b9088d6735e94b600ab9f1e42f6f7aa56eafa96f,kornia/filters/laplacian.py,Laplacian,forward,#Laplacian#Any#,127

Before Change

        return computed

    def forward(self, x: torch.Tensor):  // type: ignore
        if not torch.is_tensor(x):
            raise TypeError("Input x type is not a torch.Tensor. Got {}"
                            .format(type(x)))
        if not len(x.shape) == 4:
            raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
                             .format(x.shape))
        // prepare kernel
        b, c, h, w = x.shape
        tmp_kernel: torch.Tensor = self.kernel.to(x.device).to(x.dtype)
        kernel: torch.Tensor = tmp_kernel.repeat(c, 1, 1, 1)

        // convolve tensor with gaussian kernel
        // TODO: Filter2D convolves an image with the kernel. A wrapper fucntion
        //  for conv2d something similar to the one
        //       given in OpenCV. Also see separable filters.
        // https://github.com/opencv/opencv/blob/7fb70e170154d064ef12d8fec61c0ae70812ce3d/modules/imgproc/src/deriv.cpp//L822
        return conv2d(x, kernel, padding=self._padding, stride=1, groups=c)


////////////////////////////////////////////
// functional interface

After Change

            get_laplacian_kernel2d(kernel_size), dim=0)

    def forward(self, input: torch.Tensor):  // type: ignore
        return kornia.filter2D(input, self.kernel, self.border_type)


////////////////////////////////////////////
// functional interface

Instances