【深入pytorch】transforms.functional.py

def perspective(
    img: Tensor,
    startpoints: List[List[int]],
    endpoints: List[List[int]],
    interpolation: InterpolationMode = InterpolationMode.BILINEAR,
    fill: Optional[List[float]] = None,
) -> Tensor:
    """Perform perspective transform of the given image.
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.

    Args:
        img (PIL Image or Tensor): Image to be transformed.
        startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
            ``[top-left, top-right, bottom-right, bottom-left]`` of the original image.
        endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners
            ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image.
        interpolation (InterpolationMode): Desired interpolation enum defined by
            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
            For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
            but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
        fill (sequence or number, optional): Pixel fill value for the area outside the transformed
            image. If given a number, the value is used for all bands respectively.

            .. note::
                In torchscript mode single int/float value is not supported, please use a sequence
                of length 1: ``[value, ]``.

    Returns:
        PIL Image or Tensor: transformed Image.
    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(perspective)

    coeffs = _get_perspective_coeffs(startpoints, endpoints)

    # Backward compatibility with integer value
    if isinstance(interpolation, int):
        warnings.warn(
            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
            "Please use InterpolationMode enum."
        )
        interpolation = _interpolation_modes_from_int(interpolation)

    if not isinstance(interpolation, InterpolationMode):
        raise TypeError("Argument interpolation should be a InterpolationMode")

    if not isinstance(img, torch.Tensor):
        pil_interpolation = pil_modes_mapping[interpolation]
        return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill)

    return F_t.perspective(img, coeffs, interpolation=interpolation.value, fill=fill)