eval.py

from typing import List, Tuple

import hydra
import lightning as L
import pyrootutils
from lightning import LightningDataModule, LightningModule, Trainer
from lightning.pytorch.loggers import Logger
from omegaconf import DictConfig, OmegaConf

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
# ------------------------------------------------------------------------------------ #

from src import utils
from src.utils.configure_torch import configure_torch

log = utils.get_pylogger(__name__)


@utils.task_wrapper
def evaluate(cfg: DictConfig) -> Tuple[dict, dict]:
    """Evaluates given checkpoint on a datamodule testset.

    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.
    """

    # 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)

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

    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, logger=logger)

    converted_cfg = OmegaConf.to_container(cfg, resolve=True, throw_on_missing=True)
    object_dict = {
        "cfg": converted_cfg,
        "datamodule": datamodule,
        "model": model,
        "logger": logger,
        "trainer": trainer,
    }

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

    log.info("Starting testing!")
    trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path)

    # for predictions use trainer.predict(...)
    # predictions = trainer.predict(model=model, dataloaders=dataloaders, ckpt_path=cfg.ckpt_path)

    metric_dict = trainer.callback_metrics

    return metric_dict, object_dict


@hydra.main(version_base="1.3", config_path="../configs", config_name="eval.yaml")
def main(cfg: DictConfig) -> None:
    # apply extra utilities
    # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
    utils.extras(cfg)

    # configure torch
    configure_torch()

    # TODO: Do something about hydra eating the errors:
    # https://github.com/facebookresearch/hydra/issues/2664
    evaluate(cfg)


if __name__ == "__main__":
    main()