1 files changed, 0 insertions, 89 deletions
diff --git a/text_recognizer/datasets/base_data_module.py b/text_recognizer/datasets/base_data_module.py
deleted file mode 100644
index f5e7300..0000000
--- a/text_recognizer/datasets/base_data_module.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""Base lightning DataModule class."""
-from pathlib import Path
-from typing import Dict
-
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader
-
-
-def load_and_print_info(data_module_class: type) -> None:
-    """Load EMNISTLines and prints info."""
-    dataset = data_module_class()
-    dataset.prepare_data()
-    dataset.setup()
-    print(dataset)
-
-
-class BaseDataModule(pl.LightningDataModule):
-    """Base PyTorch Lightning DataModule."""
-
-    def __init__(self, batch_size: int = 128, num_workers: int = 0) -> None:
-        super().__init__()
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-
-        # Placeholders for subclasses.
-        self.dims = None
-        self.output_dims = None
-        self.mapping = None
-
-    @classmethod
-    def data_dirname(cls) -> Path:
-        """Return the path to the base data directory."""
-        return Path(__file__).resolve().parents[2] / "data"
-
-    def config(self) -> Dict:
-        """Return important settings of the dataset."""
-        return {
-            "input_dim": self.dims,
-            "output_dims": self.output_dims,
-            "mapping": self.mapping,
-        }
-
-    def prepare_data(self) -> None:
-        """Prepare data for training."""
-        pass
-
-    def setup(self, stage: str = None) -> None:
-        """Split into train, val, test, and set dims.
-
-        Should assign `torch Dataset` objects to self.data_train, self.data_val, and
-            optionally self.data_test.
-
-        Args:
-            stage (Any): Variable to set splits.
-
-        """
-        self.data_train = None
-        self.data_val = None
-        self.data_test = None
-
-    def train_dataloader(self) -> DataLoader:
-        """Retun DataLoader for train data."""
-        return DataLoader(
-            self.data_train,
-            shuffle=True,
-            batch_size=self.batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-        )
-
-    def val_dataloader(self) -> DataLoader:
-        """Return DataLoader for val data."""
-        return DataLoader(
-            self.data_val,
-            shuffle=False,
-            batch_size=self.batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-        )
-
-    def test_dataloader(self) -> DataLoader:
-        """Return DataLoader for val data."""
-        return DataLoader(
-            self.data_test,
-            shuffle=False,
-            batch_size=self.batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-        )