Inital commit for refactoring to lightning

1 files changed, 152 insertions, 0 deletions

diff --git a/text_recognizer/datasets/dataset.py b/text_recognizer/datasets/dataset.py
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+"""Abstract dataset class."""
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.utils import data
+from torchvision.transforms import ToTensor
+
+import text_recognizer.datasets.transforms as transforms
+from text_recognizer.datasets.util import EmnistMapper
+
+
+class Dataset(data.Dataset):
+    """Abstract class for with common methods for all datasets."""
+
+    def __init__(
+        self,
+        train: bool,
+        subsample_fraction: float = None,
+        transform: Optional[List[Dict]] = None,
+        target_transform: Optional[List[Dict]] = None,
+        init_token: Optional[str] = None,
+        pad_token: Optional[str] = None,
+        eos_token: Optional[str] = None,
+        lower: bool = False,
+    ) -> None:
+        """Initialization of Dataset class.
+
+        Args:
+            train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False.
+            subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None.
+            transform (Optional[List[Dict]]): List of Transform types and args for input data. Defaults to None.
+            target_transform (Optional[List[Dict]]): List of Transform types and args for output data. Defaults to None.
+            init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
+            pad_token (Optional[str]): String representing the pad token. Defaults to None.
+            eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
+            lower (bool): Only use lower case letters. Defaults to False.
+
+        Raises:
+            ValueError: If subsample_fraction is not None and outside the range (0, 1).
+
+        """
+        self.train = train
+        self.split = "train" if self.train else "test"
+
+        if subsample_fraction is not None:
+            if not 0.0 < subsample_fraction < 1.0:
+                raise ValueError("The subsample fraction must be in (0, 1).")
+        self.subsample_fraction = subsample_fraction
+
+        self._mapper = EmnistMapper(
+            init_token=init_token, eos_token=eos_token, pad_token=pad_token, lower=lower
+        )
+        self._input_shape = self._mapper.input_shape
+        self._output_shape = self._mapper._num_classes
+        self.num_classes = self.mapper.num_classes
+
+        # Set transforms.
+        self.transform = self._configure_transform(transform)
+        self.target_transform = self._configure_target_transform(target_transform)
+
+        self._data = None
+        self._targets = None
+
+    def _configure_transform(self, transform: List[Dict]) -> transforms.Compose:
+        transform_list = []
+        if transform is not None:
+            for t in transform:
+                t_type = t["type"]
+                t_args = t["args"] or {}
+                transform_list.append(getattr(transforms, t_type)(**t_args))
+        else:
+            transform_list.append(ToTensor())
+        return transforms.Compose(transform_list)
+
+    def _configure_target_transform(
+        self, target_transform: List[Dict]
+    ) -> transforms.Compose:
+        target_transform_list = [torch.tensor]
+        if target_transform is not None:
+            for t in target_transform:
+                t_type = t["type"]
+                t_args = t["args"] or {}
+                target_transform_list.append(getattr(transforms, t_type)(**t_args))
+        return transforms.Compose(target_transform_list)
+
+    @property
+    def data(self) -> Tensor:
+        """The input data."""
+        return self._data
+
+    @property
+    def targets(self) -> Tensor:
+        """The target data."""
+        return self._targets
+
+    @property
+    def input_shape(self) -> Tuple:
+        """Input shape of the data."""
+        return self._input_shape
+
+    @property
+    def output_shape(self) -> Tuple:
+        """Output shape of the data."""
+        return self._output_shape
+
+    @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
+
+    @property
+    def inverse_mapping(self) -> Dict:
+        """Returns the inverse mapping from character to index."""
+        return self.mapper.inverse_mapping
+
+    def _subsample(self) -> None:
+        """Only this fraction of the data will be loaded."""
+        if self.subsample_fraction is None:
+            return
+        num_subsample = int(self.data.shape[0] * self.subsample_fraction)
+        self._data = self.data[:num_subsample]
+        self._targets = self.targets[:num_subsample]
+
+    def __len__(self) -> int:
+        """Returns the length of the dataset."""
+        return len(self.data)
+
+    def load_or_generate_data(self) -> None:
+        """Load or generate dataset data."""
+        raise NotImplementedError
+
+    def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
+        """Fetches samples from the dataset.
+
+        Args:
+            index (Union[int, torch.Tensor]): The indices of the samples to fetch.
+
+        Raises:
+            NotImplementedError: If the method is not implemented in child class.
+
+        """
+        raise NotImplementedError
+
+    def __repr__(self) -> str:
+        """Returns information about the dataset."""
+        raise NotImplementedError