Inital commit for refactoring to lightning

1 files changed, 0 insertions, 110 deletions

diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py
deleted file mode 100644
index 1cb84bd..0000000
--- a/src/text_recognizer/datasets/iam_lines_dataset.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""IamLinesDataset class."""
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import h5py
-from loguru import logger
-import torch
-from torch import Tensor
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.util import (
-    compute_sha256,
-    DATA_DIRNAME,
-    download_url,
-    EmnistMapper,
-)
-
-
-PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_lines"
-PROCESSED_DATA_FILENAME = PROCESSED_DATA_DIRNAME / "iam_lines.h5"
-PROCESSED_DATA_URL = (
-    "https://s3-us-west-2.amazonaws.com/fsdl-public-assets/iam_lines.h5"
-)
-
-
-class IamLinesDataset(Dataset):
-    """IAM lines datasets for handwritten text lines."""
-
-    def __init__(
-        self,
-        train: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        init_token: Optional[str] = None,
-        pad_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        self.pad_token = "_" if pad_token is None else pad_token
-
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-            init_token=init_token,
-            pad_token=pad_token,
-            eos_token=eos_token,
-            lower=lower,
-        )
-
-    @property
-    def input_shape(self) -> Tuple:
-        """Input shape of the data."""
-        return self.data.shape[1:] if self.data is not None else None
-
-    @property
-    def output_shape(self) -> Tuple:
-        """Output shape of the data."""
-        return (
-            self.targets.shape[1:] + (self.num_classes,)
-            if self.targets is not None
-            else None
-        )
-
-    def load_or_generate_data(self) -> None:
-        """Load or generate dataset data."""
-        if not PROCESSED_DATA_FILENAME.exists():
-            PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-            logger.info("Downloading IAM lines...")
-            download_url(PROCESSED_DATA_URL, PROCESSED_DATA_FILENAME)
-        with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
-            self._data = f[f"x_{self.split}"][:]
-            self._targets = f[f"y_{self.split}"][:]
-        self._subsample()
-
-    def __repr__(self) -> str:
-        """Print info about the dataset."""
-        return (
-            "IAM Lines Dataset\n"  # pylint: disable=no-member
-            f"Number classes: {self.num_classes}\n"
-            f"Mapping: {self.mapper.mapping}\n"
-            f"Data: {self.data.shape}\n"
-            f"Targets: {self.targets.shape}\n"
-        )
-
-    def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]:
-        """Fetches data, target pair of the dataset for a given and index or indices.
-
-        Args:
-            index (Union[int, Tensor]): Either a list or int of indices/index.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target pair.
-
-        """
-        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