grid_sample() and align_corners parameter - new default value in pytorch v1.4.0

It is said pytorch devs are going to change the default behavior of grid_sample() in pytorch v1.4.0: from align_corners=True to align_corners=False

Here is the link: https://pytorch.org/docs/stable/nn.functional.html#grid-sample

What do you think about explicitly adding align_corners=True everywhere in grid_sample() so as not to ruin the current behavior of kornia (and your tests of course)?

[poxyu]

@edgarriba I just don’t have time right now to write tests. But the main part is ready and used since that time…

def create_meshgrid(
        height: int,
        width: int,
        normalized_coordinates: Optional[bool] = True,
        align_corners: Optional[bool] = True,
        device: Optional[torch.device] = torch.device("cpu")) -> torch.Tensor:
    """Generates a coordinate grid for an image.
    When the flag ``normalized_coordinates`` is set to True, the grid is
    normalized to be in the range [-1, 1] to be consistent with the pytorch
    function grid_sample.
    http://pytorch.org/docs/master/nn.html#torch.nn.functional.grid_sample
    Args:
        height (int): the image height (rows).
        width (int): the image width (cols).
        normalized_coordinates (Optional[bool]): whether to normalize
          coordinates in the range [-1, 1] in order to be consistent with the
          PyTorch function grid_sample.
        align_corners (Optional[bool]): if set to ``True``, the extrema [-1, 1]
          are considered as referring to the center points of the input's corner pixels;
          if set to ``False``, they are instead considered as referring to the corner points
          of the input's corner pixels, making the sampling more resolution agnostic 
          (if ``normalized_coordinates`` is set to ``True``).
    Return:
        torch.Tensor: returns a grid tensor with shape :math:`(1, H, W, 2)`.
    """
    # generate coordinates
    xs: Optional[torch.Tensor] = None
    ys: Optional[torch.Tensor] = None
    if normalized_coordinates:
        # extrema [-1, 1]
        x_start, x_stop = -1., 1.
        y_start, y_stop = -1., 1.
        # calculate corner points
        if not align_corners:
            x_half_pixel_size = (x_stop - x_start) / float(width) / 2.
            y_half_pixel_size = (y_stop - y_start) / float(height) / 2.
            # update extrema
            x_start, x_stop = x_start + x_half_pixel_size, x_stop - x_half_pixel_size
            y_start, y_stop = y_start + y_half_pixel_size, y_stop - y_half_pixel_size
        # generate coordinates
        xs = torch.linspace(x_start, x_stop, width, device=device, dtype=torch.float)
        ys = torch.linspace(y_start, y_stop, height, device=device, dtype=torch.float)
    else:
        # generate coordinates
        xs = torch.linspace(0, width - 1, width, device=device, dtype=torch.float)
        ys = torch.linspace(0, height - 1, height, device=device, dtype=torch.float)
    # generate grid by stacking coordinates
    base_grid: torch.Tensor = torch.stack(
        torch.meshgrid([xs, ys])).transpose(1, 2)  # 2xHxW
    return torch.unsqueeze(base_grid, dim=0).permute(0, 2, 3, 1)  # 1xHxWx2

[stale[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions, and happy coding day 😎