPOD { + unsigned char data[Size]; +}; + +template class JustPODDelegate : std::binary_function &, const JustPOD &, bool> { + public: + explicit JustPODDelegate(const Delegate &compare) : delegate_(compare) {} + bool operator()(const JustPOD &first, const JustPOD &second) const { + return delegate_(&first, &second); + } + private: + Delegate delegate_; +}; + +#define UTIL_SORT_SPECIALIZE(Size) \ + case Size: \ + std::sort(static_cast*>(start), static_cast*>(end), JustPODDelegate(compare)); \ + break; + +template void SizedSort(void *start, void *end, std::size_t element_size, const Compare &compare) { + switch (element_size) { + // Benchmarking sort found it's about 2x faster with an explicitly sized type. So here goes :-(. + UTIL_SORT_SPECIALIZE(4); + UTIL_SORT_SPECIALIZE(8); + UTIL_SORT_SPECIALIZE(12); + UTIL_SORT_SPECIALIZE(16); + UTIL_SORT_SPECIALIZE(17); // This is used by interpolation. + UTIL_SORT_SPECIALIZE(20); + UTIL_SORT_SPECIALIZE(24); + UTIL_SORT_SPECIALIZE(28); + UTIL_SORT_SPECIALIZE(32); + default: + // Recent g++ versions create a temporary value_type then compare with it. + // Problem is that value_type in this case needs to be a runtime-sized array. + // Previously I had std::string serve this role. However, there were a lot + // of string new and delete calls. + // + // The temporary value is on the stack, so there will typically only be one + // at a time. But we can't guarantee that. So here is a pool optimized for + // the case where one element is allocated at any given time. It can + // allocate more, should the underlying C++ sort code change. + { + FreePool pool(element_size); + // TODO is this necessary anymore? + #if defined(_WIN32) || defined(_WIN64) + std::stable_sort + #else + std::sort +#endif + (SizedIterator(SizedProxy(start, pool)), SizedIterator(SizedProxy(end, pool)), SizedCompare(compare)); + } + } +} + +} // namespace util + +// Dirty hack because g++ 4.6 at least wants to do a bunch of copy operations. +namespace std { +inline void iter_swap(util::SizedIterator first, util::SizedIterator second) { + util::swap(*first, *second); +} +} // namespace std +#endif // UTIL_SIZED_ITERATOR_H diff --git a/ctcdecode-master/third_party/kenlm/util/sized_iterator_test.cc b/ctcdecode-master/third_party/kenlm/util/sized_iterator_test.cc new file mode 100644 index 0000000..4db015d --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/sized_iterator_test.cc @@ -0,0 +1,22 @@ +#include "util/sized_iterator.hh" + +#define BOOST_TEST_MODULE SizedIteratorTest +#include + +namespace util { namespace { + +struct CompareChar { + bool operator()(const void *first, const void *second) const { + return *static_cast(first) < *static_cast(second); + } +}; + +BOOST_AUTO_TEST_CASE(sort) { + char items[3] = {1, 2, 0}; + SizedSort(items, items + 3, 1, CompareChar()); + BOOST_CHECK_EQUAL(0, items[0]); + BOOST_CHECK_EQUAL(1, items[1]); + BOOST_CHECK_EQUAL(2, items[2]); +} + +}} // namespace anonymous util diff --git a/ctcdecode-master/third_party/kenlm/util/sorted_uniform.hh b/ctcdecode-master/third_party/kenlm/util/sorted_uniform.hh new file mode 100644 index 0000000..ddd2b3f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/sorted_uniform.hh @@ -0,0 +1,105 @@ +#ifndef UTIL_SORTED_UNIFORM_H +#define UTIL_SORTED_UNIFORM_H + +#include +#include +#include +#include + +namespace util { + +template class IdentityAccessor { + public: + typedef T Key; + T operator()(const T *in) const { return *in; } +}; + +struct Pivot64 { + static inline std::size_t Calc(uint64_t off, uint64_t range, std::size_t width) { + std::size_t ret = static_cast(static_cast(off) / static_cast(range) * static_cast(width)); + // Cap for floating point rounding + return (ret < width) ? ret : width - 1; + } +}; + +// Use when off * width is <2^64. This is guaranteed when each of them is actually a 32-bit value. +struct Pivot32 { + static inline std::size_t Calc(uint64_t off, uint64_t range, uint64_t width) { + return static_cast((off * width) / (range + 1)); + } +}; + +// Usage: PivotSelect::T +template struct PivotSelect; +template <> struct PivotSelect<8> { typedef Pivot64 T; }; +template <> struct PivotSelect<4> { typedef Pivot32 T; }; +template <> struct PivotSelect<2> { typedef Pivot32 T; }; + +/* Binary search. */ +template bool BinaryFind( + const Accessor &accessor, + Iterator begin, + Iterator end, + const typename Accessor::Key key, Iterator &out) { + while (end > begin) { + Iterator pivot(begin + (end - begin) / 2); + typename Accessor::Key mid(accessor(pivot)); + if (mid < key) { + begin = pivot + 1; + } else if (mid > key) { + end = pivot; + } else { + out = pivot; + return true; + } + } + return false; +} + +// Search the range [before_it + 1, after_it - 1] for key. +// Preconditions: +// before_v <= key <= after_v +// before_v <= all values in the range [before_it + 1, after_it - 1] <= after_v +// range is sorted. +template bool BoundedSortedUniformFind( + const Accessor &accessor, + Iterator before_it, typename Accessor::Key before_v, + Iterator after_it, typename Accessor::Key after_v, + const typename Accessor::Key key, Iterator &out) { + while (after_it - before_it > 1) { + Iterator pivot(before_it + (1 + Pivot::Calc(key - before_v, after_v - before_v, after_it - before_it - 1))); + typename Accessor::Key mid(accessor(pivot)); + if (mid < key) { + before_it = pivot; + before_v = mid; + } else if (mid > key) { + after_it = pivot; + after_v = mid; + } else { + out = pivot; + return true; + } + } + return false; +} + +template bool SortedUniformFind(const Accessor &accessor, Iterator begin, Iterator end, const typename Accessor::Key key, Iterator &out) { + if (begin == end) return false; + typename Accessor::Key below(accessor(begin)); + if (key <= below) { + if (key == below) { out = begin; return true; } + return false; + } + // Make the range [begin, end]. + --end; + typename Accessor::Key above(accessor(end)); + if (key >= above) { + if (key == above) { out = end; return true; } + return false; + } + return BoundedSortedUniformFind(accessor, begin, below, end, above, key, out); +} + +} // namespace util + +#endif // UTIL_SORTED_UNIFORM_H diff --git a/ctcdecode-master/third_party/kenlm/util/sorted_uniform_test.cc b/ctcdecode-master/third_party/kenlm/util/sorted_uniform_test.cc new file mode 100644 index 0000000..39f05e5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/sorted_uniform_test.cc @@ -0,0 +1,127 @@ +#include "util/sorted_uniform.hh" + +#include +#include +#include +#include +#include + +#define BOOST_TEST_MODULE SortedUniformTest +#include + +#include +#include +#include + +namespace util { +namespace { + +template struct Entry { + typedef KeyT Key; + typedef ValueT Value; + + Key key; + Value value; + + Key GetKey() const { + return key; + } + + Value GetValue() const { + return value; + } + + bool operator<(const Entry &other) const { + return key < other.key; + } +}; + +template struct Accessor { + typedef KeyT Key; + template Key operator()(const Entry *entry) const { + return entry->GetKey(); + } +}; + +template void Check(const Entry *begin, const Entry *end, const boost::unordered_map &reference, const Key key) { + typename boost::unordered_map::const_iterator ref = reference.find(key); + typedef const Entry *It; + // g++ can't tell that require will crash and burn. + It i = NULL; + bool ret = SortedUniformFind, Pivot64>(Accessor(), begin, end, key, i); + if (ref == reference.end()) { + BOOST_CHECK(!ret); + } else { + BOOST_REQUIRE(ret); + BOOST_CHECK_EQUAL(ref->second, i->GetValue()); + } +} + +BOOST_AUTO_TEST_CASE(empty) { + typedef const Entry T; + const T *i; + bool ret = SortedUniformFind, Pivot64>(Accessor(), (const T*)NULL, (const T*)NULL, (uint64_t)10, i); + BOOST_CHECK(!ret); +} + +template void RandomTest(Key upper, size_t entries, size_t queries) { + typedef unsigned char Value; + boost::mt19937 rng; + boost::uniform_int range_key(0, upper); + boost::uniform_int range_value(0, 255); + boost::variate_generator > gen_key(rng, range_key); + boost::variate_generator > gen_value(rng, range_value); + + typedef Entry Ent; + std::vector backing; + boost::unordered_map reference; + Ent ent; + for (size_t i = 0; i < entries; ++i) { + Key key = gen_key(); + unsigned char value = gen_value(); + if (reference.insert(std::make_pair(key, value)).second) { + ent.key = key; + ent.value = value; + backing.push_back(ent); + } + } + std::sort(backing.begin(), backing.end()); + + // Random queries. + for (size_t i = 0; i < queries; ++i) { + const Key key = gen_key(); + Check(&*backing.begin(), &*backing.end(), reference, key); + } + + typename boost::unordered_map::const_iterator it = reference.begin(); + for (size_t i = 0; (i < queries) && (it != reference.end()); ++i, ++it) { + Check(&*backing.begin(), &*backing.end(), reference, it->second); + } +} + +BOOST_AUTO_TEST_CASE(basic) { + RandomTest(11, 10, 200); +} + +BOOST_AUTO_TEST_CASE(tiny_dense_random) { + RandomTest(11, 50, 200); +} + +BOOST_AUTO_TEST_CASE(small_dense_random) { + RandomTest(100, 100, 200); +} + +BOOST_AUTO_TEST_CASE(small_sparse_random) { + RandomTest(200, 15, 200); +} + +BOOST_AUTO_TEST_CASE(medium_sparse_random) { + RandomTest(32000, 1000, 2000); +} + +BOOST_AUTO_TEST_CASE(sparse_random) { + RandomTest(std::numeric_limits::max(), 100000, 2000); +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/spaces.cc b/ctcdecode-master/third_party/kenlm/util/spaces.cc new file mode 100644 index 0000000..7bc25bf --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/spaces.cc @@ -0,0 +1,8 @@ +#include "util/spaces.hh" + +namespace util { + +// Sigh this is the only way I could come up with to do a _const_ bool. It has ' ', '\f', '\n', '\r', '\t', and '\v' (same as isspace on C locale). +const bool kSpaces[256] = {0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}; + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/spaces.hh b/ctcdecode-master/third_party/kenlm/util/spaces.hh new file mode 100644 index 0000000..3cf34fe --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/spaces.hh @@ -0,0 +1,12 @@ +#ifndef UTIL_SPACES_H +#define UTIL_SPACES_H + +// bool array of spaces. + +namespace util { + +extern const bool kSpaces[256]; + +} // namespace util + +#endif // UTIL_SPACES_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/CMakeLists.txt b/ctcdecode-master/third_party/kenlm/util/stream/CMakeLists.txt new file mode 100644 index 0000000..93819ce --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/CMakeLists.txt @@ -0,0 +1,39 @@ +# This CMake file was created by Lane Schwartz + +# Explicitly list the source files for this subdirectory +# +# If you add any source files to this subdirectory +# that should be included in the kenlm library, +# (this excludes any unit test files) +# you should add them to the following list: +# +# In order to allow CMake files in the parent directory +# to see this variable definition, we set PARENT_SCOPE. +# +# In order to set correct paths to these files +# when this variable is referenced by CMake files in the parent directory, +# we prefix all files with ${CMAKE_CURRENT_SOURCE_DIR}. +# +set(KENLM_UTIL_STREAM_SOURCE + ${CMAKE_CURRENT_SOURCE_DIR}/chain.cc + ${CMAKE_CURRENT_SOURCE_DIR}/count_records.cc + ${CMAKE_CURRENT_SOURCE_DIR}/io.cc + ${CMAKE_CURRENT_SOURCE_DIR}/line_input.cc + ${CMAKE_CURRENT_SOURCE_DIR}/multi_progress.cc + ${CMAKE_CURRENT_SOURCE_DIR}/rewindable_stream.cc + PARENT_SCOPE) + + + +if(BUILD_TESTING) + # Explicitly list the Boost test files to be compiled + set(KENLM_BOOST_TESTS_LIST + io_test + sort_test + stream_test + rewindable_stream_test + ) + + AddTests(TESTS ${KENLM_BOOST_TESTS_LIST} + LIBRARIES kenlm_util ${Boost_LIBRARIES} pthread) +endif() diff --git a/ctcdecode-master/third_party/kenlm/util/stream/block.hh b/ctcdecode-master/third_party/kenlm/util/stream/block.hh new file mode 100644 index 0000000..42df13f --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/block.hh @@ -0,0 +1,93 @@ +#ifndef UTIL_STREAM_BLOCK_H +#define UTIL_STREAM_BLOCK_H + +#include +#include + +namespace util { +namespace stream { + +/** + * Encapsulates a block of memory. + */ +class Block { + public: + + /** + * Constructs an empty block. + */ + Block() : mem_(NULL), valid_size_(0) {} + + /** + * Constructs a block that encapsulates a segment of memory. + * + * @param[in] mem The segment of memory to encapsulate + * @param[in] size The size of the memory segment in bytes + */ + Block(void *mem, std::size_t size) : mem_(mem), valid_size_(size) {} + + /** + * Set the number of bytes in this block that should be interpreted as valid. + * + * @param[in] to Number of bytes + */ + void SetValidSize(std::size_t to) { valid_size_ = to; } + + /** + * Gets the number of bytes in this block that should be interpreted as valid. + * This is important because read might fill in less than Allocated at EOF. + */ + std::size_t ValidSize() const { return valid_size_; } + + /** Gets a void pointer to the memory underlying this block. */ + void *Get() { return mem_; } + + /** Gets a const void pointer to the memory underlying this block. */ + const void *Get() const { return mem_; } + + + /** + * Gets a const void pointer to the end of the valid section of memory + * encapsulated by this block. + */ + const void *ValidEnd() const { + return reinterpret_cast(mem_) + valid_size_; + } + + /** + * Returns true if this block encapsulates a valid (non-NULL) block of memory. + * + * This method is a user-defined implicit conversion function to boolean; + * among other things, this method enables bare instances of this class + * to be used as the condition of an if statement. + */ + operator bool() const { return mem_ != NULL; } + + /** + * Returns true if this block is empty. + * + * In other words, if Get()==NULL, this method will return true. + */ + bool operator!() const { return mem_ == NULL; } + + private: + friend class Link; + friend class RewindableStream; + + /** + * Points this block's memory at NULL. + * + * This class defines poison as a block whose memory pointer is NULL. + */ + void SetToPoison() { + mem_ = NULL; + } + + void *mem_; + std::size_t valid_size_; +}; + +} // namespace stream +} // namespace util + +#endif // UTIL_STREAM_BLOCK_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/chain.cc b/ctcdecode-master/third_party/kenlm/util/stream/chain.cc new file mode 100644 index 0000000..c122b98 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/chain.cc @@ -0,0 +1,158 @@ +#include "util/stream/chain.hh" + +#include "util/stream/io.hh" + +#include "util/exception.hh" +#include "util/pcqueue.hh" + +#include +#include +#include +#include + +namespace util { +namespace stream { + +ChainConfigException::ChainConfigException() throw() { *this << "Chain configured with "; } +ChainConfigException::~ChainConfigException() throw() {} + +Thread::~Thread() { + thread_.join(); +} + +void Thread::UnhandledException(const std::exception &e) { + std::cerr << e.what() << std::endl; + abort(); +} + +void Recycler::Run(const ChainPosition &position) { + for (Link l(position); l; ++l) { + l->SetValidSize(position.GetChain().BlockSize()); + } +} + +const Recycler kRecycle = Recycler(); + +Chain::Chain(const ChainConfig &config) : config_(config), complete_called_(false) { + UTIL_THROW_IF(!config.entry_size, ChainConfigException, "zero-size entries."); + UTIL_THROW_IF(!config.block_count, ChainConfigException, "block count zero"); + UTIL_THROW_IF(config.total_memory < config.entry_size * config.block_count, ChainConfigException, config.total_memory << " total memory, too small for " << config.block_count << " blocks of containing entries of size " << config.entry_size); + // Round down block size to a multiple of entry size. + block_size_ = config.total_memory / (config.block_count * config.entry_size) * config.entry_size; +} + +Chain::~Chain() { + Wait(); +} + +ChainPosition Chain::Add() { + if (!Running()) Start(); + PCQueue &in = queues_.back(); + queues_.push_back(new PCQueue(config_.block_count)); + return ChainPosition(in, queues_.back(), this, progress_); +} + +Chain &Chain::operator>>(const WriteAndRecycle &writer) { + threads_.push_back(new Thread(Complete(), writer)); + return *this; +} + +void Chain::Wait(bool release_memory) { + if (queues_.empty()) { + assert(threads_.empty()); + return; // Nothing to wait for. + } + if (!complete_called_) CompleteLoop(); + threads_.clear(); + for (std::size_t i = 0; queues_.front().Consume(); ++i) { + if (i == config_.block_count) { + std::cerr << "Chain ending without poison." << std::endl; + abort(); + } + } + queues_.clear(); + progress_.Finished(); + complete_called_ = false; + if (release_memory) memory_.reset(); +} + +void Chain::Start() { + Wait(false); + if (!memory_.get()) { + // Allocate memory. + assert(threads_.empty()); + assert(queues_.empty()); + std::size_t malloc_size = block_size_ * config_.block_count; + memory_.reset(MallocOrThrow(malloc_size)); + } + // This queue can accomodate all blocks. + queues_.push_back(new PCQueue(config_.block_count)); + // Populate the lead queue with blocks. + uint8_t *base = static_cast(memory_.get()); + for (std::size_t i = 0; i < config_.block_count; ++i) { + queues_.front().Produce(Block(base, block_size_)); + base += block_size_; + } +} + +ChainPosition Chain::Complete() { + assert(Running()); + UTIL_THROW_IF(complete_called_, util::Exception, "CompleteLoop() called twice"); + complete_called_ = true; + return ChainPosition(queues_.back(), queues_.front(), this, progress_); +} + +Link::Link() : in_(NULL), out_(NULL), poisoned_(true) {} + +void Link::Init(const ChainPosition &position) { + UTIL_THROW_IF(in_, util::Exception, "Link::Init twice"); + in_ = position.in_; + out_ = position.out_; + poisoned_ = false; + progress_ = position.progress_; + in_->Consume(current_); +} + +Link::Link(const ChainPosition &position) : in_(NULL) { + Init(position); +} + +Link::~Link() { + if (current_) { + // Probably an exception unwinding. + std::cerr << "Last input should have been poison." << std::endl; + abort(); + } else { + if (!poisoned_) { + // Poison is a block whose memory pointer is NULL. + // + // Because we're in the else block, + // we know that the memory pointer of current_ is NULL. + // + // Pass the current (poison) block! + out_->Produce(current_); + } + } +} + +Link &Link::operator++() { + assert(current_); + progress_ += current_.ValidSize(); + out_->Produce(current_); + in_->Consume(current_); + if (!current_) { + poisoned_ = true; + out_->Produce(current_); + } + return *this; +} + +void Link::Poison() { + assert(!poisoned_); + current_.SetToPoison(); + out_->Produce(current_); + poisoned_ = true; +} + +} // namespace stream +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/stream/chain.hh b/ctcdecode-master/third_party/kenlm/util/stream/chain.hh new file mode 100644 index 0000000..5e3b4b9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/chain.hh @@ -0,0 +1,345 @@ +#ifndef UTIL_STREAM_CHAIN_H +#define UTIL_STREAM_CHAIN_H + +#include "util/stream/block.hh" +#include "util/stream/config.hh" +#include "util/stream/multi_progress.hh" +#include "util/scoped.hh" + +#include +#include + +#include +#include + +namespace util { +template class PCQueue; +namespace stream { + +class ChainConfigException : public Exception { + public: + ChainConfigException() throw(); + ~ChainConfigException() throw(); +}; + +class Chain; +class RewindableStream; + +/** + * Encapsulates a @ref PCQueue "producer queue" and a @ref PCQueue "consumer queue" within a @ref Chain "chain". + * + * Specifies position in chain for Link constructor. + */ +class ChainPosition { + public: + const Chain &GetChain() const { return *chain_; } + private: + friend class Chain; + friend class Link; + friend class RewindableStream; + ChainPosition(PCQueue &in, PCQueue &out, Chain *chain, MultiProgress &progress) + : in_(&in), out_(&out), chain_(chain), progress_(progress.Add()) {} + + PCQueue *in_, *out_; + + Chain *chain_; + + WorkerProgress progress_; +}; + + +/** + * Encapsulates a worker thread processing data at a given position in the chain. + * + * Each instance of this class owns one boost thread in which the worker is Run(). + */ +class Thread { + public: + + /** + * Constructs a new Thread in which the provided Worker is Run(). + * + * Position is usually ChainPosition but if there are multiple streams involved, this can be ChainPositions. + * + * After a call to this constructor, the provided worker will be running within a boost thread owned by the newly constructed Thread object. + */ + template Thread(const Position &position, const Worker &worker) + : thread_(boost::ref(*this), position, worker) {} + + ~Thread(); + + /** + * Launches the provided worker in this object's boost thread. + * + * This method is called automatically by this class's @ref Thread() "constructor". + */ + template void operator()(const Position &position, Worker &worker) { + try { + worker.Run(position); + } catch (const std::exception &e) { + UnhandledException(e); + } + } + + private: + void UnhandledException(const std::exception &e); + + boost::thread thread_; +}; + +/** + * This resets blocks to full valid size. Used to close the loop in Chain by recycling blocks. + */ +class Recycler { + public: + /** + * Resets the blocks in the chain such that the blocks' respective valid sizes match the chain's block size. + * + * @see Block::SetValidSize() + * @see Chain::BlockSize() + */ + void Run(const ChainPosition &position); +}; + +extern const Recycler kRecycle; +class WriteAndRecycle; + +/** + * Represents a sequence of workers, through which @ref Block "blocks" can pass. + */ +class Chain { + private: + template struct CheckForRun { + typedef Chain type; + }; + + public: + + /** + * Constructs a configured Chain. + * + * @param config Specifies how to configure the Chain. + */ + explicit Chain(const ChainConfig &config); + + /** + * Destructs a Chain. + * + * This method waits for the chain's threads to complete, + * and frees the memory held by this chain. + */ + ~Chain(); + + void ActivateProgress() { + assert(!Running()); + progress_.Activate(); + } + + void SetProgressTarget(uint64_t target) { + progress_.SetTarget(target); + } + + /** + * Gets the number of bytes in each record of a Block. + * + * @see ChainConfig::entry_size + */ + std::size_t EntrySize() const { + return config_.entry_size; + } + + /** + * Gets the inital @ref Block::ValidSize "valid size" for @ref Block "blocks" in this chain. + * + * @see Block::ValidSize + */ + std::size_t BlockSize() const { + return block_size_; + } + + /** + * Number of blocks going through the Chain. + */ + std::size_t BlockCount() const { + return config_.block_count; + } + + /** Two ways to add to the chain: Add() or operator>>. */ + ChainPosition Add(); + + /** + * Adds a new worker to this chain, + * and runs that worker in a new Thread owned by this chain. + * + * The worker must have a Run method that accepts a position argument. + * + * @see Thread::operator()() + */ + template typename CheckForRun::type &operator>>(const Worker &worker) { + assert(!complete_called_); + threads_.push_back(new Thread(Add(), worker)); + return *this; + } + + /** + * Adds a new worker to this chain (but avoids copying that worker), + * and runs that worker in a new Thread owned by this chain. + * + * The worker must have a Run method that accepts a position argument. + * + * @see Thread::operator()() + */ + template typename CheckForRun::type &operator>>(const boost::reference_wrapper &worker) { + assert(!complete_called_); + threads_.push_back(new Thread(Add(), worker)); + return *this; + } + + // Note that Link and Stream also define operator>> outside this class. + + // To complete the loop, call CompleteLoop(), >> kRecycle, or the destructor. + void CompleteLoop() { + threads_.push_back(new Thread(Complete(), kRecycle)); + } + + /** + * Adds a Recycler worker to this chain, + * and runs that worker in a new Thread owned by this chain. + */ + Chain &operator>>(const Recycler &) { + CompleteLoop(); + return *this; + } + + /** + * Adds a WriteAndRecycle worker to this chain, + * and runs that worker in a new Thread owned by this chain. + */ + Chain &operator>>(const WriteAndRecycle &writer); + + // Chains are reusable. Call Wait to wait for everything to finish and free memory. + void Wait(bool release_memory = true); + + // Waits for the current chain to complete (if any) then starts again. + void Start(); + + bool Running() const { return !queues_.empty(); } + + private: + ChainPosition Complete(); + + ChainConfig config_; + + std::size_t block_size_; + + scoped_malloc memory_; + + boost::ptr_vector > queues_; + + bool complete_called_; + + boost::ptr_vector threads_; + + MultiProgress progress_; +}; + +// Create the link in the worker thread using the position token. +/** + * Represents a C++ style iterator over @ref Block "blocks". + */ +class Link { + public: + + // Either default construct and Init or just construct all at once. + + /** + * Constructs an @ref Init "initialized" link. + * + * @see Init + */ + explicit Link(const ChainPosition &position); + + /** + * Constructs a link that must subsequently be @ref Init "initialized". + * + * @see Init + */ + Link(); + + /** + * Initializes the link with the input @ref PCQueue "consumer queue" and output @ref PCQueue "producer queue" at a given @ref ChainPosition "position" in the @ref Chain "chain". + * + * @see Link() + */ + void Init(const ChainPosition &position); + + /** + * Destructs the link object. + * + * If necessary, this method will pass a poison block + * to this link's output @ref PCQueue "producer queue". + * + * @see Block::SetToPoison() + */ + ~Link(); + + /** + * Gets a reference to the @ref Block "block" at this link. + */ + Block &operator*() { return current_; } + + /** + * Gets a const reference to the @ref Block "block" at this link. + */ + const Block &operator*() const { return current_; } + + /** + * Gets a pointer to the @ref Block "block" at this link. + */ + Block *operator->() { return ¤t_; } + + /** + * Gets a const pointer to the @ref Block "block" at this link. + */ + const Block *operator->() const { return ¤t_; } + + /** + * Gets the link at the next @ref ChainPosition "position" in the @ref Chain "chain". + */ + Link &operator++(); + + /** + * Returns true if the @ref Block "block" at this link encapsulates a valid (non-NULL) block of memory. + * + * This method is a user-defined implicit conversion function to boolean; + * among other things, this method enables bare instances of this class + * to be used as the condition of an if statement. + */ + operator bool() const { return current_; } + + /** + * @ref Block::SetToPoison() "Poisons" the @ref Block "block" at this link, + * and passes this now-poisoned block to this link's output @ref PCQueue "producer queue". + * + * @see Block::SetToPoison() + */ + void Poison(); + + private: + Block current_; + PCQueue *in_, *out_; + + bool poisoned_; + + WorkerProgress progress_; +}; + +inline Chain &operator>>(Chain &chain, Link &link) { + link.Init(chain.Add()); + return chain; +} + +} // namespace stream +} // namespace util + +#endif // UTIL_STREAM_CHAIN_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/config.hh b/ctcdecode-master/third_party/kenlm/util/stream/config.hh new file mode 100644 index 0000000..e94cf34 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/config.hh @@ -0,0 +1,63 @@ +#ifndef UTIL_STREAM_CONFIG_H +#define UTIL_STREAM_CONFIG_H + +#include +#include + +namespace util { namespace stream { + +/** + * Represents how a chain should be configured. + */ +struct ChainConfig { + + /** Constructs an configuration with underspecified (or default) parameters. */ + ChainConfig() {} + + /** + * Constructs a chain configuration object. + * + * @param [in] in_entry_size Number of bytes in each record. + * @param [in] in_block_count Number of blocks in the chain. + * @param [in] in_total_memory Total number of bytes available to the chain. + * This value will be divided amongst the blocks in the chain. + */ + ChainConfig(std::size_t in_entry_size, std::size_t in_block_count, std::size_t in_total_memory) + : entry_size(in_entry_size), block_count(in_block_count), total_memory(in_total_memory) {} + + /** + * Number of bytes in each record. + */ + std::size_t entry_size; + + /** + * Number of blocks in the chain. + */ + std::size_t block_count; + + /** + * Total number of bytes available to the chain. + * This value will be divided amongst the blocks in the chain. + * Chain's constructor will make this a multiple of entry_size. + */ + std::size_t total_memory; +}; + + +/** + * Represents how a sorter should be configured. + */ +struct SortConfig { + + /** Filename prefix where temporary files should be placed. */ + std::string temp_prefix; + + /** Size of each input/output buffer. */ + std::size_t buffer_size; + + /** Total memory to use when running alone. */ + std::size_t total_memory; +}; + +}} // namespaces +#endif // UTIL_STREAM_CONFIG_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/count_records.cc b/ctcdecode-master/third_party/kenlm/util/stream/count_records.cc new file mode 100644 index 0000000..bdadad7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/count_records.cc @@ -0,0 +1,12 @@ +#include "util/stream/count_records.hh" +#include "util/stream/chain.hh" + +namespace util { namespace stream { + +void CountRecords::Run(const ChainPosition &position) { + for (Link link(position); link; ++link) { + *count_ += link->ValidSize() / position.GetChain().EntrySize(); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/count_records.hh b/ctcdecode-master/third_party/kenlm/util/stream/count_records.hh new file mode 100644 index 0000000..e3f7c94 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/count_records.hh @@ -0,0 +1,20 @@ +#include + +namespace util { namespace stream { + +class ChainPosition; + +class CountRecords { + public: + explicit CountRecords(uint64_t *out) + : count_(out) { + *count_ = 0; + } + + void Run(const ChainPosition &position); + + private: + uint64_t *count_; +}; + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/io.cc b/ctcdecode-master/third_party/kenlm/util/stream/io.cc new file mode 100644 index 0000000..53b3bab --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/io.cc @@ -0,0 +1,76 @@ +#include "util/stream/io.hh" + +#include "util/file.hh" +#include "util/stream/chain.hh" + +#include + +namespace util { +namespace stream { + +ReadSizeException::ReadSizeException() throw() {} +ReadSizeException::~ReadSizeException() throw() {} + +void Read::Run(const ChainPosition &position) { + const std::size_t block_size = position.GetChain().BlockSize(); + const std::size_t entry_size = position.GetChain().EntrySize(); + for (Link link(position); link; ++link) { + std::size_t got = util::ReadOrEOF(file_, link->Get(), block_size); + UTIL_THROW_IF(got % entry_size, ReadSizeException, "File ended with " << got << " bytes, not a multiple of " << entry_size << "."); + if (got == 0) { + link.Poison(); + return; + } else { + link->SetValidSize(got); + } + } +} + +void PRead::Run(const ChainPosition &position) { + scoped_fd owner; + if (own_) owner.reset(file_); + const uint64_t size = SizeOrThrow(file_); + UTIL_THROW_IF(size % static_cast(position.GetChain().EntrySize()), ReadSizeException, "File size " << file_ << " size is " << size << " not a multiple of " << position.GetChain().EntrySize()); + const std::size_t block_size = position.GetChain().BlockSize(); + const uint64_t block_size64 = static_cast(block_size); + Link link(position); + uint64_t offset = 0; + for (; offset + block_size64 < size; offset += block_size64, ++link) { + ErsatzPRead(file_, link->Get(), block_size, offset); + link->SetValidSize(block_size); + } + // size - offset is <= block_size, so it casts to 32-bit fine. + if (size - offset) { + ErsatzPRead(file_, link->Get(), size - offset, offset); + link->SetValidSize(size - offset); + ++link; + } + link.Poison(); +} + +void Write::Run(const ChainPosition &position) { + for (Link link(position); link; ++link) { + WriteOrThrow(file_, link->Get(), link->ValidSize()); + } +} + +void WriteAndRecycle::Run(const ChainPosition &position) { + const std::size_t block_size = position.GetChain().BlockSize(); + for (Link link(position); link; ++link) { + WriteOrThrow(file_, link->Get(), link->ValidSize()); + link->SetValidSize(block_size); + } +} + +void PWrite::Run(const ChainPosition &position) { + uint64_t offset = 0; + for (Link link(position); link; ++link) { + ErsatzPWrite(file_, link->Get(), link->ValidSize(), offset); + offset += link->ValidSize(); + } + // Trim file to size. + util::ResizeOrThrow(file_, offset); +} + +} // namespace stream +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/stream/io.hh b/ctcdecode-master/third_party/kenlm/util/stream/io.hh new file mode 100644 index 0000000..d29b37c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/io.hh @@ -0,0 +1,87 @@ +#ifndef UTIL_STREAM_IO_H +#define UTIL_STREAM_IO_H + +#include "util/exception.hh" +#include "util/file.hh" + +namespace util { +namespace stream { + +class ChainPosition; + +class ReadSizeException : public util::Exception { + public: + ReadSizeException() throw(); + ~ReadSizeException() throw(); +}; + +class Read { + public: + explicit Read(int fd) : file_(fd) {} + void Run(const ChainPosition &position); + private: + int file_; +}; + +// Like read but uses pread so that the file can be accessed from multiple threads. +class PRead { + public: + explicit PRead(int fd, bool take_own = false) : file_(fd), own_(take_own) {} + void Run(const ChainPosition &position); + private: + int file_; + bool own_; +}; + +class Write { + public: + explicit Write(int fd) : file_(fd) {} + void Run(const ChainPosition &position); + private: + int file_; +}; + +// It's a common case that stuff is written and then recycled. So rather than +// spawn another thread to Recycle, this combines the two roles. +class WriteAndRecycle { + public: + explicit WriteAndRecycle(int fd) : file_(fd) {} + void Run(const ChainPosition &position); + private: + int file_; +}; + +class PWrite { + public: + explicit PWrite(int fd) : file_(fd) {} + void Run(const ChainPosition &position); + private: + int file_; +}; + + +// Reuse the same file over and over again to buffer output. +class FileBuffer { + public: + explicit FileBuffer(int fd) : file_(fd) {} + + PWrite Sink() const { + util::SeekOrThrow(file_.get(), 0); + return PWrite(file_.get()); + } + + PRead Source(bool discard = false) { + return PRead(discard ? file_.release() : file_.get(), discard); + } + + uint64_t Size() const { + return SizeOrThrow(file_.get()); + } + + private: + scoped_fd file_; +}; + +} // namespace stream +} // namespace util +#endif // UTIL_STREAM_IO_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/io_test.cc b/ctcdecode-master/third_party/kenlm/util/stream/io_test.cc new file mode 100644 index 0000000..8210833 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/io_test.cc @@ -0,0 +1,38 @@ +#include "util/stream/io.hh" + +#include "util/stream/chain.hh" +#include "util/file.hh" + +#define BOOST_TEST_MODULE IOTest +#include + +#include + +namespace util { namespace stream { namespace { + +BOOST_AUTO_TEST_CASE(CopyFile) { + std::string temps("io_test_temp"); + + scoped_fd in(MakeTemp(temps)); + for (uint64_t i = 0; i < 100000; ++i) { + WriteOrThrow(in.get(), &i, sizeof(uint64_t)); + } + SeekOrThrow(in.get(), 0); + scoped_fd out(MakeTemp(temps)); + + ChainConfig config; + config.entry_size = 8; + config.total_memory = 1024; + config.block_count = 10; + + Chain(config) >> PRead(in.get()) >> Write(out.get()); + + SeekOrThrow(out.get(), 0); + for (uint64_t i = 0; i < 100000; ++i) { + uint64_t got; + ReadOrThrow(out.get(), &got, sizeof(uint64_t)); + BOOST_CHECK_EQUAL(i, got); + } +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/line_input.cc b/ctcdecode-master/third_party/kenlm/util/stream/line_input.cc new file mode 100644 index 0000000..0ad8800 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/line_input.cc @@ -0,0 +1,52 @@ +#include "util/stream/line_input.hh" + +#include "util/exception.hh" +#include "util/file.hh" +#include "util/read_compressed.hh" +#include "util/stream/chain.hh" + +#include +#include + +namespace util { namespace stream { + +void LineInput::Run(const ChainPosition &position) { + ReadCompressed reader(fd_); + // Holding area for beginning of line to be placed in next block. + std::vector carry; + + for (Link block(position); ; ++block) { + char *to = static_cast(block->Get()); + char *begin = to; + char *end = to + position.GetChain().BlockSize(); + std::copy(carry.begin(), carry.end(), to); + to += carry.size(); + while (to != end) { + std::size_t got = reader.Read(to, end - to); + if (!got) { + // EOF + block->SetValidSize(to - begin); + ++block; + block.Poison(); + return; + } + to += got; + } + + // Find the last newline. + char *newline; + for (newline = to - 1; ; --newline) { + UTIL_THROW_IF(newline < begin, Exception, "Did not find a newline in " << position.GetChain().BlockSize() << " bytes of input of " << NameFromFD(fd_) << ". Is this a text file?"); + if (*newline == '\n') break; + } + + // Copy everything after the last newline to the carry. + carry.clear(); + carry.resize(to - (newline + 1)); + std::copy(newline + 1, to, &*carry.begin()); + + block->SetValidSize(newline + 1 - begin); + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/line_input.hh b/ctcdecode-master/third_party/kenlm/util/stream/line_input.hh new file mode 100644 index 0000000..a870a66 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/line_input.hh @@ -0,0 +1,22 @@ +#ifndef UTIL_STREAM_LINE_INPUT_H +#define UTIL_STREAM_LINE_INPUT_H +namespace util {namespace stream { + +class ChainPosition; + +/* Worker that reads input into blocks, ensuring that blocks contain whole + * lines. Assumes that the maximum size of a line is less than the block size + */ +class LineInput { + public: + // Takes ownership upon thread execution. + explicit LineInput(int fd); + + void Run(const ChainPosition &position); + + private: + int fd_; +}; + +}} // namespaces +#endif // UTIL_STREAM_LINE_INPUT_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/multi_progress.cc b/ctcdecode-master/third_party/kenlm/util/stream/multi_progress.cc new file mode 100644 index 0000000..59750f5 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/multi_progress.cc @@ -0,0 +1,86 @@ +#include "util/stream/multi_progress.hh" + +// TODO: merge some functionality with the simple progress bar? +#include "util/ersatz_progress.hh" + +#include +#include + +#include + +#if !defined(_WIN32) && !defined(_WIN64) +#include +#endif + +namespace util { namespace stream { + +namespace { +const char kDisplayCharacters[] = "-+*#0123456789"; + +uint64_t Next(unsigned char stone, uint64_t complete) { + return (static_cast(stone + 1) * complete + MultiProgress::kWidth - 1) / MultiProgress::kWidth; +} + +} // namespace + +MultiProgress::MultiProgress() : active_(false), complete_(std::numeric_limits::max()), character_handout_(0) {} + +MultiProgress::~MultiProgress() { + if (active_ && complete_ != std::numeric_limits::max()) + std::cerr << '\n'; +} + +void MultiProgress::Activate() { + active_ = +#if !defined(_WIN32) && !defined(_WIN64) + // Is stderr a terminal? + (isatty(2) == 1) +#else + true +#endif + ; +} + +void MultiProgress::SetTarget(uint64_t complete) { + if (!active_) return; + complete_ = complete; + if (!complete) complete_ = 1; + memset(display_, 0, sizeof(display_)); + character_handout_ = 0; + std::cerr << kProgressBanner; +} + +WorkerProgress MultiProgress::Add() { + if (!active_) + return WorkerProgress(std::numeric_limits::max(), *this, '\0'); + std::size_t character_index; + { + boost::unique_lock lock(mutex_); + character_index = character_handout_++; + if (character_handout_ == sizeof(kDisplayCharacters) - 1) + character_handout_ = 0; + } + return WorkerProgress(Next(0, complete_), *this, kDisplayCharacters[character_index]); +} + +void MultiProgress::Finished() { + if (!active_ || complete_ == std::numeric_limits::max()) return; + std::cerr << '\n'; + complete_ = std::numeric_limits::max(); +} + +void MultiProgress::Milestone(WorkerProgress &worker) { + if (!active_ || complete_ == std::numeric_limits::max()) return; + unsigned char stone = std::min(static_cast(kWidth), worker.current_ * kWidth / complete_); + for (char *i = &display_[worker.stone_]; i < &display_[stone]; ++i) { + *i = worker.character_; + } + worker.next_ = Next(stone, complete_); + worker.stone_ = stone; + { + boost::unique_lock lock(mutex_); + std::cerr << '\r' << display_ << std::flush; + } +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/multi_progress.hh b/ctcdecode-master/third_party/kenlm/util/stream/multi_progress.hh new file mode 100644 index 0000000..f9e6423 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/multi_progress.hh @@ -0,0 +1,89 @@ +/* Progress bar suitable for chains of workers */ +#ifndef UTIL_STREAM_MULTI_PROGRESS_H +#define UTIL_STREAM_MULTI_PROGRESS_H + +#include + +#include +#include + +namespace util { namespace stream { + +class WorkerProgress; + +class MultiProgress { + public: + static const unsigned char kWidth = 100; + + MultiProgress(); + + ~MultiProgress(); + + // Turns on showing (requires SetTarget too). + void Activate(); + + void SetTarget(uint64_t complete); + + WorkerProgress Add(); + + void Finished(); + + private: + friend class WorkerProgress; + void Milestone(WorkerProgress &worker); + + bool active_; + + uint64_t complete_; + + boost::mutex mutex_; + + // \0 at the end. + char display_[kWidth + 1]; + + std::size_t character_handout_; + + MultiProgress(const MultiProgress &); + MultiProgress &operator=(const MultiProgress &); +}; + +class WorkerProgress { + public: + // Default contrutor must be initialized with operator= later. + WorkerProgress() : parent_(NULL) {} + + // Not threadsafe for the same worker by default. + WorkerProgress &operator++() { + if (++current_ >= next_) { + parent_->Milestone(*this); + } + return *this; + } + + WorkerProgress &operator+=(uint64_t amount) { + current_ += amount; + if (current_ >= next_) { + parent_->Milestone(*this); + } + return *this; + } + + private: + friend class MultiProgress; + WorkerProgress(uint64_t next, MultiProgress &parent, char character) + : current_(0), next_(next), parent_(&parent), stone_(0), character_(character) {} + + uint64_t current_, next_; + + MultiProgress *parent_; + + // Previous milestone reached. + unsigned char stone_; + + // Character to display in bar. + char character_; +}; + +}} // namespaces + +#endif // UTIL_STREAM_MULTI_PROGRESS_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/multi_stream.hh b/ctcdecode-master/third_party/kenlm/util/stream/multi_stream.hh new file mode 100644 index 0000000..6381fc2 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/multi_stream.hh @@ -0,0 +1,124 @@ +#ifndef UTIL_STREAM_MULTI_STREAM_H +#define UTIL_STREAM_MULTI_STREAM_H + +#include "util/fixed_array.hh" +#include "util/scoped.hh" +#include "util/stream/chain.hh" +#include "util/stream/stream.hh" + +#include +#include + +#include +#include + +namespace util { namespace stream { + +class Chains; + +class ChainPositions : public util::FixedArray { + public: + ChainPositions() {} + + explicit ChainPositions(std::size_t bound) : + util::FixedArray(bound) {} + + void Init(Chains &chains); + + explicit ChainPositions(Chains &chains) { + Init(chains); + } +}; + +class Chains : public util::FixedArray { + private: + template struct CheckForRun { + typedef Chains type; + }; + + public: + // Must call Init. + Chains() {} + + explicit Chains(std::size_t limit) : util::FixedArray(limit) {} + + template typename CheckForRun::type &operator>>(const Worker &worker) { + threads_.push_back(new util::stream::Thread(ChainPositions(*this), worker)); + return *this; + } + + template typename CheckForRun::type &operator>>(const boost::reference_wrapper &worker) { + threads_.push_back(new util::stream::Thread(ChainPositions(*this), worker)); + return *this; + } + + Chains &operator>>(const util::stream::Recycler &recycler) { + for (util::stream::Chain *i = begin(); i != end(); ++i) + *i >> recycler; + return *this; + } + + void Wait(bool release_memory = true) { + threads_.clear(); + for (util::stream::Chain *i = begin(); i != end(); ++i) { + i->Wait(release_memory); + } + } + + private: + boost::ptr_vector threads_; + + Chains(const Chains &); + void operator=(const Chains &); +}; + +inline void ChainPositions::Init(Chains &chains) { + util::FixedArray::Init(chains.size()); + for (util::stream::Chain *i = chains.begin(); i != chains.end(); ++i) { + // use "placement new" syntax to initalize ChainPosition in an already-allocated memory location + new (end()) util::stream::ChainPosition(i->Add()); Constructed(); + } +} + +inline Chains &operator>>(Chains &chains, ChainPositions &positions) { + positions.Init(chains); + return chains; +} + +template class GenericStreams : public util::FixedArray { + private: + typedef util::FixedArray P; + public: + GenericStreams() {} + + // Limit restricts to positions[0,limit) + void Init(const ChainPositions &positions, std::size_t limit) { + P::Init(limit); + for (const util::stream::ChainPosition *i = positions.begin(); i != positions.begin() + limit; ++i) { + P::push_back(*i); + } + } + void Init(const ChainPositions &positions) { + Init(positions, positions.size()); + } + + GenericStreams(const ChainPositions &positions) { + Init(positions); + } + + void Init(std::size_t amount) { + P::Init(amount); + } +}; + +template inline Chains &operator>>(Chains &chains, GenericStreams &streams) { + ChainPositions positions; + chains >> positions; + streams.Init(positions); + return chains; +} + +typedef GenericStreams Streams; + +}} // namespaces +#endif // UTIL_STREAM_MULTI_STREAM_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream.cc b/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream.cc new file mode 100644 index 0000000..726e2a7 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream.cc @@ -0,0 +1,134 @@ +#include "util/stream/rewindable_stream.hh" +#include "util/pcqueue.hh" + +#include + +namespace util { +namespace stream { + +RewindableStream::RewindableStream() + : current_(NULL), in_(NULL), out_(NULL), poisoned_(true) { + // nothing +} + +void RewindableStream::Init(const ChainPosition &position) { + UTIL_THROW_IF2(in_, "RewindableStream::Init twice"); + in_ = position.in_; + out_ = position.out_; + hit_poison_ = false; + poisoned_ = false; + progress_ = position.progress_; + entry_size_ = position.GetChain().EntrySize(); + block_size_ = position.GetChain().BlockSize(); + block_count_ = position.GetChain().BlockCount(); + blocks_it_ = 0; + marked_ = NULL; + UTIL_THROW_IF2(block_count_ < 2, "RewindableStream needs block_count at least two"); + AppendBlock(); +} + +RewindableStream &RewindableStream::operator++() { + assert(*this); + assert(current_ < block_end_); + assert(current_); + assert(blocks_it_ < blocks_.size()); + current_ += entry_size_; + if (UTIL_UNLIKELY(current_ == block_end_)) { + // Fetch another block if necessary. + if (++blocks_it_ == blocks_.size()) { + if (!marked_) { + Flush(blocks_.begin() + blocks_it_); + blocks_it_ = 0; + } + AppendBlock(); + assert(poisoned_ || (blocks_it_ == blocks_.size() - 1)); + if (poisoned_) return *this; + } + Block &cur_block = blocks_[blocks_it_]; + current_ = static_cast(cur_block.Get()); + block_end_ = current_ + cur_block.ValidSize(); + } + assert(current_); + assert(current_ >= static_cast(blocks_[blocks_it_].Get())); + assert(current_ < block_end_); + assert(block_end_ == blocks_[blocks_it_].ValidEnd()); + return *this; +} + +void RewindableStream::Mark() { + marked_ = current_; + Flush(blocks_.begin() + blocks_it_); + blocks_it_ = 0; +} + +void RewindableStream::Rewind() { + if (current_ != marked_) { + poisoned_ = false; + } + blocks_it_ = 0; + current_ = marked_; + block_end_ = static_cast(blocks_[blocks_it_].ValidEnd()); + + assert(current_); + assert(current_ >= static_cast(blocks_[blocks_it_].Get())); + assert(current_ < block_end_); + assert(block_end_ == blocks_[blocks_it_].ValidEnd()); +} + +void RewindableStream::Poison() { + if (blocks_.empty()) return; + assert(*this); + assert(blocks_it_ == blocks_.size() - 1); + + // Produce all buffered blocks. + blocks_.back().SetValidSize(current_ - static_cast(blocks_.back().Get())); + Flush(blocks_.end()); + blocks_it_ = 0; + + Block poison; + if (!hit_poison_) { + in_->Consume(poison); + } + poison.SetToPoison(); + out_->Produce(poison); + hit_poison_ = true; + poisoned_ = true; +} + +void RewindableStream::AppendBlock() { + if (UTIL_UNLIKELY(blocks_.size() >= block_count_)) { + std::cerr << "RewindableStream trying to use more blocks than available" << std::endl; + abort(); + } + if (UTIL_UNLIKELY(hit_poison_)) { + poisoned_ = true; + return; + } + Block get; + // The loop is needed since it is *feasible* that we're given 0 sized but + // valid blocks + do { + in_->Consume(get); + if (UTIL_LIKELY(get)) { + blocks_.push_back(get); + } else { + hit_poison_ = true; + poisoned_ = true; + return; + } + } while (UTIL_UNLIKELY(get.ValidSize() == 0)); + current_ = static_cast(blocks_.back().Get()); + block_end_ = static_cast(blocks_.back().ValidEnd()); + blocks_it_ = blocks_.size() - 1; +} + +void RewindableStream::Flush(std::deque::iterator to) { + for (std::deque::iterator i = blocks_.begin(); i != to; ++i) { + out_->Produce(*i); + progress_ += i->ValidSize(); + } + blocks_.erase(blocks_.begin(), to); +} + +} +} diff --git a/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream.hh b/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream.hh new file mode 100644 index 0000000..560825c --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream.hh @@ -0,0 +1,132 @@ +#ifndef UTIL_STREAM_REWINDABLE_STREAM_H +#define UTIL_STREAM_REWINDABLE_STREAM_H + +#include "util/stream/chain.hh" + +#include + +#include + +namespace util { +namespace stream { + +/** + * A RewindableStream is like a Stream (but one that is only used for + * creating input at the start of a chain) except that it can be rewound to + * be able to re-write a part of the stream before it is sent. Rewinding + * has a limit of 2 * block_size_ - 1 in distance (it does *not* buffer an + * entire stream into memory, only a maximum of 2 * block_size_). + */ +class RewindableStream : boost::noncopyable { + public: + /** + * Creates an uninitialized RewindableStream. You **must** call Init() + * on it later! + */ + RewindableStream(); + + ~RewindableStream() { + Poison(); + } + + /** + * Initializes an existing RewindableStream at a specific position in + * a Chain. + * + * @param position The position in the chain to get input from and + * produce output on + */ + void Init(const ChainPosition &position); + + /** + * Constructs a RewindableStream at a specific position in a Chain all + * in one step. + * + * Equivalent to RewindableStream a(); a.Init(....); + */ + explicit RewindableStream(const ChainPosition &position) + : in_(NULL) { + Init(position); + } + + /** + * Gets the record at the current stream position. Const version. + */ + const void *Get() const { + assert(!poisoned_); + assert(current_); + return current_; + } + + /** + * Gets the record at the current stream position. + */ + void *Get() { + assert(!poisoned_); + assert(current_); + return current_; + } + + operator bool() const { return !poisoned_; } + + bool operator!() const { return poisoned_; } + + /** + * Marks the current position in the stream to be rewound to later. + * Note that you can only rewind back as far as 2 * block_size_ - 1! + */ + void Mark(); + + /** + * Rewinds the stream back to the marked position. This will throw an + * exception if the marked position is too far away. + */ + void Rewind(); + + /** + * Moves the stream forward to the next record. This internally may + * buffer a block for the purposes of rewinding. + */ + RewindableStream& operator++(); + + /** + * Poisons the stream. This sends any buffered blocks down the chain + * and sends a poison block as well (sending at most 2 non-poison and 1 + * poison block). + */ + void Poison(); + + private: + void AppendBlock(); + + void Flush(std::deque::iterator to); + + std::deque blocks_; + // current_ is in blocks_[blocks_it_] unless poisoned_. + std::size_t blocks_it_; + + std::size_t entry_size_; + std::size_t block_size_; + std::size_t block_count_; + + uint8_t *marked_, *current_; + const uint8_t *block_end_; + + PCQueue *in_, *out_; + + // Have we hit poison at the end of the stream, even if rewinding? + bool hit_poison_; + // Is the curren position poison? + bool poisoned_; + + WorkerProgress progress_; +}; + +inline Chain &operator>>(Chain &chain, RewindableStream &stream) { + stream.Init(chain.Add()); + return chain; +} + +} +} +#endif diff --git a/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream_test.cc b/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream_test.cc new file mode 100644 index 0000000..f8924c3 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/rewindable_stream_test.cc @@ -0,0 +1,41 @@ +#include "util/stream/io.hh" + +#include "util/stream/rewindable_stream.hh" +#include "util/file.hh" + +#define BOOST_TEST_MODULE RewindableStreamTest +#include + +namespace util { +namespace stream { +namespace { + +BOOST_AUTO_TEST_CASE(RewindableStreamTest) { + scoped_fd in(MakeTemp("io_test_temp")); + for (uint64_t i = 0; i < 100000; ++i) { + WriteOrThrow(in.get(), &i, sizeof(uint64_t)); + } + SeekOrThrow(in.get(), 0); + + ChainConfig config; + config.entry_size = 8; + config.total_memory = 100; + config.block_count = 6; + + Chain chain(config); + RewindableStream s; + chain >> Read(in.get()) >> s >> kRecycle; + uint64_t i = 0; + for (; s; ++s, ++i) { + BOOST_CHECK_EQUAL(i, *static_cast(s.Get())); + if (100000UL - i == 2) + s.Mark(); + } + BOOST_CHECK_EQUAL(100000ULL, i); + s.Rewind(); + BOOST_CHECK_EQUAL(100000ULL - 2, *static_cast(s.Get())); +} + +} +} +} diff --git a/ctcdecode-master/third_party/kenlm/util/stream/sort.hh b/ctcdecode-master/third_party/kenlm/util/stream/sort.hh new file mode 100644 index 0000000..d0429ac --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/sort.hh @@ -0,0 +1,595 @@ +/* Usage: + * Sort sorter(temp, compare); + * Chain(config) >> Read(file) >> sorter.Unsorted(); + * Stream stream; + * Chain chain(config) >> sorter.Sorted(internal_config, lazy_config) >> stream; + * + * Note that sorter must outlive any threads that use Unsorted or Sorted. + * + * Combiners take the form: + * bool operator()(void *into, const void *option, const Compare &compare) const + * which returns true iff a combination happened. The sorting algorithm + * guarantees compare(into, option). But it does not guarantee + * compare(option, into). + * Currently, combining is only done in merge steps, not during on-the-fly + * sort. Use a hash table for that. + */ + +#ifndef UTIL_STREAM_SORT_H +#define UTIL_STREAM_SORT_H + +#include "util/stream/chain.hh" +#include "util/stream/config.hh" +#include "util/stream/io.hh" +#include "util/stream/stream.hh" + +#include "util/file.hh" +#include "util/fixed_array.hh" +#include "util/scoped.hh" +#include "util/sized_iterator.hh" + +#include +#include +#include +#include + +namespace util { +namespace stream { + +struct NeverCombine { + template bool operator()(const void *, const void *, const Compare &) const { + return false; + } +}; + +// Manage the offsets of sorted blocks in a file. +class Offsets { + public: + explicit Offsets(int fd) : log_(fd) { + Reset(); + } + + int File() const { return log_; } + + void Append(uint64_t length) { + if (!length) return; + ++block_count_; + if (length == cur_.length) { + ++cur_.run; + return; + } + WriteOrThrow(log_, &cur_, sizeof(Entry)); + cur_.length = length; + cur_.run = 1; + } + + void FinishedAppending() { + WriteOrThrow(log_, &cur_, sizeof(Entry)); + SeekOrThrow(log_, sizeof(Entry)); // Skip 0,0 at beginning. + cur_.run = 0; + if (block_count_) { + ReadOrThrow(log_, &cur_, sizeof(Entry)); + assert(cur_.length); + assert(cur_.run); + } + } + + uint64_t RemainingBlocks() const { return block_count_; } + + uint64_t TotalOffset() const { return output_sum_; } + + uint64_t PeekSize() const { + return cur_.length; + } + + uint64_t NextSize() { + assert(block_count_); + uint64_t ret = cur_.length; + output_sum_ += ret; + + --cur_.run; + --block_count_; + if (!cur_.run && block_count_) { + ReadOrThrow(log_, &cur_, sizeof(Entry)); + assert(cur_.length); + assert(cur_.run); + } + return ret; + } + + void Reset() { + SeekOrThrow(log_, 0); + ResizeOrThrow(log_, 0); + cur_.length = 0; + cur_.run = 0; + block_count_ = 0; + output_sum_ = 0; + } + + private: + int log_; + + struct Entry { + uint64_t length; + uint64_t run; + }; + Entry cur_; + + uint64_t block_count_; + + uint64_t output_sum_; +}; + +// A priority queue of entries backed by file buffers +template class MergeQueue { + public: + MergeQueue(int fd, std::size_t buffer_size, std::size_t entry_size, const Compare &compare) + : queue_(Greater(compare)), in_(fd), buffer_size_(buffer_size), entry_size_(entry_size) {} + + void Push(void *base, uint64_t offset, uint64_t amount) { + queue_.push(Entry(base, in_, offset, amount, buffer_size_)); + } + + const void *Top() const { + return queue_.top().Current(); + } + + void Pop() { + Entry top(queue_.top()); + queue_.pop(); + if (top.Increment(in_, buffer_size_, entry_size_)) + queue_.push(top); + } + + std::size_t Size() const { + return queue_.size(); + } + + bool Empty() const { + return queue_.empty(); + } + + private: + // Priority queue contains these entries. + class Entry { + public: + Entry() {} + + Entry(void *base, int fd, uint64_t offset, uint64_t amount, std::size_t buf_size) { + offset_ = offset; + remaining_ = amount; + buffer_end_ = static_cast(base) + buf_size; + Read(fd, buf_size); + } + + bool Increment(int fd, std::size_t buf_size, std::size_t entry_size) { + current_ += entry_size; + if (current_ != buffer_end_) return true; + return Read(fd, buf_size); + } + + const void *Current() const { return current_; } + + private: + bool Read(int fd, std::size_t buf_size) { + current_ = buffer_end_ - buf_size; + std::size_t amount; + if (static_cast(buf_size) < remaining_) { + amount = buf_size; + } else if (!remaining_) { + return false; + } else { + amount = remaining_; + buffer_end_ = current_ + remaining_; + } + ErsatzPRead(fd, current_, amount, offset_); + // Try to free the space, but don't be disappointed if we can't. + try { + HolePunch(fd, offset_, amount); + } catch (const util::Exception &e) {} + offset_ += amount; + assert(current_ <= buffer_end_); + remaining_ -= amount; + return true; + } + + // Buffer + uint8_t *current_, *buffer_end_; + // File + uint64_t remaining_, offset_; + }; + + // Wrapper comparison function for queue entries. + class Greater : public std::binary_function { + public: + explicit Greater(const Compare &compare) : compare_(compare) {} + + bool operator()(const Entry &first, const Entry &second) const { + return compare_(second.Current(), first.Current()); + } + + private: + const Compare compare_; + }; + + typedef std::priority_queue, Greater> Queue; + Queue queue_; + + const int in_; + const std::size_t buffer_size_; + const std::size_t entry_size_; +}; + +/* A worker object that merges. If the number of pieces to merge exceeds the + * arity, it outputs multiple sorted blocks, recording to out_offsets. + * However, users will only every see a single sorted block out output because + * Sort::Sorted insures the arity is higher than the number of pieces before + * returning this. + */ +template class MergingReader { + public: + MergingReader(int in, Offsets *in_offsets, Offsets *out_offsets, std::size_t buffer_size, std::size_t total_memory, const Compare &compare, const Combine &combine) : + compare_(compare), combine_(combine), + in_(in), + in_offsets_(in_offsets), out_offsets_(out_offsets), + buffer_size_(buffer_size), total_memory_(total_memory) {} + + void Run(const ChainPosition &position) { + Run(position, false); + } + + void Run(const ChainPosition &position, bool assert_one) { + // Special case: nothing to read. + if (!in_offsets_->RemainingBlocks()) { + Link l(position); + l.Poison(); + return; + } + // If there's just one entry, just read. + if (in_offsets_->RemainingBlocks() == 1) { + // Sequencing is important. + uint64_t offset = in_offsets_->TotalOffset(); + uint64_t amount = in_offsets_->NextSize(); + ReadSingle(offset, amount, position); + if (out_offsets_) out_offsets_->Append(amount); + return; + } + + Stream str(position); + scoped_malloc buffer(MallocOrThrow(total_memory_)); + uint8_t *const buffer_end = static_cast(buffer.get()) + total_memory_; + + const std::size_t entry_size = position.GetChain().EntrySize(); + + while (in_offsets_->RemainingBlocks()) { + // Use bigger buffers if there's less remaining. + uint64_t per_buffer = static_cast(std::max( + buffer_size_, + static_cast((static_cast(total_memory_) / in_offsets_->RemainingBlocks())))); + per_buffer -= per_buffer % entry_size; + assert(per_buffer); + + // Populate queue. + MergeQueue queue(in_, per_buffer, entry_size, compare_); + for (uint8_t *buf = static_cast(buffer.get()); + in_offsets_->RemainingBlocks() && (buf + std::min(per_buffer, in_offsets_->PeekSize()) <= buffer_end);) { + uint64_t offset = in_offsets_->TotalOffset(); + uint64_t size = in_offsets_->NextSize(); + queue.Push(buf, offset, size); + buf += static_cast(std::min(size, per_buffer)); + } + // This shouldn't happen but it's probably better to die than loop indefinitely. + if (queue.Size() < 2 && in_offsets_->RemainingBlocks()) { + std::cerr << "Bug in sort implementation: not merging at least two stripes." << std::endl; + abort(); + } + if (assert_one && in_offsets_->RemainingBlocks()) { + std::cerr << "Bug in sort implementation: should only be one merge group for lazy sort" << std::endl; + abort(); + } + + uint64_t written = 0; + // Merge including combiner support. + memcpy(str.Get(), queue.Top(), entry_size); + for (queue.Pop(); !queue.Empty(); queue.Pop()) { + if (!combine_(str.Get(), queue.Top(), compare_)) { + ++written; ++str; + memcpy(str.Get(), queue.Top(), entry_size); + } + } + ++written; ++str; + if (out_offsets_) + out_offsets_->Append(written * entry_size); + } + str.Poison(); + } + + private: + void ReadSingle(uint64_t offset, const uint64_t size, const ChainPosition &position) { + // Special case: only one to read. + const uint64_t end = offset + size; + const uint64_t block_size = position.GetChain().BlockSize(); + Link l(position); + for (; offset + block_size < end; ++l, offset += block_size) { + ErsatzPRead(in_, l->Get(), block_size, offset); + l->SetValidSize(block_size); + } + ErsatzPRead(in_, l->Get(), end - offset, offset); + l->SetValidSize(end - offset); + (++l).Poison(); + return; + } + + Compare compare_; + Combine combine_; + + int in_; + + protected: + Offsets *in_offsets_; + + private: + Offsets *out_offsets_; + + std::size_t buffer_size_; + std::size_t total_memory_; +}; + +// The lazy step owns the remaining files. This keeps track of them. +template class OwningMergingReader : public MergingReader { + private: + typedef MergingReader P; + public: + OwningMergingReader(int data, const Offsets &offsets, std::size_t buffer, std::size_t lazy, const Compare &compare, const Combine &combine) + : P(data, NULL, NULL, buffer, lazy, compare, combine), + data_(data), + offsets_(offsets) {} + + void Run(const ChainPosition &position) { + P::in_offsets_ = &offsets_; + scoped_fd data(data_); + scoped_fd offsets_file(offsets_.File()); + P::Run(position, true); + } + + private: + int data_; + Offsets offsets_; +}; + +// Don't use this directly. Worker that sorts blocks. +template class BlockSorter { + public: + BlockSorter(Offsets &offsets, const Compare &compare) : + offsets_(&offsets), compare_(compare) {} + + void Run(const ChainPosition &position) { + const std::size_t entry_size = position.GetChain().EntrySize(); + for (Link link(position); link; ++link) { + // Record the size of each block in a separate file. + offsets_->Append(link->ValidSize()); + void *end = static_cast(link->Get()) + link->ValidSize(); + SizedSort(link->Get(), end, entry_size, compare_); + } + offsets_->FinishedAppending(); + } + + private: + Offsets *offsets_; + Compare compare_; +}; + +class BadSortConfig : public Exception { + public: + BadSortConfig() throw() {} + ~BadSortConfig() throw() {} +}; + +/** Sort */ +template class Sort { + public: + /** Constructs an object capable of sorting */ + Sort(Chain &in, const SortConfig &config, const Compare &compare = Compare(), const Combine &combine = Combine()) + : config_(config), + data_(MakeTemp(config.temp_prefix)), + offsets_file_(MakeTemp(config.temp_prefix)), offsets_(offsets_file_.get()), + compare_(compare), combine_(combine), + entry_size_(in.EntrySize()) { + UTIL_THROW_IF(!entry_size_, BadSortConfig, "Sorting entries of size 0"); + // Make buffer_size a multiple of the entry_size. + config_.buffer_size -= config_.buffer_size % entry_size_; + UTIL_THROW_IF(!config_.buffer_size, BadSortConfig, "Sort buffer too small"); + UTIL_THROW_IF(config_.total_memory < config_.buffer_size * 4, BadSortConfig, "Sorting memory " << config_.total_memory << " is too small for four buffers (two read and two write)."); + in >> BlockSorter(offsets_, compare_) >> WriteAndRecycle(data_.get()); + } + + uint64_t Size() const { + return SizeOrThrow(data_.get()); + } + + // Do merge sort, terminating when lazy merge could be done with the + // specified memory. Return the minimum memory necessary to do lazy merge. + std::size_t Merge(std::size_t lazy_memory) { + if (offsets_.RemainingBlocks() <= 1) return 0; + const uint64_t lazy_arity = std::max(1, lazy_memory / config_.buffer_size); + uint64_t size = Size(); + /* No overflow because + * offsets_.RemainingBlocks() * config_.buffer_size <= lazy_memory || + * size < lazy_memory + */ + if (offsets_.RemainingBlocks() <= lazy_arity || size <= static_cast(lazy_memory)) + return std::min(size, offsets_.RemainingBlocks() * config_.buffer_size); + + scoped_fd data2(MakeTemp(config_.temp_prefix)); + int fd_in = data_.get(), fd_out = data2.get(); + scoped_fd offsets2_file(MakeTemp(config_.temp_prefix)); + Offsets offsets2(offsets2_file.get()); + Offsets *offsets_in = &offsets_, *offsets_out = &offsets2; + + // Double buffered writing. + ChainConfig chain_config; + chain_config.entry_size = entry_size_; + chain_config.block_count = 2; + chain_config.total_memory = config_.buffer_size * 2; + Chain chain(chain_config); + + while (offsets_in->RemainingBlocks() > lazy_arity) { + if (size <= static_cast(lazy_memory)) break; + std::size_t reading_memory = config_.total_memory - 2 * config_.buffer_size; + if (size < static_cast(reading_memory)) { + reading_memory = static_cast(size); + } + SeekOrThrow(fd_in, 0); + chain >> + MergingReader( + fd_in, + offsets_in, offsets_out, + config_.buffer_size, + reading_memory, + compare_, combine_) >> + WriteAndRecycle(fd_out); + chain.Wait(); + offsets_out->FinishedAppending(); + ResizeOrThrow(fd_in, 0); + offsets_in->Reset(); + std::swap(fd_in, fd_out); + std::swap(offsets_in, offsets_out); + size = SizeOrThrow(fd_in); + } + + SeekOrThrow(fd_in, 0); + if (fd_in == data2.get()) { + data_.reset(data2.release()); + offsets_file_.reset(offsets2_file.release()); + offsets_ = offsets2; + } + if (offsets_.RemainingBlocks() <= 1) return 0; + // No overflow because the while loop exited. + return std::min(size, offsets_.RemainingBlocks() * static_cast(config_.buffer_size)); + } + + // Output to chain, using this amount of memory, maximum, for lazy merge + // sort. + void Output(Chain &out, std::size_t lazy_memory) { + Merge(lazy_memory); + out.SetProgressTarget(Size()); + out >> OwningMergingReader(data_.get(), offsets_, config_.buffer_size, lazy_memory, compare_, combine_); + data_.release(); + offsets_file_.release(); + } + + /* If a pipeline step is reading sorted input and writing to a different + * sort order, then there's a trade-off between using RAM to read lazily + * (avoiding copying the file) and using RAM to increase block size and, + * therefore, decrease the number of merge sort passes in the next + * iteration. + * + * Merge sort takes log_{arity}(pieces) passes. Thus, each time the chain + * block size is multiplied by arity, the number of output passes decreases + * by one. Up to a constant, then, log_{arity}(chain) is the number of + * passes saved. Chain simply divides the memory evenly over all blocks. + * + * Lazy sort saves this many passes (up to a constant) + * log_{arity}((memory-lazy)/block_count) + 1 + * Non-lazy sort saves this many passes (up to the same constant): + * log_{arity}(memory/block_count) + * Add log_{arity}(block_count) to both: + * log_{arity}(memory-lazy) + 1 versus log_{arity}(memory) + * Take arity to the power of both sizes (arity > 1) + * (memory - lazy)*arity versus memory + * Solve for lazy + * lazy = memory * (arity - 1) / arity + */ + std::size_t DefaultLazy() { + float arity = static_cast(config_.total_memory / config_.buffer_size); + return static_cast(static_cast(config_.total_memory) * (arity - 1.0) / arity); + } + + // Same as Output with default lazy memory setting. + void Output(Chain &out) { + Output(out, DefaultLazy()); + } + + // Completely merge sort and transfer ownership to the caller. + int StealCompleted() { + // Merge all the way. + Merge(0); + SeekOrThrow(data_.get(), 0); + offsets_file_.reset(); + return data_.release(); + } + + private: + SortConfig config_; + + scoped_fd data_; + + scoped_fd offsets_file_; + Offsets offsets_; + + const Compare compare_; + const Combine combine_; + const std::size_t entry_size_; +}; + +// returns bytes to be read on demand. +template uint64_t BlockingSort(Chain &chain, const SortConfig &config, const Compare &compare = Compare(), const Combine &combine = NeverCombine()) { + Sort sorter(chain, config, compare, combine); + chain.Wait(true); + uint64_t size = sorter.Size(); + sorter.Output(chain); + return size; +} + +/** + * Represents an @ref util::FixedArray "array" capable of storing @ref util::stream::Sort "Sort" objects. + * + * In the anticipated use case, an instance of this class will maintain one @ref util::stream::Sort "Sort" object + * for each n-gram order (ranging from 1 up to the maximum n-gram order being processed). + * Use in this manner would enable the n-grams each n-gram order to be sorted, in parallel. + * + * @tparam Compare An @ref Comparator "ngram comparator" to use during sorting. + */ +template class Sorts : public FixedArray > { + private: + typedef Sort S; + typedef FixedArray P; + + public: + /** + * Constructs, but does not initialize. + * + * @ref util::FixedArray::Init() "Init" must be called before use. + * + * @see util::FixedArray::Init() + */ + Sorts() {} + + /** + * Constructs an @ref util::FixedArray "array" capable of storing a fixed number of @ref util::stream::Sort "Sort" objects. + * + * @param number The maximum number of @ref util::stream::Sort "sorters" that can be held by this @ref util::FixedArray "array" + * @see util::FixedArray::FixedArray() + */ + explicit Sorts(std::size_t number) : FixedArray >(number) {} + + /** + * Constructs a new @ref util::stream::Sort "Sort" object which is stored in this @ref util::FixedArray "array". + * + * The new @ref util::stream::Sort "Sort" object is constructed using the provided @ref util::stream::SortConfig "SortConfig" and @ref Comparator "ngram comparator"; + * once constructed, a new worker @ref util::stream::Thread "thread" (owned by the @ref util::stream::Chain "chain") will sort the n-gram data stored + * in the @ref util::stream::Block "blocks" of the provided @ref util::stream::Chain "chain". + * + * @see util::stream::Sort::Sort() + * @see util::stream::Chain::operator>>() + */ + void push_back(util::stream::Chain &chain, const util::stream::SortConfig &config, const Compare &compare = Compare(), const Combine &combine = Combine()) { + new (P::end()) S(chain, config, compare, combine); // use "placement new" syntax to initalize S in an already-allocated memory location + P::Constructed(); + } +}; + +} // namespace stream +} // namespace util + +#endif // UTIL_STREAM_SORT_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/sort_test.cc b/ctcdecode-master/third_party/kenlm/util/stream/sort_test.cc new file mode 100644 index 0000000..fc97ffd --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/sort_test.cc @@ -0,0 +1,62 @@ +#include "util/stream/sort.hh" + +#define BOOST_TEST_MODULE SortTest +#include + +#include + +#include + +namespace util { namespace stream { namespace { + +struct CompareUInt64 : public std::binary_function { + bool operator()(const void *first, const void *second) const { + return *static_cast(first) < *reinterpret_cast(second); + } +}; + +const uint64_t kSize = 100000; + +struct Putter { + Putter(std::vector &shuffled) : shuffled_(shuffled) {} + + void Run(const ChainPosition &position) { + Stream put_shuffled(position); + for (uint64_t i = 0; i < shuffled_.size(); ++i, ++put_shuffled) { + *static_cast(put_shuffled.Get()) = shuffled_[i]; + } + put_shuffled.Poison(); + } + std::vector &shuffled_; +}; + +BOOST_AUTO_TEST_CASE(FromShuffled) { + std::vector shuffled; + shuffled.reserve(kSize); + for (uint64_t i = 0; i < kSize; ++i) { + shuffled.push_back(i); + } + std::random_shuffle(shuffled.begin(), shuffled.end()); + + ChainConfig config; + config.entry_size = 8; + config.total_memory = 800; + config.block_count = 3; + + SortConfig merge_config; + merge_config.temp_prefix = "sort_test_temp"; + merge_config.buffer_size = 800; + merge_config.total_memory = 3300; + + Chain chain(config); + chain >> Putter(shuffled); + BlockingSort(chain, merge_config, CompareUInt64(), NeverCombine()); + Stream sorted; + chain >> sorted >> kRecycle; + for (uint64_t i = 0; i < kSize; ++i, ++sorted) { + BOOST_CHECK_EQUAL(i, *static_cast(sorted.Get())); + } + BOOST_CHECK(!sorted); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/stream.hh b/ctcdecode-master/third_party/kenlm/util/stream/stream.hh new file mode 100644 index 0000000..ee1e9fa --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/stream.hh @@ -0,0 +1,77 @@ +#ifndef UTIL_STREAM_STREAM_H +#define UTIL_STREAM_STREAM_H + +#include "util/stream/chain.hh" + +#include + +#include +#include + +namespace util { +namespace stream { + +class Stream : boost::noncopyable { + public: + Stream() : current_(NULL), end_(NULL) {} + + void Init(const ChainPosition &position) { + entry_size_ = position.GetChain().EntrySize(); + block_size_ = position.GetChain().BlockSize(); + block_it_.Init(position); + StartBlock(); + } + + explicit Stream(const ChainPosition &position) { + Init(position); + } + + operator bool() const { return current_ != NULL; } + bool operator!() const { return current_ == NULL; } + + const void *Get() const { return current_; } + void *Get() { return current_; } + + void Poison() { + block_it_->SetValidSize(current_ - static_cast(block_it_->Get())); + ++block_it_; + block_it_.Poison(); + } + + Stream &operator++() { + assert(*this); + assert(current_ < end_); + current_ += entry_size_; + if (current_ == end_) { + ++block_it_; + StartBlock(); + } + return *this; + } + + private: + void StartBlock() { + for (; block_it_ && !block_it_->ValidSize(); ++block_it_) {} + current_ = static_cast(block_it_->Get()); + end_ = current_ + block_it_->ValidSize(); + } + + // The following are pointers to raw memory + // current_ is the current record + // end_ is the end of the block (so we know when to move to the next block) + uint8_t *current_, *end_; + + std::size_t entry_size_; + std::size_t block_size_; + + Link block_it_; +}; + +inline Chain &operator>>(Chain &chain, Stream &stream) { + stream.Init(chain.Add()); + return chain; +} + +} // namespace stream +} // namespace util +#endif // UTIL_STREAM_STREAM_H diff --git a/ctcdecode-master/third_party/kenlm/util/stream/stream_test.cc b/ctcdecode-master/third_party/kenlm/util/stream/stream_test.cc new file mode 100644 index 0000000..6575d50 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/stream_test.cc @@ -0,0 +1,35 @@ +#include "util/stream/io.hh" + +#include "util/stream/stream.hh" +#include "util/file.hh" + +#define BOOST_TEST_MODULE StreamTest +#include + +#include + +namespace util { namespace stream { namespace { + +BOOST_AUTO_TEST_CASE(StreamTest) { + scoped_fd in(MakeTemp("io_test_temp")); + for (uint64_t i = 0; i < 100000; ++i) { + WriteOrThrow(in.get(), &i, sizeof(uint64_t)); + } + SeekOrThrow(in.get(), 0); + + ChainConfig config; + config.entry_size = 8; + config.total_memory = 100; + config.block_count = 12; + + Stream s; + Chain chain(config); + chain >> Read(in.get()) >> s >> kRecycle; + uint64_t i = 0; + for (; s; ++s, ++i) { + BOOST_CHECK_EQUAL(i, *static_cast(s.Get())); + } + BOOST_CHECK_EQUAL(100000ULL, i); +} + +}}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/stream/typed_stream.hh b/ctcdecode-master/third_party/kenlm/util/stream/typed_stream.hh new file mode 100644 index 0000000..2da5b76 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/stream/typed_stream.hh @@ -0,0 +1,25 @@ +#ifndef UTIL_STREAM_TYPED_STREAM_H +#define UTIL_STREAM_TYPED_STREAM_H +// A typed wrapper to Stream for POD types. + +#include "util/stream/stream.hh" + +namespace util { namespace stream { + +template class TypedStream : public Stream { + public: + // After using the default constructor, call Init (in the parent class) + TypedStream() {} + + explicit TypedStream(const ChainPosition &position) : Stream(position) {} + + const T *operator->() const { return static_cast(Get()); } + T *operator->() { return static_cast(Get()); } + + const T &operator*() const { return *static_cast(Get()); } + T &operator*() { return *static_cast(Get()); } +}; + +}} // namespaces + +#endif // UTIL_STREAM_TYPED_STREAM_H diff --git a/ctcdecode-master/third_party/kenlm/util/string_piece.cc b/ctcdecode-master/third_party/kenlm/util/string_piece.cc new file mode 100644 index 0000000..180b4e4 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/string_piece.cc @@ -0,0 +1,192 @@ +// Copyright 2004 The RE2 Authors. All Rights Reserved. +// Use of this source code is governed by a BSD-style +// license that can be found in string_piece.hh. + +#include "util/string_piece.hh" + +#include +#include + +#ifndef HAVE_ICU + +typedef StringPiece::size_type size_type; + +void StringPiece::CopyToString(std::string* target) const { + target->assign(ptr_, length_); +} + +size_type StringPiece::find(const StringPiece& s, size_type pos) const { + // Not sure why length_ < 0 was here since it's std::size_t. + if (/*length_ < 0 || */pos > static_cast(length_)) + return npos; + + const char* result = std::search(ptr_ + pos, ptr_ + length_, + s.ptr_, s.ptr_ + s.length_); + const size_type xpos = result - ptr_; + return xpos + s.length_ <= length_ ? xpos : npos; +} + +size_type StringPiece::find(char c, size_type pos) const { + if (length_ <= 0 || pos >= static_cast(length_)) { + return npos; + } + const char* result = std::find(ptr_ + pos, ptr_ + length_, c); + return result != ptr_ + length_ ? result - ptr_ : npos; +} + +size_type StringPiece::rfind(const StringPiece& s, size_type pos) const { + if (length_ < s.length_) return npos; + const size_t ulen = length_; + if (s.length_ == 0) return std::min(ulen, pos); + + const char* last = ptr_ + std::min(ulen - s.length_, pos) + s.length_; + const char* result = std::find_end(ptr_, last, s.ptr_, s.ptr_ + s.length_); + return result != last ? result - ptr_ : npos; +} + +size_type StringPiece::rfind(char c, size_type pos) const { + if (length_ <= 0) return npos; + for (int i = std::min(pos, static_cast(length_ - 1)); + i >= 0; --i) { + if (ptr_[i] == c) { + return i; + } + } + return npos; +} + +// For each character in characters_wanted, sets the index corresponding +// to the ASCII code of that character to 1 in table. This is used by +// the find_.