# External-Attention-pytorch

**Repository Path**: sk345/External-Attention-pytorch

## Basic Information

- **Project Name**: External-Attention-pytorch
- **Description**: 各种注意力机制pytorch实现以及论文
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 9
- **Forks**: 5
- **Created**: 2021-10-19
- **Last Updated**: 2024-04-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


<img src="./FightingCVimg/LOGO.gif" height="200" width="400"/>


# FightingCV Codebase For [***Attention***](#attention-series),[***Backbone***](#backbone-series), [***MLP***](#mlp-series), [***Re-parameter***](#re-parameter-series), [**Convolution**](#convolution-series)

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Hello，大家好，我是小马🚀🚀🚀

***For 小白（Like Me）：***
最近在读论文的时候会发现一个问题，有时候论文核心思想非常简单，核心代码可能也就十几行。但是打开作者release的源码时，却发现提出的模块嵌入到分类、检测、分割等任务框架中，导致代码比较冗余，对于特定任务框架不熟悉的我，**很难找到核心代码**，导致在论文和网络思想的理解上会有一定困难。

***For 进阶者（Like You）：***
如果把Conv、FC、RNN这些基本单元看做小的Lego积木，把Transformer、ResNet这些结构看成已经搭好的Lego城堡。那么本项目提供的模块就是一个个具有完整语义信息的Lego组件。**让科研工作者们避免反复造轮子**，只需思考如何利用这些“Lego组件”，搭建出更多绚烂多彩的作品。

***For 大神（May Be Like You）：***
能力有限，**不喜轻喷**！！！

***For All：***
本项目就是要实现一个既能**让深度学习小白也能搞懂**，又能**服务科研和工业社区**的代码库。作为[【论文解析项目】](https://github.com/xmu-xiaoma666/FightingCV-Paper-Reading)的补充，本项目的宗旨是从代码角度，实现🚀**让世界上没有难读的论文**🚀。

（同时也非常欢迎各位科研工作者将自己的工作的核心代码整理到本项目中，推动科研社区的发展，会在readme中注明代码的作者~）




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强烈推荐大家关注[**知乎**](https://www.zhihu.com/people/jason-14-58-38/posts)账号和[**FightingCV公众号**](https://mp.weixin.qq.com/s/sgNw6XFBPcD20Ef3ddfE1w)，可以快速了解到最新优质的干货资源。




***

# Contents

- [Attention Series](#attention-series)
    - [1. External Attention Usage](#1-external-attention-usage)

    - [2. Self Attention Usage](#2-self-attention-usage)

    - [3. Simplified Self Attention Usage](#3-simplified-self-attention-usage)

    - [4. Squeeze-and-Excitation Attention Usage](#4-squeeze-and-excitation-attention-usage)

    - [5. SK Attention Usage](#5-sk-attention-usage)

    - [6. CBAM Attention Usage](#6-cbam-attention-usage)

    - [7. BAM Attention Usage](#7-bam-attention-usage)
    
    - [8. ECA Attention Usage](#8-eca-attention-usage)

    - [9. DANet Attention Usage](#9-danet-attention-usage)

    - [10. Pyramid Split Attention (PSA) Usage](#10-Pyramid-Split-Attention-Usage)

    - [11. Efficient Multi-Head Self-Attention(EMSA) Usage](#11-Efficient-Multi-Head-Self-Attention-Usage)

    - [12. Shuffle Attention Usage](#12-Shuffle-Attention-Usage)
    
    - [13. MUSE Attention Usage](#13-MUSE-Attention-Usage)
  
    - [14. SGE Attention Usage](#14-SGE-Attention-Usage)

    - [15. A2 Attention Usage](#15-A2-Attention-Usage)

    - [16. AFT Attention Usage](#16-AFT-Attention-Usage)

    - [17. Outlook Attention Usage](#17-Outlook-Attention-Usage)

    - [18. ViP Attention Usage](#18-ViP-Attention-Usage)

    - [19. CoAtNet Attention Usage](#19-CoAtNet-Attention-Usage)

    - [20. HaloNet Attention Usage](#20-HaloNet-Attention-Usage)

    - [21. Polarized Self-Attention Usage](#21-Polarized-Self-Attention-Usage)

    - [22. CoTAttention Usage](#22-CoTAttention-Usage)

    - [23. Residual Attention Usage](#23-Residual-Attention-Usage)
  
    - [24. S2 Attention Usage](#24-S2-Attention-Usage)

    - [25. GFNet Attention Usage](#25-GFNet-Attention-Usage)

    - [26. Triplet Attention Usage](#26-TripletAttention-Usage)

    - [27. Coordinate Attention Usage](#27-Coordinate-Attention-Usage)

    - [28. MobileViT Attention Usage](#28-MobileViT-Attention-Usage)

    - [29. ParNet Attention Usage](#29-ParNet-Attention-Usage)

    - [30. UFO Attention Usage](#30-UFO-Attention-Usage)
  

- [Backbone Series](#Backbone-series)

    - [1. ResNet Usage](#1-ResNet-Usage)

    - [2. ResNeXt Usage](#2-ResNeXt-Usage)

    - [3. MobileViT Usage](#3-MobileViT-Usage)

    - [4. ConvMixer Usage](#4-ConvMixer-Usage)


- [MLP Series](#mlp-series)

    - [1. RepMLP Usage](#1-RepMLP-Usage)

