Hi Pytorch,

I’m trying to implement a custom piecewise loss function in pytorch. Specifically the reverse huber loss with an adaptive threshold (`Loss = |x| if |x| <c, x^2+c^2/2*c otherwise`

) . There doesn’t seem to be a great way to do this. Below is my code

`adiff = torch.abs(output-target)

batch_max = 0.2*torch.max(adiff).data[0]
t1_mask = adiff.le(batch_max).float()
t2_mask = adiff.gt(batch_max).float()
t1 = adiff*t1_mask

t2 = (adiff

*adiff+batch_max*batch_max)/(2

*batch_max)*

t2 = t2t2_mask

t2 = t2

return (torch.sum(t1)+torch.sum(t2))/torch.numel(output.data)``

not the most straightforward. I’m sort of struggling to figure out how I can implement a piecewise loss function easily without using this cumbersome masking thing (in fact, I’m struggling to determine if this is actually correct or not). Can someone help, point me towards an example, or otherwise provide guidance?