Inital commit for refactoring to lightning

1 files changed, 0 insertions, 152 deletions

diff --git a/src/text_recognizer/datasets/dataset.py b/src/text_recognizer/datasets/dataset.py
deleted file mode 100644
index e794605..0000000
--- a/src/text_recognizer/datasets/dataset.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""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