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.

This repository includes a pure PyTorch implementation of a 2D and 3D involution.

Please note that the official implementation provides a more memory efficient CuPy implementation of the 2D involution. Additionally, shikishima-TasakiLab provides a fast and memory efficent CUDA implementation of the 2D Involution.

Installation

The 2D and 3D involution can be easily installed by using pip. shell script pip install git+https://github.com/ChristophReich1996/Involution

Example Usage

Additional examples, such as strided involutions or transposed convolution like involutions, can be found in the example.py file.

The 2D involution can be used as a nn.Module as follows: ````python import torch from involution import Involution2d

involution = Involution2d(inchannels=32, outchannels=64) output = involution(torch.rand(1, 32, 128, 128)) ````

The 2D involution takes the following parameters.

| Parameter | Description | Type | | ------------- | ------------- | ------------- | | inchannels | Number of input channels | int | | outchannels | Number of output channels | int | | sigmamapping | Non-linear mapping as introduced in the paper. If none BN + ReLU is utilized (default=None) | Optional[nn.Module] | | kernelsize | Kernel size to be used (default=(7, 7)) | Union[int, Tuple[int, int]] | | stride | Stride factor to be utilized (default=(1, 1)) | Union[int, Tuple[int, int]] | | groups | Number of groups to be employed (default=1) | int | | reduceratio | Reduce ration of involution channels (default=1) | int | | dilation | Dilation in unfold to be employed (default=(1, 1)) | Union[int, Tuple[int, int]] | | padding | Padding to be used in unfold operation (default=(3, 3)) | Union[int, Tuple[int, int]] | | bias | If true bias is utilized in each convolution layer (default=False) | bool | | forceshape_match | If true potential shape mismatch is solved by performing avg pool (default=False) | bool | | **kwargs | Unused additional key word arguments | Any |

The 3D involution can be used as a nn.Module as follows: ````python import torch from involution import Involution3d

involution = Involution3d(inchannels=8, outchannels=16) output = involution(torch.rand(1, 8, 32, 32, 32)) ````

The 3D involution takes the following parameters.

| Parameter | Description | Type | | ------------- | ------------- | ------------- | | inchannels | Number of input channels | int | | outchannels | Number of output channels | int | | sigmamapping | Non-linear mapping as introduced in the paper. If none BN + ReLU is utilized | Optional[nn.Module] | | kernelsize | Kernel size to be used (default=(7, 7, 7)) | Union[int, Tuple[int, int, int]] | | stride | Stride factor to be utilized (default=(1, 1, 1)) | Union[int, Tuple[int, int, int]] | | groups | Number of groups to be employed (default=1) | int | | reduceratio | Reduce ration of involution channels (default=1) | int | | dilation | Dilation in unfold to be employed (default=(1, 1, 1)) | Union[int, Tuple[int, int, int]] | | padding | Padding to be used in unfold operation (default=(3, 3, 3)) | Union[int, Tuple[int, int, int]] | | bias | If true bias is utilized in each convolution layer (default=False) | bool | | forceshape_match | If true potential shape mismatch is solved by performing avg pool (default=False) | bool | | **kwargs | Unused additional key word arguments | Any |

Reference

bibtex @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} }