Inital commit for refactoring to lightning

1 files changed, 131 insertions, 0 deletions

diff --git a/text_recognizer/datasets/emnist_dataset.py b/text_recognizer/datasets/emnist_dataset.py
new file mode 100644
index 0000000..9884fdf
--- /dev/null
+++ b/text_recognizer/datasets/emnist_dataset.py
@@ -0,0 +1,131 @@
+"""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()