Hello pytorch community,
I’ve been working on an implementation 16 of Octave Convolution 15 in pytorch, and now I’m trying to benchmark it.
This new octave convolution layer is supposed to be a “drop-in” replacement for nn.Conv2d and according to the paper it should have better performance, both in accuracy and speed, than its vanilla counterpart.
In my implementation, I benchmarked the convolutions individually and the OctConv2d is indeed faster than nn.Conv2d. However, the same is not true for the ResNet implementation 16, which I modified from the original torchvision implementation 5.
The benchmark code can be found here 24
Any clue of why this may be?
Thanks!
Miguel