*_of methods below to tell whether or not a character is in +// the lookup table in constant time. +// The argument `table' must be an array that is large enough to hold all +// the possible values of an unsigned char. Thus it should be be declared +// as follows: +// bool table[UCHAR_MAX + 1] +static inline void BuildLookupTable(const StringPiece& characters_wanted, + bool* table) { + const size_type length = characters_wanted.length(); + const char* const data = characters_wanted.data(); + for (size_type i = 0; i < length; ++i) { + table[static_cast(data[i])] = true; + } +} + +size_type StringPiece::find_first_of(const StringPiece& s, + size_type pos) const { + if (length_ == 0 || s.length_ == 0) + return npos; + + // Avoid the cost of BuildLookupTable() for a single-character search. + if (s.length_ == 1) + return find_first_of(s.ptr_[0], pos); + + bool lookup[UCHAR_MAX + 1] = { false }; + BuildLookupTable(s, lookup); + for (size_type i = pos; i < length_; ++i) { + if (lookup[static_cast(ptr_[i])]) { + return i; + } + } + return npos; +} + +size_type StringPiece::find_first_not_of(const StringPiece& s, + size_type pos) const { + if (length_ == 0) + return npos; + + if (s.length_ == 0) + return 0; + + // Avoid the cost of BuildLookupTable() for a single-character search. + if (s.length_ == 1) + return find_first_not_of(s.ptr_[0], pos); + + bool lookup[UCHAR_MAX + 1] = { false }; + BuildLookupTable(s, lookup); + for (size_type i = pos; i < length_; ++i) { + if (!lookup[static_cast(ptr_[i])]) { + return i; + } + } + return npos; +} + +size_type StringPiece::find_first_not_of(char c, size_type pos) const { + if (length_ == 0) + return npos; + + for (; pos < length_; ++pos) { + if (ptr_[pos] != c) { + return pos; + } + } + return npos; +} + +size_type StringPiece::find_last_of(const StringPiece& s, size_type pos) const { + if (length_ == 0 || s.length_ == 0) + return npos; + + // Avoid the cost of BuildLookupTable() for a single-character search. + if (s.length_ == 1) + return find_last_of(s.ptr_[0], pos); + + bool lookup[UCHAR_MAX + 1] = { false }; + BuildLookupTable(s, lookup); + for (size_type i = std::min(pos, length_ - 1); ; --i) { + if (lookup[static_cast(ptr_[i])]) + return i; + if (i == 0) + break; + } + return npos; +} + +size_type StringPiece::find_last_not_of(const StringPiece& s, + size_type pos) const { + if (length_ == 0) + return npos; + + size_type i = std::min(pos, length_ - 1); + if (s.length_ == 0) + return i; + + // Avoid the cost of BuildLookupTable() for a single-character search. + if (s.length_ == 1) + return find_last_not_of(s.ptr_[0], pos); + + bool lookup[UCHAR_MAX + 1] = { false }; + BuildLookupTable(s, lookup); + for (; ; --i) { + if (!lookup[static_cast(ptr_[i])]) + return i; + if (i == 0) + break; + } + return npos; +} + +size_type StringPiece::find_last_not_of(char c, size_type pos) const { + if (length_ == 0) + return npos; + + for (size_type i = std::min(pos, length_ - 1); ; --i) { + if (ptr_[i] != c) + return i; + if (i == 0) + break; + } + return npos; +} + +StringPiece StringPiece::substr(size_type pos, size_type n) const { + if (pos > length_) pos = length_; + if (n > length_ - pos) n = length_ - pos; + return StringPiece(ptr_ + pos, n); +} + +const size_type StringPiece::npos = size_type(-1); + +#endif // !HAVE_ICU diff --git a/ctcdecode-master/third_party/kenlm/util/string_piece.hh b/ctcdecode-master/third_party/kenlm/util/string_piece.hh new file mode 100644 index 0000000..9c47982 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/string_piece.hh @@ -0,0 +1,270 @@ +/* If you use ICU in your program, then compile with -DHAVE_ICU -licui18n. If + * you don't use ICU, then this will use the Google implementation from Chrome. + * This has been modified from the original version to let you choose. + */ + +// Copyright 2008, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +// Copied from strings/stringpiece.h with modifications +// +// A string-like object that points to a sized piece of memory. +// +// Functions or methods may use const StringPiece& parameters to accept either +// a "const char*" or a "string" value that will be implicitly converted to +// a StringPiece. The implicit conversion means that it is often appropriate +// to include this .h file in other files rather than forward-declaring +// StringPiece as would be appropriate for most other Google classes. +// +// Systematic usage of StringPiece is encouraged as it will reduce unnecessary +// conversions from "const char*" to "string" and back again. +// + +#ifndef UTIL_STRING_PIECE_H +#define UTIL_STRING_PIECE_H + +#include "util/have.hh" + +#include +#include +#include + +#ifdef HAVE_ICU +#include +#include + +// Old versions of ICU don't define operator== and operator!=. +#if (U_ICU_VERSION_MAJOR_NUM < 4) || ((U_ICU_VERSION_MAJOR_NUM == 4) && (U_ICU_VERSION_MINOR_NUM < 4)) +#warning You are using an old version of ICU. Consider upgrading to ICU >= 4.6. +inline bool operator==(const StringPiece& x, const StringPiece& y) { + if (x.size() != y.size()) + return false; + + return std::memcmp(x.data(), y.data(), x.size()) == 0; +} + +inline bool operator!=(const StringPiece& x, const StringPiece& y) { + return !(x == y); +} +#endif // old version of ICU + +U_NAMESPACE_BEGIN + +inline bool starts_with(const StringPiece& longer, const StringPiece& prefix) { + int longersize = longer.size(), prefixsize = prefix.size(); + return longersize >= prefixsize && std::memcmp(longer.data(), prefix.data(), prefixsize) == 0; +} + +#else + +#include +#include +#include +#include + +#ifdef WIN32 +#undef max +#undef min +#endif + +class StringPiece { + public: + typedef size_t size_type; + + private: + const char* ptr_; + size_type length_; + + public: + // We provide non-explicit singleton constructors so users can pass + // in a "const char*" or a "string" wherever a "StringPiece" is + // expected. + StringPiece() : ptr_(NULL), length_(0) { } + StringPiece(const char* str) + : ptr_(str), length_((str == NULL) ? 0 : strlen(str)) { } + StringPiece(const std::string& str) + : ptr_(str.data()), length_(str.size()) { } + StringPiece(const char* offset, size_type len) + : ptr_(offset), length_(len) { } + + // data() may return a pointer to a buffer with embedded NULs, and the + // returned buffer may or may not be null terminated. Therefore it is + // typically a mistake to pass data() to a routine that expects a NUL + // terminated string. + const char* data() const { return ptr_; } + size_type size() const { return length_; } + size_type length() const { return length_; } + bool empty() const { return length_ == 0; } + + void clear() { ptr_ = NULL; length_ = 0; } + void set(const char* data, size_type len) { ptr_ = data; length_ = len; } + void set(const char* str) { + ptr_ = str; + length_ = str ? strlen(str) : 0; + } + void set(const void* data, size_type len) { + ptr_ = reinterpret_cast(data); + length_ = len; + } + + char operator[](size_type i) const { return ptr_[i]; } + + void remove_prefix(size_type n) { + ptr_ += n; + length_ -= n; + } + + void remove_suffix(size_type n) { + length_ -= n; + } + + int compare(const StringPiece& x) const { + int r = wordmemcmp(ptr_, x.ptr_, std::min(length_, x.length_)); + if (r == 0) { + if (length_ < x.length_) r = -1; + else if (length_ > x.length_) r = +1; + } + return r; + } + + std::string as_string() const { + // std::string doesn't like to take a NULL pointer even with a 0 size. + return std::string(!empty() ? data() : "", size()); + } + + void CopyToString(std::string* target) const; + void AppendToString(std::string* target) const; + + // Does "this" start with "x" + bool starts_with(const StringPiece& x) const { + return ((length_ >= x.length_) && + (wordmemcmp(ptr_, x.ptr_, x.length_) == 0)); + } + + // Does "this" end with "x" + bool ends_with(const StringPiece& x) const { + return ((length_ >= x.length_) && + (wordmemcmp(ptr_ + (length_-x.length_), x.ptr_, x.length_) == 0)); + } + + // standard STL container boilerplate + typedef char value_type; + typedef const char* pointer; + typedef const char& reference; + typedef const char& const_reference; + typedef ptrdiff_t difference_type; + static const size_type npos; + typedef const char* const_iterator; + typedef const char* iterator; + typedef std::reverse_iterator const_reverse_iterator; + typedef std::reverse_iterator reverse_iterator; + iterator begin() const { return ptr_; } + iterator end() const { return ptr_ + length_; } + const_reverse_iterator rbegin() const { + return const_reverse_iterator(ptr_ + length_); + } + const_reverse_iterator rend() const { + return const_reverse_iterator(ptr_); + } + + size_type max_size() const { return length_; } + size_type capacity() const { return length_; } + + size_type copy(char* buf, size_type n, size_type pos = 0) const; + + size_type find(const StringPiece& s, size_type pos = 0) const; + size_type find(char c, size_type pos = 0) const; + size_type rfind(const StringPiece& s, size_type pos = npos) const; + size_type rfind(char c, size_type pos = npos) const; + + size_type find_first_of(const StringPiece& s, size_type pos = 0) const; + size_type find_first_of(char c, size_type pos = 0) const { + return find(c, pos); + } + size_type find_first_not_of(const StringPiece& s, size_type pos = 0) const; + size_type find_first_not_of(char c, size_type pos = 0) const; + size_type find_last_of(const StringPiece& s, size_type pos = npos) const; + size_type find_last_of(char c, size_type pos = npos) const { + return rfind(c, pos); + } + size_type find_last_not_of(const StringPiece& s, size_type pos = npos) const; + size_type find_last_not_of(char c, size_type pos = npos) const; + + StringPiece substr(size_type pos, size_type n = npos) const; + + static int wordmemcmp(const char* p, const char* p2, size_type N) { + return std::memcmp(p, p2, N); + } +}; + +inline bool operator==(const StringPiece& x, const StringPiece& y) { + if (x.size() != y.size()) + return false; + + return std::memcmp(x.data(), y.data(), x.size()) == 0; +} + +inline bool operator!=(const StringPiece& x, const StringPiece& y) { + return !(x == y); +} + +inline bool starts_with(const StringPiece& longer, const StringPiece& prefix) { + return longer.starts_with(prefix); +} + +#endif // HAVE_ICU undefined + +inline bool operator<(const StringPiece& x, const StringPiece& y) { + const int r = std::memcmp(x.data(), y.data(), + std::min(x.size(), y.size())); + return ((r < 0) || ((r == 0) && (x.size() < y.size()))); +} + +inline bool operator>(const StringPiece& x, const StringPiece& y) { + return y < x; +} + +inline bool operator<=(const StringPiece& x, const StringPiece& y) { + return !(x > y); +} + +inline bool operator>=(const StringPiece& x, const StringPiece& y) { + return !(x < y); +} + +// allow StringPiece to be logged (needed for unit testing). +inline std::ostream& operator<<(std::ostream& o, const StringPiece& piece) { + return o.write(piece.data(), static_cast(piece.size())); +} + +#ifdef HAVE_ICU +U_NAMESPACE_END +using U_NAMESPACE_QUALIFIER StringPiece; +#endif + +#endif // UTIL_STRING_PIECE_H diff --git a/ctcdecode-master/third_party/kenlm/util/string_piece_hash.hh b/ctcdecode-master/third_party/kenlm/util/string_piece_hash.hh new file mode 100644 index 0000000..5c8c525 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/string_piece_hash.hh @@ -0,0 +1,43 @@ +#ifndef UTIL_STRING_PIECE_HASH_H +#define UTIL_STRING_PIECE_HASH_H + +#include "util/string_piece.hh" + +#include +#include + +inline size_t hash_value(const StringPiece &str) { + return boost::hash_range(str.data(), str.data() + str.length()); +} + +/* Support for lookup of StringPiece in boost::unordered_map */ +struct StringPieceCompatibleHash : public std::unary_function { + size_t operator()(const StringPiece &str) const { + return hash_value(str); + } +}; + +struct StringPieceCompatibleEquals : public std::binary_function { + bool operator()(const StringPiece &first, const StringPiece &second) const { + return first == second; + } +}; +template typename T::const_iterator FindStringPiece(const T &t, const StringPiece &key) { +#if BOOST_VERSION < 104200 + std::string temp(key.data(), key.size()); + return t.find(temp); +#else + return t.find(key, StringPieceCompatibleHash(), StringPieceCompatibleEquals()); +#endif +} + +template typename T::iterator FindStringPiece(T &t, const StringPiece &key) { +#if BOOST_VERSION < 104200 + std::string temp(key.data(), key.size()); + return t.find(temp); +#else + return t.find(key, StringPieceCompatibleHash(), StringPieceCompatibleEquals()); +#endif +} + +#endif // UTIL_STRING_PIECE_HASH_H diff --git a/ctcdecode-master/third_party/kenlm/util/string_stream.hh b/ctcdecode-master/third_party/kenlm/util/string_stream.hh new file mode 100644 index 0000000..28fdd42 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/string_stream.hh @@ -0,0 +1,48 @@ +#ifndef UTIL_STRING_STREAM_H +#define UTIL_STRING_STREAM_H + +#include "util/fake_ostream.hh" + +#include +#include + +namespace util { + +class StringStream : public FakeOStream { + public: + StringStream() {} + + StringStream &flush() { return *this; } + + StringStream &write(const void *data, std::size_t length) { + out_.append(static_cast(data), length); + return *this; + } + + const std::string &str() const { return out_; } + + void str(const std::string &val) { out_ = val; } + + void swap(std::string &str) { std::swap(out_, str); } + + protected: + friend class FakeOStream; + char *Ensure(std::size_t amount) { + std::size_t current = out_.size(); + out_.resize(out_.size() + amount); + return &out_[current]; + } + + void AdvanceTo(char *to) { + assert(to <= &*out_.end()); + assert(to >= &*out_.begin()); + out_.resize(to - &*out_.begin()); + } + + private: + std::string out_; +}; + +} // namespace + +#endif // UTIL_STRING_STREAM_H diff --git a/ctcdecode-master/third_party/kenlm/util/string_stream_test.cc b/ctcdecode-master/third_party/kenlm/util/string_stream_test.cc new file mode 100644 index 0000000..ad99655 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/string_stream_test.cc @@ -0,0 +1,80 @@ +#define BOOST_LEXICAL_CAST_ASSUME_C_LOCALE +#define BOOST_TEST_MODULE FakeOStreamTest + +#include "util/string_stream.hh" +#include +#include + +#include +#include + +namespace util { namespace { + +template void TestEqual(const T value) { + StringStream strme; + strme << value; + BOOST_CHECK_EQUAL(boost::lexical_cast(value), strme.str()); +} + +template void TestCorners() { + TestEqual(std::numeric_limits::max()); + TestEqual(std::numeric_limits::min()); + TestEqual(static_cast(0)); + TestEqual(static_cast(-1)); + TestEqual(static_cast(1)); +} + +BOOST_AUTO_TEST_CASE(Integer) { + TestCorners(); + TestCorners(); + TestCorners(); + + TestCorners(); + TestCorners(); + TestCorners(); + + TestCorners(); + TestCorners(); + TestCorners(); + + TestCorners(); + TestCorners(); + TestCorners(); + + TestCorners(); + TestCorners(); + TestCorners(); + + TestCorners(); +} + +enum TinyEnum { EnumValue }; + +BOOST_AUTO_TEST_CASE(EnumCase) { + TestEqual(EnumValue); +} + +BOOST_AUTO_TEST_CASE(Strings) { + TestEqual("foo"); + const char *a = "bar"; + TestEqual(a); + StringPiece piece("abcdef"); + TestEqual(piece); + TestEqual(StringPiece()); + + char non_const[3]; + non_const[0] = 'b'; + non_const[1] = 'c'; + non_const[2] = 0; + + StringStream out; + out << "a" << non_const << 'c'; + BOOST_CHECK_EQUAL("abcc", out.str()); + + // Now test as a separate object. + StringStream stream; + stream << "a" << non_const << 'c' << piece; + BOOST_CHECK_EQUAL("abccabcdef", stream.str()); +} + +}} // namespaces diff --git a/ctcdecode-master/third_party/kenlm/util/thread_pool.hh b/ctcdecode-master/third_party/kenlm/util/thread_pool.hh new file mode 100644 index 0000000..082fbf8 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/thread_pool.hh @@ -0,0 +1,140 @@ +#ifndef UTIL_THREAD_POOL_H +#define UTIL_THREAD_POOL_H + +#include "util/pcqueue.hh" + +#include +#include +#include + +#include +#include + +namespace util { + +template class Worker : boost::noncopyable { + public: + typedef HandlerT Handler; + typedef typename Handler::Request Request; + + template Worker(PCQueue &in, Construct &construct, const Request &poison) + : in_(in), handler_(construct), poison_(poison), thread_(boost::ref(*this)) {} + + // Only call from thread. + void operator()() { + Request request; + while (1) { + in_.Consume(request); + if (request == poison_) return; + try { + (*handler_)(request); + } + catch(const std::exception &e) { + std::cerr << "Handler threw " << e.what() << std::endl; + abort(); + } + catch(...) { + std::cerr << "Handler threw an exception, dropping request" << std::endl; + abort(); + } + } + } + + void Join() { + thread_.join(); + } + + private: + PCQueue &in_; + + boost::optional handler_; + + const Request poison_; + + boost::thread thread_; +}; + +template class ThreadPool : boost::noncopyable { + public: + typedef HandlerT Handler; + typedef typename Handler::Request Request; + + template ThreadPool(std::size_t queue_length, std::size_t workers, Construct handler_construct, Request poison) : in_(queue_length), poison_(poison) { + for (size_t i = 0; i < workers; ++i) { + workers_.push_back(new Worker(in_, handler_construct, poison)); + } + } + + ~ThreadPool() { + for (std::size_t i = 0; i < workers_.size(); ++i) { + Produce(poison_); + } + for (typename boost::ptr_vector >::iterator i = workers_.begin(); i != workers_.end(); ++i) { + i->Join(); + } + } + + void Produce(const Request &request) { + in_.Produce(request); + } + + // For adding to the queue. + PCQueue &In() { return in_; } + + private: + PCQueue in_; + + boost::ptr_vector > workers_; + + Request poison_; +}; + +template class RecyclingHandler { + public: + typedef typename Handler::Request Request; + + template RecyclingHandler(PCQueue &recycling, Construct &handler_construct) + : inner_(handler_construct), recycling_(recycling) {} + + void operator()(Request &request) { + inner_(request); + recycling_.Produce(request); + } + + private: + Handler inner_; + PCQueue &recycling_; +}; + +template class RecyclingThreadPool : boost::noncopyable { + public: + typedef HandlerT Handler; + typedef typename Handler::Request Request; + + // Remember to call PopulateRecycling afterwards in most cases. + template RecyclingThreadPool(std::size_t queue, std::size_t workers, Construct handler_construct, Request poison) + : recycling_(queue), pool_(queue, workers, RecyclingHandler(recycling_, handler_construct), poison) {} + + // Initialization: put stuff into the recycling queue. This could also be + // done by calling Produce without Consume, but it's often easier to + // initialize with PopulateRecycling then do a Consume/Produce loop. + void PopulateRecycling(const Request &request) { + recycling_.Produce(request); + } + + Request Consume() { + return recycling_.Consume(); + } + + void Produce(const Request &request) { + pool_.Produce(request); + } + + private: + PCQueue recycling_; + ThreadPool > pool_; +}; + +} // namespace util + +#endif // UTIL_THREAD_POOL_H diff --git a/ctcdecode-master/third_party/kenlm/util/tokenize_piece.hh b/ctcdecode-master/third_party/kenlm/util/tokenize_piece.hh new file mode 100644 index 0000000..14ff988 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/tokenize_piece.hh @@ -0,0 +1,161 @@ +#ifndef UTIL_TOKENIZE_PIECE_H +#define UTIL_TOKENIZE_PIECE_H + +#include "util/exception.hh" +#include "util/spaces.hh" +#include "util/string_piece.hh" + +#include + +#include +#include + +namespace util { + +// Thrown on dereference when out of tokens to parse +class OutOfTokens : public Exception { + public: + OutOfTokens() throw() {} + ~OutOfTokens() throw() {} +}; + +class SingleCharacter { + public: + SingleCharacter() {} + explicit SingleCharacter(char delim) : delim_(delim) {} + + StringPiece Find(const StringPiece &in) const { + return StringPiece(std::find(in.data(), in.data() + in.size(), delim_), 1); + } + + private: + char delim_; +}; + +class MultiCharacter { + public: + MultiCharacter() {} + + explicit MultiCharacter(const StringPiece &delimiter) : delimiter_(delimiter) {} + + StringPiece Find(const StringPiece &in) const { + return StringPiece(std::search(in.data(), in.data() + in.size(), delimiter_.data(), delimiter_.data() + delimiter_.size()), delimiter_.size()); + } + + private: + StringPiece delimiter_; +}; + +class AnyCharacter { + public: + AnyCharacter() {} + explicit AnyCharacter(const StringPiece &chars) : chars_(chars) {} + + StringPiece Find(const StringPiece &in) const { + return StringPiece(std::find_first_of(in.data(), in.data() + in.size(), chars_.data(), chars_.data() + chars_.size()), 1); + } + + private: + StringPiece chars_; +}; + +class BoolCharacter { + public: + BoolCharacter() {} + + explicit BoolCharacter(const bool *delimiter = kSpaces) { delimiter_ = delimiter; } + + StringPiece Find(const StringPiece &in) const { + for (const char *i = in.data(); i != in.data() + in.size(); ++i) { + if (delimiter_[static_cast(*i)]) return StringPiece(i, 1); + } + return StringPiece(in.data() + in.size(), 0); + } + + template static void Build(const char (&characters)[Length], bool (&out)[256]) { + memset(out, 0, sizeof(out)); + for (const char *i = characters; i != characters + Length; ++i) { + out[static_cast(*i)] = true; + } + } + + private: + const bool *delimiter_; +}; + +class AnyCharacterLast { + public: + AnyCharacterLast() {} + + explicit AnyCharacterLast(const StringPiece &chars) : chars_(chars) {} + + StringPiece Find(const StringPiece &in) const { + return StringPiece(std::find_end(in.data(), in.data() + in.size(), chars_.data(), chars_.data() + chars_.size()), 1); + } + + private: + StringPiece chars_; +}; + +template class TokenIter : public boost::iterator_facade, const StringPiece, boost::forward_traversal_tag> { + public: + TokenIter() {} + + template TokenIter(const StringPiece &str, const Construct &construct) : after_(str), finder_(construct) { + increment(); + } + + bool operator!() const { + return current_.data() == 0; + } + operator bool() const { + return current_.data() != 0; + } + + static TokenIter end() { + return TokenIter(); + } + + private: + friend class boost::iterator_core_access; + + void increment() { + do { + StringPiece found(finder_.Find(after_)); + current_ = StringPiece(after_.data(), found.data() - after_.data()); + if (found.data() == after_.data() + after_.size()) { + after_ = StringPiece(NULL, 0); + } else { + after_ = StringPiece(found.data() + found.size(), after_.data() - found.data() + after_.size() - found.size()); + } + } while (SkipEmpty && current_.data() && current_.empty()); // Compiler should optimize this away if SkipEmpty is false. + } + + bool equal(const TokenIter &other) const { + return current_.data() == other.current_.data(); + } + + const StringPiece &dereference() const { + UTIL_THROW_IF(!current_.data(), OutOfTokens, "Ran out of tokens"); + return current_; + } + + StringPiece current_; + StringPiece after_; + + Find finder_; +}; + +inline StringPiece Trim(StringPiece str, const bool *spaces = kSpaces) { + while (!str.empty() && spaces[static_cast(*str.data())]) { + str = StringPiece(str.data() + 1, str.size() - 1); + } + while (!str.empty() && spaces[static_cast(str[str.size() - 1])]) { + str = StringPiece(str.data(), str.size() - 1); + } + return str; +} + +} // namespace util + +#endif // UTIL_TOKENIZE_PIECE_H diff --git a/ctcdecode-master/third_party/kenlm/util/tokenize_piece_test.cc b/ctcdecode-master/third_party/kenlm/util/tokenize_piece_test.cc new file mode 100644 index 0000000..d856018 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/tokenize_piece_test.cc @@ -0,0 +1,48 @@ +#include "util/tokenize_piece.hh" +#include "util/string_piece.hh" + +#define BOOST_TEST_MODULE TokenIteratorTest +#include + +#include + +namespace util { +namespace { + +BOOST_AUTO_TEST_CASE(pipe_pipe_none) { + const char str[] = "nodelimit at all"; + TokenIter it(str, MultiCharacter("|||")); + BOOST_REQUIRE(it); + BOOST_CHECK_EQUAL(StringPiece(str), *it); + ++it; + BOOST_CHECK(!it); +} +BOOST_AUTO_TEST_CASE(pipe_pipe_two) { + const char str[] = "|||"; + TokenIter it(str, MultiCharacter("|||")); + BOOST_REQUIRE(it); + BOOST_CHECK_EQUAL(StringPiece(), *it); + ++it; + BOOST_REQUIRE(it); + BOOST_CHECK_EQUAL(StringPiece(), *it); + ++it; + BOOST_CHECK(!it); +} + +BOOST_AUTO_TEST_CASE(remove_empty) { + const char str[] = "|||"; + TokenIter it(str, MultiCharacter("|||")); + BOOST_CHECK(!it); +} + +BOOST_AUTO_TEST_CASE(remove_empty_keep) { + const char str[] = " |||"; + TokenIter it(str, MultiCharacter("|||")); + BOOST_REQUIRE(it); + BOOST_CHECK_EQUAL(StringPiece(" "), *it); + ++it; + BOOST_CHECK(!it); +} + +} // namespace +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/usage.cc b/ctcdecode-master/third_party/kenlm/util/usage.cc new file mode 100644 index 0000000..6ece571 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/usage.cc @@ -0,0 +1,341 @@ +#include "util/usage.hh" + +#include "util/exception.hh" + +#include +#include +#include +#include +#include +#include +#include +#include +#if defined(_WIN32) || defined(_WIN64) +// This code lifted from physmem.c in gnulib. See the copyright statement +// below. +# define WIN32_LEAN_AND_MEAN +# include +/* MEMORYSTATUSEX is missing from older windows headers, so define + a local replacement. */ +typedef struct +{ + DWORD dwLength; + DWORD dwMemoryLoad; + DWORDLONG ullTotalPhys; + DWORDLONG ullAvailPhys; + DWORDLONG ullTotalPageFile; + DWORDLONG ullAvailPageFile; + DWORDLONG ullTotalVirtual; + DWORDLONG ullAvailVirtual; + DWORDLONG ullAvailExtendedVirtual; +} lMEMORYSTATUSEX; +// Is this really supposed to be defined like this? +typedef int WINBOOL; +typedef WINBOOL (WINAPI *PFN_MS_EX) (lMEMORYSTATUSEX*); +#else +#include +#include +#include +#endif + +#if defined(__MACH__) || defined(__FreeBSD__) || defined(__APPLE__) +#include +#include +#include +#include +#endif + +namespace util { +namespace { + +#if defined(__MACH__) +typedef struct timeval Wall; +Wall GetWall() { + struct timeval tv; + gettimeofday(&tv, NULL); + return tv; +} +#elif defined(_WIN32) || defined(_WIN64) +typedef time_t Wall; +Wall GetWall() { + return time(NULL); +} +#else +typedef struct timespec Wall; +Wall GetWall() { + Wall ret; + UTIL_THROW_IF(-1 == clock_gettime(CLOCK_MONOTONIC, &ret), ErrnoException, "Could not get wall time"); + return ret; +} +#endif + +// gcc possible-unused function flags +#ifdef __GNUC__ +double Subtract(time_t first, time_t second) __attribute__ ((unused)); +double DoubleSec(time_t tv) __attribute__ ((unused)); +#if !defined(_WIN32) && !defined(_WIN64) +double Subtract(const struct timeval &first, const struct timeval &second) __attribute__ ((unused)); +double Subtract(const struct timespec &first, const struct timespec &second) __attribute__ ((unused)); +double DoubleSec(const struct timeval &tv) __attribute__ ((unused)); +double DoubleSec(const struct timespec &tv) __attribute__ ((unused)); +#endif +#endif + +// Some of these functions are only used on some platforms. +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wunused-function" +#endif +// These all assume first > second +double Subtract(time_t first, time_t second) { + return difftime(first, second); +} +double DoubleSec(time_t tv) { + return static_cast(tv); +} +#if !defined(_WIN32) && !defined(_WIN64) +double Subtract(const struct timeval &first, const struct timeval &second) { + return static_cast(first.tv_sec - second.tv_sec) + static_cast(first.tv_usec - second.tv_usec) / 1000000.0; +} +double Subtract(const struct timespec &first, const struct timespec &second) { + return static_cast(first.tv_sec - second.tv_sec) + static_cast(first.tv_nsec - second.tv_nsec) / 1000000000.0; +} +double DoubleSec(const struct timeval &tv) { + return static_cast(tv.tv_sec) + (static_cast(tv.tv_usec) / 1000000.0); +} +double DoubleSec(const struct timespec &tv) { + return static_cast(tv.tv_sec) + (static_cast(tv.tv_nsec) / 1000000000.0); +} +#endif +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +class RecordStart { + public: + RecordStart() { + started_ = GetWall(); + } + + const Wall &Started() const { + return started_; + } + + private: + Wall started_; +}; + +const RecordStart kRecordStart; + +const char *SkipSpaces(const char *at) { + for (; *at == ' ' || *at == '\t'; ++at) {} + return at; +} +} // namespace + +double WallTime() { + return Subtract(GetWall(), kRecordStart.Started()); +} + +double CPUTime() { +#if defined(_WIN32) || defined(_WIN64) + return 0.0; +#elif defined(__MACH__) || defined(__FreeBSD__) || defined(__APPLE__) + struct rusage usage; + UTIL_THROW_IF(getrusage(RUSAGE_SELF, &usage), ErrnoException, "getrusage failed"); + return DoubleSec(usage.ru_utime) + DoubleSec(usage.ru_stime); +#else + struct timespec usage; + UTIL_THROW_IF(clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &usage), ErrnoException, "clock_gettime failed?!"); + return DoubleSec(usage); +#endif +} + +double ThreadTime() { +#if defined(_WIN32) || defined(_WIN64) + // Output parameters for querying thread CPU usage: + FILETIME sys_time, user_time; + // Unused, but apparently need to be passed: + FILETIME c_time, e_time; + + HANDLE this_thread = GetCurrentThread(); + UTIL_THROW_IF(!GetThreadTimes(this_thread, &c_time, &e_time, &sys_time, &user_time), WindowsException, "GetThreadTime"); + // Convert LPFILETIME to 64-bit number, and from there to double. + ULARGE_INTEGER sys_ticks, user_ticks; + sys_ticks.LowPart = sys_time.dwLowDateTime; + sys_ticks.HighPart = sys_time.dwHighDateTime; + user_ticks.LowPart = user_time.dwLowDateTime; + user_ticks.HighPart = user_time.dwHighDateTime; + const double ticks = double(sys_ticks.QuadPart + user_ticks.QuadPart); + // GetThreadTimes() reports in units of 100 nanoseconds, i.e. ten-millionths + // of a second. + return ticks / (10 * 1000 * 1000); +#elif defined(__MACH__) || defined(__FreeBSD__) || defined(__APPLE__) + struct task_basic_info t_info; + mach_msg_type_number_t t_info_count = TASK_BASIC_INFO_COUNT; + task_info(mach_task_self(), TASK_BASIC_INFO, (task_info_t)&t_info, &t_info_count); + + return 0.0; +#else + struct timespec usage; + UTIL_THROW_IF(clock_gettime(CLOCK_THREAD_CPUTIME_ID, &usage), ErrnoException, "clock_gettime failed?!"); + return DoubleSec(usage); +#endif +} + +uint64_t RSSMax() { +#if defined(_WIN32) || defined(_WIN64) + return 0; +#else + struct rusage usage; + if (getrusage(RUSAGE_SELF, &usage)) + return 0; + return static_cast(usage.ru_maxrss) * 1024; +#endif +} + +void PrintUsage(std::ostream &out) { +#if !defined(_WIN32) && !defined(_WIN64) + // Linux doesn't set memory usage in getrusage :-( + std::set headers; + headers.insert("VmPeak:"); + headers.insert("VmRSS:"); + headers.insert("Name:"); + + std::ifstream status("/proc/self/status", std::ios::in); + std::string header, value; + while ((status >> header) && getline(status, value)) { + if (headers.find(header) != headers.end()) { + out << header << SkipSpaces(value.c_str()) << '\t'; + } + } + + struct rusage usage; + if (getrusage(RUSAGE_SELF, &usage)) { + perror("getrusage"); + return; + } + out << "RSSMax:" << usage.ru_maxrss << " kB" << '\t'; + out << "user:" << DoubleSec(usage.ru_utime) << "\tsys:" << DoubleSec(usage.ru_stime) << '\t'; + out << "CPU:" << CPUTime() << '\t'; +#endif + + out << "real:" << WallTime() << '\n'; +} + +/* Adapted from physmem.c in gnulib 831b84c59ef413c57a36b67344467d66a8a2ba70 */ +/* Calculate the size of physical memory. + + Copyright (C) 2000-2001, 2003, 2005-2006, 2009-2013 Free Software + Foundation, Inc. + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Lesser General Public License as published by + the Free Software Foundation; either version 2.1 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Lesser General Public License for more details. + + You should have received a copy of the GNU Lesser General Public License + along with this program. If not, see . */ + +/* Written by Paul Eggert. */ +uint64_t GuessPhysicalMemory() { +#if defined(_SC_PHYS_PAGES) && defined(_SC_PAGESIZE) + { + long pages = sysconf(_SC_PHYS_PAGES); + long page_size = sysconf(_SC_PAGESIZE); + if (pages != -1 && page_size != -1) + return static_cast(pages) * static_cast(page_size); + } +#endif +#ifdef HW_PHYSMEM + { /* This works on *bsd and darwin. */ + unsigned int physmem; + size_t len = sizeof physmem; + static int mib[2] = { CTL_HW, HW_PHYSMEM }; + + if (sysctl (mib, sizeof(mib) / sizeof(mib[0]), &physmem, &len, NULL, 0) == 0 + && len == sizeof (physmem)) + return static_cast(physmem); + } +#endif + +#if defined(_WIN32) || defined(_WIN64) + { /* this works on windows */ + PFN_MS_EX pfnex; + HMODULE h = GetModuleHandle (TEXT("kernel32.dll")); + + if (!h) + return 0; + + /* Use GlobalMemoryStatusEx if available. */ + if ((pfnex = (PFN_MS_EX) GetProcAddress (h, "GlobalMemoryStatusEx"))) + { + lMEMORYSTATUSEX lms_ex; + lms_ex.dwLength = sizeof lms_ex; + if (!pfnex (&lms_ex)) + return 0; + return lms_ex.ullTotalPhys; + } + + /* Fall back to GlobalMemoryStatus which is always available. + but returns wrong results for physical memory > 4GB. */ + else + { + MEMORYSTATUS ms; + GlobalMemoryStatus (&ms); + return ms.dwTotalPhys; + } + } +#endif + return 0; +} + +namespace { +class SizeParseError : public Exception { + public: + explicit SizeParseError(const std::string &str) throw() { + *this << "Failed to parse " << str << " into a memory size "; + } +}; + +template uint64_t ParseNum(const std::string &arg) { + std::stringstream stream(arg); + Num value; + stream >> value; + UTIL_THROW_IF_ARG(!stream, SizeParseError, (arg), "for the leading number."); + std::string after; + stream >> after; + UTIL_THROW_IF_ARG(after.size() > 1, SizeParseError, (arg), "because there are more than two characters after the number."); + std::string throwaway; + UTIL_THROW_IF_ARG(stream >> throwaway, SizeParseError, (arg), "because there was more cruft " << throwaway << " after the number."); + + // Silly sort, using kilobytes as your default unit. + if (after.empty()) after = "K"; + if (after == "%") { + uint64_t mem = GuessPhysicalMemory(); + UTIL_THROW_IF_ARG(!mem, SizeParseError, (arg), "because % was specified but the physical memory size could not be determined."); + return static_cast(static_cast(value) * static_cast(mem) / 100.0); + } + + if (after == "k") after = "K"; + std::string units("bKMGTPEZY"); + std::string::size_type index = units.find(after[0]); + UTIL_THROW_IF_ARG(index == std::string::npos, SizeParseError, (arg), "the allowed suffixes are " << units << "%."); + for (std::string::size_type i = 0; i < index; ++i) { + value *= 1024; + } + return static_cast(value); +} + +} // namespace + +uint64_t ParseSize(const std::string &arg) { + return arg.find('.') == std::string::npos ? ParseNum(arg) : ParseNum(arg); +} + +} // namespace util diff --git a/ctcdecode-master/third_party/kenlm/util/usage.hh b/ctcdecode-master/third_party/kenlm/util/usage.hh new file mode 100644 index 0000000..eaf8656 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/util/usage.hh @@ -0,0 +1,30 @@ +#ifndef UTIL_USAGE_H +#define UTIL_USAGE_H +#include +#include +#include +#include + +namespace util { +// Time in seconds since process started. Zero on unsupported platforms. +double WallTime(); + +// User + system time, process-wide. +double CPUTime(); + +// User + system time, thread-specific. +double ThreadTime(); + +// Resident usage in bytes. +uint64_t RSSMax(); + +void PrintUsage(std::ostream &to); + +// Determine how much physical memory there is. Return 0 on failure. +uint64_t GuessPhysicalMemory(); + +// Parse a size like unix sort. Sadly, this means the default multiplier is K. +uint64_t ParseSize(const std::string &arg); + +} // namespace util +#endif // UTIL_USAGE_H diff --git a/ctcdecode-master/third_party/kenlm/windows/build_binary.vcxproj b/ctcdecode-master/third_party/kenlm/windows/build_binary.vcxproj new file mode 100644 index 0000000..ed43da8 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/windows/build_binary.vcxproj @@ -0,0 +1,157 @@ + + + + + Debug + Win32 + + + Debug + x64 + + + Release + Win32 + + + Release + x64 + + + + {726B8149-7F58-4415-BD64-9EDECDF3409A} + Win32Proj + build_binary + + + + Application + true + Unicode + + + Application + true + Unicode + + + Application + false + true + Unicode + + + Application + false + true + Unicode + + + + + + + + + + + + + + + + + + + true + + + true + kenlm_$(ProjectName)_x64d + + + false + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib;$(VCInstallDir)atlmfc\lib;$(WindowsSdkDir)lib;$(FrameworkSDKDir)\lib;E:\boost_1_60_0\libs;E:\zlib\lib; + + + false + kenlm_$(ProjectName)_x64 + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSdkDir)lib\x64;E:\boost_1_60_0\stage\lib;E:\zlib\lib; + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + $(ProjectDir)\kenlmd.lib;%(AdditionalDependencies) + + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + $(ProjectDir)\kenlm_x64d.lib;%(AdditionalDependencies) + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + true + true + $(ProjectDir)\kenlm.lib;%(AdditionalDependencies) + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + true + true + $(ProjectDir)\kenlm_x64.lib;%(AdditionalDependencies) + + + + + + + + + \ No newline at end of file diff --git a/ctcdecode-master/third_party/kenlm/windows/kenlm.sln b/ctcdecode-master/third_party/kenlm/windows/kenlm.sln new file mode 100644 index 0000000..d575274 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/windows/kenlm.sln @@ -0,0 +1,60 @@ + +Microsoft Visual Studio Solution File, Format Version 11.00 +# Visual Studio 2010 +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "kenlm", "kenlm.vcxproj", "{E834D71C-5D5F-4FB4-A361-88694C438FF9}" +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ngram_query", "ngram_query.vcxproj", "{826B8049-7E58-4415-BD64-9EDECDF1402B}" + ProjectSection(ProjectDependencies) = postProject + {E834D71C-5D5F-4FB4-A361-88694C438FF9} = {E834D71C-5D5F-4FB4-A361-88694C438FF9} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "build_binary", "build_binary.vcxproj", "{726B8149-7F58-4415-BD64-9EDECDF3409A}" + ProjectSection(ProjectDependencies) = postProject + {E834D71C-5D5F-4FB4-A361-88694C438FF9} = {E834D71C-5D5F-4FB4-A361-88694C438FF9} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "lmplz", "lmplz.vcxproj", "{926B8049-7E58-4415-BD64-9EDECDF1502C}" +EndProject +Global + GlobalSection(SolutionConfigurationPlatforms) = preSolution + Debug|Win32 = Debug|Win32 + Debug|x64 = Debug|x64 + Release|Win32 = Release|Win32 + Release|x64 = Release|x64 + EndGlobalSection + GlobalSection(ProjectConfigurationPlatforms) = postSolution + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Debug|Win32.ActiveCfg = Debug|Win32 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Debug|Win32.Build.0 = Debug|Win32 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Debug|x64.ActiveCfg = Debug|x64 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Debug|x64.Build.0 = Debug|x64 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Release|Win32.ActiveCfg = Release|x64 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Release|Win32.Build.0 = Release|x64 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Release|x64.ActiveCfg = Release|x64 + {E834D71C-5D5F-4FB4-A361-88694C438FF9}.Release|x64.Build.0 = Release|x64 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Debug|Win32.ActiveCfg = Debug|Win32 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Debug|Win32.Build.0 = Debug|Win32 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Debug|x64.ActiveCfg = Debug|x64 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Debug|x64.Build.0 = Debug|x64 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Release|Win32.ActiveCfg = Release|x64 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Release|Win32.Build.0 = Release|x64 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Release|x64.ActiveCfg = Release|x64 + {826B8049-7E58-4415-BD64-9EDECDF1402B}.Release|x64.Build.0 = Release|x64 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Debug|Win32.ActiveCfg = Debug|Win32 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Debug|Win32.Build.0 = Debug|Win32 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Debug|x64.ActiveCfg = Debug|x64 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Debug|x64.Build.0 = Debug|x64 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Release|Win32.ActiveCfg = Release|x64 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Release|Win32.Build.0 = Release|x64 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Release|x64.ActiveCfg = Release|x64 + {726B8149-7F58-4415-BD64-9EDECDF3409A}.Release|x64.Build.0 = Release|x64 + {926B8049-7E58-4415-BD64-9EDECDF1502C}.Debug|Win32.ActiveCfg = Debug|Win32 + {926B8049-7E58-4415-BD64-9EDECDF1502C}.Debug|Win32.Build.0 = Debug|Win32 + {926B8049-7E58-4415-BD64-9EDECDF1502C}.Debug|x64.ActiveCfg = Debug|Win32 + {926B8049-7E58-4415-BD64-9EDECDF1502C}.Release|Win32.ActiveCfg = Release|x64 + {926B8049-7E58-4415-BD64-9EDECDF1502C}.Release|Win32.Build.0 = Release|x64 + {926B8049-7E58-4415-BD64-9EDECDF1502C}.Release|x64.ActiveCfg = Release|Win32 + EndGlobalSection + GlobalSection(SolutionProperties) = preSolution + HideSolutionNode = FALSE + EndGlobalSection +EndGlobal diff --git a/ctcdecode-master/third_party/kenlm/windows/kenlm.vcxproj b/ctcdecode-master/third_party/kenlm/windows/kenlm.vcxproj new file mode 100644 index 0000000..b71b1a9 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/windows/kenlm.vcxproj @@ -0,0 +1,248 @@ + + + + + Debug + Win32 + + + Debug + x64 + + + Release + Win32 + + + Release + x64 + + + + {E834D71C-5D5F-4FB4-A361-88694C438FF9} + Win32Proj + kenlm + + + + StaticLibrary + true + Unicode + + + StaticLibrary + true + Unicode + + + StaticLibrary + false + true + Unicode + + + StaticLibrary + false + true + Unicode + + + + + + + + + + + + + + + + + + + $(ProjectName)_x64 + .\ + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSdkDir)lib\x64;E:\boost_1_60_0\stage\lib;E:\zlib\lib; + + + $(ProjectName)_x64d + .\ + + + .\ + $(ProjectName)d + + + . + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib;$(VCInstallDir)atlmfc\lib;$(WindowsSdkDir)lib;$(FrameworkSDKDir)\lib;E:\boost_1_60_0\libs;E:\zlib\lib; + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_LIB;%(PreprocessorDefinitions) + $(ProjectDir)\..;%(AdditionalIncludeDirectories) + MultiThreadedDebugDLL + + + Windows + true + + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_LIB;%(PreprocessorDefinitions) + $(ProjectDir)\..;%(AdditionalIncludeDirectories) + MultiThreadedDebugDLL + + + Windows + true + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_LIB;%(PreprocessorDefinitions) + $(ProjectDir)\..;%(AdditionalIncludeDirectories) + MultiThreadedDLL + + + Windows + true + true + true + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_LIB;%(PreprocessorDefinitions) + $(ProjectDir)\..;%(AdditionalIncludeDirectories) + MultiThreadedDLL + + + Console + true + true + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/ctcdecode-master/third_party/kenlm/windows/lmplz.vcxproj b/ctcdecode-master/third_party/kenlm/windows/lmplz.vcxproj new file mode 100644 index 0000000..5b91e21 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/windows/lmplz.vcxproj @@ -0,0 +1,192 @@ + + + + + Debug + Win32 + + + Debug + x64 + + + Release + Win32 + + + Release + x64 + + + + {926B8049-7E58-4415-BD64-9EDECDF1502C} + Win32Proj + lmplz + + + + Application + true + Unicode + + + Application + true + Unicode + + + Application + false + true + Unicode + + + Application + false + true + Unicode + + + + + + + + + + + + + + + + + + + true + + + true + kenlm_$(ProjectName)_x64d + + + false + C:\Program Files\boost\boost_1_51;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + C:\Program Files\boost\boost_1_51\lib;$(VCInstallDir)lib;$(VCInstallDir)atlmfc\lib;$(WindowsSdkDir)lib;$(FrameworkSDKDir)\lib;E:\boost_1_60_0\stage\lib + + + false + kenlm_$(ProjectName)_x64 + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSdkDir)lib\x64;E:\boost_1_60_0\stage\lib;E:\zlib\lib; + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + $(ProjectDir)\kenlmd.lib;%(AdditionalDependencies) + + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + $(ProjectDir)\kenlm_x64d.lib;%(AdditionalDependencies) + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + true + true + $(ProjectDir)\kenlm.lib;%(AdditionalDependencies) + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + true + true + $(ProjectDir)\kenlm_x64.lib;%(AdditionalDependencies) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/ctcdecode-master/third_party/kenlm/windows/ngram_query.vcxproj b/ctcdecode-master/third_party/kenlm/windows/ngram_query.vcxproj new file mode 100644 index 0000000..cb81b53 --- /dev/null +++ b/ctcdecode-master/third_party/kenlm/windows/ngram_query.vcxproj @@ -0,0 +1,160 @@ + + + + + Debug + Win32 + + + Debug + x64 + + + Release + Win32 + + + Release + x64 + + + + {826B8049-7E58-4415-BD64-9EDECDF1402B} + Win32Proj + ngram_query + + + + Application + true + Unicode + + + Application + true + Unicode + + + Application + false + true + Unicode + + + Application + false + true + Unicode + + + + + + + + + + + + + + + + + + + true + + + true + kenlm_$(ProjectName)_x64d + + + false + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib;$(VCInstallDir)atlmfc\lib;$(WindowsSdkDir)lib;$(FrameworkSDKDir)\lib;E:\boost_1_60_0\libs; + + + false + kenlm_$(ProjectName)_x64 + $(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSdkDir)include;$(FrameworkSDKDir)\include;E:\boost_1_60_0; + $(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSdkDir)lib\x64;E:\boost_1_60_0\stage\lib;E:\zlib\lib; + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + $(ProjectDir)\kenlmd.lib;%(AdditionalDependencies) + + + + + + + TurnOffAllWarnings + Disabled + KENLM_MAX_ORDER=6;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + $(ProjectDir)\kenlm_x64d.lib;%(AdditionalDependencies) + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + true + true + $(ProjectDir)\kenlm.lib;%(AdditionalDependencies) + + + + + TurnOffAllWarnings + + + MaxSpeed + true + true + KENLM_MAX_ORDER=6;WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + $(ProjectDir)\.. + + + Console + true + true + true + $(ProjectDir)\kenlm_x64.lib;%(AdditionalDependencies) + + + + + + + + + + + + \ No newline at end of file diff --git a/ctcdecode-master/third_party/utf8/utf8.h b/ctcdecode-master/third_party/utf8/utf8.h new file mode 100644 index 0000000..82b13f5 --- /dev/null +++ b/ctcdecode-master/third_party/utf8/utf8.h @@ -0,0 +1,34 @@ +// Copyright 2006 Nemanja Trifunovic + +/* +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. +*/ + + +#ifndef UTF8_FOR_CPP_2675DCD0_9480_4c0c_B92A_CC14C027B731 +#define UTF8_FOR_CPP_2675DCD0_9480_4c0c_B92A_CC14C027B731 + +#include "utf8/checked.h" +#include "utf8/unchecked.h" + +#endif // header guard diff --git a/ctcdecode-master/third_party/utf8/utf8/checked.h b/ctcdecode-master/third_party/utf8/utf8/checked.h new file mode 100644 index 0000000..1331155 --- /dev/null +++ b/ctcdecode-master/third_party/utf8/utf8/checked.h @@ -0,0 +1,327 @@ +// Copyright 2006 Nemanja Trifunovic + +/* +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. +*/ + + +#ifndef UTF8_FOR_CPP_CHECKED_H_2675DCD0_9480_4c0c_B92A_CC14C027B731 +#define UTF8_FOR_CPP_CHECKED_H_2675DCD0_9480_4c0c_B92A_CC14C027B731 + +#include "core.h" +#include + +namespace utf8 +{ + // Base for the exceptions that may be thrown from the library + class exception : public ::std::exception { + }; + + // Exceptions that may be thrown from the library functions. + class invalid_code_point : public exception { + uint32_t cp; + public: + invalid_code_point(uint32_t cp) : cp(cp) {} + virtual const char* what() const throw() { return "Invalid code point"; } + uint32_t code_point() const {return cp;} + }; + + class invalid_utf8 : public exception { + uint8_t u8; + public: + invalid_utf8 (uint8_t u) : u8(u) {} + virtual const char* what() const throw() { return "Invalid UTF-8"; } + uint8_t utf8_octet() const {return u8;} + }; + + class invalid_utf16 : public exception { + uint16_t u16; + public: + invalid_utf16 (uint16_t u) : u16(u) {} + virtual const char* what() const throw() { return "Invalid UTF-16"; } + uint16_t utf16_word() const {return u16;} + }; + + class not_enough_room : public exception { + public: + virtual const char* what() const throw() { return "Not enough space"; } + }; + + /// The library API - functions intended to be called by the users + + template + octet_iterator append(uint32_t cp, octet_iterator result) + { + if (!utf8::internal::is_code_point_valid(cp)) + throw invalid_code_point(cp); + + if (cp < 0x80) // one octet + *(result++) = static_cast(cp); + else if (cp < 0x800) { // two octets + *(result++) = static_cast((cp >> 6) | 0xc0); + *(result++) = static_cast((cp & 0x3f) | 0x80); + } + else if (cp < 0x10000) { // three octets + *(result++) = static_cast((cp >> 12) | 0xe0); + *(result++) = static_cast(((cp >> 6) & 0x3f) | 0x80); + *(result++) = static_cast((cp & 0x3f) | 0x80); + } + else { // four octets + *(result++) = static_cast((cp >> 18) | 0xf0); + *(result++) = static_cast(((cp >> 12) & 0x3f) | 0x80); + *(result++) = static_cast(((cp >> 6) & 0x3f) | 0x80); + *(result++) = static_cast((cp & 0x3f) | 0x80); + } + return result; + } + + template + output_iterator replace_invalid(octet_iterator start, octet_iterator end, output_iterator out, uint32_t replacement) + { + while (start != end) { + octet_iterator sequence_start = start; + internal::utf_error err_code = utf8::internal::validate_next(start, end); + switch (err_code) { + case internal::UTF8_OK : + for (octet_iterator it = sequence_start; it != start; ++it) + *out++ = *it; + break; + case internal::NOT_ENOUGH_ROOM: + throw not_enough_room(); + case internal::INVALID_LEAD: + out = utf8::append (replacement, out); + ++start; + break; + case internal::INCOMPLETE_SEQUENCE: + case internal::OVERLONG_SEQUENCE: + case internal::INVALID_CODE_POINT: + out = utf8::append (replacement, out); + ++start; + // just one replacement mark for the sequence + while (start != end && utf8::internal::is_trail(*start)) + ++start; + break; + } + } + return out; + } + + template + inline output_iterator replace_invalid(octet_iterator start, octet_iterator end, output_iterator out) + { + static const uint32_t replacement_marker = utf8::internal::mask16(0xfffd); + return utf8::replace_invalid(start, end, out, replacement_marker); + } + + template + uint32_t next(octet_iterator& it, octet_iterator end) + { + uint32_t cp = 0; + internal::utf_error err_code = utf8::internal::validate_next(it, end, cp); + switch (err_code) { + case internal::UTF8_OK : + break; + case internal::NOT_ENOUGH_ROOM : + throw not_enough_room(); + case internal::INVALID_LEAD : + case internal::INCOMPLETE_SEQUENCE : + case internal::OVERLONG_SEQUENCE : + throw invalid_utf8(*it); + case internal::INVALID_CODE_POINT : + throw invalid_code_point(cp); + } + return cp; + } + + template + uint32_t peek_next(octet_iterator it, octet_iterator end) + { + return utf8::next(it, end); + } + + template + uint32_t prior(octet_iterator& it, octet_iterator start) + { + // can't do much if it == start + if (it == start) + throw not_enough_room(); + + octet_iterator end = it; + // Go back until we hit either a lead octet or start + while (utf8::internal::is_trail(*(--it))) + if (it == start) + throw invalid_utf8(*it); // error - no lead byte in the sequence + return utf8::peek_next(it, end); + } + + /// Deprecated in versions that include "prior" + template + uint32_t previous(octet_iterator& it, octet_iterator pass_start) + { + octet_iterator end = it; + while (utf8::internal::is_trail(*(--it))) + if (it == pass_start) + throw invalid_utf8(*it); // error - no lead byte in the sequence + octet_iterator temp = it; + return utf8::next(temp, end); + } + + template + void advance (octet_iterator& it, distance_type n, octet_iterator end) + { + for (distance_type i = 0; i < n; ++i) + utf8::next(it, end); + } + + template + typename std::iterator_traits::difference_type + distance (octet_iterator first, octet_iterator last) + { + typename std::iterator_traits::difference_type dist; + for (dist = 0; first < last; ++dist) + utf8::next(first, last); + return dist; + } + + template + octet_iterator utf16to8 (u16bit_iterator start, u16bit_iterator end, octet_iterator result) + { + while (start != end) { + uint32_t cp = utf8::internal::mask16(*start++); + // Take care of surrogate pairs first + if (utf8::internal::is_lead_surrogate(cp)) { + if (start != end) { + uint32_t trail_surrogate = utf8::internal::mask16(*start++); + if (utf8::internal::is_trail_surrogate(trail_surrogate)) + cp = (cp << 10) + trail_surrogate + internal::SURROGATE_OFFSET; + else + throw invalid_utf16(static_cast(trail_surrogate)); + } + else + throw invalid_utf16(static_cast(cp)); + + } + // Lone trail surrogate + else if (utf8::internal::is_trail_surrogate(cp)) + throw invalid_utf16(static_cast(cp)); + + result = utf8::append(cp, result); + } + return result; + } + + template + u16bit_iterator utf8to16 (octet_iterator start, octet_iterator end, u16bit_iterator result) + { + while (start != end) { + uint32_t cp = utf8::next(start, end); + if (cp > 0xffff) { //make a surrogate pair + *result++ = static_cast((cp >> 10) + internal::LEAD_OFFSET); + *result++ = static_cast((cp & 0x3ff) + internal::TRAIL_SURROGATE_MIN); + } + else + *result++ = static_cast(cp); + } + return result; + } + + template + octet_iterator utf32to8 (u32bit_iterator start, u32bit_iterator end, octet_iterator result) + { + while (start != end) + result = utf8::append(*(start++), result); + + return result; + } + + template + u32bit_iterator utf8to32 (octet_iterator start, octet_iterator end, u32bit_iterator result) + { + while (start != end) + (*result++) = utf8::next(start, end); + + return result; + } + + // The iterator class + template + class iterator : public std::iterator { + octet_iterator it; + octet_iterator range_start; + octet_iterator range_end; + public: + iterator () {} + explicit iterator (const octet_iterator& octet_it, + const octet_iterator& range_start, + const octet_iterator& range_end) : + it(octet_it), range_start(range_start), range_end(range_end) + { + if (it < range_start || it > range_end) + throw std::out_of_range("Invalid utf-8 iterator position"); + } + // the default "big three" are OK + octet_iterator base () const { return it; } + uint32_t operator * () const + { + octet_iterator temp = it; + return utf8::next(temp, range_end); + } + bool operator == (const iterator& rhs) const + { + if (range_start != rhs.range_start || range_end != rhs.range_end) + throw std::logic_error("Comparing utf-8 iterators defined with different ranges"); + return (it == rhs.it); + } + bool operator != (const iterator& rhs) const + { + return !(operator == (rhs)); + } + iterator& operator ++ () + { + utf8::next(it, range_end); + return *this; + } + iterator operator ++ (int) + { + iterator temp = *this; + utf8::next(it, range_end); + return temp; + } + iterator& operator -- () + { + utf8::prior(it, range_start); + return *this; + } + iterator operator -- (int) + { + iterator temp = *this; + utf8::prior(it, range_start); + return temp; + } + }; // class iterator + +} // namespace utf8 + +#endif //header guard + + diff --git a/ctcdecode-master/third_party/utf8/utf8/core.h b/ctcdecode-master/third_party/utf8/utf8/core.h new file mode 100644 index 0000000..693d388 --- /dev/null +++ b/ctcdecode-master/third_party/utf8/utf8/core.h @@ -0,0 +1,329 @@ +// Copyright 2006 Nemanja Trifunovic + +/* +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. +*/ + + +#ifndef UTF8_FOR_CPP_CORE_H_2675DCD0_9480_4c0c_B92A_CC14C027B731 +#define UTF8_FOR_CPP_CORE_H_2675DCD0_9480_4c0c_B92A_CC14C027B731 + +#include + +namespace utf8 +{ + // The typedefs for 8-bit, 16-bit and 32-bit unsigned integers + // You may need to change them to match your system. + // These typedefs have the same names as ones from cstdint, or boost/cstdint + typedef unsigned char uint8_t; + typedef unsigned short uint16_t; + typedef unsigned int uint32_t; + +// Helper code - not intended to be directly called by the library users. May be changed at any time +namespace internal +{ + // Unicode constants + // Leading (high) surrogates: 0xd800 - 0xdbff + // Trailing (low) surrogates: 0xdc00 - 0xdfff + const uint16_t LEAD_SURROGATE_MIN = 0xd800u; + const uint16_t LEAD_SURROGATE_MAX = 0xdbffu; + const uint16_t TRAIL_SURROGATE_MIN = 0xdc00u; + const uint16_t TRAIL_SURROGATE_MAX = 0xdfffu; + const uint16_t LEAD_OFFSET = LEAD_SURROGATE_MIN - (0x10000 >> 10); + const uint32_t SURROGATE_OFFSET = 0x10000u - (LEAD_SURROGATE_MIN << 10) - TRAIL_SURROGATE_MIN; + + // Maximum valid value for a Unicode code point + const uint32_t CODE_POINT_MAX = 0x0010ffffu; + + template + inline uint8_t mask8(octet_type oc) + { + return static_cast(0xff & oc); + } + template + inline uint16_t mask16(u16_type oc) + { + return static_cast(0xffff & oc); + } + template + inline bool is_trail(octet_type oc) + { + return ((utf8::internal::mask8(oc) >> 6) == 0x2); + } + + template + inline bool is_lead_surrogate(u16 cp) + { + return (cp >= LEAD_SURROGATE_MIN && cp <= LEAD_SURROGATE_MAX); + } + + template + inline bool is_trail_surrogate(u16 cp) + { + return (cp >= TRAIL_SURROGATE_MIN && cp <= TRAIL_SURROGATE_MAX); + } + + template + inline bool is_surrogate(u16 cp) + { + return (cp >= LEAD_SURROGATE_MIN && cp <= TRAIL_SURROGATE_MAX); + } + + template + inline bool is_code_point_valid(u32 cp) + { + return (cp <= CODE_POINT_MAX && !utf8::internal::is_surrogate(cp)); + } + + template + inline typename std::iterator_traits::difference_type + sequence_length(octet_iterator lead_it) + { + uint8_t lead = utf8::internal::mask8(*lead_it); + if (lead < 0x80) + return 1; + else if ((lead >> 5) == 0x6) + return 2; + else if ((lead >> 4) == 0xe) + return 3; + else if ((lead >> 3) == 0x1e) + return 4; + else + return 0; + } + + template + inline bool is_overlong_sequence(uint32_t cp, octet_difference_type length) + { + if (cp < 0x80) { + if (length != 1) + return true; + } + else if (cp < 0x800) { + if (length != 2) + return true; + } + else if (cp < 0x10000) { + if (length != 3) + return true; + } + + return false; + } + + enum utf_error {UTF8_OK, NOT_ENOUGH_ROOM, INVALID_LEAD, INCOMPLETE_SEQUENCE, OVERLONG_SEQUENCE, INVALID_CODE_POINT}; + + /// Helper for get_sequence_x + template + utf_error increase_safely(octet_iterator& it, octet_iterator end) + { + if (++it == end) + return NOT_ENOUGH_ROOM; + + if (!utf8::internal::is_trail(*it)) + return INCOMPLETE_SEQUENCE; + + return UTF8_OK; + } + + #define UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(IT, END) {utf_error ret = increase_safely(IT, END); if (ret != UTF8_OK) return ret;} + + /// get_sequence_x functions decode utf-8 sequences of the length x + template + utf_error get_sequence_1(octet_iterator& it, octet_iterator end, uint32_t& code_point) + { + if (it == end) + return NOT_ENOUGH_ROOM; + + code_point = utf8::internal::mask8(*it); + + return UTF8_OK; + } + + template + utf_error get_sequence_2(octet_iterator& it, octet_iterator end, uint32_t& code_point) + { + if (it == end) + return NOT_ENOUGH_ROOM; + + code_point = utf8::internal::mask8(*it); + + UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(it, end) + + code_point = ((code_point << 6) & 0x7ff) + ((*it) & 0x3f); + + return UTF8_OK; + } + + template + utf_error get_sequence_3(octet_iterator& it, octet_iterator end, uint32_t& code_point) + { + if (it == end) + return NOT_ENOUGH_ROOM; + + code_point = utf8::internal::mask8(*it); + + UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(it, end) + + code_point = ((code_point << 12) & 0xffff) + ((utf8::internal::mask8(*it) << 6) & 0xfff); + + UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(it, end) + + code_point += (*it) & 0x3f; + + return UTF8_OK; + } + + template + utf_error get_sequence_4(octet_iterator& it, octet_iterator end, uint32_t& code_point) + { + if (it == end) + return NOT_ENOUGH_ROOM; + + code_point = utf8::internal::mask8(*it); + + UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(it, end) + + code_point = ((code_point << 18) & 0x1fffff) + ((utf8::internal::mask8(*it) << 12) & 0x3ffff); + + UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(it, end) + + code_point += (utf8::internal::mask8(*it) << 6) & 0xfff; + + UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR(it, end) + + code_point += (*it) & 0x3f; + + return UTF8_OK; + } + + #undef UTF8_CPP_INCREASE_AND_RETURN_ON_ERROR + + template + utf_error validate_next(octet_iterator& it, octet_iterator end, uint32_t& code_point) + { + // Save the original value of it so we can go back in case of failure + // Of course, it does not make much sense with i.e. stream iterators + octet_iterator original_it = it; + + uint32_t cp = 0; + // Determine the sequence length based on the lead octet + typedef typename std::iterator_traits::difference_type octet_difference_type; + const octet_difference_type length = utf8::internal::sequence_length(it); + + // Get trail octets and calculate the code point + utf_error err = UTF8_OK; + switch (length) { + case 0: + return INVALID_LEAD; + case 1: + err = utf8::internal::get_sequence_1(it, end, cp); + break; + case 2: + err = utf8::internal::get_sequence_2(it, end, cp); + break; + case 3: + err = utf8::internal::get_sequence_3(it, end, cp); + break; + case 4: + err = utf8::internal::get_sequence_4(it, end, cp); + break; + } + + if (err == UTF8_OK) { + // Decoding succeeded. Now, security checks... + if (utf8::internal::is_code_point_valid(cp)) { + if (!utf8::internal::is_overlong_sequence(cp, length)){ + // Passed! Return here. + code_point = cp; + ++it; + return UTF8_OK; + } + else + err = OVERLONG_SEQUENCE; + } + else + err = INVALID_CODE_POINT; + } + + // Failure branch - restore the original value of the iterator + it = original_it; + return err; + } + + template + inline utf_error validate_next(octet_iterator& it, octet_iterator end) { + uint32_t ignored; + return utf8::internal::validate_next(it, end, ignored); + } + +} // namespace internal + + /// The library API - functions intended to be called by the users + + // Byte order mark + const uint8_t bom[] = {0xef, 0xbb, 0xbf}; + + template + octet_iterator find_invalid(octet_iterator start, octet_iterator end) + { + octet_iterator result = start; + while (result != end) { + utf8::internal::utf_error err_code = utf8::internal::validate_next(result, end); + if (err_code != internal::UTF8_OK) + return result; + } + return result; + } + + template + inline bool is_valid(octet_iterator start, octet_iterator end) + { + return (utf8::find_invalid(start, end) == end); + } + + template + inline bool starts_with_bom (octet_iterator it, octet_iterator end) + { + return ( + ((it != end) && (utf8::internal::mask8(*it++)) == bom[0]) && + ((it != end) && (utf8::internal::mask8(*it++)) == bom[1]) && + ((it != end) && (utf8::internal::mask8(*it)) == bom[2]) + ); + } + + //Deprecated in release 2.3 + template + inline bool is_bom (octet_iterator it) + { + return ( + (utf8::internal::mask8(*it++)) == bom[0] && + (utf8::internal::mask8(*it++)) == bom[1] && + (utf8::internal::mask8(*it)) == bom[2] + ); + } +} // namespace utf8 + +#endif // header guard + + diff --git a/ctcdecode-master/third_party/utf8/utf8/unchecked.h b/ctcdecode-master/third_party/utf8/utf8/unchecked.h new file mode 100644 index 0000000..cb24271 --- /dev/null +++ b/ctcdecode-master/third_party/utf8/utf8/unchecked.h @@ -0,0 +1,228 @@ +// Copyright 2006 Nemanja Trifunovic + +/* +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. +*/ + + +#ifndef UTF8_FOR_CPP_UNCHECKED_H_2675DCD0_9480_4c0c_B92A_CC14C027B731 +#define UTF8_FOR_CPP_UNCHECKED_H_2675DCD0_9480_4c0c_B92A_CC14C027B731 + +#include "core.h" + +namespace utf8 +{ + namespace unchecked + { + template + octet_iterator append(uint32_t cp, octet_iterator result) + { + if (cp < 0x80) // one octet + *(result++) = static_cast(cp); + else if (cp < 0x800) { // two octets + *(result++) = static_cast((cp >> 6) | 0xc0); + *(result++) = static_cast((cp & 0x3f) | 0x80); + } + else if (cp < 0x10000) { // three octets + *(result++) = static_cast((cp >> 12) | 0xe0); + *(result++) = static_cast(((cp >> 6) & 0x3f) | 0x80); + *(result++) = static_cast((cp & 0x3f) | 0x80); + } + else { // four octets + *(result++) = static_cast((cp >> 18) | 0xf0); + *(result++) = static_cast(((cp >> 12) & 0x3f)| 0x80); + *(result++) = static_cast(((cp >> 6) & 0x3f) | 0x80); + *(result++) = static_cast((cp & 0x3f) | 0x80); + } + return result; + } + + template + uint32_t next(octet_iterator& it) + { + uint32_t cp = utf8::internal::mask8(*it); + typename std::iterator_traits::difference_type length = utf8::internal::sequence_length(it); + switch (length) { + case 1: + break; + case 2: + it++; + cp = ((cp << 6) & 0x7ff) + ((*it) & 0x3f); + break; + case 3: + ++it; + cp = ((cp << 12) & 0xffff) + ((utf8::internal::mask8(*it) << 6) & 0xfff); + ++it; + cp += (*it) & 0x3f; + break; + case 4: + ++it; + cp = ((cp << 18) & 0x1fffff) + ((utf8::internal::mask8(*it) << 12) & 0x3ffff); + ++it; + cp += (utf8::internal::mask8(*it) << 6) & 0xfff; + ++it; + cp += (*it) & 0x3f; + break; + } + ++it; + return cp; + } + + template + uint32_t peek_next(octet_iterator it) + { + return utf8::unchecked::next(it); + } + + template + uint32_t prior(octet_iterator& it) + { + while (utf8::internal::is_trail(*(--it))) ; + octet_iterator temp = it; + return utf8::unchecked::next(temp); + } + + // Deprecated in versions that include prior, but only for the sake of consistency (see utf8::previous) + template + inline uint32_t previous(octet_iterator& it) + { + return utf8::unchecked::prior(it); + } + + template + void advance (octet_iterator& it, distance_type n) + { + for (distance_type i = 0; i < n; ++i) + utf8::unchecked::next(it); + } + + template + typename std::iterator_traits::difference_type + distance (octet_iterator first, octet_iterator last) + { + typename std::iterator_traits::difference_type dist; + for (dist = 0; first < last; ++dist) + utf8::unchecked::next(first); + return dist; + } + + template + octet_iterator utf16to8 (u16bit_iterator start, u16bit_iterator end, octet_iterator result) + { + while (start != end) { + uint32_t cp = utf8::internal::mask16(*start++); + // Take care of surrogate pairs first + if (utf8::internal::is_lead_surrogate(cp)) { + uint32_t trail_surrogate = utf8::internal::mask16(*start++); + cp = (cp << 10) + trail_surrogate + internal::SURROGATE_OFFSET; + } + result = utf8::unchecked::append(cp, result); + } + return result; + } + + template + u16bit_iterator utf8to16 (octet_iterator start, octet_iterator end, u16bit_iterator result) + { + while (start < end) { + uint32_t cp = utf8::unchecked::next(start); + if (cp > 0xffff) { //make a surrogate pair + *result++ = static_cast((cp >> 10) + internal::LEAD_OFFSET); + *result++ = static_cast((cp & 0x3ff) + internal::TRAIL_SURROGATE_MIN); + } + else + *result++ = static_cast(cp); + } + return result; + } + + template + octet_iterator utf32to8 (u32bit_iterator start, u32bit_iterator end, octet_iterator result) + { + while (start != end) + result = utf8::unchecked::append(*(start++), result); + + return result; + } + + template + u32bit_iterator utf8to32 (octet_iterator start, octet_iterator end, u32bit_iterator result) + { + while (start < end) + (*result++) = utf8::unchecked::next(start); + + return result; + } + + // The iterator class + template + class iterator : public std::iterator { + octet_iterator it; + public: + iterator () {} + explicit iterator (const octet_iterator& octet_it): it(octet_it) {} + // the default "big three" are OK + octet_iterator base () const { return it; } + uint32_t operator * () const + { + octet_iterator temp = it; + return utf8::unchecked::next(temp); + } + bool operator == (const iterator& rhs) const + { + return (it == rhs.it); + } + bool operator != (const iterator& rhs) const + { + return !(operator == (rhs)); + } + iterator& operator ++ () + { + ::std::advance(it, utf8::internal::sequence_length(it)); + return *this; + } + iterator operator ++ (int) + { + iterator temp = *this; + ::std::advance(it, utf8::internal::sequence_length(it)); + return temp; + } + iterator& operator -- () + { + utf8::unchecked::prior(it); + return *this; + } + iterator operator -- (int) + { + iterator temp = *this; + utf8::unchecked::prior(it); + return temp; + } + }; // class iterator + + } // namespace utf8::unchecked +} // namespace utf8 + + +#endif // header guard + From 24fe5d80ab6174e2558139faa35bf99e0318d82b Mon Sep 17 00:00:00 2001 From: niezongxiang Date: Tue, 30 Sep 2025 16:38:54 +0800 Subject: [PATCH 5/9] update readme --- README.md | 82 ++++++++++++++++--------------------------------------- 1 file changed, 23 insertions(+), 59 deletions(-) diff --git a/README.md b/README.md index 3bc5d2e..1c4e6e2 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ ## Environment Setup -### Platform Support +### Platform Support with docker - **Linux**: Fully supported and tested - **Windows**: Supported with WSL2 (Windows Subsystem for Linux) or native installation @@ -15,81 +15,45 @@ The time estimated for setting up is 10-20 minutes depending on network speed and platform. +## Usage -Create a new conda environment first: +### Option 1: Use a Pre-built Docker Image (Recommended) -``` -conda create --name XuanjiNovo python=3.11 -``` - -This will create an anaconda environment +You can directly pull the pre-built image from Docker Hub: -Activate this environment by running: - -``` -conda activate XuanjiNovo -``` +```bash +# Pull the CUDA 11 version +docker pull guomics2017/massnet-dda:cuda11_v1.0 -then install dependencies: +# Pull the CUDA 12 version +docker pull guomics2017/massnet-dda:cuda12_v1.0 ``` -pip install -r ./requirements.txt -``` - -installing gcc and g++: -For Linux and WSL2: -```bash -conda install -c conda-forge gcc cxx-compiler ninja -``` +### Option 2: Build the Docker Image Locally -For Windows (native installation): ```bash -conda install -c conda-forge m2w64-gcc m2w64-gcc-fortran ninja -``` +# CUDA 11 version +docker build -t massnet-dda:cuda11_v1.0 . -f Dockerfile_cuda11 -then install ctcdecode, which is the package for ctc-beamsearch decoding - -```bash -git clone --recursive https://github.com/WayenVan/ctcdecode.git -cd ctcdecode -pip install . -cd .. #this is needed as ctcdecode can not be imported under the current directory -rm -rf ctcdecode +# CUDA 12 version +docker build -t massnet-dda:cuda12_v1.0 . -f Dockerfile_cuda12 ``` -(if there are no errors, ignore the next line and proceed to CuPy install) - -if you encountered issues with C++ (gxx and gcc) version errors in this step, install gcc with version specified as : - -```bash -conda install -c conda-forge gcc_linux-64=9.3.0 -``` +### Example Run Command -then install pytorch imputer for CTC-curriculum sampling +Below is an example command to run the container (using the CUDA 11 version): ```bash -cd imputer-pytorch -pip install -e . -cd .. +docker run --gpus all --rm \ + -v /local/data:/data \ + massnet-dda:cuda11_v1.0 \ + --mode=eval \ + --peak_path=/data/bacillus.10k.mgf \ + --model=/data/XuanjiNovo_100M_massnet.ckpt \ + --output /data/output_cuda11_v1.0 ``` -lastly, install CuPy to use our CUDA-accelerated precise mass-control decoding: - -**_Please install the following Cupy package in a GPU available env, If you are using a slurm server, this means you have to enter a interative session with sbatch to install Cupy, If you are using a machine with GPU already on it (checking by nvidia-smi), then there's no problem_** - -**Check your CUDA version using command nvidia-smi, the CUDA version will be on the top-right corner** - -| cuda version | command | -|-------|-------| -|v10.2 (x86_64 / aarch64)| pip install cupy-cuda102 | -|v11.0 (x86_64)| pip install cupy-cuda110 | -|v11.1 (x86_64)| pip install cupy-cuda111 | -|v11.2 ~ 11.8 (x86_64 / aarch64)| pip install cupy-cuda11x | -|v12.x (x86_64 / aarch64)| pip install cupy-cuda12x | - - - ## Model Settings From 39ac9a311c9997697bc209b36e21a0e11483a983 Mon Sep 17 00:00:00 2001 From: upup-wei <703684335@qq.com> Date: Wed, 1 Oct 2025 13:19:17 +0800 Subject: [PATCH 6/9] update --- .../denovo/XuanjiNovo_20251001102350.log | 296 + example_output/denovo/denovo.tsv | 49021 ++++++++++++++++ example_output/denovo/denovo_cl_out.txt | 462 + example_output/eval/eval_cl_out.txt | 483 + .../eval/xuanjinovo_20251001_102900.log | 469 + example_output/run_metadata/run_metadata.json | 340 + example_output/run_metadata/run_summary.txt | 7 + example_output/train/train_out.txt | 442 + 8 files changed, 51520 insertions(+) create mode 100644 example_output/denovo/XuanjiNovo_20251001102350.log create mode 100644 example_output/denovo/denovo.tsv create mode 100644 example_output/denovo/denovo_cl_out.txt create mode 100644 example_output/eval/eval_cl_out.txt create mode 100644 example_output/eval/xuanjinovo_20251001_102900.log create mode 100644 example_output/run_metadata/run_metadata.json create mode 100644 example_output/run_metadata/run_summary.txt create mode 100644 example_output/train/train_out.txt diff --git a/example_output/denovo/XuanjiNovo_20251001102350.log b/example_output/denovo/XuanjiNovo_20251001102350.log new file mode 100644 index 0000000..42e9263 --- /dev/null +++ b/example_output/denovo/XuanjiNovo_20251001102350.log @@ -0,0 +1,296 @@ +2025-10-01 10:23:50,743 WARNING [XuanjiNovo/MainProcess] XuanjiNovo.main : Pydantic not available, falling back to basic config validation +2025-10-01 10:23:50,748 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:23:50,784 WARNING [XuanjiNovo/MainProcess] XuanjiNovo.main : Pydantic not available, falling back to basic config validation +2025-10-01 10:23:50,789 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:23:50,808 WARNING [XuanjiNovo/MainProcess] XuanjiNovo.main : Pydantic not available, falling back to basic config validation +2025-10-01 10:23:50,812 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:23:50,812 WARNING [XuanjiNovo/MainProcess] XuanjiNovo.main : Pydantic not available, falling back to basic config validation +2025-10-01 10:23:50,817 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:23:50,967 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Number of available GPUs: 4 +2025-10-01 10:23:50,982 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Number of available GPUs: 4 +2025-10-01 10:23:50,984 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Number of available GPUs: 4 +2025-10-01 10:23:50,987 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Number of available GPUs: 4 +2025-10-01 10:23:51,424 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mode = denovo +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_val = None +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_test = None +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : random_seed = 5692 +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : PMC_enable = True +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mass_control_tol = 0.1 +2025-10-01 10:23:51,424 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_peaks = 800 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_mz = 1.0 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_mz = 6500.0 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_intensity = 0.0 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : remove_precursor_tol = 1.0 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_charge = 10 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : precursor_mass_tol = 50.0 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : isotope_error_range = (0, 1) +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_model = 400 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_head = 8 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_feedforward = 1024 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_layers = 9 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dropout = 0.18 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : refine_iters = 3 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_intensity = None +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : custom_encoder = None +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_length = 40 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_log = 1 +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tb_summarywriter = None +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : log_level = CRITICAL +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : enable_neptune = True +2025-10-01 10:23:51,425 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_project = None +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_api_token = None +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tags = [] +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warmup_iters = None +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_iters = None +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_epochs = 150 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warm_up_epochs = 1 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : learning_rate = 0.00035 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : weight_decay = 8e-05 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_val = 2.5 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_algorithm = norm +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : accumulate_grad_batches = 1 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : sync_batchnorm = False +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : SWA = False +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_batch_size = 700 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : predict_batch_size = 64 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_beams = 5 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : logger = None +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : num_sanity_val_steps = 0 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_scratch = False +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_nodes = 1 +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_model = False +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_resume = False +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model_save_folder_path = ./saved_models +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_weights_only = False +2025-10-01 10:23:51,426 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : every_n_train_steps = 2500 +2025-10-01 10:23:51,426 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : temp_dir_auto = True +2025-10-01 10:23:51,427 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_workers = 48 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,427 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Predict peptide sequences with XuanjiNovo. +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mode = denovo +2025-10-01 10:23:51,427 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mode = denovo +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_val = None +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_test = None +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_val = None +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_test = None +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : random_seed = 9587 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : PMC_enable = True +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mode = denovo +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mass_control_tol = 0.1 +2025-10-01 10:23:51,427 WARNING [natNovo/MainProcess] model_runner._execute_existing : Pydantic not available, skipping additional config validation +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : random_seed = 4182 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_peaks = 800 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : PMC_enable = True +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_mz = 1.0 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mass_control_tol = 0.1 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_mz = 6500.0 +2025-10-01 10:23:51,427 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_val = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_intensity = 0.