train.py

import os
import torch
import hydra
from omegaconf import DictConfig
from typing import List, Optional, Tuple
import pyrootutils

import pytorch_lightning as pl
from pytorch_lightning.loggers import Logger
from pytorch_lightning import Callback

# from einops._torch_specific import allow_ops_in_compiled_graph  # requires einops>=0.6.1
# allow_ops_in_compiled_graph()

from src.utils.logging import setup_logging
from src.utils.distributed import get_rank, get_world_size
import src.utils as utils

pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
# ------------------------------------------------------------------------------------ #
# the setup_root above is equivalent to:
# - adding project root dir to PYTHONPATH
#       (so you don't need to force user to install project as a package)
#       (necessary before importing any local modules e.g. `from src import utils`)
# - setting up PROJECT_ROOT environment variable
#       (which is used as a base for paths in "configs/paths/default.yaml")
#       (this way all filepaths are the same no matter where you run the code)
# - loading environment variables from ".env" in root dir
#
# you can remove it if you:
# 1. either install project as a package or move entry files to project root dir
# 2. set `root_dir` to "." in "configs/paths/default.yaml"
#
# more info: https://github.com/ashleve/pyrootutils
# ------------------------------------------------------------------------------------ #


torch.set_num_threads(8)

@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, that controls the behavior during
    failure. Useful for multiruns, saving info about the crash, etc.

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

    Returns:
        Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
    """
    # Clear up residual cache from previous runs
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

    log = setup_logging(
        output=cfg.paths.output_dir,
        num_gpu=get_rank(),
        num_shards=get_world_size(),
        name="smart_canada_goose",
        color=True,
    )
    log.info("Environment info:\n" + utils.collect_env_info())

    # 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: pl.LightningDataModule = hydra.utils.instantiate(cfg.data)

    log.info(f"Instantiating model <{cfg.model._target_}>")
    # Hard code: to determine the shape of model's cls head
    cfg.model.model.num_class = datamodule.dataset.n_classes
    model: pl.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: pl.Trainer = hydra.utils.instantiate(
        cfg.trainer, callbacks=callbacks, logger=logger
    )
    #, fast_dev_run = True
    #, overfit_batches=1
    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("compile"):
        log.info("Compiling model!")
        # model = torch.compile(model)
        model = torch.compile(model, mode="reduce-overhead")
        

    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]:
    # apply extra utilities
    # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
    utils.extras(cfg)

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

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

    # return optimized metric
    return metric_value


if __name__ == "__main__":
    main()