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Unified training script for deterministic segmentation baselines.
Uses official libraries: smp (U-Net, U-Net++), MONAI (Attention U-Net), smp MAnet (TransUNet-style).
"""
import argparse
import os
import sys
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, random_split
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from dataset import LIDCFlatDataset
from models import get_model
class DiceBCELoss(nn.Module):
"""Combined Dice + BCE loss for binary segmentation."""
def __init__(self, dice_weight=0.5, bce_weight=0.5):
super().__init__()
self.dice_weight = dice_weight
self.bce_weight = bce_weight
self.bce = nn.BCEWithLogitsLoss()
def forward(self, logits, targets):
# BCE loss
bce_loss = self.bce(logits, targets)
# Dice loss
probs = torch.sigmoid(logits)
smooth = 1e-5
intersection = (probs * targets).sum(dim=(2, 3))
dice = (2. * intersection + smooth) / (probs.sum(dim=(2, 3)) + targets.sum(dim=(2, 3)) + smooth)
dice_loss = 1 - dice.mean()
return self.dice_weight * dice_loss + self.bce_weight * bce_loss
def compute_dice(pred, target):
"""Compute Dice coefficient for evaluation."""
smooth = 1e-5
pred = (torch.sigmoid(pred) > 0.5).float()
intersection = (pred * target).sum()
return (2. * intersection + smooth) / (pred.sum() + target.sum() + smooth)
def train_one_epoch(model, loader, optimizer, criterion, device):
model.train()
total_loss = 0
total_dice = 0
for images, masks, _ in loader:
images = images.to(device)
masks = masks.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, masks)
loss.backward()
optimizer.step()
total_loss += loss.item()
total_dice += compute_dice(outputs, masks).item()
return total_loss / len(loader), total_dice / len(loader)
@torch.no_grad()
def validate(model, loader, criterion, device):
model.eval()
total_loss = 0
total_dice = 0
for images, masks, _ in loader:
images = images.to(device)
masks = masks.to(device)
outputs = model(images)
loss = criterion(outputs, masks)
total_loss += loss.item()
total_dice += compute_dice(outputs, masks).item()
return total_loss / len(loader), total_dice / len(loader)
def main():
parser = argparse.ArgumentParser(description="Train deterministic segmentation baseline")
parser.add_argument("--model", type=str, required=True,
choices=["unet", "attention_unet", "unetpp", "transunet", "nnunet"],
help="Model architecture")
parser.add_argument("--data_dir", type=str, default="data/flat_train",
help="Path to flat training data")
parser.add_argument("--epochs", type=int, default=100, help="Number of epochs")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate")
parser.add_argument("--val_split", type=float, default=0.1, help="Validation split ratio")
parser.add_argument("--checkpoint_dir", type=str, default="checkpoints", help="Checkpoint directory")
parser.add_argument("--num_workers", type=int, default=4, help="DataLoader workers")
parser.add_argument("--patience", type=int, default=20, help="Early stopping patience")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
os.makedirs(args.checkpoint_dir, exist_ok=True)
# Create dataset
full_dataset = LIDCFlatDataset(args.data_dir, augment=True)
# Split into train/val
val_size = int(len(full_dataset) * args.val_split)
train_size = len(full_dataset) - val_size
generator = torch.Generator().manual_seed(42)
train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size], generator=generator)
# Disable augmentation for validation
val_dataset_no_aug = LIDCFlatDataset(args.data_dir, augment=False)
val_indices = val_dataset.indices
val_dataset_final = torch.utils.data.Subset(val_dataset_no_aug, val_indices)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.num_workers, pin_memory=True)
val_loader = DataLoader(val_dataset_final, batch_size=args.batch_size,
shuffle=False, num_workers=args.num_workers, pin_memory=True)
print(f"Train: {train_size}, Val: {val_size}")
# Create model
model = get_model(args.model, in_channels=1, num_classes=1)
model = model.to(device)
# Count parameters
n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Model: {args.model}, Parameters: {n_params:,}")
# Loss, optimizer, scheduler
criterion = DiceBCELoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
# Training loop
best_val_dice = 0
patience_counter = 0
checkpoint_path = os.path.join(args.checkpoint_dir, f"{args.model}_best.pth")
print(f"\nTraining {args.model} for {args.epochs} epochs...")
print("-" * 70)
start_time = time.time()
for epoch in range(1, args.epochs + 1):
train_loss, train_dice = train_one_epoch(model, train_loader, optimizer, criterion, device)
val_loss, val_dice = validate(model, val_loader, criterion, device)
scheduler.step()
# Save best model
if val_dice > best_val_dice:
best_val_dice = val_dice
patience_counter = 0
torch.save({
"epoch": epoch,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"val_dice": val_dice,
"model_name": args.model,
}, checkpoint_path)
else:
patience_counter += 1
# Print progress every 5 epochs or at start/end
if epoch % 5 == 0 or epoch == 1 or epoch == args.epochs:
elapsed = time.time() - start_time
lr = optimizer.param_groups[0]['lr']
print(f"Epoch {epoch:3d}/{args.epochs} | "
f"Train Loss: {train_loss:.4f} Dice: {train_dice:.4f} | "
f"Val Loss: {val_loss:.4f} Dice: {val_dice:.4f} | "
f"Best: {best_val_dice:.4f} | "
f"LR: {lr:.2e} | "
f"Time: {elapsed:.0f}s")
# Early stopping
if patience_counter >= args.patience:
print(f"\nEarly stopping at epoch {epoch} (patience={args.patience})")
break
total_time = time.time() - start_time
print("-" * 70)
print(f"Training complete! Best val Dice: {best_val_dice:.4f}")
print(f"Total time: {total_time:.0f}s ({total_time/60:.1f}min)")
print(f"Checkpoint saved: {checkpoint_path}")
if __name__ == "__main__":
main()
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