    - [2. MLP-Mixer Usage](#2-MLP-Mixer-Usage)

    - [3. ResMLP Usage](#3-ResMLP-Usage)

    - [4. gMLP Usage](#4-gMLP-Usage)

    - [5. sMLP Usage](#5-sMLP-Usage)

- [Re-Parameter(ReP) Series](#Re-Parameter-series)

    - [1. RepVGG Usage](#1-RepVGG-Usage)

    - [2. ACNet Usage](#2-ACNet-Usage)

    - [3. Diverse Branch Block(DDB) Usage](#3-Diverse-Branch-Block-Usage)

- [Convolution Series](#Convolution-series)

    - [1. Depthwise Separable Convolution Usage](#1-Depthwise-Separable-Convolution-Usage)

    - [2. MBConv Usage](#2-MBConv-Usage)

    - [3. Involution Usage](#3-Involution-Usage)

    - [4. DynamicConv Usage](#4-DynamicConv-Usage)

    - [5. CondConv Usage](#5-CondConv-Usage)

***


# Attention Series

- Pytorch implementation of ["Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks---arXiv 2021.05.05"](https://arxiv.org/abs/2105.02358)

- Pytorch implementation of ["Attention Is All You Need---NIPS2017"](https://arxiv.org/pdf/1706.03762.pdf)

- Pytorch implementation of ["Squeeze-and-Excitation Networks---CVPR2018"](https://arxiv.org/abs/1709.01507)

- Pytorch implementation of ["Selective Kernel Networks---CVPR2019"](https://arxiv.org/pdf/1903.06586.pdf)

- Pytorch implementation of ["CBAM: Convolutional Block Attention Module---ECCV2018"](https://openaccess.thecvf.com/content_ECCV_2018/papers/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.pdf)

- Pytorch implementation of ["BAM: Bottleneck Attention Module---BMCV2018"](https://arxiv.org/pdf/1807.06514.pdf)

- Pytorch implementation of ["ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks---CVPR2020"](https://arxiv.org/pdf/1910.03151.pdf)

- Pytorch implementation of ["Dual Attention Network for Scene Segmentation---CVPR2019"](https://arxiv.org/pdf/1809.02983.pdf)

- Pytorch implementation of ["EPSANet: An Efficient Pyramid Split Attention Block on Convolutional Neural Network---arXiv 2021.05.30"](https://arxiv.org/pdf/2105.14447.pdf)

- Pytorch implementation of ["ResT: An Efficient Transformer for Visual Recognition---arXiv 2021.05.28"](https://arxiv.org/abs/2105.13677)

- Pytorch implementation of ["SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS---ICASSP 2021"](https://arxiv.org/pdf/2102.00240.pdf)

- Pytorch implementation of ["MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning---arXiv 2019.11.17"](https://arxiv.org/abs/1911.09483)

- Pytorch implementation of ["Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks---arXiv 2019.05.23"](https://arxiv.org/pdf/1905.09646.pdf)

- Pytorch implementation of ["A2-Nets: Double Attention Networks---NIPS2018"](https://arxiv.org/pdf/1810.11579.pdf)


- Pytorch implementation of ["An Attention Free Transformer---ICLR2021 (Apple New Work)"](https://arxiv.org/pdf/2105.14103v1.pdf)


- Pytorch implementation of [VOLO: Vision Outlooker for Visual Recognition---arXiv 2021.06.24"](https://arxiv.org/abs/2106.13112) 
  [【论文解析】](https://zhuanlan.zhihu.com/p/385561050)


- Pytorch implementation of [Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition---arXiv 2021.06.23](https://arxiv.org/abs/2106.12368) 
  [【论文解析】](https://mp.weixin.qq.com/s/5gonUQgBho_m2O54jyXF_Q)


- Pytorch implementation of [CoAtNet: Marrying Convolution and Attention for All Data Sizes---arXiv 2021.06.09](https://arxiv.org/abs/2106.04803) 
  [【论文解析】](https://zhuanlan.zhihu.com/p/385578588)


- Pytorch implementation of [Scaling Local Self-Attention for Parameter Efficient Visual Backbones---CVPR2021 Oral](https://arxiv.org/pdf/2103.12731.pdf)  [【论文解析】](https://zhuanlan.zhihu.com/p/388598744)



- Pytorch implementation of [Polarized Self-Attention: Towards High-quality Pixel-wise Regression---arXiv 2021.07.02](https://arxiv.org/abs/2107.00782)  [【论文解析】](https://zhuanlan.zhihu.com/p/389770482) 


- Pytorch implementation of [Contextual Transformer Networks for Visual Recognition---arXiv 2021.07.26](https://arxiv.org/abs/2107.12292)  [【论文解析】](https://zhuanlan.zhihu.com/p/394795481) 


- Pytorch implementation of [Residual Attention: A Simple but Effective Method for Multi-Label Recognition---ICCV2021](https://arxiv.org/abs/2108.02456) 


- Pytorch implementation of [S²-MLPv2: Improved Spatial-Shift MLP Architecture for Vision---arXiv 2021.08.02](https://arxiv.org/abs/2108.01072) [【论文解析】](https://zhuanlan.zhihu.com/p/397003638) 

- Pytorch implementation of [Global Filter Networks for Image Classification---arXiv 2021.07.01](https://arxiv.org/abs/2107.00645) 

- Pytorch implementation of [Rotate to Attend: Convolutional Triplet Attention Module---WACV 2021](https://arxiv.org/abs/2010.03045) 

