Changed project layout again

1 files changed, 203 insertions, 0 deletions

diff --git a/training/run_experiment.py b/training/run_experiment.py
new file mode 100644
index 0000000..f46803f
--- /dev/null
+++ b/training/run_experiment.py
@@ -0,0 +1,203 @@
+"""Script to run experiments."""
+import importlib
+from pathlib import Path
+from typing import Dict, List, Optional, Type
+
+import click
+from loguru import logger
+from omegaconf import DictConfig, OmegaConf
+import pytorch_lightning as pl
+import torch
+from torch import nn
+from tqdm import tqdm
+import wandb
+
+
+SEED = 4711
+CONFIGS_DIRNAME = Path(__file__).parent.resolve() / "configs"
+LOGS_DIRNAME = Path(__file__).parent.resolve() / "runs" / "logs"
+
+
+def _configure_logging(log_dir: Optional[Path], verbose: int = 0) -> None:
+    """Configure the loguru logger for output to terminal and disk."""
+
+    def _get_level(verbose: int) -> str:
+        """Sets the logger level."""
+        levels = {0: "WARNING", 1: "INFO", 2: "DEBUG"}
+        verbose = min(verbose, 2)
+        return levels[verbose]
+
+    # Remove default logger to get tqdm to work properly.
+    logger.remove()
+
+    # Fetch verbosity level.
+    level = _get_level(verbose)
+
+    logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level)
+    if log_dir is not None:
+        logger.add(
+            str(log_dir / "train.log"),
+            format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
+        )
+
+
+def _load_config(file_path: Path) -> DictConfig:
+    """Return experiment config."""
+    logger.info(f"Loading config from: {file_path}")
+    if not file_path.exists():
+        raise FileNotFoundError(f"Experiment config not found at: {file_path}")
+    return OmegaConf.load(file_path)
+
+
+def _import_class(module_and_class_name: str) -> type:
+    """Import class from module."""
+    module_name, class_name = module_and_class_name.rsplit(".", 1)
+    module = importlib.import_module(module_name)
+    return getattr(module, class_name)
+
+
+def _configure_callbacks(
+    callbacks: List[DictConfig],
+) -> List[Type[pl.callbacks.Callback]]:
+    """Configures lightning callbacks."""
+    pl_callbacks = [
+        getattr(pl.callbacks, callback.type)(**callback.args) for callback in callbacks
+    ]
+    return pl_callbacks
+
+
+def _configure_logger(
+        network: Type[nn.Module], args: Dict, log_dir: str, use_wandb: bool
+) -> Type[pl.loggers.LightningLoggerBase]:
+    """Configures lightning logger."""
+    if use_wandb:
+        pl_logger = pl.loggers.WandbLogger(save_dir=log_dir)
+        pl_logger.watch(network)
+        pl_logger.log_hyperparams(vars(args))
+        return pl_logger
+    return pl.logger.TensorBoardLogger(save_dir=log_dir)
+
+
+def _save_best_weights(
+    callbacks: List[Type[pl.callbacks.Callback]], use_wandb: bool
+) -> None:
+    """Saves the best model."""
+    model_checkpoint_callback = next(
+        callback
+        for callback in callbacks
+        if isinstance(callback, pl.callbacks.ModelCheckpoint)
+    )
+    best_model_path = model_checkpoint_callback.best_model_path
+    if best_model_path:
+        logger.info(f"Best model saved at: {best_model_path}")
+        if use_wandb:
+            logger.info("Uploading model to W&B...")
+            wandb.save(best_model_path)
+
+
+def _load_lit_model(
+    lit_model_class: type, network: Type[nn.Module], config: DictConfig
+) -> Type[pl.LightningModule]:
+    """Load lightning model."""
+    if config.load_checkpoint is not None:
+        logger.info(
+            f"Loading network weights from checkpoint: {config.load_checkpoint}"
+        )
+        return lit_model_class.load_from_checkpoint(
+            config.load_checkpoint, network=network, **config.model.args
+        )
+    return lit_model_class(network=network, **config.model.args)
+
+
+def run(
+    filename: str,
+    train: bool,
+    test: bool,
+    tune: bool,
+    use_wandb: bool,
+    verbose: int = 0,
+) -> None:
+    """Runs experiment."""
+    # Set log dir where logging output and weights are saved to.
+    log_dir = str(LOGS_DIRNAME)
+
+    _configure_logging(None, verbose=verbose)
+    logger.info("Starting experiment...")
+
+
+    # Seed everything in the experiment.
+    logger.info(f"Seeding everthing with seed={SEED}")
+    pl.utilities.seed.seed_everything(SEED)
+
+    # Load config.
+    file_path = CONFIGS_DIRNAME / filename
+    config = _load_config(file_path)
+
+    # Load classes.
+    data_module_class = _import_class(f"text_recognizer.data.{config.data.type}")
+    network_class = _import_class(f"text_recognizer.networks.{config.network.type}")
+    lit_model_class = _import_class(f"text_recognizer.models.{config.model.type}")
+
+    # Initialize data object and network.
+    data_module = data_module_class(**config.data.args)
+    network = network_class(**data_module.config(), **config.network.args)
+
+    # Load callback and logger.
+    callbacks = _configure_callbacks(config.callbacks)
+    pl_logger = _configure_logger(network, config, log_dir, use_wandb)
+
+    # Load ligtning model.
+    lit_model = _load_lit_model(lit_model_class, network, config)
+
+    trainer = pl.Trainer(
+        **config.trainer.args,
+        callbacks=callbacks,
+        logger=pl_logger,
+        weigths_save_path=log_dir,
+    )
+
+    if tune:
+        logger.info(f"Tuning learning rate and batch size...")
+        trainer.tune(lit_model, datamodule=data_module)
+
+    if train:
+        logger.info(f"Training network...")
+        trainer.fit(lit_model, datamodule=data_module)
+
+    if test:
+        logger.info(f"Testing network...")
+        trainer.test(lit_model, datamodule=data_module)
+
+    _save_best_weights(callbacks, use_wandb)
+
+
+@click.command()
+@click.option("-f", "--experiment_config", type=str, help="Path to experiment config.")
+@click.option("--use_wandb", is_flag=True, help="If true, do use wandb for logging.")
+@click.option(
+    "--tune", is_flag=True, help="If true, tune hyperparameters for training."
+)
+@click.option("--train", is_flag=True, help="If true, train the model.")
+@click.option("--test", is_flag=True, help="If true, test the model.")
+@click.option("-v", "--verbose", count=True)
+def cli(
+    experiment_config: str,
+    use_wandb: bool,
+    tune: bool,
+    train: bool,
+    test: bool,
+    verbose: int,
+) -> None:
+    """Run experiment."""
+    run(
+        filename=experiment_config,
+        train=train,
+        test=test,
+        tune=tune,
+        use_wandb=use_wandb,
+        verbose=verbose,
+    )
+
+
+if __name__ == "__main__":
+    cli()