Involution: Inverting the Inherence of Convolution for Visual Recognition

Unofficial PyTorch reimplementation of the paper Involution: Inverting the Inherence of Convolution for Visual Recognition by Duo Li, Jie Hu, Changhu Wang et al. published at CVPR 2021.

Please note that the official implementation provides a more memory efficient CuPy implementation of the 2d involution.

Example usage

The 2d involution can be used as a nn.Module as follows:

import torch
from involution import Involution2d

involution = Involution2d(in_channels=32, out_channels=64)
output = involution(torch.rand(1, 32, 128, 128))

Installation

The 2d involution can be easily installed by utilizing pip.

pip install git+https://github.com/ChristophReich1996/Involution

Reference

@inproceedings{Li2021,
    author = {Li, Duo and Hu, Jie and Wang, Changhu and Li, Xiangtai and She, Qi and Zhu, Lei and Zhang, Tong and Chen, Qifeng},
    title = {Involution: Inverting the Inherence of Convolution for Visual Recognition},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2021}
}