- Pytorch implementation of [Coordinate Attention for Efficient Mobile Network Design ---CVPR 2021](https://arxiv.org/abs/2103.02907)

- Pytorch implementation of [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2021.10.05](https://arxiv.org/abs/2103.02907)

- Pytorch implementation of [Non-deep Networks---ArXiv 2021.10.20](https://arxiv.org/abs/2110.07641)

- Pytorch implementation of [UFO-ViT: High Performance Linear Vision Transformer without Softmax---ArXiv 2021.09.29](https://arxiv.org/abs/2109.14382)

***

### 1. External Attention Usage
#### 1.1. Paper
["Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks"](https://arxiv.org/abs/2105.02358)

#### 1.2. Overview
![](./model/img/External_Attention.png)

#### 1.3. Usage Code
```python
from model.attention.ExternalAttention import ExternalAttention
import torch

input=torch.randn(50,49,512)
ea = ExternalAttention(d_model=512,S=8)
output=ea(input)
print(output.shape)
```

***


### 2. Self Attention Usage
#### 2.1. Paper
["Attention Is All You Need"](https://arxiv.org/pdf/1706.03762.pdf)

#### 1.2. Overview
![](./model/img/SA.png)

#### 1.3. Usage Code
```python
from model.attention.SelfAttention import ScaledDotProductAttention
import torch

input=torch.randn(50,49,512)
sa = ScaledDotProductAttention(d_model=512, d_k=512, d_v=512, h=8)
output=sa(input,input,input)
print(output.shape)
```

***

### 3. Simplified Self Attention Usage
#### 3.1. Paper
[None]()

#### 3.2. Overview
![](./model/img/SSA.png)

#### 3.3. Usage Code
```python
from model.attention.SimplifiedSelfAttention import SimplifiedScaledDotProductAttention
import torch

input=torch.randn(50,49,512)
ssa = SimplifiedScaledDotProductAttention(d_model=512, h=8)
output=ssa(input,input,input)
print(output.shape)

```

***

### 4. Squeeze-and-Excitation Attention Usage
#### 4.1. Paper
["Squeeze-and-Excitation Networks"](https://arxiv.org/abs/1709.01507)

#### 4.2. Overview
![](./model/img/SE.png)

#### 4.3. Usage Code
```python
from model.attention.SEAttention import SEAttention
import torch

input=torch.randn(50,512,7,7)
se = SEAttention(channel=512,reduction=8)
output=se(input)
print(output.shape)

```

***

### 5. SK Attention Usage
#### 5.1. Paper
["Selective Kernel Networks"](https://arxiv.org/pdf/1903.06586.pdf)

#### 5.2. Overview
![](./model/img/SK.png)

#### 5.3. Usage Code
```python
from model.attention.SKAttention import SKAttention
import torch

input=torch.randn(50,512,7,7)
se = SKAttention(channel=512,reduction=8)
output=se(input)
print(output.shape)

```
***

### 6. CBAM Attention Usage
#### 6.1. Paper
["CBAM: Convolutional Block Attention Module"](https://openaccess.thecvf.com/content_ECCV_2018/papers/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.pdf)

#### 6.2. Overview
![](./model/img/CBAM1.png)

![](./model/img/CBAM2.png)

#### 6.3. Usage Code
```python
from model.attention.CBAM import CBAMBlock
import torch

input=torch.randn(50,512,7,7)
kernel_size=input.shape[2]
cbam = CBAMBlock(channel=512,reduction=16,kernel_size=kernel_size)
output=cbam(input)
print(output.shape)

```

***

### 7. BAM Attention Usage
#### 7.1. Paper
["BAM: Bottleneck Attention Module"](https://arxiv.org/pdf/1807.06514.pdf)

#### 7.2. Overview
![](./model/img/BAM.png)

#### 7.3. Usage Code
```python
from model.attention.BAM import BAMBlock
import torch

input=torch.randn(50,512,7,7)
bam = BAMBlock(channel=512,reduction=16,dia_val=2)
output=bam(input)
print(output.shape)

```

***

### 8. ECA Attention Usage
#### 8.1. Paper
["ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks"](https://arxiv.org/pdf/1910.03151.pdf)

#### 8.2. Overview
![](./model/img/ECA.png)

#### 8.3. Usage Code
```python
from model.attention.ECAAttention import ECAAttention
import torch

input=torch.randn(50,512,7,7)
eca = ECAAttention(kernel_size=3)
output=eca(input)
print(output.shape)

```

***

### 9. DANet Attention Usage
#### 9.1. Paper
["Dual Attention Network for Scene Segmentation"](https://arxiv.org/pdf/1809.02983.pdf)

#### 9.2. Overview
![](./model/img/danet.png)

#### 9.3. Usage Code
```python
from model.attention.DANet import DAModule
import torch

input=torch.randn(50,512,7,7)
danet=DAModule(d_model=512,kernel_size=3,H=7,W=7)
print(danet(input).shape)

```

***

### 10. Pyramid Split Attention Usage

#### 10.1. Paper
["EPSANet: An Efficient Pyramid Split Attention Block on Convolutional Neural Network"](https://arxiv.org/pdf/2105.14447.pdf)

#### 10.2. Overview
![](./model/img/psa.png)

#### 10.3. Usage Code
```python
from model.attention.PSA import PSA
import torch

input=torch.randn(50,512,7,7)
psa = PSA(channel=512,reduction=8)
output=psa(input)
print(output.shape)

