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from typing import List, Tuple
import hydra
from omegaconf import DictConfig
from lightning import LightningDataModule, LightningModule, Trainer, Callback
from deepscreen.utils.hydra import checkpoint_rerun_config
from deepscreen.utils import get_logger, job_wrapper, instantiate_callbacks
log = get_logger(__name__)
# def fix_dict_config(cfg: DictConfig):
# """fix all vars in the cfg config
# this is an in-place operation"""
# keys = list(cfg.keys())
# for k in keys:
# if type(cfg[k]) is DictConfig:
# fix_dict_config(cfg[k])
# else:
# setattr(cfg, k, getattr(cfg, k))
@job_wrapper(extra_utils=True)
def predict(cfg: DictConfig) -> Tuple[list, dict]:
"""Predict given checkpoint on a data predict set.
This method is wrapped in optional @job_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.
"""
log.info(f"Instantiating data <{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] = instantiate_callbacks(cfg.get("callbacks"))
log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
trainer: Trainer = hydra.utils.instantiate(cfg.trainer, logger=False, callbacks=callbacks)
object_dict = {
"cfg": cfg,
"datamodule": datamodule,
"model": model,
"callbacks": callbacks,
"trainer": trainer,
}
log.info("Start predicting.")
predictions = trainer.predict(model=model, datamodule=datamodule,
ckpt_path=cfg.ckpt_path, return_predictions=True)
return predictions, object_dict
@hydra.main(version_base="1.3", config_path="../configs", config_name="predict.yaml")
def main(cfg: DictConfig):
assert cfg.ckpt_path, "Checkpoint path (`ckpt_path`) must be specified for predicting."
cfg = checkpoint_rerun_config(cfg)
predictions, _ = predict(cfg)
return predictions
if __name__ == "__main__":
main()
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