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"""
Training script for MNIST diffusion model.
"""
from models import UNetMNIST
from src.utils import (
ensure_dirs, save_checkpoint, tensor_to_image,
figure_to_image,
)
from src.metrics import fid, kl_divergence, jsd
from src.diffusion import p_xt_1_xt_x0_pred
from src.dataloader import get_diffusion_dataloader, get_mnist_dataset
from src.config import (
DEVICE, EPOCHS, LEARNING_RATE, BATCH_SIZE, NUM_WORKERS,
DIFFU_STEPS, NB_CHANNEL, IMG_SIZE, NB_LABEL, CHECKPOINT_DIR, PLOTS_DIR
)
import os
import torch
import torch.nn as nn
import numpy as np
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_diffusion(
epochs: int = EPOCHS,
learning_rate: float = LEARNING_RATE,
batch_size: int = BATCH_SIZE,
predict_x0: bool = True,
use_attention: bool = False,
checkpoint_path: str | None = None,
run_name: str | None = None,
):
"""
Train the diffusion model.
Args:
epochs: Number of training epochs
learning_rate: Learning rate for optimizer
batch_size: Training batch size
predict_x0: If True, model predicts x0. Otherwise predicts noise.
use_attention: If True, use self-attention in UNet
checkpoint_path: Path to checkpoint to resume training from
run_name: Name for this training run (for logging)
"""
ensure_dirs()
# Setup run name and logging
if run_name is None:
from datetime import datetime
run_name = f"diffusion_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
mlflow.set_experiment("MNIST Diffusion")
mlflow.start_run(run_name=run_name)
# Initialize model
model = UNetMNIST(use_attention=use_attention).to(DEVICE)
if checkpoint_path and os.path.exists(checkpoint_path):
model.load_state_dict(torch.load(checkpoint_path, weights_only=True))
print(f"Loaded checkpoint from {checkpoint_path}")
# Setup training
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()
# Get dataloader
dataloader = get_diffusion_dataloader(
predict_x0=predict_x0,
batch_size=batch_size,
num_workers=NUM_WORKERS,
)
# Training loop
global_step = 0
for epoch in range(1, epochs + 1):
model.train()
epoch_loss = 0.0
for batch_idx, (xt, t, vec, target) in enumerate(track(dataloader, description=f"Epoch {epoch}/{epochs}")):
optimizer.zero_grad()
# Forward pass
pred = model(xt, t, vec)
loss = criterion(pred, target)
# Backward pass
loss.backward()
optimizer.step()
epoch_loss += loss.item()
global_step += 1
# Log training loss every 100 steps
if global_step % 100 == 0:
avg_loss = epoch_loss / (batch_idx + 1)
mlflow.log_metric("train_loss", avg_loss, step=global_step)
avg_loss = epoch_loss / len(dataloader)
mlflow.log_metric("epoch_loss", avg_loss, step=epoch)
# Save checkpoint every epoch
save_checkpoint(model, f"diffusion_epoch_{epoch:03d}.pt")
save_checkpoint(model, "diffusion_latest.pt")
# Generate and log sample images
if epoch % 1 == 0:
evaluate_and_log(model, epoch, predict_x0)
mlflow.end_run()
return model
def evaluate_and_log(model: nn.Module, epoch: int, predict_x0: bool = True):
"""Generate samples and log metrics."""
model.eval()
with torch.no_grad():
# Generate samples
batch_size = 64
n_batches = 4
fakes = []
for _ in range(n_batches):
x = torch.randn(batch_size, NB_CHANNEL, IMG_SIZE, IMG_SIZE, device=DEVICE)
vec = torch.randint(0, NB_LABEL, (batch_size,), device=DEVICE)
vec = torch.nn.functional.one_hot(vec, num_classes=NB_LABEL).to(dtype=torch.float32)
# Reverse diffusion
for t in range(DIFFU_STEPS, 0, -1):
t_tensor = torch.tensor([[t]] * batch_size, device=DEVICE, dtype=torch.float32)
x = p_xt_1_xt_x0_pred(model, x, t_tensor, vec)
x = x.cpu().numpy()
x = (x - x.min()) / (x.max() - x.min() + 1e-8)
fakes.append(x)
fakes = np.concatenate(fakes)
# Get real samples for comparison
dataset = get_mnist_dataset(train=True)
n_samples = len(fakes)
reals = torch.stack([dataset[i][0] for i in range(n_samples)]).numpy()
reals = reals * 2 - 1
# Log metrics
fid_score = fid(reals, fakes)
kl_score = kl_divergence(reals, fakes)
jsd_score = jsd(reals, fakes)
mlflow.log_metric("FID", fid_score, step=epoch)
mlflow.log_metric("KL Divergence", kl_score, step=epoch)
mlflow.log_metric("JSD", jsd_score, step=epoch)
# Log sample images
fig, axes = plt.subplots(4, 8, figsize=(16, 8))
for i, ax in enumerate(axes.flat):
if i < len(fakes):
ax.imshow(fakes[i].transpose(1, 2, 0).squeeze(), cmap='gray')
ax.axis('off')
fig.suptitle(f"Generated Samples - Epoch {epoch}")
plt.tight_layout()
mlflow.log_figure(fig, f"samples_epoch_{epoch:03d}.png")
# Save to plots folder
fig.savefig(os.path.join(PLOTS_DIR, f"samples_epoch_{epoch:03d}.png"))
plt.close(fig)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Train MNIST diffusion model")
parser.add_argument("--epochs", type=int, default=EPOCHS, help="Number of epochs")
parser.add_argument("--lr", type=float, default=LEARNING_RATE, help="Learning rate")
parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Batch size")
parser.add_argument("--attention", action="store_true", help="Use self-attention")
parser.add_argument("--checkpoint", type=str, default=None, help="Resume from checkpoint")
parser.add_argument("--name", type=str, default=None, help="Run name")
args = parser.parse_args()
torch.multiprocessing.set_start_method("spawn", force=True)
train_diffusion(
epochs=args.epochs,
learning_rate=args.lr,
batch_size=args.batch_size,
use_attention=args.attention,
checkpoint_path=args.checkpoint,
run_name=args.name,
)
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