```

***


### 11. Efficient Multi-Head Self-Attention Usage

#### 11.1. Paper
["ResT: An Efficient Transformer for Visual Recognition"](https://arxiv.org/abs/2105.13677)

#### 11.2. Overview
![](./model/img/EMSA.png)

#### 11.3. Usage Code
```python

from model.attention.EMSA import EMSA
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,64,512)
emsa = EMSA(d_model=512, d_k=512, d_v=512, h=8,H=8,W=8,ratio=2,apply_transform=True)
output=emsa(input,input,input)
print(output.shape)
    
```

***


### 12. Shuffle Attention Usage

#### 12.1. Paper
["SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS"](https://arxiv.org/pdf/2102.00240.pdf)

#### 12.2. Overview
![](./model/img/ShuffleAttention.jpg)

#### 12.3. Usage Code
```python

from model.attention.ShuffleAttention import ShuffleAttention
import torch
from torch import nn
from torch.nn import functional as F


input=torch.randn(50,512,7,7)
se = ShuffleAttention(channel=512,G=8)
output=se(input)
print(output.shape)

    
```


***


### 13. MUSE Attention Usage

#### 13.1. Paper
["MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning"](https://arxiv.org/abs/1911.09483)

#### 13.2. Overview
![](./model/img/MUSE.png)

#### 13.3. Usage Code
```python
from model.attention.MUSEAttention import MUSEAttention
import torch
from torch import nn
from torch.nn import functional as F


input=torch.randn(50,49,512)
sa = MUSEAttention(d_model=512, d_k=512, d_v=512, h=8)
output=sa(input,input,input)
print(output.shape)

```

***


### 14. SGE Attention Usage

#### 14.1. Paper
[Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks](https://arxiv.org/pdf/1905.09646.pdf)

#### 14.2. Overview
![](./model/img/SGE.png)

#### 14.3. Usage Code
```python
from model.attention.SGE import SpatialGroupEnhance
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,512,7,7)
sge = SpatialGroupEnhance(groups=8)
output=sge(input)
print(output.shape)

```

***


### 15. A2 Attention Usage

#### 15.1. Paper
[A2-Nets: Double Attention Networks](https://arxiv.org/pdf/1810.11579.pdf)

#### 15.2. Overview
![](./model/img/A2.png)

#### 15.3. Usage Code
```python
from model.attention.A2Atttention import DoubleAttention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,512,7,7)
a2 = DoubleAttention(512,128,128,True)
output=a2(input)
print(output.shape)

```



### 16. AFT Attention Usage

#### 16.1. Paper
[An Attention Free Transformer](https://arxiv.org/pdf/2105.14103v1.pdf)

#### 16.2. Overview
![](./model/img/AFT.jpg)

#### 16.3. Usage Code
```python
from model.attention.AFT import AFT_FULL
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,49,512)
aft_full = AFT_FULL(d_model=512, n=49)
output=aft_full(input)
print(output.shape)

```






### 17. Outlook Attention Usage

#### 17.1. Paper


[VOLO: Vision Outlooker for Visual Recognition"](https://arxiv.org/abs/2106.13112)


#### 17.2. Overview
![](./model/img/OutlookAttention.png)

#### 17.3. Usage Code
```python
from model.attention.OutlookAttention import OutlookAttention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,28,28,512)
outlook = OutlookAttention(dim=512)
output=outlook(input)
print(output.shape)

```


***






### 18. ViP Attention Usage

#### 18.1. Paper


[Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition"](https://arxiv.org/abs/2106.12368)


#### 18.2. Overview
![](./model/img/ViP.png)

#### 18.3. Usage Code
```python

from model.attention.ViP import WeightedPermuteMLP
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(64,8,8,512)
seg_dim=8
vip=WeightedPermuteMLP(512,seg_dim)
out=vip(input)
print(out.shape)

```


***





### 19. CoAtNet Attention Usage

#### 19.1. Paper


[CoAtNet: Marrying Convolution and Attention for All Data Sizes"](https://arxiv.org/abs/2106.04803) 


#### 19.2. Overview
None


#### 19.3. Usage Code
```python

from model.attention.CoAtNet import CoAtNet
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,3,224,224)
mbconv=CoAtNet(in_ch=3,image_size=224)
out=mbconv(input)
print(out.shape)

```


***






### 20. HaloNet Attention Usage

#### 20.1. Paper


[Scaling Local Self-Attention for Parameter Efficient Visual Backbones"](https://arxiv.org/pdf/2103.12731.pdf) 


#### 20.2. Overview

![](./model/img/HaloNet.png)

#### 20.3. Usage Code
```python

from model.attention.HaloAttention import HaloAttention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,512,8,8)
halo = HaloAttention(dim=512,
    block_size=2,
    halo_size=1,)
output=halo(input)
print(output.shape)

```


***

### 21. Polarized Self-Attention Usage

#### 21.1. Paper

[Polarized Self-Attention: Towards High-quality Pixel-wise Regression"](https://arxiv.org/abs/2107.00782)  


#### 21.2. Overview

![](./model/img/PoSA.png)

#### 21.3. Usage Code
```python

from model.attention.PolarizedSelfAttention import ParallelPolarizedSelfAttention,SequentialPolarizedSelfAttention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,512,7,7)
psa = SequentialPolarizedSelfAttention(channel=512)
output=psa(input)
print(output.shape)


