(img: Tensor, size: List[int],
             vertical_flip: bool=False)
return first_five + second_five
Generate ten cropped images from the given image.
    Crop the given image into four corners and the central crop plus the
    flipped version of these (horizontal flipping is used by default).
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    .. Note::
        This transform returns a tuple of images and there may be a
        mismatch in the number of inputs and targets your ``Dataset`` returns.

    Args:
        img (PIL Image or Tensor): Image to be cropped.
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
        vertical_flip (bool): Use vertical flipping instead of horizontal

    Returns:
        tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
        Corresponding top left, top right, bottom left, bottom right and
        center crop and same for the flipped image.
Generate ten cropped images from the given image.
    Crop the given image into four corners and the central crop plus the
    flipped version of these (horizontal flipping is used by default).
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
Generate ten cropped images from the given image . Crop the given image into four corners and the central crop plus the flipped version of these ( horizontal flipping is used by default ) . If the image is torch Tensor it is expected to have [ ... H W ] shape where ... means an arbitrary number of leading dimensions
def ten_crop(img: Tensor, size: List[int],
             vertical_flip: bool=False) -> List[Tensor]:
    """Generate ten cropped images from the given image.
    Crop the given image into four corners and the central crop plus the
    flipped version of these (horizontal flipping is used by default).
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    .. Note::
        This transform returns a tuple of images and there may be a
        mismatch in the number of inputs and targets your ``Dataset`` returns.

    Args:
        img (PIL Image or Tensor): Image to be cropped.
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
        vertical_flip (bool): Use vertical flipping instead of horizontal

    Returns:
        tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
        Corresponding top left, top right, bottom left, bottom right and
        center crop and same for the flipped image.
    """
    if isinstance(size, numbers.Number):
        size = (int(size), int(size))
    elif isinstance(size, (tuple, list)) and len(size) == 1:
        size = (size[0], size[0])

    if len(size) != 2:
        raise ValueError("Please provide only two dimensions (h, w) for size.")

    first_five = five_crop(img, size)

    if vertical_flip:
        img = vflip(img)
    else:
        img = hflip(img)

    second_five = five_crop(img, size)
    return first_five + second_five
def ten_crop ( img : Tensor , size : List [ int ] , vertical_flip : bool = False ) -> List [ Tensor ] : if isinstance ( size , numbers . Number ) : size = ( int ( size ) , int ( size ) ) elif isinstance ( size , ( tuple , list ) ) and len ( size ) == 1 : size = ( size [ 0 ] , size [ 0 ] ) if len ( size ) != 2 : raise ValueError ( "Please provide only two dimensions (h, w) for size." ) first_five = five_crop ( img , size ) if vertical_flip : img = vflip ( img ) else : img = hflip ( img ) second_five = five_crop ( img , size ) return first_five + second_five