# How to write an new convolution operator in pytorch

We all know that convolution operator is just a linear operator acting in sliding window way.

I am trying to implement SimNet Convolotion, which apply merely lp norm before the linear operation.

Or we can say, the original 1d convolution is:

And I want:

What’s the best way to do in pytorch?

Wouldn’t something like this do the trick:

class CustomConv(torch.nn.Module):
def __init__(p, *args, **kwargs):
super().__init__(self)
self.conv = torch.nn.Conv2d(*args, **kwargs)
self.p = p

def forward(self, x, z):
diff = x-z
p_norm = torch.abs(x-z).pow(self.p)
return self.conv(p_norm)


There might be some typos or little mistakes as I’m typing from my smartphone.

But…no
The z in there has the same size with self.conv.weight
That’s the problem. But if z is the same size as the convolutional weight x must be of equal size to calculate a norm or am I mistaken there?

Maybe I was not so clear.
I said “in the sliding window way” means, convolution operate take a patch of x to do the linear operation.
Looks like: Every point of the output feature map is got from a patch of x. Note x_patch here.
Now, the lp norm is also implemented in x_patch.

Or we can say, the original 1d convolution is:

And I want:

I’m afraid you have to implement the convolution yourself in python using the already available pytorch functions as the Convolutions are implemented in C.

Alternatively you could have a look at the C-Implementation which can be found here and check whether it would be easier to modify this implementation. On this tutorial page you can find examples towards how to extend pytorch.

I think the best approach would be to write your own autograd function as suggested in this post

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The C-implementation way is little tricky, for I found the pytorch->torch->Aten->CUDNN dependency chain, , which I am not good at.