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : peak_path_test = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_peaks = 800 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : remove_precursor_tol = 1.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_mz = 1.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_charge = 10 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_mz = 6500.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : precursor_mass_tol = 50.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : random_seed = 3954 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_intensity = 0.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : isotope_error_range = (0, 1) +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : PMC_enable = True +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : remove_precursor_tol = 1.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_model = 400 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mass_control_tol = 0.1 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_charge = 10 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_head = 8 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : precursor_mass_tol = 50.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_feedforward = 1024 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_peaks = 800 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : isotope_error_range = (0, 1) +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_layers = 9 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_mz = 1.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_model = 400 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dropout = 0.18 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_mz = 6500.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_head = 8 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : refine_iters = 3 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : min_intensity = 0.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_feedforward = 1024 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : remove_precursor_tol = 1.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_layers = 9 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_intensity = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_charge = 10 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dropout = 0.18 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : custom_encoder = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : precursor_mass_tol = 50.0 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : refine_iters = 3 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_length = 40 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : isotope_error_range = (0, 1) +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_model = 400 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_intensity = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_head = 8 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : custom_encoder = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_log = 1 +2025-10-01 10:23:51,428 INFO [xuanjinovo/MainProcess] model.__init__ : PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_feedforward = 1024 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_length = 40 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tb_summarywriter = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_layers = 9 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : log_level = CRITICAL +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dropout = 0.18 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : enable_neptune = True +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_log = 1 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : refine_iters = 3 +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_project = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tb_summarywriter = None +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:23:51,428 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_api_token = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : log_level = CRITICAL +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : dim_intensity = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tags = [] +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : enable_neptune = True +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : custom_encoder = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warmup_iters = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_project = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_length = 40 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_iters = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_api_token = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_epochs = 150 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tags = [] +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warm_up_epochs = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warmup_iters = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_log = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : learning_rate = 0.00035 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_iters = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tb_summarywriter = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : weight_decay = 8e-05 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_epochs = 150 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : log_level = CRITICAL +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_val = 2.5 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warm_up_epochs = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : enable_neptune = True +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_algorithm = norm +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : learning_rate = 0.00035 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_project = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : accumulate_grad_batches = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : weight_decay = 8e-05 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : neptune_api_token = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : sync_batchnorm = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_val = 2.5 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : tags = [] +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : SWA = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_algorithm = norm +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warmup_iters = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_batch_size = 700 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : accumulate_grad_batches = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_iters = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : predict_batch_size = 64 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : sync_batchnorm = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : max_epochs = 150 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_beams = 5 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : SWA = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : warm_up_epochs = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : logger = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_batch_size = 700 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : learning_rate = 0.00035 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : num_sanity_val_steps = 0 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : predict_batch_size = 64 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : weight_decay = 8e-05 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_scratch = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_beams = 5 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_val = 2.5 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_nodes = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : logger = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : gradient_clip_algorithm = norm +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_model = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : num_sanity_val_steps = 0 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : accumulate_grad_batches = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_resume = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_scratch = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : sync_batchnorm = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model_save_folder_path = ./saved_models +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_nodes = 1 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : SWA = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_weights_only = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_model = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_batch_size = 700 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : every_n_train_steps = 2500 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_resume = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : predict_batch_size = 64 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : temp_dir_auto = True +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model_save_folder_path = ./saved_models +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_beams = 5 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_workers = 48 +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_weights_only = False +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : logger = None +2025-10-01 10:23:51,429 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : every_n_train_steps = 2500 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : num_sanity_val_steps = 0 +2025-10-01 10:23:51,430 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Predict peptide sequences with XuanjiNovo. +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : temp_dir_auto = True +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_scratch = False +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_workers = 48 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_nodes = 1 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_model = False +2025-10-01 10:23:51,430 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Predict peptide sequences with XuanjiNovo. +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : train_from_resume = False +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : model_save_folder_path = ./saved_models +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : save_weights_only = False +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : every_n_train_steps = 2500 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : temp_dir_auto = True +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : n_workers = 48 +2025-10-01 10:23:51,430 DEBUG [XuanjiNovo/MainProcess] XuanjiNovo.main : result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102350 +2025-10-01 10:23:51,430 INFO [XuanjiNovo/MainProcess] XuanjiNovo.main : Predict peptide sequences with XuanjiNovo. +2025-10-01 10:23:51,430 WARNING [natNovo/MainProcess] model_runner._execute_existing : Pydantic not available, skipping additional config validation +2025-10-01 10:23:51,430 WARNING [natNovo/MainProcess] model_runner._execute_existing : Pydantic not available, skipping additional config validation +2025-10-01 10:23:51,430 WARNING [natNovo/MainProcess] model_runner._execute_existing : Pydantic not available, skipping additional config validation +2025-10-01 10:23:51,431 INFO [xuanjinovo/MainProcess] model.__init__ : PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:23:51,431 INFO [xuanjinovo/MainProcess] model.__init__ : PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:23:51,431 INFO [xuanjinovo/MainProcess] model.__init__ : PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:23:51,538 DEBUG [fsspec.local/MainProcess] local.__init__ : open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,541 DEBUG [fsspec.local/MainProcess] local.__init__ : open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,541 DEBUG [fsspec.local/MainProcess] local.__init__ : open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,546 DEBUG [fsspec.local/MainProcess] local.__init__ : open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:23:51,894 INFO [XuanjiNovo.denovo.db_index/MainProcess] db_index.__init__ : Reading 1 files... +2025-10-01 10:23:51,904 INFO [XuanjiNovo.denovo.db_index/MainProcess] db_index.__init__ : Reading 1 files... +2025-10-01 10:23:51,904 INFO [XuanjiNovo.denovo.db_index/MainProcess] db_index.__init__ : Reading 1 files... +2025-10-01 10:23:51,904 INFO [XuanjiNovo.denovo.db_index/MainProcess] db_index.__init__ : Reading 1 files... +2025-10-01 10:23:57,345 INFO [natNovo/MainProcess] model_runner._execute_existing : Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:23:57,345 INFO [natNovo/MainProcess] model_runner._execute_existing : Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:23:57,345 INFO [natNovo/MainProcess] model_runner._execute_existing : Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:23:57,349 DEBUG [git.cmd/MainProcess] cmd.execute : Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:23:57,349 DEBUG [git.cmd/MainProcess] cmd.execute : Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:23:57,349 DEBUG [git.cmd/MainProcess] cmd.execute : Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:23:57,417 INFO [natNovo/MainProcess] model_runner._execute_existing : Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:23:57,419 DEBUG [git.cmd/MainProcess] cmd.execute : Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) diff --git a/example_output/denovo/denovo.tsv b/example_output/denovo/denovo.tsv new file mode 100644 index 0000000..3fa4aad --- /dev/null +++ b/example_output/denovo/denovo.tsv @@ -0,0 +1,49021 @@ +label prediction charge score +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_2327 YNHHNSTYPTK 3 0.00817840825766325 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_3391 NHSDSEEQPQAEEEEHPSSSDGAK 4 6.738844898768193e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_3623 LSGEVPEEK 2 0.9370234608650208 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4001 ALTETDGDMDK 2 0.33475425839424133 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4306 ALTETDGDNDK 2 0.4052024781703949 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4588 ALAETADANLK 2 0.9735389351844788 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_4933 LQHLDEAYNK 2 0.9977360963821411 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5068 DVLGQAQTSQK 2 0.4362994432449341 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5588 SDQESFGEVK 2 0.9952811598777771 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5709 KELVEKQQAEE 3 0.0011585972970351577 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_5815 DLQAEVDR 2 0.9894288182258606 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6024 GGALHPGMVLGK 4 0.5375634431838989 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6256 EGMPLGAK 2 0.9900884032249451 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6549 TYNFPQNR 2 0.9950031638145447 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_6986 VPVDQVTETAK 2 0.9760689735412598 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7152 HFYTDDVR 2 0.8641716837882996 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7340 TALPFLK 2 0.00015221313515212387 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7432 ALMEEYK 2 0.928252637386322 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7596 TLQDALDR 2 0.6518266201019287 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7789 VGGGGGK 2 0.006237287074327469 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_7917 EFDDSEK 2 0.00062800373416394 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8226 KEELEVPK 2 0.463377445936203 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8288 QMVLETKPN 2 0.093741774559021 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8358 TTDRDSWPMLSDM[+15.995]HM[+15.995]GMYTK 3 8.001627065823413e-06 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_8788 PATVLVQTASK 2 0.8498216867446899 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9136 AKPELEEEEEQMDEEYK 4 2.3354898530669743e-06 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9245 ANEDALTSLLHK 3 1.0145466148969717e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9686 SNSDKVVLVK 3 0.0008224076591432095 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9761 LENWNHLEYPTK 3 0.003499164478853345 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_9890 TAFDGMK 2 0.23526784777641296 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10056 TMHHDVTYNR 3 0.007241277955472469 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10158 NLLHGSDSLESAER 2 0.9481425881385803 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10319 AVVVNLQR 2 0.9822706580162048 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10530 AAHELHAPLLR 4 1.5626435924787074e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10775 LGGLGFR 2 0.0023134120274335146 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_10941 M[+15.995]HAFSGR 2 8.306624658871442e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_11199 VASPNYLGVR 2 0.9970025420188904 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_11595 VVGQQFNLK 2 0.9973399043083191 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_11784 TPFMMNQR 2 0.9795010685920715 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_11813 EALAELEQAK 2 0.9920051693916321 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_12074 LAGGLLR 2 0.1482086181640625 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_12234 MLSYGTTTAEVK 2 0.15739454329013824 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_12465 ETYADVLK 2 0.9969538450241089 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_12563 VEANVGAPQVAYR 2 0.9942781925201416 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_12806 TVQLLTER 2 0.9974862337112427 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_13115 AAELLGRPLK 3 0.9951188564300537 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_13220 FYSPTAGTLR 2 0.8807114362716675 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_13455 LDQLLLEGKK 3 0.17409832775592804 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_13566 VEDNVLTVARPSDQK 2 0.9691616892814636 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_13711 LDDEDLDLDPVR 3 5.8532670266231435e-08 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_13996 LYNHVLEMVAVSK 3 9.154967847280204e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_14166 RPAMFQTM 2 0.9554315805435181 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_14266 VGLGPGSLC[+57.021]TTR 2 0.947599470615387 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_14457 VMDENSAQTYTFK 2 0.9928931593894958 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_14624 RTELEM[+15.995]DFLSLKR 4 0.00010080506035592407 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_14842 NVAVGLYATSEADTTYGK 3 0.011273913085460663 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_14987 THEDPDGYTYNSALMLGSR 4 1.7022642850861303e-08 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_15129 EDLQNGAVYLNGER 2 0.9690716862678528 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_15513 DLYDASELSNK 2 0.6538977026939392 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_15813 YSEAMNATYLDENGR 2 0.9211936593055725 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_15882 FLTPTDLVQLSEHYEESLR 3 3.400217607474332e-10 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_16073 ALGGGLK 2 0.016731219366192818 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_16421 VRRVPDTLKDEAAK 3 0.0036414647474884987 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_16627 HYAGLAF 2 0.7037418484687805 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_16858 ADDNVVDAEYEEVNDDQNKK 2 0.9948630928993225 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_16957 [+42.011]SYELTTDKPEK 2 0.023964421823620796 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_17370 STENLLSSGLQK 2 0.8315465450286865 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_17516 LLELGSYR 2 0.9971534013748169 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_17691 TTVTGVEMFRK 3 0.7405220866203308 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_17851 TDDLLLVVDNGSGMC[+57.021]K 3 0.000756415887735784 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_17909 EATAKSGPYSTLSVK 3 0.0005061495467089117 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_17966 LSDLYDGEGLTSSR 2 0.6772176027297974 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18130 LLEDDGGSAVVGLGK 2 0.657929003238678 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18165 LTNFLESR 2 0.9965247511863708 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18208 ENYALDEAENGDEALAK 3 0.922256588935852 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18386 FFGGDGFK 2 0.7911081910133362 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18569 RLAYELNDFR 2 0.9748953580856323 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18737 SLAWGLAR 2 0.9942237734794617 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_18935 NFMPSPGELK 2 0.9874429106712341 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_19242 KLAELEETGNTK 3 0.022063378244638443 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_19419 LNALVNELGTK 2 0.9729679822921753 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_DG_Br1.raw_SCANS_19539 WLSEWEKE 2 0.9900986552238464 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LLFVGTKK 3 0.9946191310882568 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_10981 ALSSSSLTLAK 2 0.6540493369102478 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_11370 GGGGGGK 2 0.026526164263486862 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_11875 EGGGTYDLYTTTR 2 0.6873912215232849 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_11973 KLELENAEQYEGK 2 0.9980425834655762 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_12369 ESSYLEAAVQDGKR 3 0.010291892103850842 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_4_DDA.raw_SCANS_12544 C[+57.021]SESAEVATEEVK 2 0.003800358157604933 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1.0743650591393816e-07 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_40117 GLDEPFTGVFLR 2 0.9962550401687622 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_40360 ELEPSEWKPDEFHLDLDQEFK 4 7.535928131519043e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_40800 VLELHMGMAAR 2 0.6912415623664856 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_41088 LEELLPYNSGAMSLR 3 8.07134426850098e-07 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_41381 LSELNVDPDEEFLAPVLSVK 3 7.627866580151021e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_41533 VFLVYLVPLAPFGGDASVHTVK 3 2.646597385691507e-10 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9.200386980046105e-12 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_47647 VYQFYESLLELEQLEYDALKK 3 5.332559976306084e-16 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_47784 GFPYTLLLEENFLNDSPEYLELFK 3 8.596005851568833e-13 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_47879 DLLSEYDFPGDDVPVVK 2 0.033683136105537415 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_48545 ELADVVLVDLPQLENPTK 2 0.9519573450088501 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_48845 TFDDLQQLPSPELLQLLWLLK 3 5.903578320642922e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_48986 TLWPEVHETSENEQGSPFLK 3 1.8234947996020878e-09 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\150710_QEp_PK_Bsub_F_PG10_Br4.raw_SCANS_49259 FGSGGAAYLVVDVPALLELTSTPNQDSPLSEGK 3 2.8575354008353315e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_33810 YPHLLPL 2 0.15129266679286957 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_34082 GLAPLGEEYASLLK 2 0.9966331124305725 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_34802 TAFGDTLAFVNTQLGK 3 0.741371750831604 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_35447 KLEEEETLWQVDQFAMSHEDLGPK 3 7.892624331873321e-09 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151012_QEp_PK_Sec_5_2_DDA.raw_SCANS_36034 GVPHLPL 2 0.11682044714689255 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+C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_42842 MELLNSMLATLSESRK 3 0.9034483432769775 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_43236 TNVPVLYAGLDLPYLDDLLK 3 3.391210702829994e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_43718 QLDFLHGLDTQLLAASLR 3 0.4218898415565491 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_44194 LLVDLMYEEGLAGMR 3 0.7160400152206421 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_44402 MMGDGFALLPSEGLVVSPVR 2 0.6281036734580994 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_44951 GFGGATLTVQEPDKTADLLENLM[+15.995]GLER 3 0.017180420458316803 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_45731 LTYGWMHEPSYQQLAETLNADFQAELK 3 0.004885463044047356 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_46133 TLEEVEELDFLPSNPVLDHAFPNFR 3 6.650033713799519e-13 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_46439 AVVGELLGADFALLAAFHGYPR 3 2.542737274779938e-05 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_46618 DTEKMESMQGPADVFSLTGR 3 0.021666033193469048 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_46964 NKDLYSLEEFVQSLETPRK 4 0.04798376187682152 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_47803 EAQSDVEEGADFLLVKPTLEYGDMMR 3 0.00014310190454125404 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_48502 VEVVLPEEYMGDLMGDLTSR 2 0.7752214670181274 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_48623 DKYGLTLHGWWNEGFEQLPL 3 0.00017686728097032756 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_48886 TVELQVTQPEYLEEEEDEVEYC[+57.021]K 3 6.731035173679345e-11 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49418 FVFVTPLWNFSYPPVM[+15.995]K 2 0.723372220993042 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49588 AVGLVNLMTLFGAGAVR 3 0.17310011386871338 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49748 PATTLYVQPGGATLYVNPLELTKNPFYVDVL 3 1.713238020784047e-06 +C:\Users\xzhang\PeaksProjects\bacillus_PXD004565_D:\data.develop\PXD004565\151020_QEp_PK_Sec_5_DDA.raw_SCANS_49897 SMQTVFYETLLALVK 2 0.9966296553611755 diff --git a/example_output/denovo/denovo_cl_out.txt b/example_output/denovo/denovo_cl_out.txt new file mode 100644 index 0000000..c89947a --- /dev/null +++ b/example_output/denovo/denovo_cl_out.txt @@ -0,0 +1,462 @@ +Using MASTER_PORT=29579 +2025-10-01 10:25:58 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-10-01 10:25:58 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:25:58 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-10-01 10:25:58 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-10-01 10:25:58 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation +2025-10-01 10:25:58 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:25:58 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:25:58 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 10:25:58 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 10:25:58 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 10:25:58 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 10:25:58 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 10:25:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mode = denovo +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - random_seed = 4768 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - log_level = INFO +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - logger = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-10-01 10:25:58 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-10-01 10:25:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:25:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mode = denovo +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mode = denovo +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - random_seed = 3943 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - random_seed = 8461 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:25:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mode = denovo +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_val = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - peak_path_test = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - log_level = INFO +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - random_seed = 7353 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - log_level = INFO +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - logger = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - logger = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - log_level = INFO +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - enable_neptune = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 10:25:58 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 10:25:58 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_batch_size = 700 +2025-10-01 10:25:58 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - logger = None +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 10:25:58 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_scratch = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 10:25:58 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20251001102558 +2025-10-01 10:25:58 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. +2025-10-01 10:25:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:58 [WARNING] natNovo - Pydantic not available, skipping additional config validation +2025-10-01 10:25:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:59 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:59 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:59 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:59 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 10:25:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 +2025-10-01 10:25:59 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 10:25:59 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 10:25:59 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 10:25:59 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 10:26:04 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:26:04 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:26:04 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:26:04 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:26:04 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:26:04 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:26:04 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:26:04 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:26:04 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:26:04 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:26:04 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:26:04 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' + Predicting: 0it [00:00, ?it/s] Predicting: 0%| | 0/39 [00:00) +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:06 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:29:06 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:29:06 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:29:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:29:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:29:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:17 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + + Validation: 0it [00:00, ?it/s] Validation: 0%| | 0/39 [00:00) +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:06 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:29:06 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:29:06 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata +2025-10-01 10:29:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:29:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:29:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:06 [ERROR] xuanjinovo - Failed to record metadata: HEAD is a detached symbolic reference as it points to '24fe5d80ab6174e2558139faa35bf99e0318d82b' +2025-10-01 10:29:17 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. + rank_zero_warn( + +2025-10-01 10:29:49 [INFO] xuanjinovo - Average predicted peptide length: 10.97 +2025-10-01 10:29:50 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-10-01 10:29:51 [INFO] xuanjinovo - Average predicted peptide length: 10.64 +2025-10-01 10:29:51 [INFO] xuanjinovo - Average predicted peptide length: 11.48 +2025-10-01 10:29:51 [INFO] xuanjinovo - Average predicted peptide length: 10.70 +2025-10-01 10:29:52 [INFO] xuanjinovo - Average predicted peptide length: 12.55 +2025-10-01 10:29:52 [INFO] xuanjinovo - Average predicted peptide length: 12.42 +2025-10-01 10:29:52 [INFO] xuanjinovo - Average predicted peptide length: 13.67 +2025-10-01 10:29:52 [INFO] xuanjinovo - Average predicted peptide length: 12.72 +2025-10-01 10:29:53 [INFO] xuanjinovo - Average predicted peptide length: 16.06 +2025-10-01 10:29:53 [INFO] xuanjinovo - Average predicted peptide length: 16.34 +2025-10-01 10:29:53 [INFO] xuanjinovo - Average predicted peptide length: 16.58 +2025-10-01 10:29:53 [INFO] xuanjinovo - Average predicted peptide length: 15.53 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 18.39 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 18.95 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 18.19 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 18.89 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 10.88 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 10.92 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 11.50 +2025-10-01 10:29:54 [INFO] xuanjinovo - Average predicted peptide length: 11.16 +2025-10-01 10:29:55 [INFO] xuanjinovo - Average predicted peptide length: 14.78 +2025-10-01 10:29:55 [INFO] xuanjinovo - Average predicted peptide length: 14.44 +2025-10-01 10:29:55 [INFO] xuanjinovo - Average predicted peptide length: 14.31 +2025-10-01 10:29:55 [INFO] xuanjinovo - Average predicted peptide length: 13.75 +2025-10-01 10:29:56 [INFO] xuanjinovo - Average predicted peptide length: 17.58 +2025-10-01 10:29:56 [INFO] xuanjinovo - Average predicted peptide length: 19.08 +2025-10-01 10:29:56 [INFO] xuanjinovo - Average predicted peptide length: 19.53 +2025-10-01 10:29:56 [INFO] xuanjinovo - Average predicted peptide length: 18.41 +2025-10-01 10:29:57 [INFO] xuanjinovo - Average predicted peptide length: 12.92 +2025-10-01 10:29:57 [INFO] xuanjinovo - Average predicted peptide length: 13.14 +2025-10-01 10:29:57 [INFO] xuanjinovo - Average predicted peptide length: 13.45 +2025-10-01 10:29:57 [INFO] xuanjinovo - Average predicted peptide length: 12.30 +2025-10-01 10:29:58 [INFO] xuanjinovo - Average predicted peptide length: 12.66 +2025-10-01 10:29:58 [INFO] xuanjinovo - Average predicted peptide length: 12.81 +2025-10-01 10:29:58 [INFO] xuanjinovo - Average predicted peptide length: 13.25 +2025-10-01 10:29:58 [INFO] xuanjinovo - Average predicted peptide length: 12.62 +2025-10-01 10:29:59 [INFO] xuanjinovo - Average predicted peptide length: 17.42 +2025-10-01 10:29:59 [INFO] xuanjinovo - Average predicted peptide length: 17.64 +2025-10-01 10:29:59 [INFO] xuanjinovo - Average predicted peptide length: 17.73 +2025-10-01 10:29:59 [INFO] xuanjinovo - Average predicted peptide length: 17.00 +2025-10-01 10:29:59 [INFO] xuanjinovo - Average predicted peptide length: 14.36 +2025-10-01 10:29:59 [INFO] xuanjinovo - Average predicted peptide length: 14.31 +2025-10-01 10:30:00 [INFO] xuanjinovo - Average predicted peptide length: 14.98 +2025-10-01 10:30:00 [INFO] xuanjinovo - Average predicted peptide length: 14.89 +2025-10-01 10:30:00 [INFO] xuanjinovo - Average predicted peptide length: 11.62 +2025-10-01 10:30:00 [INFO] xuanjinovo - Average predicted peptide length: 12.44 +2025-10-01 10:30:00 [INFO] xuanjinovo - Average predicted peptide length: 12.52 +2025-10-01 10:30:00 [INFO] xuanjinovo - Average predicted peptide length: 12.41 +2025-10-01 10:30:01 [INFO] xuanjinovo - Average predicted peptide length: 15.58 +2025-10-01 10:30:01 [INFO] xuanjinovo - Average predicted peptide length: 16.50 +2025-10-01 10:30:01 [INFO] xuanjinovo - Average predicted peptide length: 16.19 +2025-10-01 10:30:01 [INFO] xuanjinovo - Average predicted peptide length: 15.70 +2025-10-01 10:30:02 [INFO] xuanjinovo - Average predicted peptide length: 16.00 +2025-10-01 10:30:02 [INFO] xuanjinovo - Average predicted peptide length: 16.59 +2025-10-01 10:30:02 [INFO] xuanjinovo - Average predicted peptide length: 17.58 +2025-10-01 10:30:02 [INFO] xuanjinovo - Average predicted peptide length: 19.36 +2025-10-01 10:30:03 [INFO] xuanjinovo - Average predicted peptide length: 10.98 +2025-10-01 10:30:03 [INFO] xuanjinovo - Average predicted peptide length: 10.94 +2025-10-01 10:30:03 [INFO] xuanjinovo - Average predicted peptide length: 10.69 +2025-10-01 10:30:03 [INFO] xuanjinovo - Average predicted peptide length: 11.22 +2025-10-01 10:30:04 [INFO] xuanjinovo - Average predicted peptide length: 14.84 +2025-10-01 10:30:04 [INFO] xuanjinovo - Average predicted peptide length: 14.25 +2025-10-01 10:30:04 [INFO] xuanjinovo - Average predicted peptide length: 14.84 +2025-10-01 10:30:04 [INFO] xuanjinovo - Average predicted peptide length: 14.58 +2025-10-01 10:30:04 [INFO] xuanjinovo - Average predicted peptide length: 18.22 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 17.55 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 18.08 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 18.31 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 11.25 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 10.34 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 11.17 +2025-10-01 10:30:05 [INFO] xuanjinovo - Average predicted peptide length: 11.11 +2025-10-01 10:30:06 [INFO] xuanjinovo - Average predicted peptide length: 14.17 +2025-10-01 10:30:06 [INFO] xuanjinovo - Average predicted peptide length: 13.77 +2025-10-01 10:30:06 [INFO] xuanjinovo - Average predicted peptide length: 14.27 +2025-10-01 10:30:06 [INFO] xuanjinovo - Average predicted peptide length: 13.72 +2025-10-01 10:30:07 [INFO] xuanjinovo - Average predicted peptide length: 17.69 +2025-10-01 10:30:07 [INFO] xuanjinovo - Average predicted peptide length: 18.78 +2025-10-01 10:30:07 [INFO] xuanjinovo - Average predicted peptide length: 18.62 +2025-10-01 10:30:07 [INFO] xuanjinovo - Average predicted peptide length: 17.94 +2025-10-01 10:30:07 [INFO] xuanjinovo - Average predicted peptide length: 10.83 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 10.39 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 10.33 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 10.44 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 13.53 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 13.92 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 13.48 +2025-10-01 10:30:08 [INFO] xuanjinovo - Average predicted peptide length: 12.67 +2025-10-01 10:30:09 [INFO] xuanjinovo - Average predicted peptide length: 18.14 +2025-10-01 10:30:09 [INFO] xuanjinovo - Average predicted peptide length: 18.30 +2025-10-01 10:30:09 [INFO] xuanjinovo - Average predicted peptide length: 18.23 +2025-10-01 10:30:09 [INFO] xuanjinovo - Average predicted peptide length: 17.11 +2025-10-01 10:30:10 [INFO] xuanjinovo - Average predicted peptide length: 12.66 +2025-10-01 10:30:10 [INFO] xuanjinovo - Average predicted peptide length: 13.12 +2025-10-01 10:30:10 [INFO] xuanjinovo - Average predicted peptide length: 12.70 +2025-10-01 10:30:10 [INFO] xuanjinovo - Average predicted peptide length: 12.53 +2025-10-01 10:30:11 [INFO] xuanjinovo - Average predicted peptide length: 13.44 +2025-10-01 10:30:11 [INFO] xuanjinovo - Average predicted peptide length: 12.69 +2025-10-01 10:30:11 [INFO] xuanjinovo - Average predicted peptide length: 12.69 +2025-10-01 10:30:11 [INFO] xuanjinovo - Average predicted peptide length: 12.06 +2025-10-01 10:30:12 [INFO] xuanjinovo - Average predicted peptide length: 17.58 +2025-10-01 10:30:12 [INFO] xuanjinovo - Average predicted peptide length: 17.03 +2025-10-01 10:30:12 [INFO] xuanjinovo - Average predicted peptide length: 16.58 +2025-10-01 10:30:12 [INFO] xuanjinovo - Average predicted peptide length: 17.02 +2025-10-01 10:30:12 [INFO] xuanjinovo - Average predicted peptide length: 13.89 +2025-10-01 10:30:12 [INFO] xuanjinovo - Average predicted peptide length: 14.78 +2025-10-01 10:30:13 [INFO] xuanjinovo - Average predicted peptide length: 15.94 +2025-10-01 10:30:13 [INFO] xuanjinovo - Average predicted peptide length: 14.27 +2025-10-01 10:30:13 [INFO] xuanjinovo - Average predicted peptide length: 11.06 +2025-10-01 10:30:13 [INFO] xuanjinovo - Average predicted peptide length: 10.86 +2025-10-01 10:30:13 [INFO] xuanjinovo - Average predicted peptide length: 11.75 +2025-10-01 10:30:13 [INFO] xuanjinovo - Average predicted peptide length: 11.92 +2025-10-01 10:30:14 [INFO] xuanjinovo - Average predicted peptide length: 15.14 +2025-10-01 10:30:14 [INFO] xuanjinovo - Average predicted peptide length: 15.30 +2025-10-01 10:30:14 [INFO] xuanjinovo - Average predicted peptide length: 15.44 +2025-10-01 10:30:14 [INFO] xuanjinovo - Average predicted peptide length: 14.47 +2025-10-01 10:30:15 [INFO] xuanjinovo - Average predicted peptide length: 18.97 +2025-10-01 10:30:15 [INFO] xuanjinovo - Average predicted peptide length: 18.92 +2025-10-01 10:30:15 [INFO] xuanjinovo - Average predicted peptide length: 19.16 +2025-10-01 10:30:15 [INFO] xuanjinovo - Average predicted peptide length: 18.42 +2025-10-01 10:30:16 [INFO] xuanjinovo - Average predicted peptide length: 12.80 +2025-10-01 10:30:16 [INFO] xuanjinovo - Average predicted peptide length: 12.58 +2025-10-01 10:30:16 [INFO] xuanjinovo - Average predicted peptide length: 12.73 +2025-10-01 10:30:16 [INFO] xuanjinovo - Average predicted peptide length: 13.56 +2025-10-01 10:30:17 [INFO] xuanjinovo - Average predicted peptide length: 17.25 +2025-10-01 10:30:17 [INFO] xuanjinovo - Average predicted peptide length: 18.12 +2025-10-01 10:30:17 [INFO] xuanjinovo - Average predicted peptide length: 17.97 +2025-10-01 10:30:17 [INFO] xuanjinovo - Average predicted peptide length: 17.58 +2025-10-01 10:30:18 [INFO] xuanjinovo - Average predicted peptide length: 12.44 +2025-10-01 10:30:18 [INFO] xuanjinovo - Average predicted peptide length: 12.31 +2025-10-01 10:30:18 [INFO] xuanjinovo - Average predicted peptide length: 13.39 +2025-10-01 10:30:18 [INFO] xuanjinovo - Average predicted peptide length: 12.88 +2025-10-01 10:30:18 [INFO] xuanjinovo - Average predicted peptide length: 14.94 +2025-10-01 10:30:19 [INFO] xuanjinovo - Average predicted peptide length: 15.17 +2025-10-01 10:30:19 [INFO] xuanjinovo - Average predicted peptide length: 15.91 +2025-10-01 10:30:19 [INFO] xuanjinovo - Average predicted peptide length: 15.28 +2025-10-01 10:30:19 [INFO] xuanjinovo - Average predicted peptide length: 15.53 +2025-10-01 10:30:19 [INFO] xuanjinovo - Average predicted peptide length: 16.66 +2025-10-01 10:30:20 [INFO] xuanjinovo - Average predicted peptide length: 16.16 +2025-10-01 10:30:20 [INFO] xuanjinovo - Average predicted peptide length: 15.45 +2025-10-01 10:30:20 [INFO] xuanjinovo - Average predicted peptide length: 12.72 +2025-10-01 10:30:20 [INFO] xuanjinovo - Average predicted peptide length: 14.36 +2025-10-01 10:30:20 [INFO] xuanjinovo - Average predicted peptide length: 14.66 +2025-10-01 10:30:20 [INFO] xuanjinovo - Average predicted peptide length: 12.81 +2025-10-01 10:30:21 [INFO] xuanjinovo - Average predicted peptide length: 18.11 +2025-10-01 10:30:21 [INFO] xuanjinovo - Average predicted peptide length: 19.66 +2025-10-01 10:30:22 [INFO] xuanjinovo - Average predicted peptide length: 18.70 +2025-10-01 10:30:22 [INFO] xuanjinovo - Average predicted peptide length: 18.75 +2025-10-01 10:30:22 [INFO] xuanjinovo - Average predicted peptide length: 14.72 +2025-10-01 10:30:22 [INFO] xuanjinovo - Average predicted peptide length: 13.73 +2025-10-01 10:30:22 [INFO] xuanjinovo - Average predicted peptide length: 14.28 +2025-10-01 10:30:22 [INFO] xuanjinovo - Average predicted peptide length: 14.39 +2025-10-01 10:30:22 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 19. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. + warning_cache.warn( + +2025-10-01 10:30:23 [INFO] xuanjinovo - Average predicted peptide length: 22.42 +2025-10-01 10:30:23 [INFO] xuanjinovo - Average predicted peptide length: 22.53 +2025-10-01 10:30:23 [INFO] xuanjinovo - Average predicted peptide length: 22.42 +2025-10-01 10:30:23 [INFO] xuanjinovo - Average predicted peptide length: 20.95 diff --git a/example_output/run_metadata/run_metadata.json b/example_output/run_metadata/run_metadata.json new file mode 100644 index 0000000..3f6264c --- /dev/null +++ b/example_output/run_metadata/run_metadata.json @@ -0,0 +1,340 @@ +{ + "config": { + "PMC_enable": true, + "SWA": false, + "accumulate_grad_batches": 1, + "custom_encoder": null, + "dim_feedforward": 1024, + "dim_intensity": null, + "dim_model": 400, + "dropout": 0.18, + "enable_neptune": true, + "every_n_train_steps": 2500, + "gradient_clip_algorithm": "norm", + "gradient_clip_val": 2.5, + "isotope_error_range": [ + 0, + 1 + ], + "learning_rate": 0.00035, + "load_file_name": "./NON351/epoch=5-step=65000.ckpt", + "log_level": "CRITICAL", + "logger": null, + "mask_schedule": { + "epoch_decay": 0.01, + "initial_peek": 0.93, + "min_peek": 0.0 + }, + "mass_control_tol": 0.1, + "max_charge": 10, + "max_epochs": 150, + "max_iters": null, + "max_length": 40, + "max_mz": 6500.0, + "min_intensity": 0.0, + "min_mz": 1.0, + "model_save_folder_path": "./saved_models", + "n_beams": 5, + "n_head": 8, + "n_layers": 9, + "n_log": 1, + "n_nodes": 1, + "n_peaks": 800, + "n_workers": 48, + "neptune_api_token": null, + "neptune_project": null, + "num_sanity_val_steps": 0, + "precursor_mass_tol": 50.0, + "predict_batch_size": 64, + "random_seed": 9906, + "refine_iters": 3, + "remove_precursor_tol": 1.0, + "residues": { + "+42.011": 42.010565, + "+43.006": 43.005814, + "+43.006-17.027": 25.980265, + "-17.027": -17.026549, + "A": 71.037114, + "C+57.021": 160.030649, + "D": 115.026943, + "E": 129.042593, + "F": 147.068414, + "G": 57.021464, + "H": 137.058912, + "I": 113.084064, + "K": 128.094963, + "L": 113.084064, + "M": 131.040485, + "M+15.995": 147.0354, + "N": 114.042927, + "N+0.984": 115.026943, + "P": 97.052764, + "Q": 128.058578, + "Q+0.984": 129.042594, + "R": 156.101111, + "S": 87.032028, + "T": 101.04767, + "V": 99.068414, + "W": 186.079313, + "Y": 163.063329 + }, + "save_model": false, + "save_weights_only": false, + "sync_batchnorm": false, + "tags": [], + "tb_summarywriter": null, + "temp_dir_auto": true, + "train_batch_size": 700, + "train_from_resume": false, + "train_from_scratch": false, + "warm_up_epochs": 1, + "warmup_iters": null, + "weight_decay": 8e-05 + }, + "git_info": { + "git_branch": "main", + "git_commit": "07a5329977fcacf2617354f689fd276e73ab8406", + "git_dirty": true, + "git_remote_url": "https://github.com/guomics-lab/MassNet-DDA.git" + }, + "hardware_info": { + "cpu": { + "max_frequency": "4000.00MHz", + "physical_cores": 96, + "processor": "x86_64", + "total_cores": 1 + }, + "gpus": [ + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + } + ], + "memory": { + "available": "289.11 GB", + "total": "292.97 GB" + } + }, + "model_name": "epoch=695-step=324000.ckpt", + "software_info": { + "cuda_version": "12.1", + "cudnn_version": 8902, + "installed_packages": { + "absl-py": "1.3.0", + "aiohttp": "3.8.3", + "aiosignal": "1.3.1", + "appdirs": "1.4.4", + "arrow": "1.2.3", + "asttokens": "2.0.5", + "async-timeout": "4.0.2", + "attrs": "22.2.0", + "backcall": "0.2.0", + "biopython": "1.81", + "blinker": "1.4", + "boto3": "1.26.53", + "botocore": "1.29.53", + "bravado": "11.0.3", + "bravado-core": "5.17.1", + "brotlipy": "0.7.0", + "cachetools": "4.2.2", + "captum": "0.8.0", + "casanovo": "3.2.0", + "certifi": "2022.12.7", + "cffi": "1.15.1", + "charset-normalizer": "2.0.4", + "click": "8.1.3", + "comet-ml": "3.31.22", + "comm": "0.1.2", + "configobj": "5.0.6", + "contourpy": "1.0.6", + "cryptography": "38.0.1", + "ctcdecode": "1.0.3", + "cupy-cuda12x": "13.6.0", + "cycler": "0.11.0", + "debugpy": "1.5.1", + "decorator": "5.1.1", + "deprecated": "1.2.13", + "depthcharge-ms": "0.1.0", + "dulwich": "0.20.50", + "einops": "0.6.0", + "entrypoints": "0.4", + "everett": "3.1.0", + "executing": "0.8.3", + "fastobo": "0.12.2", + "fastrlock": "0.8.3", + "filelock": "3.19.1", + "flit-core": "3.6.0", + "fonttools": "4.38.0", + "fqdn": "1.5.1", + "frozenlist": "1.3.3", + "fsspec": "2022.11.0", + "future": "0.18.3", + "gitdb": "4.0.10", + "gitpython": "3.1.30", + "google-auth": "2.6.0", + "google-auth-oauthlib": "0.4.4", + "grpcio": "1.74.0", + "h5py": "3.8.0", + 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"pyyaml": "6.0", + "pyzmq": "23.2.0", + "requests": "2.28.1", + "requests-oauthlib": "1.3.0", + "requests-toolbelt": "0.10.1", + "rfc3339-validator": "0.1.4", + "rfc3987": "1.3.8", + "rsa": "4.7.2", + "s3transfer": "0.6.0", + "scikit-learn": "1.2.0", + "scipy": "1.10.0", + "seaborn": "0.12.2", + "semantic-version": "2.10.0", + "sentry-sdk": "1.12.1", + "setuptools": "65.5.0", + "simplejson": "3.18.1", + "six": "1.16.0", + "smmap": "5.0.0", + "sparsestack": "0.4.1", + "spectrum-utils": "0.4.1", + "stack-data": "0.2.0", + "swagger-spec-validator": "3.0.3", + "sympy": "1.14.0", + "tensorboard": "2.10.0", + "tensorboard-data-server": "0.6.1", + "tensorboard-plugin-wit": "1.8.1", + "tensorboardx": "2.5.1", + "threadpoolctl": "3.1.0", + "torch": "2.1.0", + "torch-imputer": "0.1.0", + "torch-optimizer": "0.3.0", + "torchmetrics": "0.11.0", + "tornado": "6.2", + "tqdm": "4.64.1", + "traitlets": "5.7.1", + "triton": "2.1.0", + "typing-extensions": "4.4.0", + "uri-template": "1.2.0", + "urllib3": "1.26.13", + "wcwidth": "0.2.5", + "webcolors": "1.12", + "websocket-client": "1.3.3", + "werkzeug": "2.2.2", + "wheel": "0.37.1", + "wrapt": "1.14.1", + "wurlitzer": "3.0.3", + "yapf": "0.32.0", + "yarl": "1.8.2" + }, + "python": { + "executable": "/home/weijiaqi/miniconda3/envs/XuanjiNovo/bin/python3.11", + "platform": "Linux-5.15.0-89-generic-x86_64-with-glibc2.35", + "version": "3.11.13 (main, Jun 5 2025, 13:12:00) [GCC 11.2.0]" + }, + "pytorch_version": "2.1.0+cu121" + }, + "timestamp": "2025-09-30T12:42:27.044401" +} \ No newline at end of file diff --git a/example_output/run_metadata/run_summary.txt b/example_output/run_metadata/run_summary.txt new file mode 100644 index 0000000..dacebb5 --- /dev/null +++ b/example_output/run_metadata/run_summary.txt @@ -0,0 +1,7 @@ +timestamp: 2025-09-30T12:42:27.044401 +git_commit: 07a5329977fcacf2617354f689fd276e73ab8406 +python_version: 3.11.13 +pytorch_version: 2.1.0+cu121 +gpu_count: 4 +gpu_names: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +cuda_version: 12.1 diff --git a/example_output/train/train_out.txt b/example_output/train/train_out.txt new file mode 100644 index 0000000..fe9f730 --- /dev/null +++ b/example_output/train/train_out.txt @@ -0,0 +1,442 @@ +Using MASTER_PORT=24047 +2025-10-01 13:07:48 [INFO] XuanjiNovo - Configuration validated and logged: +2025-10-01 13:07:48 [INFO] XuanjiNovo - Full config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_full.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - Summary: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_summary.txt +2025-10-01 13:07:48 [INFO] XuanjiNovo - Device config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/device_config.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 13:07:48 [INFO] XuanjiNovo - Configuration validated and logged: +2025-10-01 13:07:48 [INFO] XuanjiNovo - Full config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_full.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - Configuration validated and logged: +2025-10-01 13:07:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 13:07:48 [INFO] XuanjiNovo - Configuration validated and logged: +2025-10-01 13:07:48 [INFO] XuanjiNovo - Summary: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_summary.txt +2025-10-01 13:07:48 [INFO] XuanjiNovo - Full config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_full.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 13:07:48 [INFO] XuanjiNovo - Device config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/device_config.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - Full config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_full.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - Summary: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_summary.txt +2025-10-01 13:07:48 [INFO] XuanjiNovo - Summary: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/config_summary.txt +2025-10-01 13:07:48 [INFO] XuanjiNovo - Device config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/device_config.json +2025-10-01 13:07:48 [INFO] XuanjiNovo - Device config: /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748/config_logs/20251001_130748/device_config.json +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mode = train +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_val = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_test = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - random_seed = 5277 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - enable_neptune = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_batch_size = 100 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - logger = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_scratch = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - val_interval = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [INFO] XuanjiNovo - Train the XuanjiNovo model. +2025-10-01 13:07:48 [INFO] natNovo - Training configuration validated via pydantic +config validated via pydantic +2025-10-01 13:07:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 13:07:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 13:07:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] +2025-10-01 13:07:48 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 13:07:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 13:07:48 [INFO] XuanjiNovo - Number of available GPUs: 4 +2025-10-01 13:07:48 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 13:07:48 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 13:07:48 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mode = train +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mode = train +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mode = train +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_val = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_val = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_test = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_val = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_test = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - peak_path_test = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo/config.yaml +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - random_seed = 5112 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - random_seed = 3075 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - random_seed = 478 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - PMC_enable = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_peaks = 800 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_mz = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_charge = 10 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_model = 400 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_head = 8 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_layers = 9 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dropout = 0.18 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - refine_iters = 3 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - dim_intensity = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - custom_encoder = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_length = 40 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_log = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - enable_neptune = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - enable_neptune = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tb_summarywriter = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - log_level = CRITICAL +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - enable_neptune = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_project = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - neptune_api_token = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - tags = [] +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warmup_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_iters = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - max_epochs = 150 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_batch_size = 100 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_batch_size = 100 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - sync_batchnorm = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - SWA = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - logger = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_batch_size = 100 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - logger = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_scratch = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_beams = 5 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_scratch = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - val_interval = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - logger = None +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - val_interval = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_scratch = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - val_interval = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_nodes = 1 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_model = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - train_from_resume = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - save_weights_only = False +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [INFO] XuanjiNovo - Train the XuanjiNovo model. +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - temp_dir_auto = True +2025-10-01 13:07:48 [INFO] XuanjiNovo - Train the XuanjiNovo model. +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - n_workers = 48 +2025-10-01 13:07:48 [DEBUG] XuanjiNovo - result_output_dir = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748 +2025-10-01 13:07:48 [INFO] natNovo - Training configuration validated via pydantic +2025-10-01 13:07:48 [INFO] natNovo - Training configuration validated via pydantic +2025-10-01 13:07:48 [INFO] XuanjiNovo - Train the XuanjiNovo model. +config validated via pydantic +config validated via pydantic +2025-10-01 13:07:48 [INFO] natNovo - Training configuration validated via pydantic +config validated via pydantic +2025-10-01 13:07:48 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:48 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:48 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:53 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:53 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:53 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:53 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:57 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:57 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:58 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:07:58 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... +2025-10-01 13:08:03 [INFO] natNovo - Recording metadata in: ./saved_models +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata.json +2025-10-01 13:08:03 [INFO] natNovo - Quick summary available in ./saved_models/run_summary.txt +2025-10-01 13:08:03 [INFO] natNovo - Recording metadata in: ./saved_models +2025-10-01 13:08:03 [INFO] natNovo - Recording metadata in: ./saved_models +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata.json +2025-10-01 13:08:03 [INFO] natNovo - Quick summary available in ./saved_models/run_summary.txt +2025-10-01 13:08:03 [INFO] natNovo - Recording metadata in: ./saved_models +2025-10-01 13:08:03 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata.json +2025-10-01 13:08:03 [INFO] natNovo - Quick summary available in ./saved_models/run_summary.txt +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:03 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA, universal_newlines=False, shell=None, istream=None) +2025-10-01 13:08:04 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata.json +2025-10-01 13:08:04 [INFO] natNovo - Quick summary available in ./saved_models/run_summary.txt + +Validation DataLoader 0: 0%| | 0/611 [00:00 Date: Wed, 1 Oct 2025 13:23:48 +0800 Subject: [PATCH 7/9] u --- XuanjiNovo/XuanjiNovo.py | 10 +- XuanjiNovo/config.yaml | 7 +- XuanjiNovo/config/model.py | 170 +++++- XuanjiNovo/denovo/model_runner.py | 10 +- logs/xuanjinovo_20250930_043835.log | 66 --- logs/xuanjinovo_20250930_043836.log | 0 logs/xuanjinovo_20250930_044032.log | 181 ------ logs/xuanjinovo_20250930_044033.log | 430 --------------- logs/xuanjinovo_20250930_044036.log | 86 --- logs/xuanjinovo_20250930_044037.log | 0 logs/xuanjinovo_20250930_044038.log | 0 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Device config: {config_paths['device']}") - # Convert validated config back to dict for backward compatibility - config = config_obj.dict() + # Convert validated config back to flat dict for backward compatibility + config2 = config_obj.to_flat_dict() - except ImportError: - # Fallback to old config loading if pydantic is not available - logger.warning("Pydantic not available, falling back to basic config validation") + except: + print("skipping pydantic validation") + if config2: with open(config_fn) as f_in: config = yaml.safe_load(f_in) # Old type validation code remains as fallback diff --git a/XuanjiNovo/config.yaml b/XuanjiNovo/config.yaml index 0cbbf89..214111f 100644 --- a/XuanjiNovo/config.yaml +++ b/XuanjiNovo/config.yaml @@ -78,7 +78,7 @@ tb_summarywriter: log_level: "CRITICAL" # One of: DEBUG, INFO, WARNING, ERROR, CRITICAL # Neptune logger -enable_neptune: True +enable_neptune: False neptune_project: neptune_api_token: tags: [] @@ -98,7 +98,7 @@ sync_batchnorm: False SWA: False # StochasticWeightAveraging # Training/inference options. -train_batch_size: 2800 +train_batch_size: 400 predict_batch_size: 64 # n_beams: 5 n_beams: 5 # No beam search @@ -107,7 +107,8 @@ logger: num_sanity_val_steps: 0 -train_from_scratch: False +train_from_scratch: True +val_interval: 1 n_nodes: 1 save_model: False train_from_resume: False diff --git a/XuanjiNovo/config/model.py b/XuanjiNovo/config/model.py index a126fd0..fb21ec9 100644 --- a/XuanjiNovo/config/model.py +++ b/XuanjiNovo/config/model.py @@ -24,7 +24,7 @@ class TrainingConfig(BaseModel): learning_rate: float = Field(0.00035, gt=0) weight_decay: float = Field(8e-5, ge=0) gradient_clip_val: float = Field(2.5, gt=0) - gradient_clip_algorithm: str = Field("norm", regex="^(norm|value)$") + gradient_clip_algorithm: str = Field("norm", pattern="^(norm|value)$") accumulate_grad_batches: int = Field(1, gt=0) sync_batchnorm: bool = False SWA: bool = False # StochasticWeightAveraging @@ -66,7 +66,7 @@ class XuanjiNovoConfig(BaseModel): """Complete configuration for XuanjiNovo.""" # Basic settings random_seed: int = -1 - log_level: str = Field("INFO", regex="^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$") + log_level: str = Field("INFO", pattern="^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$") # Component configurations training: TrainingConfig @@ -102,15 +102,175 @@ def save_to_file(self, filepath: str) -> None: with open(filepath, 'w') as f: f.write(self.json(indent=2)) + def to_flat_dict(self) -> Dict: + """Convert nested config to flat dictionary for backward compatibility.""" + flat_dict = { + 'random_seed': self.random_seed, + 'log_level': self.log_level, + # Training fields + 'train_batch_size': self.training.train_batch_size, + 'max_epochs': self.training.max_epochs, + 'warm_up_epochs': self.training.warm_up_epochs, + 'learning_rate': self.training.learning_rate, + 'weight_decay': self.training.weight_decay, + 'gradient_clip_val': self.training.gradient_clip_val, + 'gradient_clip_algorithm': self.training.gradient_clip_algorithm, + 'accumulate_grad_batches': self.training.accumulate_grad_batches, + 'sync_batchnorm': self.training.sync_batchnorm, + 'SWA': self.training.SWA, + # Model fields + 'dim_model': self.model.dim_model, + 'n_head': self.model.n_head, + 'dim_feedforward': self.model.dim_feedforward, + 'n_layers': self.model.n_layers, + 'dropout': self.model.dropout, + 'max_length': self.model.max_length, + # Decoding fields + 'PMC_enable': self.decoding.PMC_enable, + 'mass_control_tol': self.decoding.mass_control_tol, + 'n_beams': self.decoding.n_beams, + 'refine_iters': self.decoding.refine_iters, + # Data fields + 'n_peaks': self.data.n_peaks, + 'min_mz': self.data.min_mz, + 'max_mz': self.data.max_mz, + 'min_intensity': self.data.min_intensity, + 'remove_precursor_tol': self.data.remove_precursor_tol, + 'max_charge': self.data.max_charge, + 'precursor_mass_tol': self.data.precursor_mass_tol, + 'isotope_error_range': self.data.isotope_error_range, + # Mask schedule + 'mask_schedule': { + 'initial_peek': self.mask_schedule.initial_peek, + 'epoch_decay': self.mask_schedule.epoch_decay, + 'min_peek': self.mask_schedule.min_peek, + }, + # Residues + 'residues': self.residues, + } + + # Add optional fields if they exist + if self.neptune_config: + flat_dict['neptune_config'] = self.neptune_config + if self.custom_metadata: + flat_dict['custom_metadata'] = self.custom_metadata + + return flat_dict + @classmethod def from_yaml(cls, yaml_path: str) -> 'XuanjiNovoConfig': """Create configuration from YAML file.""" import yaml with open(yaml_path) as f: - config_dict = yaml.safe_load(f) - return cls(**config_dict) + flat_config = yaml.safe_load(f) + + # Transform flat config to nested structure + nested_dict = { + 'random_seed': flat_config.get('random_seed', -1), + 'log_level': flat_config.get('log_level', 'INFO'), + 'training': { + 'train_batch_size': flat_config.get('train_batch_size', 2800), + 'max_epochs': flat_config.get('max_epochs', 150), + 'warm_up_epochs': flat_config.get('warm_up_epochs', 1), + 'learning_rate': flat_config.get('learning_rate', 0.00035), + 'weight_decay': flat_config.get('weight_decay', 8e-5), + 'gradient_clip_val': flat_config.get('gradient_clip_val', 2.5), + 'gradient_clip_algorithm': flat_config.get('gradient_clip_algorithm', 'norm'), + 'accumulate_grad_batches': flat_config.get('accumulate_grad_batches', 1), + 'sync_batchnorm': flat_config.get('sync_batchnorm', False), + 'SWA': flat_config.get('SWA', False), + }, + 'model': { + 'dim_model': flat_config.get('dim_model', 400), + 'n_head': flat_config.get('n_head', 8), + 'dim_feedforward': flat_config.get('dim_feedforward', 1024), + 'n_layers': flat_config.get('n_layers', 9), + 'dropout': flat_config.get('dropout', 0.18), + 'max_length': flat_config.get('max_length', 40), + }, + 'decoding': { + 'PMC_enable': flat_config.get('PMC_enable', True), + 'mass_control_tol': flat_config.get('mass_control_tol', 0.1), + 'n_beams': flat_config.get('n_beams', 5), + 'refine_iters': flat_config.get('refine_iters', 3), + }, + 'data': { + 'n_peaks': flat_config.get('n_peaks', 800), + 'min_mz': flat_config.get('min_mz', 1.0), + 'max_mz': flat_config.get('max_mz', 6500.0), + 'min_intensity': flat_config.get('min_intensity', 0.0), + 'remove_precursor_tol': flat_config.get('remove_precursor_tol', 1.0), + 'max_charge': flat_config.get('max_charge', 10), + 'precursor_mass_tol': flat_config.get('precursor_mass_tol', 50.0), + 'isotope_error_range': tuple(flat_config.get('isotope_error_range', [0, 1])), + }, + 'mask_schedule': flat_config.get('mask_schedule', { + 'initial_peek': 0.93, + 'epoch_decay': 0.01, + 'min_peek': 0.00, + }), + 'residues': flat_config.get('residues', {}), + 'neptune_config': flat_config.get('neptune_config'), + 'custom_metadata': flat_config.get('custom_metadata'), + } + + return cls(**nested_dict) @classmethod def from_dict(cls, config_dict: Dict) -> 'XuanjiNovoConfig': """Create configuration from dictionary.""" - return cls(**config_dict) + # Check if it's already nested structure by looking for 'training' key + if 'training' in config_dict and isinstance(config_dict.get('training'), dict): + # Already nested, use directly + return cls(**config_dict) + else: + # Flat structure, transform to nested + nested_dict = { + 'random_seed': config_dict.get('random_seed', -1), + 'log_level': config_dict.get('log_level', 'INFO'), + 'training': { + 'train_batch_size': config_dict.get('train_batch_size', 2800), + 'max_epochs': config_dict.get('max_epochs', 150), + 'warm_up_epochs': config_dict.get('warm_up_epochs', 1), + 'learning_rate': config_dict.get('learning_rate', 0.00035), + 'weight_decay': config_dict.get('weight_decay', 8e-5), + 'gradient_clip_val': config_dict.get('gradient_clip_val', 2.5), + 'gradient_clip_algorithm': config_dict.get('gradient_clip_algorithm', 'norm'), + 'accumulate_grad_batches': config_dict.get('accumulate_grad_batches', 1), + 'sync_batchnorm': config_dict.get('sync_batchnorm', False), + 'SWA': config_dict.get('SWA', False), + }, + 'model': { + 'dim_model': config_dict.get('dim_model', 400), + 'n_head': config_dict.get('n_head', 8), + 'dim_feedforward': config_dict.get('dim_feedforward', 1024), + 'n_layers': config_dict.get('n_layers', 9), + 'dropout': config_dict.get('dropout', 0.18), + 'max_length': config_dict.get('max_length', 40), + }, + 'decoding': { + 'PMC_enable': config_dict.get('PMC_enable', True), + 'mass_control_tol': config_dict.get('mass_control_tol', 0.1), + 'n_beams': config_dict.get('n_beams', 5), + 'refine_iters': config_dict.get('refine_iters', 3), + }, + 'data': { + 'n_peaks': config_dict.get('n_peaks', 800), + 'min_mz': config_dict.get('min_mz', 1.0), + 'max_mz': config_dict.get('max_mz', 6500.0), + 'min_intensity': config_dict.get('min_intensity', 0.0), + 'remove_precursor_tol': config_dict.get('remove_precursor_tol', 1.0), + 'max_charge': config_dict.get('max_charge', 10), + 'precursor_mass_tol': config_dict.get('precursor_mass_tol', 50.0), + 'isotope_error_range': tuple(config_dict.get('isotope_error_range', [0, 1])), + }, + 'mask_schedule': config_dict.get('mask_schedule', { + 'initial_peek': 0.93, + 'epoch_decay': 0.01, + 'min_peek': 0.00, + }), + 'residues': config_dict.get('residues', {}), + 'neptune_config': config_dict.get('neptune_config'), + 'custom_metadata': config_dict.get('custom_metadata'), + } + return cls(**nested_dict) diff --git a/XuanjiNovo/denovo/model_runner.py b/XuanjiNovo/denovo/model_runner.py index 4d4935e..70997ed 100644 --- a/XuanjiNovo/denovo/model_runner.py +++ b/XuanjiNovo/denovo/model_runner.py @@ -81,7 +81,7 @@ def _execute_existing( try: from ..config import XuanjiNovoConfig if isinstance(config, dict): - config = XuanjiNovoConfig.from_dict(config) + config2 = XuanjiNovoConfig.from_dict(config) logger.info("Configuration validated via pydantic") except ImportError: logger.warning("Pydantic not available, skipping additional config validation") @@ -109,7 +109,8 @@ def _execute_existing( model_filename, ) raise FileNotFoundError("Could not find the trained model weights") - + if config2: + print("config validated via pydantic") model = Spec2Pep().load_from_checkpoint( model_filename, PMC_enable=config["PMC_enable"], @@ -249,7 +250,7 @@ def train( try: from ..config import XuanjiNovoConfig if isinstance(config, dict): - config = XuanjiNovoConfig.from_dict(config) + config2 = XuanjiNovoConfig.from_dict(config) logger.info("Training configuration validated via pydantic") except ImportError: logger.warning("Pydantic not available, skipping additional config validation") @@ -281,7 +282,8 @@ def train( train_is_not_index = any([ os.path.splitext(fn)[1] in (".mgf", ".mzxml", ".mzml") for fn in train_filenames ]) - + if config2: + print("config validated via pydantic") if (peak_path_val is None or len(val_filenames := _get_peak_filenames(peak_path_val, ext)) == 0): diff --git a/logs/xuanjinovo_20250930_043835.log b/logs/xuanjinovo_20250930_043835.log deleted file mode 100644 index 82e70fc..0000000 --- a/logs/xuanjinovo_20250930_043835.log +++ /dev/null @@ -1,66 +0,0 @@ -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930043835 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - random_seed = 2717 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:38:36 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:38:36 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:38:36 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:38:36 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:38:36 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 diff --git a/logs/xuanjinovo_20250930_043836.log b/logs/xuanjinovo_20250930_043836.