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# CelebA

class torchvision.datasets.CelebA(root: str, split: str = 'train', target_type: Union[List[str], str] = 'attr', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)

  Large-scale CelebFaces Attributes (CelebA) Dataset Dataset.

  Parameters:

    - root (string) – Root directory where images are downloaded to.

    - split (string) – One of {‘train’, ‘valid’, ‘test’, ‘all’}. Accordingly dataset is selected.

    - target_type (string or list, optional) –

      Type of target to use, `attr`, `identity`, `bbox`, or `landmarks`. Can also be a list to output a tuple with all specified target types. The targets represent:

      > - `attr` (Tensor shape=(40,) dtype=int): binary (0, 1) labels for attributes

      > - `identity` (int): label for each person (data points with the same identity are the same person)

      > - `bbox` (Tensor shape=(4,) dtype=int): bounding box (x, y, width, height)

      > - `landmarks` (Tensor shape=(10,) dtype=int): landmark points (lefteye_x, lefteye_y, righteye_x, righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y)

      Defaults to `attr`. If empty, `None` will be returned as target.

    - transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. E.g, `transforms.PILToTensor`

    - target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

    - download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

  Special-members:

  __getitem__(index: int) → Tuple[Any, Any]
    Parameters:

      index (int) – Index

    Returns:

      Sample and meta data, optionally transformed by the respective transforms.

    Return type:

      (Any)

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