```


***


### 22. CoTAttention Usage

#### 22.1. Paper

[Contextual Transformer Networks for Visual Recognition---arXiv 2021.07.26](https://arxiv.org/abs/2107.12292) 


#### 22.2. Overview

![](./model/img/CoT.png)

#### 22.3. Usage Code
```python

from model.attention.CoTAttention import CoTAttention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,512,7,7)
cot = CoTAttention(dim=512,kernel_size=3)
output=cot(input)
print(output.shape)



```

***


### 23. Residual Attention Usage

#### 23.1. Paper

[Residual Attention: A Simple but Effective Method for Multi-Label Recognition---ICCV2021](https://arxiv.org/abs/2108.02456) 


#### 23.2. Overview

![](./model/img/ResAtt.png)

#### 23.3. Usage Code
```python

from model.attention.ResidualAttention import ResidualAttention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,512,7,7)
resatt = ResidualAttention(channel=512,num_class=1000,la=0.2)
output=resatt(input)
print(output.shape)



```

***



### 24. S2 Attention Usage

#### 24.1. Paper

[S²-MLPv2: Improved Spatial-Shift MLP Architecture for Vision---arXiv 2021.08.02](https://arxiv.org/abs/2108.01072) 


#### 24.2. Overview

![](./model/img/S2Attention.png)

#### 24.3. Usage Code
```python
from model.attention.S2Attention import S2Attention
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(50,512,7,7)
s2att = S2Attention(channels=512)
output=s2att(input)
print(output.shape)

```

***



### 25. GFNet Attention Usage

#### 25.1. Paper

[Global Filter Networks for Image Classification---arXiv 2021.07.01](https://arxiv.org/abs/2107.00645) 


#### 25.2. Overview

![](./model/img/GFNet.jpg)

#### 25.3. Usage Code - Implemented by [Wenliang Zhao (Author)](https://scholar.google.com/citations?user=lyPWvuEAAAAJ&hl=en)

```python
from model.attention.gfnet import GFNet
import torch
from torch import nn
from torch.nn import functional as F

x = torch.randn(1, 3, 224, 224)
gfnet = GFNet(embed_dim=384, img_size=224, patch_size=16, num_classes=1000)
out = gfnet(x)
print(out.shape)

```

***


### 26. TripletAttention Usage

#### 26.1. Paper

[Rotate to Attend: Convolutional Triplet Attention Module---CVPR 2021](https://arxiv.org/abs/2010.03045) 

#### 26.2. Overview

![](./model/img/triplet.png)

#### 26.3. Usage Code - Implemented by [digantamisra98](https://github.com/digantamisra98)

```python
from model.attention.TripletAttention import TripletAttention
import torch
from torch import nn
from torch.nn import functional as F
input=torch.randn(50,512,7,7)
triplet = TripletAttention()
output=triplet(input)
print(output.shape)
```


***


### 27. Coordinate Attention Usage

#### 27.1. Paper

[Coordinate Attention for Efficient Mobile Network Design---CVPR 2021](https://arxiv.org/abs/2103.02907)


#### 27.2. Overview

![](./model/img/CoordAttention.png)

#### 27.3. Usage Code - Implemented by [Andrew-Qibin](https://github.com/Andrew-Qibin)

```python
from model.attention.CoordAttention import CoordAtt
import torch
from torch import nn
from torch.nn import functional as F

inp=torch.rand([2, 96, 56, 56])
inp_dim, oup_dim = 96, 96
reduction=32

coord_attention = CoordAtt(inp_dim, oup_dim, reduction=reduction)
output=coord_attention(inp)
print(output.shape)
```

***


### 28. MobileViT Attention Usage

#### 28.1. Paper

[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2021.10.05](https://arxiv.org/abs/2103.02907)


#### 28.2. Overview

![](./model/img/MobileViTAttention.png)

#### 28.3. Usage Code

```python
from model.attention.MobileViTAttention import MobileViTAttention
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    m=MobileViTAttention()
    input=torch.randn(1,3,49,49)
    output=m(input)
    print(output.shape)  #output:(1,3,49,49)
    
```

***


### 29. ParNet Attention Usage

#### 29.1. Paper

[Non-deep Networks---ArXiv 2021.10.20](https://arxiv.org/abs/2110.07641)


#### 29.2. Overview

![](./model/img/ParNet.png)

#### 29.3. Usage Code

```python
from model.attention.ParNetAttention import *
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(50,512,7,7)
    pna = ParNetAttention(channel=512)
    output=pna(input)
    print(output.shape) #50,512,7,7
    
```

***


### 30. UFO Attention Usage

#### 30.1. Paper

[UFO-ViT: High Performance Linear Vision Transformer without Softmax---ArXiv 2021.09.29](https://arxiv.org/abs/2110.07641)


#### 30.2. Overview

![](./model/img/UFO.png)

#### 30.3. Usage Code

```python
from model.attention.UFOAttention import *
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(50,49,512)
    ufo = UFOAttention(d_model=512, d_k=512, d_v=512, h=8)
    output=ufo(input,input,input)
    print(output.shape) #[50, 49, 512]
    
```

***


# Backbone Series

- Pytorch implementation of ["Deep Residual Learning for Image Recognition---CVPR2016 Best Paper"](https://arxiv.org/pdf/1512.03385.pdf)

