diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/run.py | 37 | ||||
| -rw-r--r-- | training/utils.py | 84 |
2 files changed, 90 insertions, 31 deletions
diff --git a/training/run.py b/training/run.py index ed1b372..5f7c927 100644 --- a/training/run.py +++ b/training/run.py @@ -14,35 +14,12 @@ from pytorch_lightning import ( from pytorch_lightning.loggers import LightningLoggerBase from torch import nn -from utils import configure_logging - - -def configure_callbacks( - config: DictConfig, -) -> List[Type[Callback]]: - """Configures lightning callbacks.""" - callbacks = [] - if config.get("callbacks"): - for callback_config in config.callbacks.values(): - if config.get("_target_"): - log.info(f"Instantiating callback <{callback_config._target_}>") - callbacks.append(hydra.utils.instantiate(callback_config)) - return callbacks - - -def configure_logger(config: DictConfig) -> List[Type[LightningLoggerBase]]: - logger = [] - if config.get("logger"): - for logger_config in config.logger.values(): - if config.get("_target_"): - log.info(f"Instantiating callback <{logger_config._target_}>") - logger.append(hydra.utils.instantiate(logger_config)) - return logger +import utils def run(config: DictConfig) -> Optional[float]: """Runs experiment.""" - configure_logging(config.logging) + utils.configure_logging(config.logging) log.info("Starting experiment...") if config.get("seed"): @@ -65,8 +42,8 @@ def run(config: DictConfig) -> Optional[float]: ) # Load callback and logger. - callbacks = configure_callbacks(config) - logger = configure_logger(config) + 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( @@ -74,6 +51,7 @@ def run(config: DictConfig) -> Optional[float]: ) # Log hyperparameters + utils.log_hyperparameters(config=config, model=model, trainer=trainer) if config.debug: log.info("Fast development run...") @@ -81,7 +59,7 @@ def run(config: DictConfig) -> Optional[float]: return None if config.tune: - log.info("Tuning learning rate and batch size...") + log.info("Tuning hyperparameters...") trainer.tune(model, datamodule=datamodule) if config.train: @@ -92,4 +70,5 @@ def run(config: DictConfig) -> Optional[float]: log.info("Testing network...") trainer.test(model, datamodule=datamodule) - # Make sure everything closes properly + log.info(f"Best checkpoint path:\n{trainer.checkpoint_callback.best_model_path}") + utils.finish(trainer) diff --git a/training/utils.py b/training/utils.py index 7717fc5..4c31dc3 100644 --- a/training/utils.py +++ b/training/utils.py @@ -1,19 +1,52 @@ """Util functions for training hydra configs and pytorch lightning.""" +from typing import Any, List, Type import warnings +import hydra from omegaconf import DictConfig, OmegaConf import loguru.logger as log +from pytorch_lightning import ( + Callback, + LightningModule, + Trainer, +) +from pytorch_lightning.loggers import LightningLoggerBase from pytorch_lightning.loggers.wandb import WandbLogger from pytorch_lightning.utilities import rank_zero_only from tqdm import tqdm +import wandb @rank_zero_only -def configure_logging(level: str) -> None: +def configure_logging(config: DictConfig) -> None: """Configure the loguru logger for output to terminal and disk.""" # Remove default logger to get tqdm to work properly. log.remove() - log.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level) + log.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=config.logging) + + +def configure_callbacks( + config: DictConfig, +) -> List[Type[Callback]]: + """Configures Lightning callbacks.""" + callbacks = [] + if config.get("callbacks"): + for callback_config in config.callbacks.values(): + if config.get("_target_"): + log.info(f"Instantiating callback <{callback_config._target_}>") + callbacks.append(hydra.utils.instantiate(callback_config)) + return callbacks + + +def configure_logger(config: DictConfig) -> List[Type[LightningLoggerBase]]: + """Configures Lightning loggers.""" + logger = [] + if config.get("logger"): + for logger_config in config.logger.values(): + if config.get("_target_"): + log.info(f"Instantiating callback <{logger_config._target_}>") + logger.append(hydra.utils.instantiate(logger_config)) + return logger def extras(config: DictConfig) -> None: @@ -54,3 +87,50 @@ def extras(config: DictConfig) -> None: # Disable adding new keys to config OmegaConf.set_struct(config, True) + + +def empty(*args: Any, **kwargs: Any) -> None: + pass + + +@rank_zero_only +def log_hyperparameters( + config: DictConfig, + model: LightningModule, + trainer: Trainer, +) -> None: + """This method saves hyperparameters with the logger.""" + hparams = {} + + # choose which parts of hydra config will be saved to loggers + hparams["trainer"] = config["trainer"] + hparams["model"] = config["model"] + hparams["datamodule"] = config["datamodule"] + if "callbacks" in config: + hparams["callbacks"] = config["callbacks"] + + # save number of model parameters + hparams["model/params_total"] = sum(p.numel() for p in model.parameters()) + hparams["model/params_trainable"] = sum( + p.numel() for p in model.parameters() if p.requires_grad + ) + hparams["model/params_not_trainable"] = sum( + p.numel() for p in model.parameters() if not p.requires_grad + ) + + # send hparams to all loggers + trainer.logger.log_hyperparams(hparams) + + # disable logging any more hyperparameters for all loggers + # this is just a trick to prevent trainer from logging hparams of model, + # since we already did that above + trainer.logger.log_hyperparams = empty + + +def finish( + logger: List[Type[LightningLoggerBase]], +) -> None: + """Makes sure everything closed properly.""" + for lg in logger: + if isinstance(lg, WandbLogger): + wandb.finish() |