renamed datasets to data, added iam refactor

1 files changed, 209 insertions, 0 deletions

diff --git a/text_recognizer/data/util.py b/text_recognizer/data/util.py
new file mode 100644
index 0000000..da87756
--- /dev/null
+++ b/text_recognizer/data/util.py
@@ -0,0 +1,209 @@
+"""Util functions for datasets."""
+import hashlib
+import json
+import os
+from pathlib import Path
+import string
+from typing import Dict, List, Optional, Union
+from urllib.request import urlretrieve
+
+from loguru import logger
+import numpy as np
+import torch
+from torch import Tensor
+from torchvision.datasets import EMNIST
+from tqdm import tqdm
+
+DATA_DIRNAME = Path(__file__).resolve().parents[3] / "data"
+ESSENTIALS_FILENAME = Path(__file__).resolve().parents[0] / "emnist_essentials.json"
+
+
+def save_emnist_essentials(emnsit_dataset: EMNIST = EMNIST) -> None:
+    """Extract and saves EMNIST essentials."""
+    labels = emnsit_dataset.classes
+    labels.sort()
+    mapping = [(i, str(label)) for i, label in enumerate(labels)]
+    essentials = {
+        "mapping": mapping,
+        "input_shape": tuple(np.array(emnsit_dataset[0][0]).shape[:]),
+    }
+    logger.info("Saving emnist essentials...")
+    with open(ESSENTIALS_FILENAME, "w") as f:
+        json.dump(essentials, f)
+
+
+def download_emnist() -> None:
+    """Download the EMNIST dataset via the PyTorch class."""
+    logger.info(f"Data directory is: {DATA_DIRNAME}")
+    dataset = EMNIST(root=DATA_DIRNAME, split="byclass", download=True)
+    save_emnist_essentials(dataset)
+
+
+class EmnistMapper:
+    """Mapper between network output to Emnist character."""
+
+    def __init__(
+        self,
+        pad_token: str,
+        init_token: Optional[str] = None,
+        eos_token: Optional[str] = None,
+        lower: bool = False,
+    ) -> None:
+        """Loads the emnist essentials file with the mapping and input shape."""
+        self.init_token = init_token
+        self.pad_token = pad_token
+        self.eos_token = eos_token
+        self.lower = lower
+
+        self.essentials = self._load_emnist_essentials()
+        # Load dataset information.
+        self._mapping = dict(self.essentials["mapping"])
+        self._augment_emnist_mapping()
+        self._inverse_mapping = {v: k for k, v in self.mapping.items()}
+        self._num_classes = len(self.mapping)
+        self._input_shape = self.essentials["input_shape"]
+
+    def __call__(self, token: Union[str, int, np.uint8, Tensor]) -> Union[str, int]:
+        """Maps the token to emnist character or character index.
+
+        If the token is an integer (index), the method will return the Emnist character corresponding to that index.
+        If the token is a str (Emnist character), the method will return the corresponding index for that character.
+
+        Args:
+            token (Union[str, int, np.uint8, Tensor]): Either a string or index (integer).
+
+        Returns:
+            Union[str, int]: The mapping result.
+
+        Raises:
+            KeyError: If the index or string does not exist in the mapping.
+
+        """
+        if (
+            (isinstance(token, np.uint8) or isinstance(token, int))
+            or torch.is_tensor(token)
+            and int(token) in self.mapping
+        ):
+            return self.mapping[int(token)]
+        elif isinstance(token, str) and token in self._inverse_mapping:
+            return self._inverse_mapping[token]
+        else:
+            raise KeyError(f"Token {token} does not exist in the mappings.")
+
+    @property
+    def mapping(self) -> Dict:
+        """Returns the mapping between index and character."""
+        return self._mapping
+
+    @property
+    def inverse_mapping(self) -> Dict:
+        """Returns the mapping between character and index."""
+        return self._inverse_mapping
+
+    @property
+    def num_classes(self) -> int:
+        """Returns the number of classes in the dataset."""
+        return self._num_classes
+
+    @property
+    def input_shape(self) -> List[int]:
+        """Returns the input shape of the Emnist characters."""
+        return self._input_shape
+
+    def _load_emnist_essentials(self) -> Dict:
+        """Load the EMNIST mapping."""
+        with open(str(ESSENTIALS_FILENAME)) as f:
+            essentials = json.load(f)
+        return essentials
+
+    def _augment_emnist_mapping(self) -> None:
+        """Augment the mapping with extra symbols."""
+        # Extra symbols in IAM dataset
+        if self.lower:
+            self._mapping = {
+                k: str(v)
+                for k, v in enumerate(list(range(10)) + list(string.ascii_lowercase))
+            }
+
+        extra_symbols = [
+            " ",
+            "!",
+            '"',
+            "#",
+            "&",
+            "'",
+            "(",
+            ")",
+            "*",
+            "+",
+            ",",
+            "-",
+            ".",
+            "/",
+            ":",
+            ";",
+            "?",
+        ]
+
+        # padding symbol, and acts as blank symbol as well.
+        extra_symbols.append(self.pad_token)
+
+        if self.init_token is not None:
+            extra_symbols.append(self.init_token)
+
+        if self.eos_token is not None:
+            extra_symbols.append(self.eos_token)
+
+        max_key = max(self.mapping.keys())
+        extra_mapping = {}
+        for i, symbol in enumerate(extra_symbols):
+            extra_mapping[max_key + 1 + i] = symbol
+
+        self._mapping = {**self.mapping, **extra_mapping}
+
+
+def compute_sha256(filename: Union[Path, str]) -> str:
+    """Returns the SHA256 checksum of a file."""
+    with open(filename, "rb") as f:
+        return hashlib.sha256(f.read()).hexdigest()
+
+
+class TqdmUpTo(tqdm):
+    """TQDM progress bar when downloading files.
+
+    From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
+
+    """
+
+    def update_to(
+        self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None
+    ) -> None:
+        """Updates the progress bar.
+
+        Args:
+            blocks (int): Number of blocks transferred so far. Defaults to 1.
+            block_size (int): Size of each block, in tqdm units. Defaults to 1.
+            total_size (Optional[int]): Total size in tqdm units. Defaults to None.
+        """
+        if total_size is not None:
+            self.total = total_size  # pylint: disable=attribute-defined-outside-init
+        self.update(blocks * block_size - self.n)
+
+
+def download_url(url: str, filename: str) -> None:
+    """Downloads a file from url to filename, with a progress bar."""
+    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
+        urlretrieve(url, filename, reporthook=t.update_to, data=None)  # nosec
+
+
+def _download_raw_dataset(metadata: Dict) -> None:
+    if os.path.exists(metadata["filename"]):
+        return
+    logger.info(f"Downloading raw dataset from {metadata['url']}...")
+    download_url(metadata["url"], metadata["filename"])
+    logger.info("Computing SHA-256...")
+    sha256 = compute_sha256(metadata["filename"])
+    if sha256 != metadata["sha256"]:
+        raise ValueError(
+            "Downloaded data file SHA-256 does not match that listed in metadata document."
+        )