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import pytorch_lightning as pl
import hydra
from omegaconf import DictConfig
import remfx.utils as utils
log = utils.get_logger(__name__)
@hydra.main(version_base=None, config_path="../cfg", config_name="config.yaml")
def main(cfg: DictConfig):
# Apply seed for reproducibility
if cfg.seed:
pl.seed_everything(cfg.seed)
log.info(f"Instantiating datamodule <{cfg.datamodule._target_}>.")
datamodule = hydra.utils.instantiate(cfg.datamodule, _convert_="partial")
log.info(f"Instantiating model <{cfg.model._target_}>.")
model = hydra.utils.instantiate(cfg.model, _convert_="partial")
if "ckpt_path" in cfg:
log.info(f"Loading checkpoint from <{cfg.ckpt_path}>.")
model.load_from_checkpoint(
cfg.ckpt_path,
lr=model.lr,
lr_beta1=model.lr_beta1,
lr_beta2=model.lr_beta2,
lr_eps=model.lr_eps,
lr_weight_decay=model.lr_weight_decay,
sample_rate=model.sample_rate,
network=model.model,
)
# Init all callbacks
callbacks = []
if "callbacks" in cfg:
for _, cb_conf in cfg["callbacks"].items():
if "_target_" in cb_conf:
log.info(f"Instantiating callback <{cb_conf._target_}>.")
callbacks.append(hydra.utils.instantiate(cb_conf, _convert_="partial"))
logger = hydra.utils.instantiate(cfg.logger, _convert_="partial")
log.info(f"Instantiating trainer <{cfg.trainer._target_}>.")
trainer = hydra.utils.instantiate(
cfg.trainer, callbacks=callbacks, logger=logger, _convert_="partial"
)
log.info("Logging hyperparameters!")
utils.log_hyperparameters(
config=cfg,
model=model,
datamodule=datamodule,
trainer=trainer,
callbacks=callbacks,
logger=logger,
)
trainer.fit(model=model, datamodule=datamodule)
trainer.test(model=model, datamodule=datamodule, ckpt_path="best")
if __name__ == "__main__":
main()
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