Hello everybody,

I am having a hard time while I am trying to design a loss function that applies Sobel filter to the batches before computing MSE. I am quite sure that the problem is related to an “autograd computational graph detachment”, but I just cannot solve it.

Here is my code. Does anyone can see what I am missing?

```
def sobel_MSE(output, target):
dx = (torch.tensor([[1.0, 0.0, -1.0],[2.0, 0.0, -2.0],[1.0, 0.0, -1.0]], requires_grad=True)).float()
dy = (torch.tensor([[1.0, 2.0, 1.0], [0.0, 0.0, 0.0], [-1.0, -2.0, -1.0]], requires_grad=True)).float()
dx = dx.cuda()
dy = dy.cuda()
dx = dx.view((1, 1, 3, 3))
dy = dy.view((1, 1, 3, 3))
doutdx = nn.functional.conv2d(output, dx, padding=1)
doutdy = nn.functional.conv2d(output, dy, padding=1)
dtardx = nn.functional.conv2d(target, dx, padding=1)
dtardy = nn.functional.conv2d(target, dy, padding=1)
dout = torch.sqrt(torch.pow(doutdx, 2) + torch.pow(doutdy, 2))
dtar = torch.sqrt(torch.pow(dtardx, 2) + torch.pow(dtardy, 2))
out = torch.mean(torch.pow(dout-dtar,2))
return out
```