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Resnet Deneme #40

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a7ad534
Resnet Deneme
mdrmz Jul 8, 2024
16eb26c
Update utils.py
mdrmz Jul 15, 2024
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6 changes: 4 additions & 2 deletions anodet/feature_extraction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

import torch
import torch.nn.functional as F
from torchvision.models import resnet18, ResNet18_Weights, wide_resnet50_2, Wide_ResNet50_2_Weights
from torchvision.models import resnet18, ResNet18_Weights, wide_resnet50_2, Wide_ResNet50_2_Weights,resnet34,ResNet34_Weights
from tqdm import tqdm
from typing import List, Optional, Callable, cast
from torch.utils.data import DataLoader
Expand All @@ -29,12 +29,14 @@ def __init__(self, backbone_name: str, device: torch.device) -> None:
"""

super().__init__()
assert backbone_name in ['resnet18', 'wide_resnet50']
assert backbone_name in ['resnet18', 'wide_resnet50','resnet34']

if backbone_name == 'resnet18':
self.backbone = resnet18(weights=ResNet18_Weights.DEFAULT, progress=True)
elif backbone_name == 'wide_resnet50':
self.backbone = wide_resnet50_2(weights=Wide_ResNet50_2_Weights.DEFAULT, progress=True)
elif backbone_name == 'resnet34':
self.backbone = resnet34(weights=ResNet34_Weights.DEFAULT, progress=True)

self.backbone.to(device)
self.backbone.eval()
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2 changes: 2 additions & 0 deletions anodet/padim.py
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Expand Up @@ -52,6 +52,8 @@ def __init__(self, backbone: str = 'resnet18',
self.channel_indices = get_indices(100, 448, self.device)
elif backbone == 'wide_resnet50':
self.channel_indices = get_indices(550, 1792, self.device)
elif backbone == 'resnet34':
self.channel_indices = get_indices(100, 448, self.device)

self.layer_indices = layer_indices
if self.layer_indices is None:
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8 changes: 4 additions & 4 deletions anodet/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,15 +10,15 @@
import os


standard_image_transform = T.Compose([T.Resize(224),
T.CenterCrop(224),
standard_image_transform = T.Compose([T.Resize(320),
T.CenterCrop(320),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])

standard_mask_transform = T.Compose([T.Resize(224),
T.CenterCrop(224),
standard_mask_transform = T.Compose([T.Resize(320),
T.CenterCrop(320),
T.ToTensor()
])

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