Solved by ptrblck in post #2 
              
              
                You could permute the result tensor as seen here: 
conv_out = (F.conv1d(data_reshaped, torch_kernel, padding=padding_value, bias=None) / torch_kernel.shape[2])
ret = conv_out.view(torch_data.shape).permute(0, 2, 1)
print(ret)
> tensor([[[1.0000, 1.0000],
           [2.0000, 2.0000],
           [3.00…