A question about the RandZoomOut

[xiaohu2015]

a question about the augmention (https://github.com/pytorch/vision/blob/main/references/detection/transforms.py) of SSD:

class RandomZoomOut(nn.Module):
    def __init__(
        self, fill: Optional[List[float]] = None, side_range: Tuple[float, float] = (1.0, 4.0), p: float = 0.5
    ):
        super().__init__()
        if fill is None:
            fill = [0.0, 0.0, 0.0]
        self.fill = fill
        self.side_range = side_range
        if side_range[0] < 1.0 or side_range[0] > side_range[1]:
            raise ValueError(f"Invalid canvas side range provided {side_range}.")
        self.p = p

    @torch.jit.unused
    def _get_fill_value(self, is_pil):
        # type: (bool) -> int
        # We fake the type to make it work on JIT
        return tuple(int(x) for x in self.fill) if is_pil else 0

    def forward(
        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        if isinstance(image, torch.Tensor):
            if image.ndimension() not in {2, 3}:
                raise ValueError(f"image should be 2/3 dimensional. Got {image.ndimension()} dimensions.")
            elif image.ndimension() == 2:
                image = image.unsqueeze(0)

        if torch.rand(1) < self.p:
            return image, target

        orig_w, orig_h = F.get_image_size(image)

        r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
        canvas_width = int(orig_w * r)
        canvas_height = int(orig_h * r)

        r = torch.rand(2)
        left = int((canvas_width - orig_w) * r[0])
        top = int((canvas_height - orig_h) * r[1])
        right = canvas_width - (left + orig_w)
        bottom = canvas_height - (top + orig_h)

        if torch.jit.is_scripting():
            fill = 0
        else:
            fill = self._get_fill_value(F._is_pil_image(image))

        image = F.pad(image, [left, top, right, bottom], fill=fill)
        # maybe the following code is redundant?
        if isinstance(image, torch.Tensor):
            v = torch.tensor(self.fill, device=image.device, dtype=image.dtype).view(-1, 1, 1)
            image[..., :top, :] = image[..., :, :left] = image[..., (top + orig_h) :, :] = image[
                ..., :, (left + orig_w) :
            ] = v

        if target is not None:
            target["boxes"][:, 0::2] += left
            target["boxes"][:, 1::2] += top

        return image, target

since the operation of F.pad has pad the image, why you do another fill operation for torch.Tensor?

cc @datumbox

[datumbox]

@xiaohu2015 Sorry for the delayed response. I just got back from PTO and trying to get up-to-speed. I don't remember all the details now as it's been over a year since I wrote this but I can have a look on my past notes and come back to you. :)

[datumbox]

Apologies for the delayed response. It is needed, the reply is here: #5278 (comment)