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| from argparse import Namespace | |
| import os | |
| from pathlib import Path | |
| from dataloaders.JSRT import build_dataloaders | |
| import torch | |
| from trainers.utils import seed_everything, TensorboardLogger | |
| from torch.cuda.amp import GradScaler | |
| from trainers.train_baseline import train | |
| from models.datasetDM_model import DatasetDM | |
| def main(config: Namespace) -> None: | |
| # adjust logdir to include experiment name | |
| os.makedirs(config.log_dir, exist_ok=True) | |
| print('Experiment folder: %s' % (config.log_dir)) | |
| # 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) | |
| model = DatasetDM(config) | |
| if config.shared_weights_over_timesteps: | |
| import torch.nn as nn | |
| from einops.layers.torch import Rearrange | |
| model.classifier = nn.Sequential( | |
| Rearrange('b (step act) h w -> (b step) act h w', step=len(model.steps)), | |
| nn.Conv2d(960, 128, 1), | |
| nn.ReLU(), | |
| nn.BatchNorm2d(128), | |
| nn.Conv2d(128, 32, 1), | |
| nn.ReLU(), | |
| nn.BatchNorm2d(32), | |
| nn.Conv2d(32, 1, config.out_channels) | |
| ) | |
| model = model.to(config.device) | |
| model.train() | |
| optimizer = torch.optim.Adam(model.classifier.parameters(), lr=config.lr, weight_decay=config.weight_decay) # , betas=config.adam_betas) | |
| step = 0 | |
| scaler = GradScaler() | |
| 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, model, optimizer, train_dl, val_dl, logger, scaler, step) |