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 intorchvision.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:

tuple