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| import os | |
| import torch | |
| from torch import autocast | |
| from torch.cuda.amp import GradScaler | |
| from tqdm import tqdm | |
| from dataloaders.JSRT import build_dataloaders as build_dataloaders_JSRT | |
| from models.diffusion_model import DiffusionModel | |
| from trainers.utils import (TensorboardLogger, compare_configs, sample_plot_image, | |
| seed_everything) | |
| def train(config, model, optimizer, train_loader, val_loader, logger, scaler, step): | |
| best_val_loss = float('inf') | |
| train_losses = [] | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| img_losses = [] | |
| seg_losses = [] | |
| pbar = tqdm(total=config.val_freq, desc='Training') | |
| cond = None | |
| while True: | |
| for x, y in train_loader: | |
| pbar.update(1) | |
| step += 1 | |
| if config.experiment == "joint": | |
| x = torch.concat([x,y], dim=1) | |
| elif config.experiment == "conditional": | |
| cond = x # condition on the image | |
| cond = cond.to(config.device) | |
| x = y # predict the segmentation | |
| elif config.experiment == "joint_and_cond": | |
| cond = y.to(config.device) | |
| x = x.to(config.device) | |
| # Forward pass | |
| optimizer.zero_grad() | |
| with autocast(device_type=config.device, enabled=config.mixed_precision): | |
| loss = model.train_step(x, cond) | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| img_loss, seg_loss = loss | |
| loss = img_loss + seg_loss | |
| img_losses.append(img_loss.item()) | |
| seg_losses.append(seg_loss.item()) | |
| scaler.scale(loss).backward() | |
| optimizer.step() | |
| train_losses.append(loss.item()) | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| pbar.set_description(f'Training loss: {loss.item():.4f} (img: {img_loss.item():.4f}, seg: {seg_loss.item():.4f})') | |
| else: | |
| pbar.set_description(f'Training loss: {loss.item():.4f}') | |
| if step % config.log_freq == 0 or config.debug: | |
| avg_train_loss = sum(train_losses) / len(train_losses) | |
| print(f'Step {step} - Train loss: {avg_train_loss:.4f}') | |
| logger.log({'train/loss': avg_train_loss}, step=step) | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| avg_img_loss = sum(img_losses) / len(img_losses) | |
| avg_seg_loss = sum(seg_losses) / len(seg_losses) | |
| logger.log({'train_loss/img': avg_img_loss}, step=step) | |
| logger.log({'train_loss/seg': avg_seg_loss}, step=step) | |
| if step % config.val_freq == 0 or config.debug: | |
| val_results = validate(config, model, val_loader) | |
| logger.log(val_results, step=step) | |
| if val_results['val/loss'] < best_val_loss and not config.debug: | |
| print(f'Step {step} - New best validation loss: ' | |
| f'{val_results["val/loss"]:.4f}, saving model ' | |
| f'in {config.log_dir}') | |
| best_val_loss = val_results['val/loss'] | |
| save( | |
| model, | |
| optimizer, | |
| config, | |
| config.log_dir / 'best_model.pt', | |
| step | |
| ) | |
| if step >= config.max_steps or config.debug: | |
| return model | |
| def validate(config, model, val_loader): | |
| model.eval() | |
| losses = [] | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| img_losses = [] | |
| seg_losses = [] | |
| cond=None | |
| for i, (x, y) in tqdm(enumerate(val_loader), desc='Validating'): | |
| if config.experiment == "joint": | |
| x = torch.cat([x,y], dim=1) | |
| elif config.experiment == "conditional": | |
| cond = x # condition on the image | |
| cond = cond.to(config.device) | |
| x = y # predict the segmentation | |
| elif config.experiment == "joint_and_cond": | |
| cond = y.to(config.device) | |
| x = x.to(config.device) | |
| with autocast(device_type=config.device, enabled=config.mixed_precision): | |
| if len(val_loader.dataset) > 1000: | |
| # val set is too large to evaluate for each timestep, take random timesteps | |
| loss = model.train_step(x, cond) | |
| else: | |
| loss = model.val_step(x, cond, t_steps=config.val_steps) | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| img_loss, seg_loss = loss | |
| loss = img_loss + seg_loss | |
| img_losses.append(img_loss.item()) | |
| seg_losses.append(seg_loss.item()) | |
| losses.append(loss.item()) | |
| if i + 1 == config.max_val_steps or config.debug: | |
| break | |
| avg_loss = sum(losses) / len(losses) | |
