import argparse import logging import os import random import sys import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms import torchvision.transforms.functional as TF from pathlib import Path from torch import optim from torch.utils.data import DataLoader, random_split from tqdm import tqdm import wandb from evaluate import evaluate from unet import UNet from utils.data_loading import BasicDataset, CarvanaDataset from utils.dice_score import dice_loss dir_img = Path('./data/imgs/') dir_mask = Path('./data/masks/') dir_checkpoint = Path('./checkpoints/') def train_model( model, device, epochs: int = 5, batch_size: int = 1, learning_rate: float = 1e-5, val_percent: float = 0.1, save_checkpoint: bool = True, img_scale: float = 0.5, amp: bool = False, weight_decay: float = 1e-8, momentum: float = 0.999, gradient_clipping: float = 1.0, ): # 1. Create dataset try: dataset = CarvanaDataset(dir_img, dir_mask, img_scale) except (AssertionError, RuntimeError, IndexError): dataset = BasicDataset(dir_img, dir_mask, img_scale) # 2. Split into train / validation partitions n_val = int(len(dataset) * val_percent) n_train = len(dataset) - n_val train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0)) # 3. Create data loaders loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True) train_loader = DataLoader(train_set, shuffle=True, **loader_args) val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args) # (Initialize logging) experiment = wandb.init(project='U-Net', resume='allow', anonymous='must') experiment.config.update( dict(epochs=epochs, batch_size=batch_size, learning_rate=learning_rate, val_percent=val_percent, save_checkpoint=save_checkpoint, img_scale=img_scale, amp=amp) ) logging.info(f'''Starting training: Epochs: {epochs} Batch size: {batch_size} Learning rate: {learning_rate} Training size: {n_train} Validation size: {n_val} Checkpoints: {save_checkpoint} Device: {device.type} Images scaling: {img_scale} Mixed Precision: {amp} ''') # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP optimizer = optim.RMSprop(model.parameters(), lr=learning_rate, weight_decay=weight_decay, momentum=momentum, foreach=True) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=5) # goal: maximize Dice score grad_scaler = torch.cuda.amp.GradScaler(enabled=amp) criterion = nn.CrossEntropyLoss() if model.n_classes > 1 else nn.BCEWithLogitsLoss() global_step = 0 # 5. Begin training for epoch in range(1, epochs + 1): model.train() epoch_loss = 0 with tqdm(total=n_train, desc=f'Epoch {epoch}/{epochs}', unit='img') as pbar: for batch in train_loader: images, true_masks = batch['image'], batch['mask'] assert images.shape[1] == model.n_channels, \ f'Network has been defined with {model.n_channels} input channels, ' \ f'but loaded images have {images.shape[1]} channels. Please check that ' \ 'the images are loaded correctly.' images = images.to(device=device, dtype=torch.float32, memory_format=torch.channels_last) true_masks = true_masks.to(device=device, dtype=torch.long) with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp): masks_pred = model(images) if model.n_classes == 1: loss = criterion(masks_pred.squeeze(1), true_masks.float()) loss += dice_loss(F.sigmoid(masks_pred.squeeze(1)), true_masks.float(), multiclass=False) else: loss = criterion(masks_pred, true_masks) loss += dice_loss( F.softmax(masks_pred, dim=1).float(), F.one_hot(true_masks, model.n_classes).permute(0, 3, 1, 2).float(), multiclass=True ) optimizer.zero_grad(set_to_none=True) grad_scaler.scale(loss).backward() grad_scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clipping) grad_scaler.step(optimizer) grad_scaler.update() pbar.update(images.shape[0]) global_step += 1 epoch_loss += loss.item() experiment.log({ 'train loss': loss.item(), 'step': global_step, 'epoch': epoch }) pbar.set_postfix(**{'loss (batch)': loss.item()}) # Evaluation round division_step = (n_train // (5 * batch_size)) if division_step > 0: if global_step % division_step == 0: histograms = {} for tag, value in model.named_parameters(): tag = tag.replace('/', '.') if not (torch.isinf(value) | torch.isnan(value)).any(): histograms['Weights/' + tag] = wandb.Histogram(value.data.cpu()) if not (torch.isinf(value.grad) | torch.isnan(value.grad)).any(): histograms['Gradients/' + tag] = wandb.Histogram(value.grad.data.cpu()) val_score = evaluate(model, val_loader, device, amp) scheduler.step(val_score) logging.info('Validation Dice score: {}'.format(val_score)) try: experiment.log({ 'learning rate': optimizer.param_groups[0]['lr'], 'validation Dice': val_score, 'images': wandb.Image(images[0].cpu()), 'masks': { 'true': wandb.Image(true_masks[0].float().cpu()), 'pred': wandb.Image(masks_pred.argmax(dim=1)[0].float().cpu()), }, 'step': global_step, 'epoch': epoch, **histograms }) except: pass if save_checkpoint: Path(dir_checkpoint).mkdir(parents=True, exist_ok=True) state_dict = model.state_dict() state_dict['mask_values'] = dataset.mask_values torch.save(state_dict, str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch))) logging.info(f'Checkpoint {epoch} saved!') def get_args(): parser = argparse.ArgumentParser(description='Train the UNet on images and target masks') parser.add_argument('--epochs', '-e', metavar='E', type=int, default=5, help='Number of epochs') parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=1, help='Batch size') parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-5, help='Learning rate', dest='lr') parser.add_argument('--load', '-f', type=str, default=False, help='Load model from a .pth file') parser.add_argument('--scale', '-s', type=float, default=0.5, help='Downscaling factor of the images') parser.add_argument('--validation', '-v', dest='val', type=float, default=10.0, help='Percent of the data that is used as validation (0-100)') parser.add_argument('--amp', action='store_true', default=False, help='Use mixed precision') parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling') parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes') return parser.parse_args() if __name__ == '__main__': args = get_args() logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') logging.info(f'Using device {device}') # Change here to adapt to your data # n_channels=3 for RGB images # n_classes is the number of probabilities you want to get per pixel model = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear) model = model.to(memory_format=torch.channels_last) logging.info(f'Network:\n' f'\t{model.n_channels} input channels\n' f'\t{model.n_classes} output channels (classes)\n' f'\t{"Bilinear" if model.bilinear else "Transposed conv"} upscaling') if args.load: state_dict = torch.load(args.load, map_location=device) del state_dict['mask_values'] model.load_state_dict(state_dict) logging.info(f'Model loaded from {args.load}') model.to(device=device) try: train_model( model=model, epochs=args.epochs, batch_size=args.batch_size, learning_rate=args.lr, device=device, img_scale=args.scale, val_percent=args.val / 100, amp=args.amp ) except torch.cuda.OutOfMemoryError: logging.error('Detected OutOfMemoryError! ' 'Enabling checkpointing to reduce memory usage, but this slows down training. ' 'Consider enabling AMP (--amp) for fast and memory efficient training') torch.cuda.empty_cache() model.use_checkpointing() train_model( model=model, epochs=args.epochs, batch_size=args.batch_size, learning_rate=args.lr, device=device, img_scale=args.scale, val_percent=args.val / 100, amp=args.amp )