fightingcv-attention 1.0.0
FightingCV Codebase For Attention,Backbone, MLP, Re-parameter, Convolution
License: Apache
Requires Python: >=3.7.0
Keywords: AttentionBackbone

# 目录

- [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)

    - [31. ACmix Attention Usage](#31-Acmix-Attention-Usage)
  
    - [32. MobileViTv2 Attention Usage](#32-MobileViTv2-Attention-Usage)

    - [33. DAT Attention Usage](#33-DAT-Attention-Usage)

    - [34. CrossFormer Attention Usage](#34-CrossFormer-Attention-Usage)

    - [35. MOATransformer Attention Usage](#35-MOATransformer-Attention-Usage)

    - [36. CrissCrossAttention Attention Usage](#36-CrissCrossAttention-Attention-Usage)

    - [37. Axial_attention Attention Usage](#37-Axial_attention-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)

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

    - [6. ConTNet Usage](#6-ConTNet-Usage)

    - [7. HATNet Usage](#7-HATNet-Usage)

    - [8. CoaT Usage](#8-CoaT-Usage)

    - [9. PVT Usage](#9-PVT-Usage)

    - [10. CPVT Usage](#10-CPVT-Usage)

    - [11. PIT Usage](#11-PIT-Usage)

    - [12. CrossViT Usage](#12-CrossViT-Usage)

    - [13. TnT Usage](#13-TnT-Usage)

    - [14. DViT Usage](#14-DViT-Usage)

    - [15. CeiT Usage](#15-CeiT-Usage)

    - [16. ConViT Usage](#16-ConViT-Usage)

    - [17. CaiT Usage](#17-CaiT-Usage)

    - [18. PatchConvnet Usage](#18-PatchConvnet-Usage)

    - [19. DeiT Usage](#19-DeiT-Usage)

    - [20. LeViT Usage](#20-LeViT-Usage)

    - [21. VOLO Usage](#21-VOLO-Usage)
    
    - [22. Container Usage](#22-Container-Usage)

    - [23. CMT Usage](#23-CMT-Usage)

    - [24. EfficientFormer Usage](#24-EfficientFormer-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)

    - [6. vip-mlp Usage](#6-vip-mlp-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/2110.02178)

- 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)

- Pytorch implementation of [Separable Self-attention for Mobile Vision Transformers---ArXiv 2022.06.06](https://arxiv.org/abs/2206.02680)

- Pytorch implementation of [On the Integration of Self-Attention and Convolution---ArXiv 2022.03.14](https://arxiv.org/pdf/2111.14556.pdf)

- Pytorch implementation of [CROSSFORMER: A VERSATILE VISION TRANSFORMER HINGING ON CROSS-SCALE ATTENTION---ICLR 2022](https://arxiv.org/pdf/2108.00154.pdf)

- Pytorch implementation of [Aggregating Global Features into Local Vision Transformer](https://arxiv.org/abs/2201.12903)

- Pytorch implementation of [CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721)

- Pytorch implementation of [Axial Attention in Multidimensional Transformers](https://arxiv.org/abs/1912.12180)
***


### 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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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]
    
```

-

### 31. ACmix Attention Usage

#### 31.1. Paper

[On the Integration of Self-Attention and Convolution](https://arxiv.org/pdf/2111.14556.pdf)

#### 31.2. Usage Code

```python
from fightingcv_attention.attention.ACmix import ACmix
import torch

if __name__ == '__main__':
    input=torch.randn(50,256,7,7)
    acmix = ACmix(in_planes=256, out_planes=256)
    output=acmix(input)
    print(output.shape)
    
```

### 32. MobileViTv2 Attention Usage

#### 32.1. Paper

[Separable Self-attention for Mobile Vision Transformers---ArXiv 2022.06.06](https://arxiv.org/abs/2206.02680)


#### 32.2. Overview

![](./model/img/MobileViTv2.png)

#### 32.3. Usage Code

```python
from fightingcv_attention.attention.MobileViTv2Attention import MobileViTv2Attention
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(50,49,512)
    sa = MobileViTv2Attention(d_model=512)
    output=sa(input)
    print(output.shape)
    
```

### 33. DAT Attention Usage

#### 33.1. Paper

[Vision Transformer with Deformable Attention---CVPR2022](https://arxiv.org/abs/2201.00520)

#### 33.2. Usage Code