- Pytorch implementation of ["Aggregated Residual Transformations for Deep Neural Networks---CVPR2017"](https://arxiv.org/abs/1611.05431v2)

- Pytorch implementation of [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2020.10.05](https://arxiv.org/abs/2103.02907)

- Pytorch implementation of [Patches Are All You Need?---ICLR2022 (Under Review)](https://openreview.net/forum?id=TVHS5Y4dNvM)


### 1. ResNet Usage
#### 1.1. Paper
["Deep Residual Learning for Image Recognition---CVPR2016 Best Paper"](https://arxiv.org/pdf/1512.03385.pdf)

#### 1.2. Overview
![](./model/img/resnet.png)
![](./model/img/resnet2.jpg)

#### 1.3. Usage Code
```python

from model.backbone.resnet import ResNet50,ResNet101,ResNet152
import torch
if __name__ == '__main__':
    input=torch.randn(50,3,224,224)
    resnet50=ResNet50(1000)
    # resnet101=ResNet101(1000)
    # resnet152=ResNet152(1000)
    out=resnet50(input)
    print(out.shape)

```


### 2. ResNeXt Usage
#### 2.1. Paper

["Aggregated Residual Transformations for Deep Neural Networks---CVPR2017"](https://arxiv.org/abs/1611.05431v2)

#### 2.2. Overview
![](./model/img/resnext.png)

#### 2.3. Usage Code
```python

from model.backbone.resnext import ResNeXt50,ResNeXt101,ResNeXt152
import torch

if __name__ == '__main__':
    input=torch.randn(50,3,224,224)
    resnext50=ResNeXt50(1000)
    # resnext101=ResNeXt101(1000)
    # resnext152=ResNeXt152(1000)
    out=resnext50(input)
    print(out.shape)


```



### 3. MobileViT Usage
#### 3.1. Paper

[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2020.10.05](https://arxiv.org/abs/2103.02907)

#### 3.2. Overview
![](./model/img/mobileViT.jpg)

#### 3.3. Usage Code
```python

from model.backbone.MobileViT import *
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)

    ### mobilevit_xxs
    mvit_xxs=mobilevit_xxs()
    out=mvit_xxs(input)
    print(out.shape)

    ### mobilevit_xs
    mvit_xs=mobilevit_xs()
    out=mvit_xs(input)
    print(out.shape)


    ### mobilevit_s
    mvit_s=mobilevit_s()
    out=mvit_s(input)
    print(out.shape)

```





### 4. ConvMixer Usage
#### 4.1. Paper
[Patches Are All You Need?---ICLR2022 (Under Review)](https://openreview.net/forum?id=TVHS5Y4dNvM)
#### 4.2. Overview
![](./model/img/ConvMixer.png)

#### 4.3. Usage Code
```python

from model.backbone.ConvMixer import *
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    x=torch.randn(1,3,224,224)
    convmixer=ConvMixer(dim=512,depth=12)
    out=convmixer(x)
    print(out.shape)  #[1, 1000]


```







# MLP Series

- Pytorch implementation of ["RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition---arXiv 2021.05.05"](https://arxiv.org/pdf/2105.01883v1.pdf)

- Pytorch implementation of ["MLP-Mixer: An all-MLP Architecture for Vision---arXiv 2021.05.17"](https://arxiv.org/pdf/2105.01601.pdf)

- Pytorch implementation of ["ResMLP: Feedforward networks for image classification with data-efficient training---arXiv 2021.05.07"](https://arxiv.org/pdf/2105.03404.pdf)

- Pytorch implementation of ["Pay Attention to MLPs---arXiv 2021.05.17"](https://arxiv.org/abs/2105.08050)


- Pytorch implementation of ["Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?---arXiv 2021.09.12"](https://arxiv.org/abs/2109.05422)

### 1. RepMLP Usage
#### 1.1. Paper
["RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition"](https://arxiv.org/pdf/2105.01883v1.pdf)

#### 1.2. Overview
![](./model/img/repmlp.png)

#### 1.3. Usage Code
```python
from model.mlp.repmlp import RepMLP
import torch
from torch import nn

N=4 #batch size
C=512 #input dim
O=1024 #output dim
H=14 #image height
W=14 #image width
h=7 #patch height
w=7 #patch width
fc1_fc2_reduction=1 #reduction ratio
fc3_groups=8 # groups
repconv_kernels=[1,3,5,7] #kernel list
repmlp=RepMLP(C,O,H,W,h,w,fc1_fc2_reduction,fc3_groups,repconv_kernels=repconv_kernels)
x=torch.randn(N,C,H,W)
repmlp.eval()
for module in repmlp.modules():
    if isinstance(module, nn.BatchNorm2d) or isinstance(module, nn.BatchNorm1d):
        nn.init.uniform_(module.running_mean, 0, 0.1)
        nn.init.uniform_(module.running_var, 0, 0.1)
        nn.init.uniform_(module.weight, 0, 0.1)
        nn.init.uniform_(module.bias, 0, 0.1)

#training result
out=repmlp(x)
#inference result
repmlp.switch_to_deploy()
deployout = repmlp(x)

print(((deployout-out)**2).sum())
```

### 2. MLP-Mixer Usage
#### 2.1. Paper
["MLP-Mixer: An all-MLP Architecture for Vision"](https://arxiv.org/pdf/2105.01601.pdf)

