1 files changed, 70 insertions, 0 deletions
diff --git a/text_recognizer/datasets/base_dataset.py b/text_recognizer/datasets/base_dataset.py
new file mode 100644
index 0000000..7322d7f
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+++ b/text_recognizer/datasets/base_dataset.py
@@ -0,0 +1,70 @@
+"""Base PyTorch Dataset class."""
+from typing import Any, Callable, Dict, Sequence, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.utils.data import Dataset
+
+
+class BaseDataset(Dataset):
+    """
+    Base Dataset class that processes data and targets through optional transfroms.
+
+    Args:
+        data (Union[Sequence, Tensor]): Torch tensors, numpy arrays, or PIL images.
+        targets (Union[Sequence, Tensor]): Torch tensors or numpy arrays.
+        tranform (Callable): Function that takes a datum and applies transforms.
+        target_transform (Callable): Fucntion that takes a target and applies
+            target transforms.
+    """
+    def __init__(self,
+            data: Union[Sequence, Tensor],
+            targets: Union[Sequence, Tensor],
+            transform: Callable = None,
+            target_transform: Callable = None,
+            ) -> None:
+        if len(data) != len(targets):
+            raise ValueError("Data and targets must be of equal length.")
+        self.data = data
+        self.targets = targets
+        self.transform = transform
+        self.target_transform = target_transform
+
+
+    def __len__(self) -> int:
+        """Return the length of the dataset."""
+        return len(self.data)
+            
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """Return a datum and its target, after processing by transforms.
+
+        Args:
+            index (int): Index of a datum in the dataset.
+
+        Returns:
+            Tuple[Any, Any]: Datum and target pair.
+
+        """
+        datum, target = self.data[index], self.targets[index]
+
+        if self.transform is not None:
+            datum = self.transform(datum)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return datum, target
+
+
+def convert_strings_to_labels(strings: Sequence[str], mapping: Dict[str, int], length: int) -> Tensor:
+    """
+    Convert a sequence of N strings to (N, length) ndarray, with each string wrapped with <S> and </S> tokens,
+    and padded wiht the <P> token.
+    """
+    labels = torch.ones((len(strings), length), dtype=torch.long) * mapping["<P>"]
+    for i, string in enumerate(strings):
+        tokens = list(string)
+        tokens = ["<S>", *tokens, "</S>"]
+        for j, token in enumerate(tokens):
+            labels[i, j] = mapping[token]
+    return labels