Source code for torchvision.datasets.omniglot

from os.path import join
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union

from PIL import Image

from .utils import check_integrity, download_and_extract_archive, list_dir, list_files
from .vision import VisionDataset

[docs]class Omniglot(VisionDataset): """`Omniglot <>`_ Dataset. Args: root (str or ``pathlib.Path``): Root directory of dataset where directory ``omniglot-py`` exists. background (bool, optional): If True, creates dataset from the "background" set, otherwise creates from the "evaluation" set. This terminology is defined by the authors. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. download (bool, optional): If true, downloads the dataset zip files from the internet and puts it in root directory. If the zip files are already downloaded, they are not downloaded again. """ folder = "omniglot-py" download_url_prefix = "" zips_md5 = { "images_background": "68d2efa1b9178cc56df9314c21c6e718", "images_evaluation": "6b91aef0f799c5bb55b94e3f2daec811", } def __init__( self, root: Union[str, Path], background: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> None: super().__init__(join(root, self.folder), transform=transform, target_transform=target_transform) self.background = background if download: if not self._check_integrity(): raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") self.target_folder = join(self.root, self._get_target_folder()) self._alphabets = list_dir(self.target_folder) self._characters: List[str] = sum( ([join(a, c) for c in list_dir(join(self.target_folder, a))] for a in self._alphabets), [] ) self._character_images = [ [(image, idx) for image in list_files(join(self.target_folder, character), ".png")] for idx, character in enumerate(self._characters) ] self._flat_character_images: List[Tuple[str, int]] = sum(self._character_images, []) def __len__(self) -> int: return len(self._flat_character_images)
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]: """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target character class. """ image_name, character_class = self._flat_character_images[index] image_path = join(self.target_folder, self._characters[character_class], image_name) image =, mode="r").convert("L") if self.transform: image = self.transform(image) if self.target_transform: character_class = self.target_transform(character_class) return image, character_class
def _check_integrity(self) -> bool: zip_filename = self._get_target_folder() if not check_integrity(join(self.root, zip_filename + ".zip"), self.zips_md5[zip_filename]): return False return True def download(self) -> None: if self._check_integrity(): print("Files already downloaded and verified") return filename = self._get_target_folder() zip_filename = filename + ".zip" url = self.download_url_prefix + "/" + zip_filename download_and_extract_archive(url, self.root, filename=zip_filename, md5=self.zips_md5[filename]) def _get_target_folder(self) -> str: return "images_background" if self.background else "images_evaluation"


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