Solved by imaluengo in post #2 
              
              
                You can: 

Create a convolutional kernel 
conv = nn.Conv2(in, out, ksize, ...)

Copy your kernel weights to it 
conv.weights.copy_(myweights)


Just be aware that you will need to use torch.no_grad if you dont want that convoluton’s weights to be updated while training.