train.py

from typing import Any, Dict, List, Optional, Tuple

import hydra
import lightning as L
import rootutils
from lightning import Callback, LightningDataModule, LightningModule, Trainer
from lightning.pytorch.loggers import Logger
from omegaconf import DictConfig
from optuna.integration import PyTorchLightningPruningCallback

from REAPS.models.REAPS_model import REAPS_Model

rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)

from REAPS.utils import (
    RankedLogger,
    extras,
    get_metric_value,
    instantiate_callbacks,
    instantiate_loggers,
    log_hyperparameters,
    task_wrapper,
)

log = RankedLogger(__name__, rank_zero_only=True)


@task_wrapper
def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """Trains the model. Can additionally evaluate on a test-set, using best weights obtained during
    training.

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

    :param cfg: A DictConfig configuration composed by Hydra.
    :return: 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"):
        L.seed_everything(cfg.seed, workers=True)

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


    if cfg.get("pretrained_weights_path"):
        log.info(f"Manually loading pretrained weights from: {cfg.pretrained_weights_path}")

        model = REAPS_Model.load_from_checkpoint(
            checkpoint_path=cfg.pretrained_weights_path,
            strict=False,
            **cfg.model
        )
    else:
        log.info(f"Instantiating model <{cfg.model._target_}>")
        model: LightningModule = hydra.utils.instantiate(cfg.model)

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

    # Enabled only in the hyperparameter search mode
    if cfg.get("hparams_search"):
        log.info("Enabling Optuna pruning callback!")
        # The value of "monitor" must be the value of "optimized_metric"
        pruning_callback = PyTorchLightningPruningCallback(
            trial=hydra.utils.instantiate(cfg.hydra.sweeper.sampler)._trial,
            monitor=cfg.hparams_search.optimized_metric
        )
        callbacks.append(pruning_callback)

    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


@hydra.main(version_base="1.3", config_path="configs", config_name="pre_training.yaml")
def main(cfg: DictConfig) -> Optional[float]:
    """Main entry point for training.

    :param cfg: DictConfig configuration composed by Hydra.
    :return: 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


if __name__ == "__main__":
    main()