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a493204 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import os
from tqdm import tqdm
import wandb
import argparse
import random
import numpy as np
import io
import torchvision.transforms.functional as F
import torchvision.transforms.v2 as v2
class HAM10000Dataset(Dataset):
def __init__(self, root_dir, transform=None):
self.root_dir = root_dir
self.transform = transform
self.classes = ['bkl', 'mel'] # benign (0) and malignant (1)
self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
self.images = []
self.labels = []
# Load images from both classes
for class_name in self.classes:
class_dir = os.path.join(root_dir, class_name)
for img_name in os.listdir(class_dir):
if img_name.endswith(('.jpg', '.jpeg', '.png')):
self.images.append(os.path.join(class_dir, img_name))
self.labels.append(self.class_to_idx[class_name])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
img_path = self.images[idx]
label = self.labels[idx]
# Load and transform image
image = Image.open(img_path).convert('RGB')
if self.transform:
image = self.transform(image)
return image, label
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--resize', type=int, default=224,
help='Size to resize images to (default: 224)')
parser.add_argument('--seed', type=int, default=1,
help='Seed for random number generator (default: 1)')
parser.add_argument('--cuda', type=int, default=0,
help='CUDA device number (default: 0)')
parser.add_argument('--auditor_augs', action='store_true', default=False,
help='Enable auditor augmentations (default: False)')
parser.add_argument('--auto_aug', action='store_true', default=False,
help='Enable auto augmentations (default: False)')
args = parser.parse_args()
# Set seeds
random.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Initialize wandb
wandb.init(project="ModelAuditor", name="HAM10000_ResNet50_" + str(args.seed) + "_" + str(args.resize) +
("_AuditorAugs" if args.auditor_augs else "") + ("_AutoAugs" if args.auto_aug else ""))
# Define augmentations
if args.auditor_augs:
aug_list = [
# PUT HERE WHAT THE AUDITOR GIVES YOU
]
else:
aug_list = [transforms.ToTensor()]
# Define transforms
if args.auto_aug:
train_transform = transforms.Compose([
transforms.Resize((args.resize, args.resize)),
transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET)
] + aug_list + [
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
else:
train_transform = transforms.Compose([
transforms.Resize((args.resize, args.resize)),
] + aug_list + [
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
val_transform = transforms.Compose([
transforms.Resize((args.resize, args.resize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Create datasets
train_dataset = HAM10000Dataset(root_dir='data/ham10000/vidir_modern', transform=train_transform)
# Split dataset into train and validation
train_size = int(0.8 * len(train_dataset))
val_size = len(train_dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])
# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=8)
val_loader = DataLoader(val_dataset, batch_size=64, num_workers=8)
# Set device
device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() else "cpu")
# Initialize model
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
model.fc = nn.Linear(model.fc.in_features, 2) # 2 classes: benign and malignant
model = model.to(device)
# Initialize optimizer and criterion
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
# Initialize scaler for mixed precision
scaler = torch.cuda.amp.GradScaler()
# Training parameters
n_epochs = 10
# Add learning rate scheduler
warmup_epochs = 2
total_steps = len(train_loader) * n_epochs
warmup_steps = len(train_loader) * warmup_epochs
scheduler = optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=0.001,
total_steps=total_steps,
pct_start=warmup_steps/total_steps,
anneal_strategy='cos'
)
# Training loop
for epoch in range(n_epochs):
# Training phase
model.train()
train_loss = 0
for x, y in tqdm(train_loader, desc=f'Epoch {epoch+1}/{n_epochs}'):
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
# Mixed precision training
with torch.cuda.amp.autocast():
outputs = model(x)
loss = criterion(outputs, y)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
train_loss += loss.item()
train_loss /= len(train_loader)
# Validation phase
model.eval()
val_loss = 0
correct = 0
total = 0
with torch.no_grad():
for x, y in val_loader:
x, y = x.to(device), y.to(device)
with torch.cuda.amp.autocast():
outputs = model(x)
loss = criterion(outputs, y)
val_loss += loss.item()
_, predicted = outputs.max(1)
total += y.size(0)
correct += predicted.eq(y).sum().item()
val_loss /= len(val_loader)
accuracy = 100. * correct / total
# Log metrics
current_lr = scheduler.get_last_lr()[0]
wandb.log({
"train_loss": train_loss,
"val_loss": val_loss,
"val_accuracy": accuracy,
"epoch": epoch + 1,
"learning_rate": current_lr
})
print(f'Epoch {epoch+1}: Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {accuracy:.2f}%')
# Save model after each epoch
torch.save(model.state_dict(), f'ham10000_resnet50_{args.seed}_{args.resize}' +
("_AuditorAugs" if args.auditor_augs else "") +
("_AutoAugs" if args.auto_aug else "") + '.pt')
if __name__ == "__main__":
main() |