torchvision/transforms/functional.py
def five_crop(img, size):
    """Crop the given PIL Image into four corners and the central crop.

    .. 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:
       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.

    Returns:
       tuple: tuple (tl, tr, bl, br, center)
                Corresponding top left, top right, bottom left, bottom right and center crop.
    """
    if isinstance(size, numbers.Number):
        size = (int(size), int(size))
    else:
        assert len(size) == 2, "Please provide only two dimensions (h, w) for size."

    w, h = img.size
    crop_h, crop_w = size
    if crop_w > w or crop_h > h:
        raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
                                                                                      (h, w)))
    tl = img.crop((0, 0, crop_w, crop_h))
    tr = img.crop((w - crop_w, 0, w, crop_h))
    bl = img.crop((0, h - crop_h, crop_w, h))
    br = img.crop((w - crop_w, h - crop_h, w, h))
    center = center_crop(img, (crop_h, crop_w))
    return (tl, tr, bl, br, center)
def five_crop(img, size):
    """Crop the given PIL Image into four corners and the central crop.

    .. 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:
       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.

    Returns:
       tuple: tuple (tl, tr, bl, br, center)
                Corresponding top left, top right, bottom left, bottom right and center crop.
    """
    if isinstance(size, numbers.Number):
        size = (int(size), int(size))
    else:
        assert len(size) == 2, "Please provide only two dimensions (h, w) for size."

    w, h = img.size
    crop_h, crop_w = size
    if crop_w > w or crop_h > h:
        raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
                                                                                      (h, w)))
    tl = img.crop((0, 0, crop_w, crop_h))
    tr = img.crop((w - crop_w, 0, w, crop_h))
    bl = img.crop((0, h - crop_h, crop_w, h))
    br = img.crop((w - crop_w, h - crop_h, w, h))
    center = center_crop(img, (crop_h, crop_w))
    return (tl, tr, bl, br, center)
Crop the given PIL Image into four corners and the central crop.

    .. 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:
       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.

    Returns:
       tuple: tuple (tl, tr, bl, br, center)
                Corresponding top left, top right, bottom left, bottom right and center crop.