uniform scales

[gbliao]

Hi, thanks for the interesting work! How to set the value of scale when using _uniform_scales? Or how do just set one scale to train lerf?

    def __call__(self, img_points, scale=None):     
        if scale is None:
            return self._random_scales(img_points)
        else:
            return self._uniform_scales(img_points, scale)    

    def _uniform_scales(self, img_points, scale):
        # import pdb; pdb.set_trace()
        scale_bin = torch.floor(
            (scale - self.tile_sizes[0]) / (self.tile_sizes[-1] - self.tile_sizes[0]) * (self.tile_sizes.shape[0] - 1)
        ).to(torch.int64)
        scale_weight = (scale - self.tile_sizes[scale_bin]) / (
            self.tile_sizes[scale_bin + 1] - self.tile_sizes[scale_bin]
        )
        interp_lst = torch.stack([interp(img_points) for interp in self.data_dict.values()])
        point_inds = torch.arange(img_points.shape[0])
        interp = torch.lerp(
            interp_lst[scale_bin, point_inds],
            interp_lst[scale_bin + 1, point_inds],
            torch.Tensor([scale_weight]).half().to(self.device)[..., None],
        )
        return interp / interp.norm(dim=-1, keepdim=True), scale