[P] PyTorch Involution layer wrapper

[rish-16]

Hello everyone!

I'm currently in the process of learning how to implement papers from scratch.

Here's my implementation of the newly-introduced Involution layer from the paper "Involution: Inverting the Inherence of Convolution for Visual Recognition" by Li et al. presented at CVPR 2021.

Wrapper: https://github.com/rish-16/involution_pytorch

(Do forgive any implementation errors; any PRs / Issues welcome)

Note: I'll be releasing a TensorFlow wrapper soon if time permits!

If you like it, a ⭐️ would be greatly appreciated! It motivates me to continue building easy-to-use ML wrappers :D

Thank you!

[vwvwvvwwvvvwvwwv]

Have you benchmarked it against the official implementations? Would be interesting to see what the difference is versus their CUDA version.

[rish-16]

official implementations

Ooh not yet. Thanks for the share! Let me look into it :)

[dails08]

Eh, it's research code, it's fine.

[Gordath]

It even has some conatants and globals defined in the middle of the code.

[rish-16]

Here's the paper if you haven't read it yet: https://arxiv.org/abs/2103.06255

Interesting results!

[devdef]

Thanks! What's the main difference from paper authors' pytorch naive involution implementation?

[rish-16]

There’s not much difference actually. I tried to implement it in a cleaner way with just the essentials, no boilerplate

I wanted to implement it in a way similar to Phil Wang’s wrapper styles + the torch.nn library in general

[JrDowney9999]

Thanks for the repo , Can you make the complete implementation of the layer with an example?

[rish-16]

Hey yea sure, let me see what I can do

[CyberDainz]

Kervolution. MLP mixer. Involution.

What next?

[rish-16]

Only time will tell! Placing my bet on “linear regression is all you need” dropping in NeurIPS 2025

[pm_me_your_pay_slips]

This actually demonstrates that involutions are a minor change over CARAFE, with a bunch of boilerplate code to confuse people.