#### 2.2. Overview
![](./model/img/mlpmixer.png)

#### 2.3. Usage Code
```python
from model.mlp.mlp_mixer import MlpMixer
import torch
mlp_mixer=MlpMixer(num_classes=1000,num_blocks=10,patch_size=10,tokens_hidden_dim=32,channels_hidden_dim=1024,tokens_mlp_dim=16,channels_mlp_dim=1024)
input=torch.randn(50,3,40,40)
output=mlp_mixer(input)
print(output.shape)
```

***

### 3. ResMLP Usage
#### 3.1. Paper
["ResMLP: Feedforward networks for image classification with data-efficient training"](https://arxiv.org/pdf/2105.03404.pdf)

#### 3.2. Overview
![](./model/img/resmlp.png)

#### 3.3. Usage Code
```python
from model.mlp.resmlp import ResMLP
import torch

input=torch.randn(50,3,14,14)
resmlp=ResMLP(dim=128,image_size=14,patch_size=7,class_num=1000)
out=resmlp(input)
print(out.shape) #the last dimention is class_num
```

***

### 4. gMLP Usage
#### 4.1. Paper
["Pay Attention to MLPs"](https://arxiv.org/abs/2105.08050)

#### 4.2. Overview
![](./model/img/gMLP.jpg)

#### 4.3. Usage Code
```python
from model.mlp.g_mlp import gMLP
import torch

num_tokens=10000
bs=50
len_sen=49
num_layers=6
input=torch.randint(num_tokens,(bs,len_sen)) #bs,len_sen
gmlp = gMLP(num_tokens=num_tokens,len_sen=len_sen,dim=512,d_ff=1024)
output=gmlp(input)
print(output.shape)
```

***

### 5. sMLP Usage
#### 5.1. Paper
["Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?"](https://arxiv.org/abs/2109.05422)

#### 5.2. Overview
![](./model/img/sMLP.jpg)

#### 5.3. Usage Code
```python
from model.mlp.sMLP_block import sMLPBlock
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(50,3,224,224)
    smlp=sMLPBlock(h=224,w=224)
    out=smlp(input)
    print(out.shape)
```


# Re-Parameter Series

- Pytorch implementation of ["RepVGG: Making VGG-style ConvNets Great Again---CVPR2021"](https://arxiv.org/abs/2101.03697)

- Pytorch implementation of ["ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks---ICCV2019"](https://arxiv.org/abs/1908.03930)

- Pytorch implementation of ["Diverse Branch Block: Building a Convolution as an Inception-like Unit---CVPR2021"](https://arxiv.org/abs/2103.13425)


***

### 1. RepVGG Usage
#### 1.1. Paper
["RepVGG: Making VGG-style ConvNets Great Again"](https://arxiv.org/abs/2101.03697)

#### 1.2. Overview
![](./model/img/repvgg.png)

#### 1.3. Usage Code
```python

from model.rep.repvgg import RepBlock
import torch


input=torch.randn(50,512,49,49)
repblock=RepBlock(512,512)
repblock.eval()
out=repblock(input)
repblock._switch_to_deploy()
out2=repblock(input)
print('difference between vgg and repvgg')
print(((out2-out)**2).sum())
```



***

### 2. ACNet Usage
#### 2.1. Paper
["ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks"](https://arxiv.org/abs/1908.03930)

#### 2.2. Overview
![](./model/img/acnet.png)

#### 2.3. Usage Code
```python
from model.rep.acnet import ACNet
import torch
from torch import nn

input=torch.randn(50,512,49,49)
acnet=ACNet(512,512)
acnet.eval()
out=acnet(input)
acnet._switch_to_deploy()
out2=acnet(input)
print('difference:')
print(((out2-out)**2).sum())

```



***

### 2. Diverse Branch Block Usage
#### 2.1. Paper
["Diverse Branch Block: Building a Convolution as an Inception-like Unit"](https://arxiv.org/abs/2103.13425)

#### 2.2. Overview
![](./model/img/ddb.png)

#### 2.3. Usage Code
##### 2.3.1 Transform I
```python
from model.rep.ddb import transI_conv_bn
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,64,7,7)
#conv+bn
conv1=nn.Conv2d(64,64,3,padding=1)
bn1=nn.BatchNorm2d(64)
bn1.eval()
out1=bn1(conv1(input))

#conv_fuse
conv_fuse=nn.Conv2d(64,64,3,padding=1)
conv_fuse.weight.data,conv_fuse.bias.data=transI_conv_bn(conv1,bn1)
out2=conv_fuse(input)

print("difference:",((out2-out1)**2).sum().item())
```

##### 2.3.2 Transform II
```python
from model.rep.ddb import transII_conv_branch
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,64,7,7)

#conv+conv
conv1=nn.Conv2d(64,64,3,padding=1)
conv2=nn.Conv2d(64,64,3,padding=1)
out1=conv1(input)+conv2(input)

#conv_fuse
conv_fuse=nn.Conv2d(64,64,3,padding=1)
conv_fuse.weight.data,conv_fuse.bias.data=transII_conv_branch(conv1,conv2)
out2=conv_fuse(input)

print("difference:",((out2-out1)**2).sum().item())
```

##### 2.3.3 Transform III
```python
from model.rep.ddb import transIII_conv_sequential
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,64,7,7)

#conv+conv
conv1=nn.Conv2d(64,64,1,padding=0,bias=False)
conv2=nn.Conv2d(64,64,3,padding=1,bias=False)
out1=conv2(conv1(input))


#conv_fuse
conv_fuse=nn.Conv2d(64,64,3,padding=1,bias=False)
conv_fuse.weight.data=transIII_conv_sequential(conv1,conv2)
out2=conv_fuse(input)

print("difference:",((out2-out1)**2).sum().item())
```