| if config.joint_training and config.experiment == 'joint_and_cond': | |
| avg_img_loss = sum(img_losses) / len(img_losses) | |
| avg_seg_loss = sum(seg_losses) / len(seg_losses) | |
| print(f'Validation loss: {avg_loss:.4f} (img: {avg_img_loss:.4f}, seg: {avg_seg_loss:.4f})') | |
| else: | |
| print(f'Validation loss: {avg_loss:.4f}') | |
| # Build visualisations | |
| # select images for visualisation | |
| if config.experiment == "conditional": | |
| # note that there is no randomness on how the images are selected | |
| # if there are enough images on the last validation batch, then we keep those | |
| # otherwise, we take the first images from a new validation epoch batch | |
| config.n_sampled_imgs = config.n_sampled_imgs if not config.debug else 1 | |
| N_missing = config.n_sampled_imgs - cond.shape[0] | |
| iter_val_loader = iter(val_loader) # new validation epoch | |
| while N_missing > 0: # are we missing images? | |
| new_cond, _ = next(iter_val_loader) | |
| cond = torch.cat([cond, new_cond], dim=0) | |
| N_missing = config.n_sampled_imgs - cond.shape[0] | |
| del iter_val_loader | |
| if N_missing < 0: # do we have too many images? | |
| cond = cond[:config.n_sampled_imgs] | |
| with autocast(device_type=config.device, enabled=config.mixed_precision): | |
| sampled_imgs = sample_plot_image( | |
| model, | |
| config.timesteps, | |
| config.img_size, | |
| config.n_sampled_imgs, # this defaults to 1 if in debug mode | |
| config.channels if config.experiment != "joint_and_cond" else config.channels + config.out_channels, # out_channels stands for channels in segmentation | |
| cond=cond, | |
| normalized=config.normalize, | |
| ) | |
| model.train() | |
| return { | |
| 'val/loss': avg_loss, | |
| 'val/sampled images': sampled_imgs | |
| } | |
| def save(model, optimizer, config, path, step): | |
| torch.save({ | |
| 'model_state_dict': model.state_dict(), | |
| 'optimizer_state_dict': optimizer.state_dict(), | |
| 'config': config, | |
| 'step': step | |
| }, path) | |
| def load(new_config, path): | |
| checkpoint = torch.load(path, map_location=torch.device(new_config.device)) | |
| old_config = checkpoint['config'] | |
| compare_configs(old_config, new_config) | |
| model = DiffusionModel(old_config) | |
| model.to(new_config.device) | |
| model.load_state_dict(checkpoint['model_state_dict']) | |
| optimizer = torch.optim.Adam(model.parameters(), lr=new_config.lr) | |
| optimizer.load_state_dict(checkpoint['optimizer_state_dict']) | |
| step = checkpoint['step'] | |
| return model, optimizer, step | |
| def main(config): | |
| # adjust logdir to include experiment name | |
| os.makedirs(config.log_dir, exist_ok=True) | |
| # save config namespace into logdir | |
| with open(config.log_dir / 'config.txt', 'w') as f: | |
| for k, v in vars(config).items(): | |
| if type(v) not in [str, int, float, bool]: | |
| f.write(f'{k}: {str(v)}\n') | |
| else: | |
| f.write(f'{k}: {v}\n') | |
| # Random seed | |
| seed_everything(config.seed) | |
| # Init model and optimizer | |
| if config.resume_path is not None: | |
| print('Loading model from', config.resume_path) | |
| diffusion_model, optimizer, step = load(config, config.resume_path) | |
| else: | |
| if config.experiment in ["img_only", "joint", "conditional"]: | |
| diffusion_model = DiffusionModel(config) | |
| else: | |
| raise ValueError(f"Unknown experiment: {config.experiment}") | |
| optimizer = torch.optim.Adam(diffusion_model.parameters(), lr=config.lr, weight_decay=config.weight_decay) # , betas=config.adam_betas) | |
| step = 0 | |
| diffusion_model.to(config.device) | |
| diffusion_model.train() | |
| scaler = GradScaler() | |
| # Load data | |
| if config.dataset == "JSRT": | |
| build_dataloaders = build_dataloaders_JSRT | |
| else: | |
| raise ValueError(f"Unknown dataset: {config.dataset}") | |
| dataloaders = build_dataloaders( | |
| config.data_dir, | |
| config.img_size, | |
| config.batch_size, | |
| config.num_workers, | |
| config.n_labelled_images, | |
| ) | |
| train_dl = dataloaders['train'] | |
| val_dl = dataloaders['val'] | |
| # Logger | |
| logger = TensorboardLogger(config.log_dir, enabled=not config.debug) | |
| train(config, diffusion_model, optimizer, train_dl, val_dl, logger, scaler, step) | |