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_044032.log b/logs/xuanjinovo_20250930_044032.log deleted file mode 100644 index 4736392..0000000 --- a/logs/xuanjinovo_20250930_044032.log +++ /dev/null @@ -1,181 +0,0 @@ -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044032 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 7973 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044032 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 862 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:40:51 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 04:41:25 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 12.02 -2025-09-30 04:41:27 [INFO] xuanjinovo - Average predicted peptide length: 11.97 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.77 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.53 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 11.98 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.28 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.50 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.17 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.69 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.94 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.72 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.50 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.67 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 17.12 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.70 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.27 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.95 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.14 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.94 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.55 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.42 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.77 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.88 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.25 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.55 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.34 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 13.83 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.70 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.19 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.97 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 15.19 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 15.17 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 12.83 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.81 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.84 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.28 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.72 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.12 -2025-09-30 04:41:32 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 18.90 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 19.30 diff --git a/logs/xuanjinovo_20250930_044033.log b/logs/xuanjinovo_20250930_044033.log deleted file mode 100644 index 93ac930..0000000 --- a/logs/xuanjinovo_20250930_044033.log +++ /dev/null @@ -1,430 +0,0 @@ -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 4234 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 4151 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 3000 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044033 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 4597 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - random_seed = 9428 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:33 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:40:33 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 11.31 -2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 12.16 -2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 11.58 -2025-09-30 04:41:26 [INFO] xuanjinovo - Average predicted peptide length: 11.20 -2025-09-30 04:41:27 [INFO] xuanjinovo - Average predicted peptide length: 12.33 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.67 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.64 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 17.23 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 17.25 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 17.00 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.69 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 13.28 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.52 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.58 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.45 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.97 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.94 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.47 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.69 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.83 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.33 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.44 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.70 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.53 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 14.98 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 12.92 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 13.16 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.39 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.39 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.52 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.67 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.53 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.31 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.31 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.73 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.47 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.38 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.31 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.89 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.31 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 13.50 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.12 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.23 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.41 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.19 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.06 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.16 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.39 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.30 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.42 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.72 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.66 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.25 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.86 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.09 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.77 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.23 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.00 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.36 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.73 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.11 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.25 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 13.92 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.91 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.58 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 11.91 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.75 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.95 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 13.20 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.33 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.58 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.16 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 17.19 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 17.08 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 17.14 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.80 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.23 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.84 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 15.31 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.94 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.41 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.67 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.80 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.53 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.70 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 13.88 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.64 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.73 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.41 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.70 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.97 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.33 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.56 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.61 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 16.30 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 19.90 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 21.00 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 19.80 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 21.50 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 20.70 diff --git a/logs/xuanjinovo_20250930_044036.log b/logs/xuanjinovo_20250930_044036.log deleted file mode 100644 index 80ec5cb..0000000 --- a/logs/xuanjinovo_20250930_044036.log +++ /dev/null @@ -1,86 +0,0 @@ -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044036 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - random_seed = 9387 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - log_level = INFO -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:40:37 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:40:37 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:40:37 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:40:37 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:40:38 [INFO] xuanjinovo - PMC enabled: False, Mass control tolerance: 0.1 -2025-09-30 04:41:27 [INFO] xuanjinovo - Average predicted peptide length: 11.77 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 16.69 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 13.25 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.09 -2025-09-30 04:41:28 [INFO] xuanjinovo - Average predicted peptide length: 15.34 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.78 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 15.02 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 12.41 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 14.27 -2025-09-30 04:41:29 [INFO] xuanjinovo - Average predicted peptide length: 16.28 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.27 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 15.58 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 14.22 -2025-09-30 04:41:30 [INFO] xuanjinovo - Average predicted peptide length: 12.00 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 16.67 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.70 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 12.98 -2025-09-30 04:41:31 [INFO] xuanjinovo - Average predicted peptide length: 14.75 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 15.69 -2025-09-30 04:41:32 [INFO] xuanjinovo - Average predicted peptide length: 20.40 diff --git a/logs/xuanjinovo_20250930_044037.log b/logs/xuanjinovo_20250930_044037.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_044038.log b/logs/xuanjinovo_20250930_044038.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_044146.log b/logs/xuanjinovo_20250930_044146.log deleted file mode 100644 index 3932fd7..0000000 --- a/logs/xuanjinovo_20250930_044146.log +++ /dev/null @@ -1,65 +0,0 @@ -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044146 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - random_seed = 6573 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:46 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:46 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:46 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt diff --git a/logs/xuanjinovo_20250930_044147.log b/logs/xuanjinovo_20250930_044147.log deleted file mode 100644 index f170c80..0000000 --- a/logs/xuanjinovo_20250930_044147.log +++ /dev/null @@ -1,464 +0,0 @@ -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 2394 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 3206 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 6278 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 614 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 4889 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 479 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930044147 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - random_seed = 6479 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 04:41:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 04:41:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 04:41:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:41:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 04:42:04 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 04:42:37 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 04:42:42 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_050709.log b/logs/xuanjinovo_20250930_050709.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_050901.log b/logs/xuanjinovo_20250930_050901.log deleted file mode 100644 index 035544d..0000000 --- a/logs/xuanjinovo_20250930_050901.log +++ /dev/null @@ -1,571 +0,0 @@ -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 9261 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 5458 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 8629 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 8842 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 9461 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 6169 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 3213 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050901 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - random_seed = 2571 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:01 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:01 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:01 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:07 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:07 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:07 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:19 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - diff --git a/logs/xuanjinovo_20250930_050948.log b/logs/xuanjinovo_20250930_050948.log deleted file mode 100644 index e033163..0000000 --- a/logs/xuanjinovo_20250930_050948.log +++ /dev/null @@ -1,571 +0,0 @@ -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - random_seed = 9420 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:48 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:48 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - random_seed = 8900 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:48 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:48 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:48 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 5423 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 2162 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 3854 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 1811 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 9764 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930050948 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - random_seed = 5398 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:09:49 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:09:49 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:09:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:54 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:54 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:54 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:54 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:54 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:09:55 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:09:55 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:10:06 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - diff --git a/logs/xuanjinovo_20250930_050949.log b/logs/xuanjinovo_20250930_050949.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_051203.log b/logs/xuanjinovo_20250930_051203.log deleted file mode 100644 index 4a233a2..0000000 --- a/logs/xuanjinovo_20250930_051203.log +++ /dev/null @@ -1,71 +0,0 @@ -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051203 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - random_seed = 2937 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_051204.log b/logs/xuanjinovo_20250930_051204.log deleted file mode 100644 index bc87d39..0000000 --- a/logs/xuanjinovo_20250930_051204.log +++ /dev/null @@ -1,500 +0,0 @@ -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 2496 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 8559 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 4215 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 9581 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 278 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 6674 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051204 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - random_seed = 685 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:12:04 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:12:04 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:12:04 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:04 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:12:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:12:22 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - diff --git a/logs/xuanjinovo_20250930_051503.log b/logs/xuanjinovo_20250930_051503.log deleted file mode 100644 index bf435fa..0000000 --- a/logs/xuanjinovo_20250930_051503.log +++ /dev/null @@ -1,574 +0,0 @@ -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 5594 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 1900 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 471 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 9613 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 2174 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 1966 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051503 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 5978 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - random_seed = 9143 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:15:03 [DEBUG] XuanjiNovo - n_workers = 1 -2025-09-30 05:15:03 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:10 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:15:10 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:15:21 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 05:15:28 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_051504.log b/logs/xuanjinovo_20250930_051504.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_051747.log b/logs/xuanjinovo_20250930_051747.log deleted file mode 100644 index eb8254f..0000000 --- a/logs/xuanjinovo_20250930_051747.log +++ /dev/null @@ -1,577 +0,0 @@ -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 1216 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 5205 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 4618 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 3571 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 4879 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 7617 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 1487 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051747 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - random_seed = 6533 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:17:47 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:17:47 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:17:47 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:48 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:17:53 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:18:06 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 05:18:41 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 05:18:46 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_051748.log b/logs/xuanjinovo_20250930_051748.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_051859.log b/logs/xuanjinovo_20250930_051859.log deleted file mode 100644 index a3b0318..0000000 --- a/logs/xuanjinovo_20250930_051859.log +++ /dev/null @@ -1,577 +0,0 @@ -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 469 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 1407 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 5743 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 9967 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 2658 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 9177 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 6165 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930051859 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - random_seed = 6028 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:18:59 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:18:59 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:18:59 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:00 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:06 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:19:06 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:19:17 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 05:19:49 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 05:19:55 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_051900.log b/logs/xuanjinovo_20250930_051900.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052157.log b/logs/xuanjinovo_20250930_052157.log deleted file mode 100644 index 7d41bcc..0000000 --- a/logs/xuanjinovo_20250930_052157.log +++ /dev/null @@ -1,577 +0,0 @@ -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 5248 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 2232 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 4746 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 1735 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 8131 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 3047 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 2641 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052157 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - random_seed = 4999 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_batch_size = 350 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:21:58 [DEBUG] XuanjiNovo - n_workers = 24 -2025-09-30 05:21:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:21:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:21:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:04 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:05 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:22:05 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:22:16 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 05:22:50 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 05:22:55 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_052158.log b/logs/xuanjinovo_20250930_052158.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052159.log b/logs/xuanjinovo_20250930_052159.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052318.log b/logs/xuanjinovo_20250930_052318.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052319.log b/logs/xuanjinovo_20250930_052319.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052326.log b/logs/xuanjinovo_20250930_052326.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052625.log b/logs/xuanjinovo_20250930_052625.log deleted file mode 100644 index 573907a..0000000 --- a/logs/xuanjinovo_20250930_052625.log +++ /dev/null @@ -1,518 +0,0 @@ -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:25 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 1510 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 3242 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 1208 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 8890 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 4285 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 2273 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052625 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - random_seed = 3120 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:26 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:26 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:26 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:27 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_052626.log b/logs/xuanjinovo_20250930_052626.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052628.log b/logs/xuanjinovo_20250930_052628.log deleted file mode 100644 index f3eec52..0000000 --- a/logs/xuanjinovo_20250930_052628.log +++ /dev/null @@ -1,74 +0,0 @@ -2025-09-30 05:26:28 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:28 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:28 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052628 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - random_seed = 601 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:28 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:28 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:28 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:29 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:29 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:29 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:29 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_052629.log b/logs/xuanjinovo_20250930_052629.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052657.log b/logs/xuanjinovo_20250930_052657.log deleted file mode 100644 index 2868f6f..0000000 --- a/logs/xuanjinovo_20250930_052657.log +++ /dev/null @@ -1,157 +0,0 @@ -2025-09-30 05:26:57 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:57 [INFO] XuanjiNovo - Using GPUs: ['0', '1'] -2025-09-30 05:26:58 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:58 [INFO] XuanjiNovo - Number of available GPUs: 2 -2025-09-30 05:26:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:58 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052657 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - random_seed = 2088 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052657 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - random_seed = 5178 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_batch_size = 1400 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:26:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:26:58 [DEBUG] XuanjiNovo - n_workers = 96 -2025-09-30 05:26:58 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:26:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:58 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:26:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:58 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:59 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:26:59 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:59 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:26:59 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:26:59 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:27:04 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 05:27:33 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 05:27:54 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 38. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_052658.log b/logs/xuanjinovo_20250930_052658.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052806.log b/logs/xuanjinovo_20250930_052806.log deleted file mode 100644 index fe8659a..0000000 --- a/logs/xuanjinovo_20250930_052806.log +++ /dev/null @@ -1,305 +0,0 @@ -2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 05:28:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 05:28:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:28:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 6193 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930052806 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 6107 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 7726 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - random_seed = 4215 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:28:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:28:07 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:28:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:28:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 05:28:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 05:28:19 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 05:28:48 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 05:29:01 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 19. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_052807.log b/logs/xuanjinovo_20250930_052807.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_052808.log b/logs/xuanjinovo_20250930_052808.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_104735.log b/logs/xuanjinovo_20250930_104735.log deleted file mode 100644 index 6489bd6..0000000 --- a/logs/xuanjinovo_20250930_104735.log +++ /dev/null @@ -1,305 +0,0 @@ -2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 10:47:35 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 10:47:35 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 9700 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 2425 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 10:47:35 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mode = eval -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 -2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 8714 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930104735 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - random_seed = 3519 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - logger = None -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 10:47:35 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 10:47:35 [INFO] XuanjiNovo - Evaluate a trained XuanjiNovo model. -2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 10:47:35 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:35 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 10:47:37 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 10:47:37 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 10:47:48 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:315: PossibleUserWarning: Using `DistributedSampler` with the dataloaders. During `trainer.validate()`, it is recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates some samples to make sure all devices have same batch size in case of uneven inputs. - rank_zero_warn( - -2025-09-30 10:48:21 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 64. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - -2025-09-30 10:48:29 [WARNING] py.warnings - /home/weijiaqi/miniconda3/envs/XuanjiNovo/lib/python3.11/site-packages/pytorch_lightning/utilities/data.py:85: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 19. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`. - warning_cache.warn( - diff --git a/logs/xuanjinovo_20250930_104736.log b/logs/xuanjinovo_20250930_104736.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_110305.log b/logs/xuanjinovo_20250930_110305.log deleted file mode 100644 index 66b7adc..0000000 --- a/logs/xuanjinovo_20250930_110305.log +++ /dev/null @@ -1,296 +0,0 @@ -2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:03:05 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:03:06 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 865 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 5242 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 4410 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:03:06 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110305 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - random_seed = 7393 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:03:06 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:03:06 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:03:06 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:06 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:03:08 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:03:08 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_110306.log b/logs/xuanjinovo_20250930_110306.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_110307.log b/logs/xuanjinovo_20250930_110307.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_110706.log b/logs/xuanjinovo_20250930_110706.log deleted file mode 100644 index 65039f3..0000000 --- a/logs/xuanjinovo_20250930_110706.log +++ /dev/null @@ -1,304 +0,0 @@ -2025-09-30 11:07:06 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:07:06 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:07:06 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:07:06 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:07:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:07:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:07:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:07:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 726 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 5094 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 6938 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:07:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930110706 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - random_seed = 2828 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./NON355 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:07:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:07:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:07:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:07:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:07 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:07:09 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:07:09 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_110707.log b/logs/xuanjinovo_20250930_110707.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_111048.log b/logs/xuanjinovo_20250930_111048.log deleted file mode 100644 index 476007d..0000000 --- a/logs/xuanjinovo_20250930_111048.log +++ /dev/null @@ -1,304 +0,0 @@ -2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:10:48 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:10:48 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:10:48 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 6113 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 7307 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 6720 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:10:49 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111048 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - random_seed = 2785 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:10:49 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:10:49 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:10:49 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:10:49 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:49 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:50 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt -2025-09-30 11:10:51 [INFO] natNovo - Run metadata recorded in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_metadata.json -2025-09-30 11:10:51 [INFO] natNovo - Quick summary available in /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_111049.log b/logs/xuanjinovo_20250930_111049.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_111520.log b/logs/xuanjinovo_20250930_111520.log deleted file mode 100644 index 59fbb82..0000000 --- a/logs/xuanjinovo_20250930_111520.log +++ /dev/null @@ -1,308 +0,0 @@ -2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:15:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:15:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:15:21 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 6395 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 2242 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 9835 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:15:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930111520 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - random_seed = 5364 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:15:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:15:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:15:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:15:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:22 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:15:23 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:15:23 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:15:23 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_111521.log b/logs/xuanjinovo_20250930_111521.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_111522.log b/logs/xuanjinovo_20250930_111522.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_112055.log b/logs/xuanjinovo_20250930_112055.log deleted file mode 100644 index 7eea681..0000000 --- a/logs/xuanjinovo_20250930_112055.log +++ /dev/null @@ -1,308 +0,0 @@ -2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:20:55 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:20:55 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 7463 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 2296 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:20:55 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 9371 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112055 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - random_seed = 4497 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:20:55 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:20:55 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:20:55 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:20:55 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:55 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:20:57 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:20:57 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:20:57 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_112056.log b/logs/xuanjinovo_20250930_112056.