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"""
Training script for MNIST autoencoder.
"""
from models import Autoencoder, AEModule
from src.utils import ensure_dirs, save_checkpoint
from src.dataloader import get_autoencoder_dataloader
from src.config import DEVICE, BATCH_SIZE, NUM_WORKERS, CHECKPOINT_DIR, PLOTS_DIR
import os
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from rich.progress import track
import mlflow
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
def train_autoencoder(
epochs: int = 10,
learning_rate: float = 1e-3,
batch_size: int = BATCH_SIZE,
latent_channels: int = 1,
checkpoint_path: str | None = None,
run_name: str | None = None,
):
"""
Train the autoencoder model.
Args:
epochs: Number of training epochs
learning_rate: Learning rate for optimizer
batch_size: Training batch size
latent_channels: Number of channels in latent space
checkpoint_path: Path to checkpoint to resume training from
run_name: Name for this training run (for logging)
"""
ensure_dirs()
# Initialize model
model = Autoencoder(input_channels=1, latent_channels=latent_channels).to(DEVICE)
if checkpoint_path:
if not os.path.exists(checkpoint_path):
print(f"Checkpoint not found: {checkpoint_path}")
return
model.load_state_dict(torch.load(checkpoint_path, weights_only=True))
if run_name is None:
from datetime import datetime
run_name = f"autoencoder_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
mlflow.set_experiment("MNIST Autoencoder")
mlflow.start_run(run_name=run_name)
# Setup training
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Use MSE loss instead of BCE for better reconstruction of continuous values
criterion = nn.MSELoss()
# Get dataloader
dataloader = get_autoencoder_dataloader(
batch_size=batch_size,
num_workers=NUM_WORKERS,
)
# Training loop
for epoch in range(1, epochs + 1):
model.train()
epoch_loss = 0.0
for batch_idx, (x, target) in enumerate(track(dataloader, description=f"Epoch {epoch}/{epochs}")):
optimizer.zero_grad()
# Forward pass
x_recon = model(x)
loss = criterion(x_recon, target)
# Backward pass
loss.backward()
# Gradient clipping to prevent exploding gradients
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
epoch_loss += loss.item()
avg_loss = epoch_loss / len(dataloader)
mlflow.log_metric("epoch_loss", avg_loss, step=epoch)
# Save checkpoint
save_checkpoint(model, f"autoencoder_epoch_{epoch:03d}.pt")
save_checkpoint(model, "autoencoder_latest.pt")
# Visualize results
visualize_reconstructions(model, dataloader)
mlflow.end_run()
return model
def visualize_reconstructions(model: AEModule, dataloader, n_samples: int = 10):
"""Visualize original, latent, and reconstructed images."""
model.eval()
ensure_dirs()
# Get a batch of samples
x_batch, _ = next(iter(dataloader))
x_batch = x_batch[:n_samples]
with torch.no_grad():
latent = model.encode(x_batch)
x_recon = model.decode(latent)
# Create visualization
fig, axes = plt.subplots(3, n_samples + 1, figsize=(2 * n_samples, 6))
# Labels
axes[0, 0].text(0.5, 0.5, 'Original', ha='center', va='center', fontsize=12)
axes[0, 0].axis('off')
axes[1, 0].text(0.5, 0.5, 'Latent', ha='center', va='center', fontsize=12)
axes[1, 0].axis('off')
axes[2, 0].text(0.5, 0.5, 'Reconstructed', ha='center', va='center', fontsize=12)
axes[2, 0].axis('off')
# Plot images
for i in range(n_samples):
axes[0, i + 1].imshow(x_batch[i].cpu().squeeze().numpy(), cmap='gray')
axes[0, i + 1].axis('off')
axes[1, i + 1].imshow(latent[i].cpu().squeeze().numpy(), cmap='gray')
axes[1, i + 1].axis('off')
axes[2, i + 1].imshow(x_recon[i].cpu().squeeze().numpy(), cmap='gray')
axes[2, i + 1].axis('off')
fig.suptitle('Autoencoder Results')
plt.tight_layout()
save_path = os.path.join(PLOTS_DIR, 'autoencoder_results.png')
fig.savefig(save_path)
plt.close(fig)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Train MNIST autoencoder")
parser.add_argument("--epochs", type=int, default=10, help="Number of epochs")
parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate")
parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Batch size")
parser.add_argument("--latent-channels", type=int, default=1, help="Latent channels")
parser.add_argument("--checkpoint", type=str, default=None, help="Resume from checkpoint")
args = parser.parse_args()
torch.multiprocessing.set_start_method("spawn", force=True)
train_autoencoder(
epochs=args.epochs,
learning_rate=args.lr,
batch_size=args.batch_size,
latent_channels=args.latent_channels,
checkpoint_path=args.checkpoint,
)
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