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"""Script to run experiments."""
from typing import List, Optional, Type
import hydra
from loguru import logger as log
from omegaconf import DictConfig
from pytorch_lightning import (
Callback,
LightningDataModule,
LightningModule,
seed_everything,
Trainer,
)
from pytorch_lightning.loggers import LightningLoggerBase
from torch import nn
from torchinfo import summary
from text_recognizer.data.base_mapping import AbstractMapping
import utils
def run(config: DictConfig) -> Optional[float]:
"""Runs experiment."""
utils.configure_logging(config)
log.info("Starting experiment...")
if config.get("seed"):
seed_everything(config.seed, workers=True)
log.info(f"Instantiating datamodule <{config.datamodule._target_}>")
datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule)
log.info(f"Instantiating network <{config.network._target_}>")
network: nn.Module = hydra.utils.instantiate(config.network)
log.info(f"Instantiating criterion <{config.criterion._target_}>")
loss_fn: Type[nn.Module] = hydra.utils.instantiate(config.criterion)
log.info(f"Instantiating model <{config.model._target_}>")
model: LightningModule = hydra.utils.instantiate(
config.model,
network=network,
loss_fn=loss_fn,
optimizer_configs=config.optimizers,
lr_scheduler_configs=config.lr_schedulers,
_recursive_=False,
)
# Load callback and logger.
callbacks: List[Type[Callback]] = utils.configure_callbacks(config)
logger: List[Type[LightningLoggerBase]] = utils.configure_logger(config)
log.info(f"Instantiating trainer <{config.trainer._target_}>")
trainer: Trainer = hydra.utils.instantiate(
config.trainer, callbacks=callbacks, logger=logger, _convert_="partial"
)
# Log hyperparameters
log.info("Logging hyperparameters")
utils.log_hyperparameters(config=config, model=model, trainer=trainer)
utils.save_config(config)
if config.get("summary"):
summary(
network, list(map(lambda x: list(x), config.summary)), depth=1, device="cpu"
)
if config.debug:
log.info("Fast development run...")
trainer.fit(model, datamodule=datamodule)
return None
if config.tune:
log.info("Tuning hyperparameters...")
trainer.tune(model, datamodule=datamodule)
if config.train:
log.info("Training network...")
trainer.fit(model, datamodule=datamodule)
if config.test:
log.info("Testing network...")
trainer.test(model, datamodule=datamodule)
log.info(f"Best checkpoint path:\n{trainer.checkpoint_callback.best_model_path}")
utils.finish(logger)
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