# TransUNet Training Script for Medical Image Segmentation # R50-ViT-B/16 with ImageNet-21k pretrained weights import os import sys import argparse import time import numpy as np import torch import torch.nn as nn from torch.utils.data import DataLoader from models.transunet.vit_seg_modeling import VisionTransformer as ViT_seg from models.transunet.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg from datasets import SegmentationDataset, get_dataset_config from losses import BCEDiceLoss, CEDiceLoss from metrics import compute_dice_iou_binary, compute_dice_iou_multiclass, MetricTracker def parse_args(): parser = argparse.ArgumentParser(description='TransUNet Medical Segmentation') parser.add_argument('--dataset', type=str, required=True, choices=['cvc', 'kvasir', 'refuge2']) parser.add_argument('--resolution', type=int, default=224) parser.add_argument('--batch_size', type=int, default=24) parser.add_argument('--epochs', type=int, default=150) parser.add_argument('--lr', type=float, default=0.01) parser.add_argument('--weight_decay', type=float, default=1e-4) parser.add_argument('--momentum', type=float, default=0.9) parser.add_argument('--poly_power', type=float, default=0.9) parser.add_argument('--num_workers', type=int, default=4) parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--save_dir', type=str, default='checkpoints') parser.add_argument('--vit_name', type=str, default='R50-ViT-B_16') parser.add_argument('--pretrained_path', type=str, default='pretrained/R50+ViT-B_16.npz') return parser.parse_args() def poly_lr(optimizer, init_lr, epoch, max_epoch, power=0.9): """Polynomial learning rate decay.""" lr = init_lr * (1 - epoch / max_epoch) ** power for param_group in optimizer.param_groups: param_group['lr'] = lr return lr def train_one_epoch(model, loader, criterion, optimizer, device, task): model.train() tracker = MetricTracker() total_loss = 0.0 for images, masks in loader: images = images.to(device) masks = masks.to(device) logits = model(images) if task == 'binary': loss = criterion(logits, masks) dice, iou = compute_dice_iou_binary(logits, masks) else: loss = criterion(logits, masks) dice, iou, _, _ = compute_dice_iou_multiclass(logits, masks, num_classes=logits.shape[1]) optimizer.zero_grad() loss.backward() optimizer.step() total_loss += loss.item() * images.size(0) tracker.update(dice, iou, images.size(0)) n = len(loader.dataset) return total_loss / n, tracker.avg_dice, tracker.avg_iou @torch.no_grad() def validate(model, loader, criterion, device, task, num_classes=1): model.eval() tracker = MetricTracker() total_loss = 0.0 for images, masks in loader: images = images.to(device) masks = masks.to(device) logits = model(images) if task == 'binary': loss = criterion(logits, masks) dice, iou = compute_dice_iou_binary(logits, masks) else: loss = criterion(logits, masks) dice, iou, _, _ = compute_dice_iou_multiclass(logits, masks, num_classes=num_classes) total_loss += loss.item() * images.size(0) tracker.update(dice, iou, images.size(0)) n = len(loader.dataset) return total_loss / n, tracker.avg_dice, tracker.avg_iou def main(): args = parse_args() torch.manual_seed(args.seed) device = torch.device(f'cuda:{args.gpu}') cfg = get_dataset_config(args.dataset) task = cfg['task'] num_classes = cfg['num_classes'] # For TransUNet: binary uses 2 classes (background + foreground) with CE # but we keep 1-class sigmoid for consistency with UNet # Actually TransUNet originally uses softmax with n_classes>=2 # We adapt: binary tasks use 1 output + sigmoid, multi-class use n outputs + softmax # Datasets train_dataset = SegmentationDataset(args.dataset, split='train', resolution=args.resolution, seed=args.seed) val_dataset = SegmentationDataset(args.dataset, split='val', resolution=args.resolution, seed=args.seed) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True, drop_last=True) val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) # Model: TransUNet R50-ViT-B_16 vit_config = CONFIGS_ViT_seg[args.vit_name] # Adjust grid size for input resolution (grid = img_size // 16) grid_size = args.resolution // 16 vit_config.patches.grid = (grid_size, grid_size) if task == 'binary': vit_config.n_classes = 1 criterion = BCEDiceLoss() else: vit_config.n_classes = num_classes criterion = CEDiceLoss(num_classes=num_classes) model = ViT_seg(vit_config, img_size=args.resolution, num_classes=vit_config.n_classes, zero_head=True) # Load pretrained weights if os.path.exists(args.pretrained_path): pretrained_weights = np.load(args.pretrained_path) model.load_from(pretrained_weights) print(f"Loaded pretrained weights from {args.pretrained_path}") else: print(f"WARNING: Pretrained weights not found at {args.pretrained_path}") model = model.to(device) # Optimizer: SGD with Polynomial LR decay optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # Output directory save_dir = os.path.join(args.save_dir, f'transunet_{args.dataset}') os.makedirs(save_dir, exist_ok=True) best_dice = 0.0 print(f"\n{'='*60}") print(f"TransUNet Training: {cfg['name']}") print(f"Task: {task}, Classes: {num_classes}") print(f"Train: {len(train_dataset)}, Val: {len(val_dataset)}") print(f"Epochs: {args.epochs}, LR: {args.lr}, BS: {args.batch_size}") print(f"{'='*60}\n") for epoch in range(1, args.epochs + 1): t0 = time.time() # Polynomial LR decay current_lr = poly_lr(optimizer, args.lr, epoch - 1, args.epochs, power=args.poly_power) train_loss, train_dice, train_iou = train_one_epoch( model, train_loader, criterion, optimizer, device, task) val_loss, val_dice, val_iou = validate( model, val_loader, criterion, device, task, num_classes) elapsed = time.time() - t0 # Save best if val_dice > best_dice: best_dice = val_dice torch.save({ 'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'best_dice': best_dice, 'args': vars(args), }, os.path.join(save_dir, 'best.pth')) # Print progress if epoch % 10 == 0 or epoch == 1: 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} IoU: {val_iou:.4f} | " f"Best: {best_dice:.4f} | LR: {current_lr:.2e} | {elapsed:.1f}s") # Save last torch.save({ 'epoch': args.epochs, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'best_dice': best_dice, 'args': vars(args), }, os.path.join(save_dir, 'last.pth')) print(f"\nTraining complete. Best val Dice: {best_dice:.4f}") print(f"Checkpoints saved to: {save_dir}") if __name__ == '__main__': main()