1 files changed, 0 insertions, 131 deletions
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
deleted file mode 100644
index 9884fdf..0000000
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""Emnist dataset: black and white images of handwritten characters (Aa-Zz) and digits (0-9)."""
-
-import json
-from pathlib import Path
-from typing import Callable, Optional, Tuple, Union
-
-from loguru import logger
-import numpy as np
-from PIL import Image
-import torch
-from torch import Tensor
-from torchvision.datasets import EMNIST
-from torchvision.transforms import Compose, ToTensor
-
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.transforms import Transpose
-from text_recognizer.datasets.util import DATA_DIRNAME
-
-
-class EmnistDataset(Dataset):
-    """This is a class for resampling and subsampling the PyTorch EMNIST dataset."""
-
-    def __init__(
-        self,
-        pad_token: str = None,
-        train: bool = False,
-        sample_to_balance: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        seed: int = 4711,
-    ) -> None:
-        """Loads the dataset and the mappings.
-
-        Args:
-            pad_token (str): The pad token symbol. Defaults to _.
-            train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False.
-            sample_to_balance (bool): Resamples the dataset to make it balanced. Defaults to False.
-            subsample_fraction (float): Description of parameter `subsample_fraction`. Defaults to None.
-            transform (Optional[Callable]): Transform(s) for input data. Defaults to None.
-            target_transform (Optional[Callable]): Transform(s) for output data. Defaults to None.
-            seed (int): Seed number. Defaults to 4711.
-
-        """
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-            pad_token=pad_token,
-        )
-
-        self.sample_to_balance = sample_to_balance
-
-        # Have to transpose the emnist characters, ToTensor norms input between [0,1].
-        if transform is None:
-            self.transform = Compose([Transpose(), ToTensor()])
-
-        self.target_transform = None
-
-        self.seed = seed
-
-    def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
-        """Fetches samples from the dataset.
-
-        Args:
-            index (Union[int, Tensor]): The indices of the samples to fetch.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target tuple.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        if self.transform:
-            data = self.transform(data)
-
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets
-
-    def __repr__(self) -> str:
-        """Returns information about the dataset."""
-        return (
-            "EMNIST Dataset\n"
-            f"Num classes: {self.num_classes}\n"
-            f"Input shape: {self.input_shape}\n"
-            f"Mapping: {self.mapper.mapping}\n"
-        )
-
-    def _sample_to_balance(self) -> None:
-        """Because the dataset is not balanced, we take at most the mean number of instances per class."""
-        np.random.seed(self.seed)
-        x = self._data
-        y = self._targets
-        num_to_sample = int(np.bincount(y.flatten()).mean())
-        all_sampled_indices = []
-        for label in np.unique(y.flatten()):
-            inds = np.where(y == label)[0]
-            sampled_indices = np.unique(np.random.choice(inds, num_to_sample))
-            all_sampled_indices.append(sampled_indices)
-        indices = np.concatenate(all_sampled_indices)
-        x_sampled = x[indices]
-        y_sampled = y[indices]
-        self._data = x_sampled
-        self._targets = y_sampled
-
-    def load_or_generate_data(self) -> None:
-        """Fetch the EMNIST dataset."""
-        dataset = EMNIST(
-            root=DATA_DIRNAME,
-            split="byclass",
-            train=self.train,
-            download=False,
-            transform=None,
-            target_transform=None,
-        )
-
-        self._data = dataset.data
-        self._targets = dataset.targets
-
-        if self.sample_to_balance:
-            self._sample_to_balance()
-
-        if self.subsample_fraction is not None:
-            self._subsample()