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Diffstat (limited to 'text_recognizer/data/util.py')
| -rw-r--r-- | text_recognizer/data/util.py | 209 |
1 files changed, 0 insertions, 209 deletions
diff --git a/text_recognizer/data/util.py b/text_recognizer/data/util.py deleted file mode 100644 index da87756..0000000 --- a/text_recognizer/data/util.py +++ /dev/null @@ -1,209 +0,0 @@ -"""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." - ) |