6a4fa82792bddc6126eaf944956f49f0a0cb7ca7,kornia/filters/sobel.py,,sobel,#Any#Any#Any#,199

Before Change

    See :class:`~kornia.filters.Sobel` for details.
    
    return Sobel(normalized, eps)(input)

After Change

        >>> output.shape
        torch.Size([1, 3, 4, 4])
    
    if not isinstance(input, torch.Tensor):
        raise TypeError("Input type is not a torch.Tensor. Got {}"
                        .format(type(input)))

    if not len(input.shape) == 4:
        raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
                         .format(input.shape))

    // comput the x/y gradients
    edges: torch.Tensor = spatial_gradient(input, normalized=normalized)

    // unpack the edges
    gx: torch.Tensor = edges[:, :, 0]
    gy: torch.Tensor = edges[:, :, 1]

    // compute gradient maginitude
    magnitude: torch.Tensor = torch.sqrt(gx * gx + gy * gy + eps)

    return magnitude


class SpatialGradient(nn.Module):
    rComputes the first order image derivative in both x and y using a Sobel

Instances