renamed datasets to data, added iam refactor

1 files changed, 110 insertions, 0 deletions

diff --git a/text_recognizer/data/iam_lines_dataset.py b/text_recognizer/data/iam_lines_dataset.py
new file mode 100644
index 0000000..1cb84bd
--- /dev/null
+++ b/text_recognizer/data/iam_lines_dataset.py
@@ -0,0 +1,110 @@
+"""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