Is ResizeShortestEdge a data augmentation method or just resize the image in detectron2?

[Nurungyi]

❓ How to do something using detectron2

Describe what you want to do, including:
As far as I know, flip and ResizeShortestEdge are basic data augmentation if we do not control and adjust anything.
So, my question is that is ResizeShortestEdge a data augmentation method or just resize the image in detectron2?
I mean that ResizeShortestEdge creates new resized image? or just resize it without creating data.

[MathijsNL]

Data augmentation.

https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/transforms/augmentation_impl.py

class ResizeShortestEdge(Augmentation):
    """
    Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
    If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
    """

    def __init__(
        self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR
    ):
        """
        Args:
            short_edge_length (list[int]): If ``sample_style=="range"``,
                a [min, max] interval from which to sample the shortest edge length.
                If ``sample_style=="choice"``, a list of shortest edge lengths to sample from.
            max_size (int): maximum allowed longest edge length.
            sample_style (str): either "range" or "choice".
        """
        super().__init__()
        assert sample_style in ["range", "choice"], sample_style

        self.is_range = sample_style == "range"
        if isinstance(short_edge_length, int):
            short_edge_length = (short_edge_length, short_edge_length)
        if self.is_range:
            assert len(short_edge_length) == 2, (
                "short_edge_length must be two values using 'range' sample style."
                f" Got {short_edge_length}!"
            )
        self._init(locals())

    def get_transform(self, image):
        h, w = image.shape[:2]
        if self.is_range:
            size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1)
        else:
            size = np.random.choice(self.short_edge_length)
        if size == 0:
            return NoOpTransform()

        scale = size * 1.0 / min(h, w)
        if h < w:
            newh, neww = size, scale * w
        else:
            newh, neww = scale * h, size
        if max(newh, neww) > self.max_size:
            scale = self.max_size * 1.0 / max(newh, neww)
            newh = newh * scale
            neww = neww * scale
        neww = int(neww + 0.5)
        newh = int(newh + 0.5)
        return ResizeTransform(h, w, newh, neww, self.interp)