```python
from fightingcv_attention.attention.DAT import DAT
import torch

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = DAT(
        img_size=224,
        patch_size=4,
        num_classes=1000,
        expansion=4,
        dim_stem=96,
        dims=[96, 192, 384, 768],
        depths=[2, 2, 6, 2],
        stage_spec=[['L', 'S'], ['L', 'S'], ['L', 'D', 'L', 'D', 'L', 'D'], ['L', 'D']],
        heads=[3, 6, 12, 24],
        window_sizes=[7, 7, 7, 7] ,
        groups=[-1, -1, 3, 6],
        use_pes=[False, False, True, True],
        dwc_pes=[False, False, False, False],
        strides=[-1, -1, 1, 1],
        sr_ratios=[-1, -1, -1, -1],
        offset_range_factor=[-1, -1, 2, 2],
        no_offs=[False, False, False, False],
        fixed_pes=[False, False, False, False],
        use_dwc_mlps=[False, False, False, False],
        use_conv_patches=False,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.2,
    )
    output=model(input)
    print(output[0].shape)
    
```

### 34. CrossFormer Attention Usage

#### 34.1. Paper

[CROSSFORMER: A VERSATILE VISION TRANSFORMER HINGING ON CROSS-SCALE ATTENTION---ICLR 2022](https://arxiv.org/pdf/2108.00154.pdf)

#### 34.2. Usage Code

```python
from fightingcv_attention.attention.Crossformer import CrossFormer
import torch

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CrossFormer(img_size=224,
        patch_size=[4, 8, 16, 32],
        in_chans= 3,
        num_classes=1000,
        embed_dim=48,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        group_size=[7, 7, 7, 7],
        mlp_ratio=4.,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        drop_path_rate=0.1,
        ape=False,
        patch_norm=True,
        use_checkpoint=False,
        merge_size=[[2, 4], [2,4], [2, 4]]
    )
    output=model(input)
    print(output.shape)
    
```

### 35. MOATransformer Attention Usage

#### 35.1. Paper

[Aggregating Global Features into Local Vision Transformer](https://arxiv.org/abs/2201.12903)

#### 35.2. Usage Code

```python
from fightingcv_attention.attention.MOATransformer import MOATransformer
import torch

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = MOATransformer(
        img_size=224,
        patch_size=4,
        in_chans=3,
        num_classes=1000,
        embed_dim=96,
        depths=[2, 2, 6],
        num_heads=[3, 6, 12],
        window_size=14,
        mlp_ratio=4.,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        drop_path_rate=0.1,
        ape=False,
        patch_norm=True,
        use_checkpoint=False
    )
    output=model(input)
    print(output.shape)
    
```

### 36. CrissCrossAttention Attention Usage

#### 36.1. Paper

[CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721)

#### 36.2. Usage Code

```python
from fightingcv_attention.attention.CrissCrossAttention import CrissCrossAttention
import torch

if __name__ == '__main__':
    input=torch.randn(3, 64, 7, 7)
    model = CrissCrossAttention(64)
    outputs = model(input)
    print(outputs.shape)
    
```

### 37. Axial_attention Attention Usage

#### 37.1. Paper

[Axial Attention in Multidimensional Transformers](https://arxiv.org/abs/1912.12180)

#### 37.2. Usage Code

```python
from fightingcv_attention.attention.Axial_attention import AxialImageTransformer
import torch

if __name__ == '__main__':
    input=torch.randn(3, 128, 7, 7)
    model = AxialImageTransformer(
        dim = 128,
        depth = 12,
        reversible = True
    )
    outputs = model(input)
    print(outputs.shape)
    
