Omniglot — Torchvision 0.22 documentation (original) (raw)
class torchvision.datasets.Omniglot(root: Union[str, Path], background: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, loader: Optional[Callable[[Union[str, Path]], Any]] = None)[source]¶
Omniglot Dataset.
Parameters:
- root (str or
pathlib.Path
) – Root directory of dataset where directoryomniglot-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.
- loader (callable , optional) – A function to load an image given its path. By default, it uses PIL as its image loader, but users could also pass in
torchvision.io.decode_image
for decoding image data into tensors directly.
Special-members:
__getitem__(index: int) → Tuple[Any, Any][source]¶
Parameters:
index (int) – Index
Returns:
(image, target) where target is index of the target character class.
Return type: