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01fdb75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | # UNet Training Script for Medical Image Segmentation
# Uses segmentation_models_pytorch with ResNet34 (ImageNet pretrained)
import os
import argparse
import time
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import segmentation_models_pytorch as smp
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='UNet 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=16)
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--weight_decay', type=float, default=1e-4)
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')
return parser.parse_args()
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']
# 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
if task == 'binary':
model = smp.Unet(
encoder_name='resnet34',
encoder_weights='imagenet',
in_channels=3,
classes=1,
)
criterion = BCEDiceLoss()
else:
model = smp.Unet(
encoder_name='resnet34',
encoder_weights='imagenet',
in_channels=3,
classes=num_classes,
)
criterion = CEDiceLoss(num_classes=num_classes)
model = model.to(device)
# Optimizer & Scheduler
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=args.epochs)
# Output directory
save_dir = os.path.join(args.save_dir, f'unet_{args.dataset}')
os.makedirs(save_dir, exist_ok=True)
best_dice = 0.0
print(f"\n{'='*60}")
print(f"UNet 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()
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)
scheduler.step()
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:
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} IoU: {val_iou:.4f} | "
f"Best: {best_dice:.4f} | LR: {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()
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