Source code for torchvision.datasets.omniglot
from PIL import Image
from os.path import join
import os
from typing import Any, Callable, List, Optional, Tuple
from .vision import VisionDataset
from .utils import download_and_extract_archive, check_integrity, list_dir, list_files
[docs]class Omniglot(VisionDataset):
"""`Omniglot <https://github.com/brendenlake/omniglot>`_ Dataset.
Args:
root (string): 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 an 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 = 'https://github.com/brendenlake/omniglot/raw/master/python'
zips_md5 = {
'images_background': '68d2efa1b9178cc56df9314c21c6e718',
'images_evaluation': '6b91aef0f799c5bb55b94e3f2daec811'
}
def __init__(
self,
root: str,
background: bool = True,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super(Omniglot, self).__init__(join(root, self.folder), transform=transform,
target_transform=target_transform)
self.background = background
if download:
self.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)
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 = Image.open(image_path, 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'