default.py

# Load the ResNet50 model
def ResNet50(num_classes, channels=3):
    return ResNet(Bottleneck, [3,4,6,3], num_classes, channels)
    
model = ResNet50(num_classes=1000)

# Parallelize training across multiple GPUs
# model = torch.nn.DataParallel(model)

# Set the model to run on the device
model = model.to(device)

# Define the loss function and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Function to evaluate the model
def evaluate_model(model, val_loader, criterion):
    model.eval()
    val_loss = 0.0
    correct = 0
    total = 0
    class_correct = [0] * len(val_dataset.classes)
    class_total = [0] * len(val_dataset.classes)
    
    with torch.no_grad():
        for inputs, labels in tqdm(val_loader, desc="Validating"):
            inputs, labels = inputs.to(device), labels.to(device)
            
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            val_loss += loss.item()

            _, predicted = torch.max(outputs, 1)
            correct += (predicted == labels).sum().item()
            total += labels.size(0)

            for i in range(len(labels)):
                label = labels[i]
                class_correct[label] += (predicted[i] == label).item()
                class_total[label] += 1

    val_loss /= len(val_loader)
    accuracy = 100.0 * correct / total
    per_class_accuracy = {
        val_dataset.classes[i]: 100.0 * class_correct[i] / class_total[i]
        for i in range(len(val_dataset.classes))
        if class_total[i] > 0
    }
    return val_loss, accuracy, per_class_accuracy

# Train the model
print(f'Training the model on ImageNet')
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0

    for inputs, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}/{num_epochs}"):
        inputs, labels = inputs.to(device), labels.to(device)

        # Zero out the optimizer
        optimizer.zero_grad()

        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # Backward pass
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        # Calculate accuracy during training
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == labels).sum().item()
        total += labels.size(0)

    # Average loss and accuracy for the epoch
    train_loss = running_loss / len(train_loader)
    train_accuracy = 100.0 * correct / total

    print(f"Epoch {epoch+1}/{num_epochs} - Training Loss: {train_loss:.4f}, Training Accuracy: {train_accuracy:.2f}%")

# Run validation after all epochs
print(f"Validating the model on unseen data after training...")
val_loss, val_accuracy, per_class_accuracy = evaluate_model(model, val_loader, criterion)
print(f"Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_accuracy:.2f}%")
print("Per-class Accuracy:")
for class_name, acc in per_class_accuracy.items():
    print(f"{class_name}: {acc:.2f}%")

# Save the model at the end of training
torch.save(model.state_dict(), "resnet50_imagenet.pth")
print("Model saved as resnet50_imagenet_last_epoch.pth")