1 files changed, 0 insertions, 73 deletions
diff --git a/text_recognizer/datasets/base_dataset.py b/text_recognizer/datasets/base_dataset.py
deleted file mode 100644
index a9e9c24..0000000
--- a/text_recognizer/datasets/base_dataset.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""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