##### 2.3.4 Transform IV
```python
from model.rep.ddb import transIV_conv_concat
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,64,7,7)

#conv+conv
conv1=nn.Conv2d(64,32,3,padding=1)
conv2=nn.Conv2d(64,32,3,padding=1)
out1=torch.cat([conv1(input),conv2(input)],dim=1)

#conv_fuse
conv_fuse=nn.Conv2d(64,64,3,padding=1)
conv_fuse.weight.data,conv_fuse.bias.data=transIV_conv_concat(conv1,conv2)
out2=conv_fuse(input)

print("difference:",((out2-out1)**2).sum().item())
```

##### 2.3.5 Transform V
```python
from model.rep.ddb import transV_avg
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,64,7,7)

avg=nn.AvgPool2d(kernel_size=3,stride=1)
out1=avg(input)

conv=transV_avg(64,3)
out2=conv(input)

print("difference:",((out2-out1)**2).sum().item())
```


##### 2.3.6 Transform VI
```python
from model.rep.ddb import transVI_conv_scale
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,64,7,7)

#conv+conv
conv1x1=nn.Conv2d(64,64,1)
conv1x3=nn.Conv2d(64,64,(1,3),padding=(0,1))
conv3x1=nn.Conv2d(64,64,(3,1),padding=(1,0))
out1=conv1x1(input)+conv1x3(input)+conv3x1(input)

#conv_fuse
conv_fuse=nn.Conv2d(64,64,3,padding=1)
conv_fuse.weight.data,conv_fuse.bias.data=transVI_conv_scale(conv1x1,conv1x3,conv3x1)
out2=conv_fuse(input)

print("difference:",((out2-out1)**2).sum().item())
```





# Convolution Series

- Pytorch implementation of ["MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications---CVPR2017"](https://arxiv.org/abs/1704.04861)

- Pytorch implementation of ["Efficientnet: Rethinking model scaling for convolutional neural networks---PMLR2019"](http://proceedings.mlr.press/v97/tan19a.html)

- Pytorch implementation of ["Involution: Inverting the Inherence of Convolution for Visual Recognition---CVPR2021"](https://arxiv.org/abs/2103.06255)

- Pytorch implementation of ["Dynamic Convolution: Attention over Convolution Kernels---CVPR2020 Oral"](https://arxiv.org/abs/1912.03458)

- Pytorch implementation of ["CondConv: Conditionally Parameterized Convolutions for Efficient Inference---NeurIPS2019"](https://arxiv.org/abs/1904.04971)

***

### 1. Depthwise Separable Convolution Usage
#### 1.1. Paper
["MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"](https://arxiv.org/abs/1704.04861)

#### 1.2. Overview
![](./model/img/DepthwiseSeparableConv.png)

#### 1.3. Usage Code
```python
from model.conv.DepthwiseSeparableConvolution import DepthwiseSeparableConvolution
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,3,224,224)
dsconv=DepthwiseSeparableConvolution(3,64)
out=dsconv(input)
print(out.shape)
```

***


### 2. MBConv Usage
#### 2.1. Paper
["Efficientnet: Rethinking model scaling for convolutional neural networks"](http://proceedings.mlr.press/v97/tan19a.html)

#### 2.2. Overview
![](./model/img/MBConv.jpg)

#### 2.3. Usage Code
```python
from model.conv.MBConv import MBConvBlock
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,3,224,224)
mbconv=MBConvBlock(ksize=3,input_filters=3,output_filters=512,image_size=224)
out=mbconv(input)
print(out.shape)


```

***


### 3. Involution Usage
#### 3.1. Paper
["Involution: Inverting the Inherence of Convolution for Visual Recognition"](https://arxiv.org/abs/2103.06255)

#### 3.2. Overview
![](./model/img/Involution.png)

#### 3.3. Usage Code
```python
from model.conv.Involution import Involution
import torch
from torch import nn
from torch.nn import functional as F

input=torch.randn(1,4,64,64)
involution=Involution(kernel_size=3,in_channel=4,stride=2)
out=involution(input)
print(out.shape)
```

***


### 4. DynamicConv Usage
#### 4.1. Paper
["Dynamic Convolution: Attention over Convolution Kernels"](https://arxiv.org/abs/1912.03458)

#### 4.2. Overview
![](./model/img/DynamicConv.png)

#### 4.3. Usage Code
```python
from model.conv.DynamicConv import *
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(2,32,64,64)
    m=DynamicConv(in_planes=32,out_planes=64,kernel_size=3,stride=1,padding=1,bias=False)
    out=m(input)
    print(out.shape) # 2,32,64,64

```

***


### 5. CondConv Usage
#### 5.1. Paper
["CondConv: Conditionally Parameterized Convolutions for Efficient Inference"](https://arxiv.org/abs/1904.04971)

#### 5.2. Overview
![](./model/img/CondConv.png)

#### 5.3. Usage Code
```python
from model.conv.CondConv import *
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(2,32,64,64)
    m=CondConv(in_planes=32,out_planes=64,kernel_size=3,stride=1,padding=1,bias=False)
    out=m(input)
    print(out.shape)

```

***