def pad()

in torchvision/transforms/functional_tensor.py [0:0]

• 48 lines of code
• 19 McCabe index (conditional complexity)

def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
    _assert_image_tensor(img)

    if not isinstance(padding, (int, tuple, list)):
        raise TypeError("Got inappropriate padding arg")
    if not isinstance(fill, (int, float)):
        raise TypeError("Got inappropriate fill arg")
    if not isinstance(padding_mode, str):
        raise TypeError("Got inappropriate padding_mode arg")

    if isinstance(padding, tuple):
        padding = list(padding)

    if isinstance(padding, list) and len(padding) not in [1, 2, 4]:
        raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")

    if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
        raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")

    if isinstance(padding, int):
        if torch.jit.is_scripting():
            # This maybe unreachable
            raise ValueError("padding can't be an int while torchscripting, set it as a list [value, ]")
        pad_left = pad_right = pad_top = pad_bottom = padding
    elif len(padding) == 1:
        pad_left = pad_right = pad_top = pad_bottom = padding[0]
    elif len(padding) == 2:
        pad_left = pad_right = padding[0]
        pad_top = pad_bottom = padding[1]
    else:
        pad_left = padding[0]
        pad_top = padding[1]
        pad_right = padding[2]
        pad_bottom = padding[3]

    p = [pad_left, pad_right, pad_top, pad_bottom]

    if padding_mode == "edge":
        # remap padding_mode str
        padding_mode = "replicate"
    elif padding_mode == "symmetric":
        # route to another implementation
        return _pad_symmetric(img, p)

    need_squeeze = False
    if img.ndim < 4:
        img = img.unsqueeze(dim=0)
        need_squeeze = True

    out_dtype = img.dtype
    need_cast = False
    if (padding_mode != "constant") and img.dtype not in (torch.float32, torch.float64):
        # Here we temporary cast input tensor to float
        # until pytorch issue is resolved :
        # https://github.com/pytorch/pytorch/issues/40763
        need_cast = True
        img = img.to(torch.float32)

    img = torch_pad(img, p, mode=padding_mode, value=float(fill))

    if need_squeeze:
        img = img.squeeze(dim=0)

    if need_cast:
        img = img.to(out_dtype)

    return img