train.py

import random
import warnings

import hydra
import numpy as np
import torch
from hydra.utils import instantiate

from src.datasets.data_utils import get_dataloaders
from src.trainer import Trainer
from src.utils.init_utils import setup_saving_and_logging

warnings.filterwarnings("ignore", category=UserWarning)


def set_random_seed(seed):
    # fix random seeds for reproducibility
    torch.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    # benchmark=True works faster but reproducibility decreases
    torch.backends.cudnn.benchmark = False
    np.random.seed(seed)
    random.seed(seed)


@hydra.main(version_base=None, config_path="src/configs", config_name="baseline")
def main(config):
    set_random_seed(config.trainer.seed)

    logger = setup_saving_and_logging(config)

    if config.trainer.device == "auto":
        device = "cuda" if torch.cuda.is_available() else "cpu"
    else:
        device = config.trainer.device

    # setup data_loader instances
    dataloaders, batch_transforms = get_dataloaders(config)

    # build model architecture, then print to console
    model = instantiate(config.model).to(device)
    logger.info(model)

    # get function handles of loss and metrics
    loss_function = instantiate(config.loss_function).to(device)
    metrics = instantiate(config.metrics)

    # build optimizer, learning rate scheduler
    trainable_params = filter(lambda p: p.requires_grad, model.parameters())
    optimizer = instantiate(config.optimizer, params=trainable_params)
    lr_scheduler = instantiate(config.lr_scheduler, optimizer=optimizer)

    # epoch_len = number of iterations for iteration-based training
    # epoch_len = None or len(dataloader) for epoch-based training
    epoch_len = config.trainer.get("epoch_len")

    trainer = Trainer(
        model=model,
        criterion=loss_function,
        metrics=metrics,
        optimizer=optimizer,
        lr_scheduler=lr_scheduler,
        config=config,
        device=device,
        dataloaders=dataloaders,
        epoch_len=epoch_len,
        logger=logger,
        batch_transforms=batch_transforms,
        skip_oom=config.trainer.get("skip_oom", True),
    )

    trainer.train()


if __name__ == "__main__":
    main()