Remove unused code

2 files changed, 0 insertions, 192 deletions

diff --git a/text_recognizer/networks/vision_transformer.py b/text_recognizer/networks/vision_transformer.py
deleted file mode 100644
index b617c71..0000000
--- a/text_recognizer/networks/vision_transformer.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Vision transformer for character recognition."""
-from torch import nn, Tensor
-
-
-class VisionTransformer(nn.Module):
-    def __init__(self,) -> None:
-        pass
diff --git a/training/run_experiment.py b/training/run_experiment.py
deleted file mode 100644
index b3c9552..0000000
--- a/training/run_experiment.py
+++ /dev/null
@@ -1,185 +0,0 @@
-"""Script to run experiments."""
-from datetime import datetime
-import importlib
-from pathlib import Path
-from typing import List, Optional, Type
-import warnings
-
-warnings.filterwarnings("ignore")
-
-import hydra
-from loguru import logger
-from omegaconf import DictConfig, OmegaConf
-import pytorch_lightning as pl
-from torch import nn
-from tqdm import tqdm
-import wandb
-
-
-LOGS_DIRNAME = Path(__file__).parent.resolve() / "logs"
-
-
-def _create_experiment_dir(config: DictConfig) -> Path:
-    """Creates log directory for experiment."""
-    log_dir = (
-        LOGS_DIRNAME
-        / f"{config.model.type}_{config.network.type}".lower()
-        / datetime.now().strftime("%m%d_%H%M%S")
-    )
-    log_dir.mkdir(parents=True, exist_ok=True)
-    return log_dir
-
-
-def _save_config(config: DictConfig, log_dir: Path) -> None:
-    """Saves config to log directory."""
-    with (log_dir / "config.yaml").open("w") as f:
-        OmegaConf.save(config=config, f=f)
-
-
-def _configure_logging(log_dir: Optional[Path], level: str) -> None:
-    """Configure the loguru logger for output to terminal and disk."""
-    # Remove default logger to get tqdm to work properly.
-    logger.remove()
-    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 _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: DictConfig,) -> List[Type[pl.callbacks.Callback]]:
-    """Configures lightning callbacks."""
-    pl_callbacks = [
-        getattr(pl.callbacks, callback.type)(**callback.args)
-        for callback in callbacks.values()
-    ]
-    return pl_callbacks
-
-
-def _configure_logger(
-    network: Type[nn.Module], config: DictConfig, log_dir: Path
-) -> Type[pl.loggers.LightningLoggerBase]:
-    """Configures lightning logger."""
-    if config.trainer.wandb:
-        logger.info("Logging model with W&B")
-        pl_logger = pl.loggers.WandbLogger(save_dir=str(log_dir))
-        pl_logger.watch(network)
-        pl_logger.log_hyperparams(vars(config))
-        return pl_logger
-    logger.info("Logging model with Tensorboard")
-    return pl.loggers.TensorBoardLogger(save_dir=str(log_dir))
-
-
-def _save_best_weights(
-    pl_callbacks: List[Type[pl.callbacks.Callback]], use_wandb: bool
-) -> None:
-    """Saves the best model."""
-    model_checkpoint_callback = next(
-        callback
-        for callback in pl_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,
-            optimizer=config.optimizer,
-            criterion=config.criterion,
-            lr_scheduler=config.lr_scheduler,
-            **config.model.args,
-        )
-    return lit_model_class(
-        network=network,
-        optimizer=config.optimizer,
-        criterion=config.criterion,
-        lr_scheduler=config.lr_scheduler,
-        **config.model.args,
-    )
-
-
-def run(config: DictConfig) -> None:
-    """Runs experiment."""
-    log_dir = _create_experiment_dir(config)
-    _configure_logging(log_dir, level=config.logging)
-    logger.info("Starting experiment...")
-
-    pl.utilities.seed.seed_everything(config.trainer.seed)
-
-    # Load classes.
-    data_module_class = _import_class(f"text_recognizer.data.{config.dataset.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.dataset.args)
-    network = network_class(**data_module.config(), **config.network.args)
-
-    # Load callback and logger.
-    pl_callbacks = _configure_callbacks(config.callbacks)
-    pl_logger = _configure_logger(network, config, log_dir)
-
-    # Load ligtning model.
-    lit_model = _load_lit_model(lit_model_class, network, config)
-
-    # Save config to experiment dir.
-    _save_config(config, log_dir)
-
-    trainer = pl.Trainer(
-        **config.trainer.args,
-        callbacks=pl_callbacks,
-        logger=pl_logger,
-        weights_save_path=str(log_dir),
-    )
-
-    if config.trainer.args.fast_dev_run:
-        logger.info("Fast development run...")
-        trainer.fit(lit_model, datamodule=data_module)
-        return None
-
-    if config.trainer.tune:
-        logger.info("Tuning learning rate and batch size...")
-        trainer.tune(lit_model, datamodule=data_module)
-
-    if config.trainer.train:
-        logger.info("Training network...")
-        trainer.fit(lit_model, datamodule=data_module)
-
-    if config.trainer.test:
-        logger.info("Testing network...")
-        trainer.test(lit_model, datamodule=data_module)
-
-    _save_best_weights(pl_callbacks, config.trainer.wandb)
-
-
-@hydra.main(config_path="conf", config_name="config")
-def main(config: DictConfig) -> None:
-    """Loads config with hydra."""
-    print(OmegaConf.to_yaml(config))
-    run(config)
-
-
-if __name__ == "__main__":
-    main()