```

***


# 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)

- Pytorch implementation of [Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer---ArXiv 2021.06.07](https://arxiv.org/abs/2106.03650)

- Pytorch implementation of [ConTNet: Why not use convolution and transformer at the same time?---ArXiv 2021.04.27](https://arxiv.org/abs/2104.13497)

- Pytorch implementation of [Vision Transformers with Hierarchical Attention---ArXiv 2022.06.15](https://arxiv.org/abs/2106.03180)

- Pytorch implementation of [Co-Scale Conv-Attentional Image Transformers---ArXiv 2021.08.26](https://arxiv.org/abs/2104.06399)

- Pytorch implementation of [Conditional Positional Encodings for Vision Transformers](https://arxiv.org/abs/2102.10882)

- Pytorch implementation of [Rethinking Spatial Dimensions of Vision Transformers---ICCV 2021](https://arxiv.org/abs/2103.16302)

- Pytorch implementation of [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification---ICCV 2021](https://arxiv.org/abs/2103.14899)

- Pytorch implementation of [Transformer in Transformer---NeurIPS 2021](https://arxiv.org/abs/2103.00112)

- Pytorch implementation of [DeepViT: Towards Deeper Vision Transformer](https://arxiv.org/abs/2103.11886)

- Pytorch implementation of [Incorporating Convolution Designs into Visual Transformers](https://arxiv.org/abs/2103.11816)
***

- Pytorch implementation of [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://arxiv.org/abs/2103.10697)

- Pytorch implementation of [Augmenting Convolutional networks with attention-based aggregation](https://arxiv.org/abs/2112.13692)

- Pytorch implementation of [Going deeper with Image Transformers---ICCV 2021 (Oral)](https://arxiv.org/abs/2103.17239)

- Pytorch implementation of [Training data-efficient image transformers & distillation through attention---ICML 2021](https://arxiv.org/abs/2012.12877)

- Pytorch implementation of [LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference](https://arxiv.org/abs/2104.01136)

- Pytorch implementation of [VOLO: Vision Outlooker for Visual Recognition](https://arxiv.org/abs/2106.13112)

- Pytorch implementation of [Container: Context Aggregation Network---NeuIPS 2021](https://arxiv.org/abs/2106.01401)

- Pytorch implementation of [CMT: Convolutional Neural Networks Meet Vision Transformers---CVPR 2022](https://arxiv.org/abs/2107.06263)

- Pytorch implementation of [Vision Transformer with Deformable Attention---CVPR 2022](https://arxiv.org/abs/2201.00520)

- Pytorch implementation of [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)


### 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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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]


```

### 5. ShuffleTransformer Usage
#### 5.1. Paper
[Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer](https://arxiv.org/pdf/2106.03650.pdf)

#### 5.2. Usage Code
```python

from fightingcv_attention.backbone.ShuffleTransformer import ShuffleTransformer
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    sft = ShuffleTransformer()
    output=sft(input)
    print(output.shape)


```

### 6. ConTNet Usage
#### 6.1. Paper
[ConTNet: Why not use convolution and transformer at the same time?](https://arxiv.org/abs/2104.13497)

#### 6.2. Usage Code
```python

from fightingcv_attention.backbone.ConTNet import ConTNet
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == "__main__":
    model = build_model(use_avgdown=True, relative=True, qkv_bias=True, pre_norm=True)
    input = torch.randn(1, 3, 224, 224)
    out = model(input)
    print(out.shape)


```

### 7 HATNet Usage
#### 7.1. Paper
[Vision Transformers with Hierarchical Attention](https://arxiv.org/abs/2106.03180)

#### 7.2. Usage Code
```python

from fightingcv_attention.backbone.HATNet import HATNet
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    hat = HATNet(dims=[48, 96, 240, 384], head_dim=48, expansions=[8, 8, 4, 4],
        grid_sizes=[8, 7, 7, 1], ds_ratios=[8, 4, 2, 1], depths=[2, 2, 6, 3])
    output=hat(input)
    print(output.shape)


```

### 8 CoaT Usage
#### 8.1. Paper
[Co-Scale Conv-Attentional Image Transformers](https://arxiv.org/abs/2104.06399)

#### 8.2. Usage Code
```python

from fightingcv_attention.backbone.CoaT import CoaT
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CoaT(patch_size=4, embed_dims=[152, 152, 152, 152], serial_depths=[2, 2, 2, 2], parallel_depth=6, num_heads=8, mlp_ratios=[4, 4, 4, 4])
    output=model(input)
    print(output.shape) # torch.Size([1, 1000])

```

### 9 PVT Usage
#### 9.1. Paper
[PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/pdf/2106.13797.pdf)

#### 9.2. Usage Code
```python

from fightingcv_attention.backbone.PVT import PyramidVisionTransformer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1])
    output=model(input)
    print(output.shape)

```


### 10 CPVT Usage
#### 10.1. Paper
[Conditional Positional Encodings for Vision Transformers](https://arxiv.org/abs/2102.10882)

#### 10.2. Usage Code
```python

from fightingcv_attention.backbone.CPVT import CPVTV2
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CPVTV2(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1])
    output=model(input)
    print(output.shape)

```

### 11 PIT Usage
#### 11.1. Paper
[Rethinking Spatial Dimensions of Vision Transformers](https://arxiv.org/abs/2103.16302)

