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from typing import Any, Dict, List, Tuple
import hydra
import rootutils
from lightning import LightningDataModule, LightningModule, Trainer
from lightning.pytorch.loggers import Logger
from omegaconf import DictConfig
rootutils.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/rootutils
# ------------------------------------------------------------------------------------ #
from swim.utils import (
RankedLogger,
extras,
instantiate_loggers,
log_hyperparameters,
task_wrapper,
)
log = RankedLogger(__name__, rank_zero_only=True)
@task_wrapper
def evaluate(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""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.
:param cfg: DictConfig configuration composed by Hydra.
:return: Tuple[dict, dict] with metrics and dict with all instantiated objects.
"""
assert cfg.ckpt_path
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] = instantiate_loggers(cfg.get("logger"))
log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
trainer: Trainer = hydra.utils.instantiate(cfg.trainer, logger=logger)
object_dict = {
"cfg": cfg,
"datamodule": datamodule,
"model": model,
"logger": logger,
"trainer": trainer,
}
if logger:
log.info("Logging hyperparameters!")
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:
"""Main entry point for evaluation.
:param cfg: DictConfig configuration composed by Hydra.
"""
# apply extra utilities
# (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
extras(cfg)
evaluate(cfg)
if __name__ == "__main__":
main()
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