Created an abstract Dataset class for common methods.

1 files changed, 21 insertions, 47 deletions

diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py
index 477f500..4a74b2b 100644
--- a/src/text_recognizer/datasets/iam_lines_dataset.py
+++ b/src/text_recognizer/datasets/iam_lines_dataset.py
@@ -5,11 +5,15 @@ import h5py
 from loguru import logger
 import torch
 from torch import Tensor
-from torch.utils.data import Dataset
 from torchvision.transforms import ToTensor
 
-from text_recognizer.datasets.emnist_dataset import DATA_DIRNAME, EmnistMapper
-from text_recognizer.datasets.util import compute_sha256, download_url
+from text_recognizer.datasets.dataset import Dataset
+from text_recognizer.datasets.util import (
+    compute_sha256,
+    DATA_DIRNAME,
+    download_url,
+    EmnistMapper,
+)
 
 
 PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_lines"
@@ -29,47 +33,26 @@ class IamLinesDataset(Dataset):
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        self.train = train
-        self.split = "train" if self.train else "test"
-        self._mapper = EmnistMapper()
-        self.num_classes = self.mapper.num_classes
-
-        # Set transforms.
-        self.transform = transform
-        if self.transform is None:
-            self.transform = ToTensor()
-
-        self.target_transform = target_transform
-        if self.target_transform is None:
-            self.target_transform = torch.tensor
-
-        self.subsample_fraction = subsample_fraction
-        self.data = None
-        self.targets = None
-
-    @property
-    def mapper(self) -> EmnistMapper:
-        """Returns the EmnistMapper."""
-        return self._mapper
-
-    @property
-    def mapping(self) -> Dict:
-        """Return EMNIST mapping from index to character."""
-        return self._mapper.mapping
+        super().__init__(
+            train=train,
+            subsample_fraction=subsample_fraction,
+            transform=transform,
+            target_transform=target_transform,
+        )
 
     @property
     def input_shape(self) -> Tuple:
         """Input shape of the data."""
-        return self.data.shape[1:]
+        return self.data.shape[1:] if self.data is not None else None
 
     @property
     def output_shape(self) -> Tuple:
         """Output shape of the data."""
-        return self.targets.shape[1:] + (self.num_classes,)
-
-    def __len__(self) -> int:
-        """Returns the length of the dataset."""
-        return len(self.data)
+        return (
+            self.targets.shape[1:] + (self.num_classes,)
+            if self.targets is not None
+            else None
+        )
 
     def load_or_generate_data(self) -> None:
         """Load or generate dataset data."""
@@ -78,19 +61,10 @@ class IamLinesDataset(Dataset):
             logger.info("Downloading IAM lines...")
             download_url(PROCESSED_DATA_URL, PROCESSED_DATA_FILENAME)
         with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
-            self.data = f[f"x_{self.split}"][:]
-            self.targets = f[f"y_{self.split}"][:]
+            self._data = f[f"x_{self.split}"][:]
+            self._targets = f[f"y_{self.split}"][:]
         self._subsample()
 
-    def _subsample(self) -> None:
-        """Only a fraction of the data will be loaded."""
-        if self.subsample_fraction is None:
-            return
-
-        num_samples = int(self.data.shape[0] * self.subsample_fraction)
-        self.data = self.data[:num_samples]
-        self.targets = self.targets[:num_samples]
-
     def __repr__(self) -> str:
         """Print info about the dataset."""
         return (