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"""
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Debug training script using dummy data to test the pipeline without downloading CIFAR-10
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"""
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import os
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader, TensorDataset
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from tqdm import tqdm
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import config
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from model import get_model, count_parameters
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from utils import save_checkpoint, plot_training_history
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def get_dummy_data_loaders():
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"""Create dummy data loaders for testing"""
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train_size = 100
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test_size = 20
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train_images = torch.randn(train_size, 3, 32, 32)
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train_labels = torch.randint(0, 10, (train_size,))
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test_images = torch.randn(test_size, 3, 32, 32)
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test_labels = torch.randint(0, 10, (test_size,))
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train_dataset = TensorDataset(train_images, train_labels)
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test_dataset = TensorDataset(test_images, test_labels)
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train_loader = DataLoader(train_dataset, batch_size=config.BATCH_SIZE, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=config.BATCH_SIZE, shuffle=False)
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return train_loader, test_loader
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def debug_train():
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"""Debug training function"""
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os.makedirs(config.CHECKPOINT_DIR, exist_ok=True)
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os.makedirs(config.PLOTS_DIR, exist_ok=True)
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print("Creating dummy data loaders...")
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train_loader, test_loader = get_dummy_data_loaders()
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print(f"Creating model on {config.DEVICE}...")
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model = get_model(num_classes=config.NUM_CLASSES, device=config.DEVICE)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(model.parameters(), lr=0.01)
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history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}
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print("Starting debug training for 2 epochs...")
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for epoch in range(2):
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model.train()
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running_loss = 0.0
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correct = 0
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total = 0
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for inputs, labels in tqdm(train_loader, desc=f"Epoch {epoch+1}"):
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inputs, labels = inputs.to(config.DEVICE), labels.to(config.DEVICE)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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_, predicted = outputs.max(1)
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total += labels.size(0)
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correct += predicted.eq(labels).sum().item()
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train_loss = running_loss / len(train_loader)
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train_acc = 100. * correct / total
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history['train_loss'].append(train_loss)
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history['train_acc'].append(train_acc)
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history['val_loss'].append(train_loss)
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history['val_acc'].append(train_acc)
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print(f"Epoch {epoch+1}: Loss {train_loss:.4f}, Acc {train_acc:.2f}%")
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save_checkpoint(model, optimizer, epoch, train_acc, config.BEST_MODEL_PATH)
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plot_training_history(history, config.PLOTS_DIR)
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print("\nDebug training complete. 'best_model.pth' created for testing the web app.")
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if __name__ == "__main__":
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debug_train()
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