I am trying to define a loss function to compute the loss between edge reconstruction. The following is my implementation however I suspect I have made some error. I am calculating the edges using convolutions and then performing mse over it.

```
def edge_loss(out, target, cuda=True):
x_filter = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]])
y_filter = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
convx = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False)
convy = nn.Conv2d(1, 1, kernel_size=3 , stride=1, padding=1, bias=False)
weights_x = torch.from_numpy(x_filter).float().unsqueeze(0).unsqueeze(0)
weights_y = torch.from_numpy(y_filter).float().unsqueeze(0).unsqueeze(0)
if cuda:
weights_x = weights_x.cuda()
weights_y = weights_y.cuda()
convx.weight = nn.Parameter(weights_x)
convy.weight = nn.Parameter(weights_y)
g1_x = convx(out)
g2_x = convx(target)
g1_y = convy(out)
g2_y = convy(target)
g_1 = torch.sqrt(torch.pow(g1_x, 2) + torch.pow(g1_y, 2))
g_2 = torch.sqrt(torch.pow(g2_x, 2) + torch.pow(g2_y, 2))
return torch.mean((g_1 - g_2).pow(2))
```

The loss is then calculated as follows

```
loss = edge_loss(out, x)
loss.backward()
```

I do not want to update the weights of the convolution filters since these are the edge filters needed. Is this implementation correct?