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import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
from torchvision.transforms import v2
from torch.utils.data import Dataset, DataLoader
import pandas as pd
import numpy as np
from PIL import Image
import wandb
import argparse
from tqdm import tqdm
class ChexpertDataset(Dataset):
def __init__(self, csv_path, transform=None, is_train=True):
self.df = pd.read_csv(csv_path)
self.df = self.df.replace(np.nan, 0.0)
self.df = self.df.replace(-1.0, 0.0)
self.df = self.df[self.df['Frontal/Lateral'] == 'Frontal']
self.df = self.df[self.df['AP/PA'] == 'AP']
# Remove path prefix
self.df['Path'] = self.df['Path'].str.replace('CheXpert-v1.0-small/train/', '')
if not is_train:
self.df['Path'] = self.df['Path'].str.replace('CheXpert-v1.0-small/valid/', '')
self.paths = self.df['Path'].to_numpy()
self.labels = self.df[['Atelectasis', 'Consolidation', 'Cardiomegaly', 'Pleural Effusion', 'Edema']].to_numpy()
self.transform = transform if transform is not None else transforms.ToTensor()
self.base_path = 'data/chexpert_resized_224/train/' if is_train else 'data/chexpert_resized_224/valid/'
def __len__(self):
return len(self.paths)
def __getitem__(self, idx):
img_path = self.base_path + str(self.paths[idx])
image = Image.open(img_path).convert('RGB')
image = self.transform(image)
label = torch.tensor(self.labels[idx], dtype=torch.float32)
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=1,
help='CUDA device number (default: 1)')
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)')
parser.add_argument('--subset_len', type=int, default=None,
help='Length of subset to use for training (default: None, use full dataset)')
args = parser.parse_args()
# Set seeds
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Initialize wandb
wandb.init(project="ModelAuditor", name="CheXpert_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 = []
# Create transforms
if args.auto_aug:
train_transform = transforms.Compose([
transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET)
] + aug_list + [
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
else:
train_transform = transforms.Compose([
] + aug_list + [
transforms.Normalize(mean=[0.5], std=[0.5])
])
val_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
# Create datasets
train_dataset = ChexpertDataset('data/chexpert/train.csv', transform=train_transform, is_train=True)
val_dataset = ChexpertDataset('data/chexpert/valid.csv', transform=val_transform, is_train=False)
train_subset = torch.utils.data.Subset(train_dataset, torch.arange(max(0, len(train_dataset) - 5000)))
# Create data loaders
train_loader = DataLoader(train_subset, batch_size=64, shuffle=True, num_workers=1, pin_memory=True, persistent_workers=True)
val_loader = DataLoader(val_dataset, batch_size=64, num_workers=1, pin_memory=True, persistent_workers=True)
# 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, 5) # 5 classes for CheXpert
model = model.to(device)
# Calculate positive weights for each class
pos_weights = []
for col in range(train_dataset.labels.shape[1]):
num_positive = (train_dataset.labels[:, col] == 1.0).sum()
num_negative = (train_dataset.labels[:, col] == 0.0).sum()
pos_weights.append(num_negative / num_positive)
pos_weights = torch.tensor(pos_weights).to(device)
# Initialize optimizer and criterion
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weights)
# Initialize scaler for mixed precision
scaler = torch.amp.GradScaler('cuda')
# 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
train_correct = 0
train_total = 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.amp.autocast('cuda'):
outputs = model(x)
loss = criterion(outputs, y)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
train_loss += loss.item()
# Calculate training accuracy
preds = (torch.sigmoid(outputs) > 0.5).float()
train_correct += (preds == y).sum().item()
train_total += y.numel()
train_loss /= len(train_loader)
train_acc = train_correct / train_total
# Validation phase
model.eval()
val_loss = 0
val_correct = 0
val_total = 0
with torch.no_grad():
for x, y in val_loader:
x, y = x.to(device), y.to(device)
with torch.amp.autocast('cuda'):
outputs = model(x)
loss = criterion(outputs, y)
val_loss += loss.item()
# Calculate validation accuracy
preds = (torch.sigmoid(outputs) > 0.5).float()
val_correct += (preds == y).sum().item()
val_total += y.numel()
val_loss /= len(val_loader)
val_acc = val_correct / val_total
# Log metrics
current_lr = scheduler.get_last_lr()[0]
wandb.log({
"train_loss": train_loss,
"val_loss": val_loss,
"train_acc": train_acc,
"val_acc": val_acc,
"epoch": epoch + 1,
"learning_rate": current_lr
})
print(f'Epoch {epoch+1}: Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}')
# Save model after each epoch
torch.save(model.state_dict(), f'chexpert_resnet50_{args.seed}_{args.resize}' +
("_AuditorAugs" if args.auditor_augs else "") +
("_AutoAugs" if args.auto_aug else "") + '.pt')
if __name__ == "__main__":
main() |