train.py

from typing import List, Optional, Tuple

import hydra
import pytorch_lightning as pl
from omegaconf import DictConfig
from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer
from pytorch_lightning.loggers import Logger

import warnings

warnings.filterwarnings("ignore")

from src import utils

log = utils.get_pylogger(__name__)


def fix_DictConfig(cfg: DictConfig):
    """fix all vars in the cfg config
    this is a in-place operation"""
    keys = list(cfg.keys())
    for k in keys:
        if type(cfg[k]) is DictConfig:
            fix_DictConfig(cfg[k])
        else:
            setattr(cfg, k, getattr(cfg, k))


@utils.task_wrapper
def train(cfg: DictConfig) -> Tuple[dict, dict]:
    """Trains the model. Can additionally evaluate on a testset, using best weights obtained during
    training.

    This method is wrapped in optional @task_wrapper decorator which applies extra utilities
    before and after the call.

    Args:
        cfg (DictConfig): Configuration composed by Hydra.

    Returns:
        Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
    """

    # set seed for random number generators in pytorch, numpy and python.random
    if cfg.get("seed"):
        pl.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)

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

    log.info("Instantiating loggers...")
    logger: List[Logger] = utils.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!")
        utils.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="train.yaml")
def main(cfg: DictConfig) -> Optional[float]:
    # train the model
    fix_DictConfig(cfg)
    train(cfg)


if __name__ == "__main__":
    main()