eval.py

import argparse
import datetime
import json
import random
import time
import numpy as np
from pathlib import Path

import torch
from torch.utils.data import DataLoader, DistributedSampler

import utils.misc as utils
from utils.config import Config
from engine import evaluate
from datasets import build_dataset
from models import build_model


def get_args_parser():
    parser = argparse.ArgumentParser('RSVG1', add_help=False)
    
    # Model parameters
    parser.add_argument('--model_name', type=str, default='CSDNet',
                        help="Name of model to be exploited.")
    parser.add_argument('--model_type', type=str, default='ResNet', choices=('ResNet', ),
                        help="Name of model to be exploited.")
    
    parser.add_argument('--batch_size', default=32, type=int)
    parser.add_argument('--num_workers', default=2, type=int)
    parser.add_argument('--epochs', default=90, type=int)
    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--lr_drop', default=80, type=int)
    parser.add_argument('--lr', default=1e-4, type=float)
    parser.add_argument('--lr_bert', default=0., type=float)
    parser.add_argument('--lr_visu_cnn', default=0., type=float)
    parser.add_argument('--lr_visu_tra', default=1e-5, type=float)
    parser.add_argument('--optimizer', default='rmsprop', type=str)
    parser.add_argument('--weight_decay', default=1e-4, type=float)
    parser.add_argument('--lr_scheduler', default='poly', type=str)
    parser.add_argument('--lr_power', default=0.9, type=float, help='lr poly power')
    parser.add_argument('--clip_max_norm', default=0.1, type=float,
                        help='gradient clipping max norm')    
    
    # DETR parameters
    parser.add_argument('--backbone', default='resnet50', type=str,
                        help="Name of the convolutional backbone to use")
    parser.add_argument('--dilation', action='store_true',
                        help="If true, we replace stride with dilation in the last convolutional block (DC5)")
    parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'), help="Type of positional embedding to use on top of the image features")
    
    parser.add_argument('--hidden_dim', default=256, type=int,
                        help="Size of the embeddings (dimension of the transformer)")
    parser.add_argument('--dim_feedforward', default=2048, type=int,
                        help="Intermediate size of the feedforward layers in the transformer blocks")
    parser.add_argument('--nheads', default=8, type=int,
                        help="Number of attention heads inside the transformer's attentions")
    parser.add_argument('--dropout', default=0.1, type=float,
                        help="Dropout applied in the transformer")
    parser.add_argument('--pre_norm', action='store_true')

    # vl
    parser.add_argument('--vl_hidden_dim', default=256, type=int,
                        help='Size of the embeddings (dimension of the vision-language transformer)')
    parser.add_argument('--vl_dim_feedforward', default=2048, type=int,
                        help="Intermediate size of the feedforward layers in the vision-language transformer blocks")
    parser.add_argument('--vl_nheads', default=8, type=int,
                        help="Number of attention heads inside the vision-language transformer's attentions")
    parser.add_argument('--vl_dropout', default=0.1, type=float,
                        help="Dropout applied in the vision-language transformer")
    
    # Model architecture
    parser.add_argument('--bert_enc_num', default=12, type=int)
    parser.add_argument('--detr_enc_num', default=6, type=int)
    
    # new 
    parser.add_argument('--last_dwsample', default=False, type=bool, help='Downsample the features of the last layer output of resnet')
    parser.add_argument('--last_channels', default=256, type=int)
    parser.add_argument('--vl_enhancevit', default=False, type=bool)
    parser.add_argument('--vl_crosAttn', default=False, type=bool)
    parser.add_argument('--vl_enc_num', default=3, type=int)
    
    # dynamic new 
    parser.add_argument('--st_dec_dyn', default=False, type=bool)
    parser.add_argument('--vl_dec_num', default=3, type=int)
    parser.add_argument('--uniform_learnable', default=False, type=bool)
    parser.add_argument('--uniform_grid', default=False, type=bool)
    parser.add_argument('--in_points', default=32, type=int)
    
