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Train

Main training script.

main(cfg) #

Main entry point for training.

Parameters:

Name Type Description Default
cfg DictConfig

DictConfig configuration composed by Hydra.

 required

Returns:

Type Description
float | None

Optional[float] with optimized metric value.
Source code in src/train.py 
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@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml")
def main(cfg: DictConfig) -> float | None:
    """Main entry point for training.

    Args:
        cfg: DictConfig configuration composed by Hydra.

    Returns:
        Optional[float] with optimized metric value.
    """
    # apply extra utilities
    # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
    extras(cfg)

    # train the model
    metric_dict, _ = train(cfg)

    # safely retrieve metric value for hydra-based hyperparameter optimization
    metric_value = get_metric_value(metric_dict=metric_dict, metric_name=cfg.get("optimized_metric"))

    # return optimized metric
    return metric_value

train(cfg) #

Trains the model. Can additionally evaluate on a testset, using best weights obtained during training.

This method is wrapped in optional @task_wrapper decorator, that controls the behavior during failure. Useful for multiruns, saving info about the crash, etc.

Parameters:

Name Type Description Default
cfg DictConfig

A DictConfig configuration composed by Hydra.

 required

Returns:

Type Description
tuple[dict[str, Any], dict[str, Any]]

A tuple with metrics and dict with all instantiated objects.
Source code in src/train.py 
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@task_wrapper
def train(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]:
    """Trains the model. Can additionally evaluate on a testset, using best weights obtained during training.

    This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
    failure. Useful for multiruns, saving info about the crash, etc.

    Args:
        cfg: A DictConfig configuration composed by Hydra.

    Returns:
        A tuple with metrics and dict with all instantiated objects.
    """
    # set seed for random number generators in pytorch, numpy and python.random
    if cfg.get("seed"):
        lightning.seed_everything(cfg.seed, workers=True)

    log.info(f"Instantiating datamodule <{cfg.data._target_}>")
    datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)

    log.info(f"Instantiating model <{cfg.model._target_}>")
    model: LightningModule = hydra.utils.instantiate(cfg.model)

    if cfg.get("model_compile", False):
        log.info("Compiling model...")
        torch.compile(model)

    log.info("Instantiating callbacks...")
    callbacks: list[Callback] = instantiate_callbacks(cfg.get("callbacks"))

    log.info("Instantiating loggers...")
    logger: list[Logger] = instantiate_loggers(cfg.get("logger"))

    log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
    trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger)

    object_dict = {
        "cfg": cfg,
        "datamodule": datamodule,
        "model": model,
        "callbacks": callbacks,
        "logger": logger,
        "trainer": trainer,
    }

    if logger:
        log.info("Logging hyperparameters!")
        log_hyperparameters(object_dict)

    if cfg.get("train"):
        log.info("Starting training!")
        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))

    train_metrics = trainer.callback_metrics

    if cfg.get("test"):
        log.info("Starting testing!")
        ckpt_path = trainer.checkpoint_callback.best_model_path
        if ckpt_path == "":
            log.warning("Best ckpt not found! Using current weights for testing...")
            ckpt_path = None
        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
        log.info(f"Best ckpt path: {ckpt_path}")

    test_metrics = trainer.callback_metrics

    # merge train and test metrics
    metric_dict = {**train_metrics, **test_metrics}

    return metric_dict, object_dict