Hello, I am designing my loss based on edge information for segmentation. The network (i.e Unet) provides a softmax probability P. The loss defined as

```
loss = \sum_i^N ( G_i - Pedge_i)^2
```

where `Pedge_i`

is the binary edge of the softmax probability. It can obtain by using threshold `P>0.5`

or max(P,axis=1) for multiple class. and N is a number of pixel in ground-truth edge G.

Is possible to perform the loss in pytorch without writing backward function. I mean does it exist auto differentiable? Thanks