    # datasets
    parser.add_argument('--dataset', default='referit', type=str,
                        help='referit/unc/unc+/gref/gref_umd/DIOR_RSVG/OPT_RSVG/VRSBench_Ref')
    parser.add_argument('--max_query_len', default=20, type=int,
                        help='maximum time steps (lang length) per batch')
    parser.add_argument('--imsize', default=640, type=int, help='image size')
    
    # Augmentation options
    parser.add_argument('--aug_blur', action='store_true',
                        help="If true, use gaussian blur augmentation")
    parser.add_argument('--aug_crop', action='store_true',
                        help="If true, use random crop augmentation")
    parser.add_argument('--aug_scale', action='store_true',
                        help="If true, use multi-scale augmentation")
    parser.add_argument('--aug_translate', action='store_true',
                        help="If true, use random translate augmentation")
    
    # root
    parser.add_argument('--image_root', type=str, default='/',
                        help='path to ReferIt splits data folder')
    parser.add_argument('--split_root', type=str, default='data',
                        help='location of pre-parsed dataset info')
    parser.add_argument('--output_dir', default='./outputs',
                        help='path where to save, empty for no saving')
    
    parser.add_argument('--resume', default='', help='resume from checkpoint')
    parser.add_argument('--detr_model', default=None, type=str, help='detr model')
    parser.add_argument('--bert_model', default='./checkpoints/bert-base-uncased', type=str, help='bert model')
    
    parser.add_argument('--light', dest='light', default=False, action='store_true', help='if use smaller model')    
    parser.add_argument('--eval', dest='eval', default=False, action='store_true', help='if evaluation only')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=42, type=int)

    # distributed training parameters
    parser.add_argument('--distributed', action='store_true')
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')

    # evalutaion options
    parser.add_argument('--eval_set', default='text', type=str)
    parser.add_argument('--eval_model', default='', type=str)
    
    # Configure file
    parser.add_argument('--config', type=str, help='Path to the configure file.')
    parser.add_argument('--model_config')
    
    return parser


def main(args):
    utils.init_distributed_mode(args)

    device = torch.device(args.device)

    # random
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)

    # build model
    model = build_model(args)
    model.to(device)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
        model_without_ddp = model.module
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print('number of params:', n_parameters)

    # build dataset
    dataset_test = build_dataset(args.eval_set, args)
    
    if args.distributed:
        sampler_test = DistributedSampler(dataset_test, shuffle=False)
    else:
        sampler_test = torch.utils.data.SequentialSampler(dataset_test)
    
    batch_sampler_test = torch.utils.data.BatchSampler(
        sampler_test, args.batch_size, drop_last=False)

    data_loader_test = DataLoader(dataset_test, args.batch_size, sampler=sampler_test,
                                 drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)

    checkpoint = torch.load(args.eval_model, map_location='cpu')
    model_without_ddp.load_state_dict(checkpoint['model'])

    # output log
    output_dir = Path(args.output_dir)
    if args.output_dir and utils.is_main_process():
        with (output_dir / "eval_log.txt").open("a") as f:
            f.write(str(args) + "\n")
    
    start_time = time.time()
    
    # perform evaluation
    accuracy = evaluate(args, model, data_loader_test, device)
    
    if utils.is_main_process():
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print('Training time {}'.format(total_time_str))

        log_stats = {'test_model:': args.eval_model,
                    '%s_set_accuracy'%args.eval_set: accuracy,
                    }
        print(log_stats)
        if args.output_dir and utils.is_main_process():
                with (output_dir / "eval_log.txt").open("a") as f:
                    f.write(json.dumps(log_stats) + "\n")


if __name__ == '__main__':
    parser = argparse.ArgumentParser('Modle Evaluation Script', parents=[get_args_parser()])
    args = parser.parse_args()
    if args.config:
        cfg = Config(args.config)
        cfg.merge_to_args(args)
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)