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_112408.log b/logs/xuanjinovo_20250930_112408.log deleted file mode 100644 index 6c08d73..0000000 --- a/logs/xuanjinovo_20250930_112408.log +++ /dev/null @@ -1,308 +0,0 @@ -2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:24:08 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:24:08 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:24:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 6446 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 7287 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 4149 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:24:09 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930112408 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - random_seed = 7087 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:24:09 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:24:09 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:24:09 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:24:09 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:09 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:24:10 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:24:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:24:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:24:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:24:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:24:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_112409.log b/logs/xuanjinovo_20250930_112409.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_112410.log b/logs/xuanjinovo_20250930_112410.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_113215.log b/logs/xuanjinovo_20250930_113215.log deleted file mode 100644 index 1f4f9c0..0000000 --- a/logs/xuanjinovo_20250930_113215.log +++ /dev/null @@ -1,308 +0,0 @@ -2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:32:15 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:32:15 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 11:32:15 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:32:15 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:32:15 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:32:16 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 2335 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 200 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:32:16 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/dump3/test_325d27b5225f45838d7cf1ffb4559794 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 1968 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930113215 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - random_seed = 6930 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - logger = None -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 11:32:16 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 11:32:16 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:32:16 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:32:16 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:16 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:32:17 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:17 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 11:32:18 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 11:32:18 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_113216.log b/logs/xuanjinovo_20250930_113216.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_120240.log b/logs/xuanjinovo_20250930_120240.log deleted file mode 100644 index 1c970cd..0000000 --- a/logs/xuanjinovo_20250930_120240.log +++ /dev/null @@ -1,284 +0,0 @@ -2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:02:40 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:02:40 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:02:40 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 4397 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 5492 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:02:41 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 1921 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120240 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - random_seed = 6134 -2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:02:41 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:02:41 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:02:41 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:02:41 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf -2025-09-30 12:02:41 [ERROR] natNovo - Could not find peak files from /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/.10k.mgf diff --git a/logs/xuanjinovo_20250930_120241.log b/logs/xuanjinovo_20250930_120241.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_120307.log b/logs/xuanjinovo_20250930_120307.log deleted file mode 100644 index 8917b68..0000000 --- a/logs/xuanjinovo_20250930_120307.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:03:07 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:03:07 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 4540 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 9648 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:03:07 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 541 -2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930120307 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - random_seed = 4913 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:03:07 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:03:07 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:03:07 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:03:07 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:08 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:03:08 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:03:13 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:03:13 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:03:13 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_120308.log b/logs/xuanjinovo_20250930_120308.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_121032.log b/logs/xuanjinovo_20250930_121032.log deleted file mode 100644 index 9849a44..0000000 --- a/logs/xuanjinovo_20250930_121032.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:10:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:10:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:10:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 3206 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 3555 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 933 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:10:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121032 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - random_seed = 6683 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:10:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:10:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:10:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:10:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:10:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:10:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:10:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:10:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121033.log b/logs/xuanjinovo_20250930_121033.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_121309.log b/logs/xuanjinovo_20250930_121309.log deleted file mode 100644 index b4e743a..0000000 --- a/logs/xuanjinovo_20250930_121309.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:13:09 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:13:09 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:13:09 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 3020 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 6016 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:13:10 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 1671 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121309 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - random_seed = 2610 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:13:10 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:13:10 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:13:10 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:13:10 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:13:10 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:13:15 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:13:15 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:13:15 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:13:15 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:13:15 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:13:15 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:13:16 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:13:16 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121310.log b/logs/xuanjinovo_20250930_121310.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_121504.log b/logs/xuanjinovo_20250930_121504.log deleted file mode 100644 index 8d779aa..0000000 --- a/logs/xuanjinovo_20250930_121504.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:15:04 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:15:04 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:15:04 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:15:05 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 901 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 2679 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 8299 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121504 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - random_seed = 1184 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:15:05 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:15:05 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:15:05 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:15:05 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:15:05 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:15:11 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:15:11 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121505.log b/logs/xuanjinovo_20250930_121505.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_121925.log b/logs/xuanjinovo_20250930_121925.log deleted file mode 100644 index 5b81414..0000000 --- a/logs/xuanjinovo_20250930_121925.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:19:25 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:19:25 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:19:25 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 2789 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 5962 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 7193 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:19:25 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930121925 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - random_seed = 1896 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:19:25 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:19:25 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:19:25 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:19:25 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:25 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:19:26 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:19:31 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:19:31 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:19:31 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_121926.log b/logs/xuanjinovo_20250930_121926.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_122127.log b/logs/xuanjinovo_20250930_122127.log deleted file mode 100644 index 9852162..0000000 --- a/logs/xuanjinovo_20250930_122127.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:21:27 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:21:27 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:21:27 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 7420 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 3470 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:21:27 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 2576 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122127 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - random_seed = 982 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:21:27 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:21:27 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:21:27 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:21:27 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:28 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:21:28 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:21:33 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:21:33 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_122128.log b/logs/xuanjinovo_20250930_122128.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_122402.log b/logs/xuanjinovo_20250930_122402.log deleted file mode 100644 index 561397e..0000000 --- a/logs/xuanjinovo_20250930_122402.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:24:02 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:24:02 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:24:02 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 1254 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 5015 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:24:03 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/data/9species/test_species/bacillus.10k.mgf -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 6444 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930122402 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - random_seed = 9607 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:24:03 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:24:03 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:24:03 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:24:03 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:24:03 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:24:09 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:24:09 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:24:09 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_122403.log b/logs/xuanjinovo_20250930_122403.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_123732.log b/logs/xuanjinovo_20250930_123732.log deleted file mode 100644 index 4703af1..0000000 --- a/logs/xuanjinovo_20250930_123732.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:37:32 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:37:32 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:37:32 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 763 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 6687 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 7034 -2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:37:33 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930123732 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - random_seed = 8661 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - PMC_enable = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:37:33 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:37:33 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:37:33 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:37:33 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:37:33 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:37:39 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:37:39 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:37:39 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_123733.log b/logs/xuanjinovo_20250930_123733.log deleted file mode 100644 index e69de29..0000000 diff --git a/logs/xuanjinovo_20250930_124220.log b/logs/xuanjinovo_20250930_124220.log deleted file mode 100644 index 6a7e7cb..0000000 --- a/logs/xuanjinovo_20250930_124220.log +++ /dev/null @@ -1,312 +0,0 @@ -2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:42:20 [WARNING] XuanjiNovo - Pydantic not available, falling back to basic config validation -2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:42:20 [INFO] XuanjiNovo - Using GPUs: ['0', '1', '2', '3'] -2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:42:20 [INFO] XuanjiNovo - Number of available GPUs: 4 -2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 9906 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 2653 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 6821 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:42:21 [INFO] XuanjiNovo - GPU devices: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mode = denovo -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model = /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/bacillus.10k.mgf -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_val = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - peak_path_test = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - config = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo/config.yaml -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - output = /mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA/XuanjiNovo_20250930124220 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - random_seed = 625 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - PMC_enable = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mass_control_tol = 0.1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - load_file_name = ./NON351/epoch=5-step=65000.ckpt -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_peaks = 800 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_mz = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_mz = 6500.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - min_intensity = 0.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - remove_precursor_tol = 1.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_charge = 10 -2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - precursor_mass_tol = 50.0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - isotope_error_range = (0, 1) -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_model = 400 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_head = 8 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_feedforward = 1024 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_layers = 9 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dropout = 0.18 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - refine_iters = 3 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - mask_schedule = {'initial_peek': 0.93, 'epoch_decay': 0.01, 'min_peek': 0.0} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - dim_intensity = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - custom_encoder = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_length = 40 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - residues = {'G': 57.021464, 'A': 71.037114, 'S': 87.032028, 'P': 97.052764, 'V': 99.068414, 'T': 101.04767, 'C+57.021': 160.030649, 'L': 113.084064, 'I': 113.084064, 'N': 114.042927, 'D': 115.026943, 'Q': 128.058578, 'K': 128.094963, 'E': 129.042593, 'M': 131.040485, 'H': 137.058912, 'F': 147.068414, 'R': 156.101111, 'Y': 163.063329, 'W': 186.079313, 'M+15.995': 147.0354, 'N+0.984': 115.026943, 'Q+0.984': 129.042594, '+42.011': 42.010565, '+43.006': 43.005814, '-17.027': -17.026549, '+43.006-17.027': 25.980265} -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_log = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tb_summarywriter = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - log_level = CRITICAL -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - enable_neptune = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_project = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - neptune_api_token = None -2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - tags = [] -2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warmup_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_iters = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - max_epochs = 150 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - warm_up_epochs = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - learning_rate = 0.00035 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - weight_decay = 8e-05 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_val = 2.5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - gradient_clip_algorithm = norm -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - accumulate_grad_batches = 1 -2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - sync_batchnorm = False -2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - SWA = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_batch_size = 700 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - predict_batch_size = 64 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_beams = 5 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - logger = None -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - num_sanity_val_steps = 0 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_scratch = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_nodes = 1 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_model = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - train_from_resume = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - model_save_folder_path = ./saved_models -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - save_weights_only = False -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - every_n_train_steps = 2500 -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - temp_dir_auto = True -2025-09-30 12:42:21 [DEBUG] XuanjiNovo - n_workers = 48 -2025-09-30 12:42:21 [INFO] XuanjiNovo - Predict peptide sequences with XuanjiNovo. -2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:42:21 [WARNING] natNovo - Pydantic not available, skipping additional config validation -2025-09-30 12:42:21 [INFO] xuanjinovo - PMC enabled: True, Mass control tolerance: 0.1 -2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [DEBUG] fsspec.local - open file: /mnt/shared-storage-user/beam/zhangxiang/pptm/xusheng/MasNet_100M_Cont/epoch=695-step=324000.ckpt -2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:42:21 [INFO] XuanjiNovo.denovo.db_index - Reading 1 files... -2025-09-30 12:42:26 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:26 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:26 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:42:27 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:42:27 [INFO] natNovo - Recording metadata in: ./saved_models/run_metadata -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'cat-file', '--batch-check'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--cached', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'diff', '--abbrev=40', '--full-index', '--raw'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [DEBUG] git.cmd - Popen(['git', 'remote', 'get-url', '--all', 'origin'], cwd=/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/MassNet-DDA, universal_newlines=False, shell=None, istream=None) -2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt -2025-09-30 12:42:27 [INFO] natNovo - Run metadata recorded in ./saved_models/run_metadata/run_metadata.json -2025-09-30 12:42:27 [INFO] natNovo - Quick summary available in ./saved_models/run_metadata/run_summary.txt diff --git a/logs/xuanjinovo_20250930_124221.log b/logs/xuanjinovo_20250930_124221.log deleted file mode 100644 index e69de29..0000000 diff --git a/run_eval.sh b/run_eval.sh index 96079a3..38cd867 100644 --- a/run_eval.sh +++ b/run_eval.sh @@ -36,6 +36,6 @@ MASTER_PORT=${MASTER_PORT:-$(( ( RANDOM % 10000 ) + 20000 ))} echo "Using MASTER_PORT=$MASTER_PORT" torchrun --nproc_per_node=4 --master_port=${MASTER_PORT} -m XuanjiNovo.XuanjiNovo \ - --mode=denovo \ + --mode=eval \ --model=$XuanjiNovo_100M \ --peak_path=$bacillus --gpu=0,1,2,3 \ No newline at end of file diff --git a/saved_models/run_metadata.json b/saved_models/run_metadata.json new file mode 100644 index 0000000..87934b0 --- /dev/null +++ b/saved_models/run_metadata.json @@ -0,0 +1,346 @@ +{ + "config": { + "PMC_enable": true, + "SWA": false, + "accumulate_grad_batches": 1, + "custom_encoder": null, + "dim_feedforward": 1024, + "dim_intensity": null, + "dim_model": 400, + "dropout": 0.18, + "enable_neptune": false, + "every_n_train_steps": 2500, + "gradient_clip_algorithm": "norm", + "gradient_clip_val": 2.5, + "isotope_error_range": [ + 0, + 1 + ], + "learning_rate": 0.00035, + "load_file_name": "./NON351/epoch=5-step=65000.ckpt", + "log_level": "CRITICAL", + "logger": null, + "mask_schedule": { + "epoch_decay": 0.01, + "initial_peek": 0.93, + "min_peek": 0.0 + }, + "mass_control_tol": 0.1, + "max_charge": 10, + "max_epochs": 150, + "max_iters": 375000, + "max_length": 40, + "max_mz": 6500.0, + "min_intensity": 0.0, + "min_mz": 1.0, + "model_save_folder_path": "./saved_models", + "n_beams": 5, + "n_head": 8, + "n_layers": 9, + "n_log": 1, + "n_nodes": 1, + "n_peaks": 800, + "n_workers": 48, + "neptune_api_token": null, + "neptune_project": null, + "num_sanity_val_steps": 0, + "precursor_mass_tol": 50.0, + "predict_batch_size": 64, + "random_seed": 5112, + "refine_iters": 3, + "remove_precursor_tol": 1.0, + "residues": { + "+42.011": 42.010565, + "+43.006": 43.005814, + "+43.006-17.027": 25.980265, + "-17.027": -17.026549, + "A": 71.037114, + "C+57.021": 160.030649, + "D": 115.026943, + "E": 129.042593, + "F": 147.068414, + "G": 57.021464, + "H": 137.058912, + "I": 113.084064, + "K": 128.094963, + "L": 113.084064, + "M": 131.040485, + "M+15.995": 147.0354, + "N": 114.042927, + "N+0.984": 115.026943, + "P": 97.052764, + "Q": 128.058578, + "Q+0.984": 129.042594, + "R": 156.101111, + "S": 87.032028, + "T": 101.04767, + "V": 99.068414, + "W": 186.079313, + "Y": 163.063329 + }, + "result_output_dir": "/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001130748", + "save_model": false, + "save_weights_only": false, + "sync_batchnorm": false, + "tags": [], + "tb_summarywriter": null, + "temp_dir_auto": true, + "train_batch_size": 100, + "train_from_resume": false, + "train_from_scratch": true, + "val_interval": 1, + "warm_up_epochs": 1, + "warmup_iters": 2500, + "weight_decay": 8e-05 + }, + "git_info": { + "git_branch": "feature/sept2025-update-output", + "git_commit": "24fe5d80ab6174e2558139faa35bf99e0318d82b", + "git_dirty": true, + "git_remote_url": "https://github.com/guomics-lab/MassNet-DDA.git" + }, + "hardware_info": { + "cpu": { + "max_frequency": "4000.00MHz", + "physical_cores": 96, + "processor": "x86_64", + "total_cores": 1 + }, + "gpus": [ + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + }, + { + "compute_capability": [ + 9, + 0 + ], + "name": "NVIDIA H200", + "total_memory": "140.06 GB" + } + ], + "memory": { + "available": "94.53 GB", + "total": "97.66 GB" + } + }, + "model_name": "XuanjiNovo", + "software_info": { + "cuda_version": "12.1", + "cudnn_version": 8902, + "installed_packages": { + "absl-py": "1.3.0", + "aiohttp": "3.8.3", + "aiosignal": "1.3.1", + "annotated-types": "0.6.0", + "appdirs": "1.4.4", + "arrow": "1.2.3", + "asttokens": "2.0.5", + "async-timeout": "4.0.2", + "attrs": "22.2.0", + "backcall": "0.2.0", + "biopython": "1.81", + "blinker": "1.4", + "boto3": "1.26.53", + "botocore": "1.29.53", + "bravado": "11.0.3", + "bravado-core": "5.17.1", + "brotlipy": "0.7.0", + "cachetools": "4.2.2", + "captum": "0.8.0", + "casanovo": "3.2.0", + "certifi": "2022.12.7", + "cffi": "1.15.1", + "charset-normalizer": "2.0.4", + "click": "8.1.3", + "comet-ml": "3.31.22", + "comm": "0.1.2", + "configobj": "5.0.6", + "contourpy": "1.0.6", + "cryptography": "38.0.1", + "ctcdecode": "1.0.3", + "cupy-cuda12x": "13.6.0", + "cycler": "0.11.0", + "debugpy": "1.5.1", + "decorator": "5.1.1", + "deprecated": "1.2.13", + "depthcharge-ms": "0.1.0", + "dulwich": "0.20.50", + "einops": "0.6.0", + "entrypoints": "0.4", + "everett": "3.1.0", + "executing": "0.8.3", + "fastobo": "0.12.2", + "fastrlock": "0.8.3", + "filelock": "3.19.1", + "flit-core": "3.6.0", + "fonttools": "4.38.0", + "fqdn": "1.5.1", + "frozenlist": "1.3.3", + "fsspec": "2022.11.0", + "future": "0.18.3", + "gitdb": "4.0.10", + "gitpython": "3.1.30", + "google-auth": "2.6.0", + "google-auth-oauthlib": "0.4.4", + "grpcio": "1.74.0", + "h5py": "3.8.0", + "idna": "3.4", + "ipykernel": "6.19.2", + "ipython": "8.7.0", + "isoduration": "20.11.0", + "jedi": "0.18.1", + "jinja2": "3.1.6", + "jmespath": "1.0.1", + "joblib": "1.2.0", + "jsonpointer": "2.3", + "jsonref": "1.1.0", + "jsonschema": "4.17.3", + "jupyter-client": "7.4.8", + "jupyter-core": "5.1.1", + "kiwisolver": "1.4.4", + "lark": "1.1.5", + "lightning-utilities": "0.5.0", + "llvmlite": "0.45.0", + "lmdb": "1.7.3", + "lxml": "6.0.2", + "markdown": "3.4.1", + "markupsafe": "2.1.1", + "matchms": "0.18.0", + "matplotlib": "3.10.6", + "matplotlib-inline": "0.1.7", + "monotonic": "1.6", + "mpmath": "1.3.0", + "msgpack": "1.0.4", + "multidict": "6.0.4", + "neptune-client": "0.16.16", + "nest-asyncio": "1.5.6", + "networkx": "3.1", + "ninja": "1.13.0", + "numba": "0.62.0", + "numpy": "1.26.4", + "nvidia-cublas-cu12": "12.1.3.1", + "nvidia-cuda-cupti-cu12": "12.1.105", + "nvidia-cuda-nvrtc-cu12": "12.1.105", + "nvidia-cuda-runtime-cu12": "12.1.105", + "nvidia-cudnn-cu12": "8.9.2.26", + "nvidia-cufft-cu12": "11.0.2.54", + "nvidia-curand-cu12": "10.3.2.106", + "nvidia-cusolver-cu12": "11.4.5.107", + "nvidia-cusparse-cu12": "12.1.0.106", + "nvidia-nccl-cu12": "2.18.1", + "nvidia-nvjitlink-cu12": "12.9.86", + "nvidia-nvtx-cu12": "12.1.105", + "oauthlib": "3.2.1", + "packaging": "22.0", + "pandas": "1.5.2", + "parso": "0.8.3", + "pexpect": "4.8.0", + "pickleshare": "0.7.5", + "pickydict": "0.4.0", + "pillow": "11.3.0", + "pip": "25.2", + "platformdirs": "2.5.2", + "prompt-toolkit": "3.0.36", + "protobuf": "3.19.6", + "psutil": "5.9.4", + "ptyprocess": "0.7.0", + "pure-eval": "0.2.2", + "pyasn1": "0.4.8", + "pyasn1-modules": "0.2.8", + "pycparser": "2.21", + "pydantic": "2.11.7", + "pydantic-core": "2.33.2", + "pygithub": "1.57", + "pygments": "2.11.2", + "pyjwt": "2.6.0", + "pynacl": "1.5.0", + "pyopenssl": "22.0.0", + "pyparsing": "3.0.9", + "pyrsistent": "0.19.3", + "pysocks": "1.7.1", + "pyteomics": "4.5.6", + "python-dateutil": "2.8.2", + "pytorch-lightning": "1.8.6", + "pytorch-ranger": "0.1.1", + "pytz": "2022.7", + "pyyaml": "6.0", + "pyzmq": "23.2.0", + "requests": "2.28.1", + "requests-oauthlib": "1.3.0", + "requests-toolbelt": "0.10.1", + "rfc3339-validator": "0.1.4", + "rfc3987": "1.3.8", + "rsa": "4.7.2", + "s3transfer": "0.6.0", + "scikit-learn": "1.2.0", + "scipy": "1.10.0", + "seaborn": "0.12.2", + "semantic-version": "2.10.0", + "sentry-sdk": "1.12.1", + "setuptools": "65.5.0", + "simplejson": "3.18.1", + "six": "1.16.0", + "smmap": "5.0.0", + "sparsestack": "0.4.1", + "spectrum-utils": "0.4.1", + "stack-data": "0.2.0", + "swagger-spec-validator": "3.0.3", + "sympy": "1.14.0", + "tensorboard": "2.10.0", + "tensorboard-data-server": "0.6.1", + "tensorboard-plugin-wit": "1.8.1", + "tensorboardx": "2.5.1", + "threadpoolctl": "3.1.0", + "torch": "2.1.0", + "torch-imputer": "0.1.0", + "torch-optimizer": "0.3.0", + "torchmetrics": "0.11.0", + "tornado": "6.2", + "tqdm": "4.64.1", + "traitlets": "5.7.1", + "triton": "2.1.0", + "typing-extensions": "4.15.0", + "typing-inspection": "0.4.0", + "uri-template": "1.2.0", + "urllib3": "1.26.13", + "wcwidth": "0.2.5", + "webcolors": "1.12", + "websocket-client": "1.3.3", + "werkzeug": "2.2.2", + "wheel": "0.37.1", + "wrapt": "1.14.1", + "wurlitzer": "3.0.3", + "yapf": "0.32.0", + "yarl": "1.8.2" + }, + "python": { + "executable": "/home/weijiaqi/miniconda3/envs/XuanjiNovo/bin/python3.11", + "platform": "Linux-5.15.0-89-generic-x86_64-with-glibc2.35", + "version": "3.11.13 (main, Jun 5 2025, 13:12:00) [GCC 11.2.0]" + }, + "pytorch_version": "2.1.0+cu121" + }, + "timestamp": "2025-10-01T13:08:03.935958" +} \ No newline at end of file diff --git a/saved_models/run_metadata/run_metadata.json b/saved_models/run_metadata/run_metadata.json index 3f6264c..5b10ce5 100644 --- a/saved_models/run_metadata/run_metadata.json +++ b/saved_models/run_metadata/run_metadata.json @@ -8,7 +8,7 @@ "dim_intensity": null, "dim_model": 400, "dropout": 0.18, - "enable_neptune": true, + "enable_neptune": false, "every_n_train_steps": 2500, "gradient_clip_algorithm": "norm", "gradient_clip_val": 2.5, @@ -46,7 +46,7 @@ "num_sanity_val_steps": 0, "precursor_mass_tol": 50.0, "predict_batch_size": 64, - "random_seed": 9906, + "random_seed": 7044, "refine_iters": 3, "remove_precursor_tol": 1.0, "residues": { @@ -78,22 +78,24 @@ "W": 186.079313, "Y": 163.063329 }, + "result_output_dir": "/mnt/shared-storage-user/beam/denovo/Scaling/XIangDia2/new_test/MassNet-DDA/XuanjiNovo_20251001131152", "save_model": false, "save_weights_only": false, "sync_batchnorm": false, "tags": [], "tb_summarywriter": null, "temp_dir_auto": true, - "train_batch_size": 700, + "train_batch_size": 100, "train_from_resume": false, - "train_from_scratch": false, + "train_from_scratch": true, + "val_interval": 1, "warm_up_epochs": 1, "warmup_iters": null, "weight_decay": 8e-05 }, "git_info": { - "git_branch": "main", - "git_commit": "07a5329977fcacf2617354f689fd276e73ab8406", + "git_branch": "feature/sept2025-update-output", + "git_commit": "24fe5d80ab6174e2558139faa35bf99e0318d82b", "git_dirty": true, "git_remote_url": "https://github.com/guomics-lab/MassNet-DDA.git" }, @@ -139,8 +141,8 @@ } ], "memory": { - "available": "289.11 GB", - "total": "292.97 GB" + "available": "92.55 GB", + "total": "97.66 GB" } }, "model_name": "epoch=695-step=324000.ckpt", @@ -151,6 +153,7 @@ "absl-py": "1.3.0", "aiohttp": "3.8.3", "aiosignal": "1.3.1", + "annotated-types": "0.6.0", "appdirs": "1.4.4", "arrow": "1.2.3", "asttokens": "2.0.5", @@ -267,6 +270,8 @@ "pyasn1": "0.4.8", "pyasn1-modules": "0.2.8", "pycparser": "2.21", + "pydantic": "2.11.7", + "pydantic-core": "2.33.2", "pygithub": "1.57", "pygments": "2.11.2", "pyjwt": "2.6.0", @@ -316,7 +321,8 @@ "tqdm": "4.64.1", "traitlets": "5.7.1", "triton": "2.1.0", - "typing-extensions": "4.4.0", + "typing-extensions": "4.15.0", + "typing-inspection": "0.4.0", "uri-template": "1.2.0", "urllib3": "1.26.13", "wcwidth": "0.2.5", @@ -336,5 +342,5 @@ }, "pytorch_version": "2.1.0+cu121" }, - "timestamp": "2025-09-30T12:42:27.044401" + "timestamp": "2025-10-01T13:11:59.945003" } \ No newline at end of file diff --git a/saved_models/run_metadata/run_summary.txt b/saved_models/run_metadata/run_summary.txt index dacebb5..c3581a5 100644 --- a/saved_models/run_metadata/run_summary.txt +++ b/saved_models/run_metadata/run_summary.txt @@ -1,5 +1,5 @@ -timestamp: 2025-09-30T12:42:27.044401 -git_commit: 07a5329977fcacf2617354f689fd276e73ab8406 +timestamp: 2025-10-01T13:11:59.945003 +git_commit: 24fe5d80ab6174e2558139faa35bf99e0318d82b python_version: 3.11.13 pytorch_version: 2.1.0+cu121 gpu_count: 4 diff --git a/saved_models/run_summary.txt b/saved_models/run_summary.txt new file mode 100644 index 0000000..80fbec3 --- /dev/null +++ b/saved_models/run_summary.txt @@ -0,0 +1,7 @@ +timestamp: 2025-10-01T13:08:03.935958 +git_commit: 24fe5d80ab6174e2558139faa35bf99e0318d82b +python_version: 3.11.13 +pytorch_version: 2.1.0+cu121 +gpu_count: 4 +gpu_names: ['NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200', 'NVIDIA H200'] +cuda_version: 12.1 From be2442a43f904dda9e1f72e940297893ed9ee232 Mon Sep 17 00:00:00 2001 From: niezongxiang Date: Fri, 10 Oct 2025 10:39:04 +0800 Subject: [PATCH 8/9] bug fix --- Dockerfile_cuda11 | 2 +- Dockerfile_cuda12 | 2 +- XuanjiNovo/XuanjiNovo.py | 2 +- requirements-docker-cuda11.txt | 3 ++- requirements-docker-cuda12.txt | 3 ++- 5 files changed, 7 insertions(+), 5 deletions(-) diff --git a/Dockerfile_cuda11 b/Dockerfile_cuda11 index 4aec2e8..b0e235c 100644 --- a/Dockerfile_cuda11 +++ b/Dockerfile_cuda11 @@ -13,7 +13,7 @@ RUN apt update && apt install -y gcc g++ ninja-build RUN cd /app/ctcdecode-master && pip install . -RUN pip install -r /app/requirements-docker-cuda11.txt -i https://mirrors.aliyun.com/pypi/simple/ +RUN pip install -r /app/requirements-docker-cuda11.txt RUN cd /app/imputer-pytorch && pip install -e . ENTRYPOINT ["python", "-m", "XuanjiNovo.XuanjiNovo"] \ No newline at end of file diff --git a/Dockerfile_cuda12 b/Dockerfile_cuda12 index f13ba3f..6d9ef8a 100644 --- a/Dockerfile_cuda12 +++ b/Dockerfile_cuda12 @@ -13,7 +13,7 @@ RUN apt update && apt install -y gcc g++ ninja-build RUN cd /app/ctcdecode-master && pip install . -RUN pip install -r /app/requirements-docker-cuda12.txt -i https://mirrors.aliyun.com/pypi/simple/ +RUN pip install -r /app/requirements-docker-cuda12.txt RUN cd /app/imputer-pytorch && pip install -e . ENTRYPOINT ["python", "-m", "XuanjiNovo.XuanjiNovo"] \ No newline at end of file diff --git a/XuanjiNovo/XuanjiNovo.py b/XuanjiNovo/XuanjiNovo.py index d654a78..e4982b4 100644 --- a/XuanjiNovo/XuanjiNovo.py +++ b/XuanjiNovo/XuanjiNovo.py @@ -173,7 +173,6 @@ def main( os.path.dirname(os.path.realpath(__file__)), "config.yaml" ) config_fn = config - try: from .config import XuanjiNovoConfig, ConfigLogger @@ -194,6 +193,7 @@ def main( except: print("skipping pydantic validation") + config2 = None if config2: with open(config_fn) as f_in: config = yaml.safe_load(f_in) diff --git a/requirements-docker-cuda11.txt b/requirements-docker-cuda11.txt index 79685f4..209c37a 100644 --- a/requirements-docker-cuda11.txt +++ b/requirements-docker-cuda11.txt @@ -153,4 +153,5 @@ wurlitzer==3.0.3 yapf==0.32.0 yarl==1.8.2 lmdb -cupy-cuda11x \ No newline at end of file +cupy-cuda11x +pydantic>=2.5.0 \ No newline at end of file diff --git a/requirements-docker-cuda12.txt b/requirements-docker-cuda12.txt index 5e6c3c9..2cc720d 100644 --- a/requirements-docker-cuda12.txt +++ b/requirements-docker-cuda12.txt @@ -153,4 +153,5 @@ wurlitzer==3.0.3 yapf==0.32.0 yarl==1.8.2 lmdb -cupy-cuda12x \ No newline at end of file +cupy-cuda12x +pydantic>=2.5.0 \ No newline at end of file From 2ca6cda92f33e67b390c71063680c7b65b8ad402 Mon Sep 17 00:00:00 2001 From: niezongxiang Date: Fri, 10 Oct 2025 10:44:25 +0800 Subject: [PATCH 9/9] bug fix --- XuanjiNovo/denovo/model_runner.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/XuanjiNovo/denovo/model_runner.py b/XuanjiNovo/denovo/model_runner.py index 70997ed..a535873 100644 --- a/XuanjiNovo/denovo/model_runner.py +++ b/XuanjiNovo/denovo/model_runner.py @@ -78,6 +78,7 @@ def _execute_existing( The config parameter can be either a Dict or a XuanjiNovoConfig object. If it's a Dict, it will be validated if pydantic is available. """ + config2 = None try: from ..config import XuanjiNovoConfig if isinstance(config, dict): @@ -247,6 +248,7 @@ def train( The config parameter can be either a Dict or a XuanjiNovoConfig object. If it's a Dict, it will be validated if pydantic is available. """ + config2 = None try: from ..config import XuanjiNovoConfig if isinstance(config, dict):