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.

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:

import torch
from involution import Involution2d

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

The 2D involution takes the following parameters.

Parameter	Description	Type
in_channels	Number of input channels	int
out_channels	Number of output channels	int
sigma_mapping	Non-linear mapping as introduced in the paper. If none BN + ReLU is utilized (default=None)	Optional[nn.Module]
kernel_size	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
reduce_ratio	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
force_shape_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:

import torch
from involution import Involution3d

involution = Involution3d(in_channels=8, out_channels=16)
output = involution(torch.rand(1, 8, 32, 32, 32))

The 3D involution takes the following parameters.

Parameter	Description	Type
in_channels	Number of input channels	int
out_channels	Number of output channels	int
sigma_mapping	Non-linear mapping as introduced in the paper. If none BN + ReLU is utilized	Optional[nn.Module]
kernel_size	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
reduce_ratio	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
force_shape_match	If true potential shape mismatch is solved by performing avg pool (default=False)	bool
**kwargs	Unused additional key word arguments	Any

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}
}