#### 11.2. Usage Code
```python

from fightingcv_attention.backbone.PIT import PoolingTransformer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = PoolingTransformer(
        image_size=224,
        patch_size=14,
        stride=7,
        base_dims=[64, 64, 64],
        depth=[3, 6, 4],
        heads=[4, 8, 16],
        mlp_ratio=4
    )
    output=model(input)
    print(output.shape)

```

### 12 CrossViT Usage
#### 12.1. Paper
[CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://arxiv.org/abs/2103.14899)

#### 12.2. Usage Code
```python

from fightingcv_attention.backbone.CrossViT import VisionTransformer
import torch
from torch import nn

if __name__ == "__main__":
    input=torch.randn(1,3,224,224)
    model = VisionTransformer(
        img_size=[240, 224],
        patch_size=[12, 16], 
        embed_dim=[192, 384], 
        depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
        num_heads=[6, 6], 
        mlp_ratio=[4, 4, 1], 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6)
    )
    output=model(input)
    print(output.shape)

```

### 13 TnT Usage
#### 13.1. Paper
[Transformer in Transformer](https://arxiv.org/abs/2103.00112)

#### 13.2. Usage Code
```python

from fightingcv_attention.backbone.TnT import TNT
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = TNT(
        img_size=224, 
        patch_size=16, 
        outer_dim=384, 
        inner_dim=24, 
        depth=12,
        outer_num_heads=6, 
        inner_num_heads=4, 
        qkv_bias=False,
        inner_stride=4)
    output=model(input)
    print(output.shape)

```

### 14 DViT Usage
#### 14.1. Paper
[DeepViT: Towards Deeper Vision Transformer](https://arxiv.org/abs/2103.11886)

#### 14.2. Usage Code
```python

from fightingcv_attention.backbone.DViT import DeepVisionTransformer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = DeepVisionTransformer(
        patch_size=16, embed_dim=384, 
        depth=[False] * 16, 
        apply_transform=[False] * 0 + [True] * 32, 
        num_heads=12, 
        mlp_ratio=3, 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        )
    output=model(input)
    print(output.shape)

```

### 15 CeiT Usage
#### 15.1. Paper
[Incorporating Convolution Designs into Visual Transformers](https://arxiv.org/abs/2103.11816)

#### 15.2. Usage Code
```python

from fightingcv_attention.backbone.CeiT import CeIT
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CeIT(
        hybrid_backbone=Image2Tokens(),
        patch_size=4, 
        embed_dim=192, 
        depth=12, 
        num_heads=3, 
        mlp_ratio=4, 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6)
        )
    output=model(input)
    print(output.shape)

```

### 16 ConViT Usage
#### 16.1. Paper
[ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://arxiv.org/abs/2103.10697)

#### 16.2. Usage Code
```python

from fightingcv_attention.backbone.ConViT import VisionTransformer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = VisionTransformer(
        num_heads=16,
        norm_layer=partial(nn.LayerNorm, eps=1e-6)
        )
    output=model(input)
    print(output.shape)

```

### 17 CaiT Usage
#### 17.1. Paper
[Going deeper with Image Transformers](https://arxiv.org/abs/2103.17239)

#### 17.2. Usage Code
```python

from fightingcv_attention.backbone.CaiT import CaiT
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CaiT(
        img_size= 224,
        patch_size=16, 
        embed_dim=192, 
        depth=24, 
        num_heads=4, 
        mlp_ratio=4, 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        init_scale=1e-5,
        depth_token_only=2
        )
    output=model(input)
    print(output.shape)

```

### 18 PatchConvnet Usage
#### 18.1. Paper
[Augmenting Convolutional networks with attention-based aggregation](https://arxiv.org/abs/2112.13692)

#### 18.2. Usage Code
```python

from fightingcv_attention.backbone.PatchConvnet import PatchConvnet
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = PatchConvnet(
        patch_size=16,
        embed_dim=384,
        depth=60,
        num_heads=1,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        Patch_layer=ConvStem,
        Attention_block=Conv_blocks_se,
        depth_token_only=1,
        mlp_ratio_clstk=3.0,
    )
    output=model(input)
    print(output.shape)

```

### 19 DeiT Usage
#### 19.1. Paper
[Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877)

#### 19.2. Usage Code
```python

from fightingcv_attention.backbone.DeiT import DistilledVisionTransformer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = DistilledVisionTransformer(
        patch_size=16, 
        embed_dim=384, 
        depth=12, 
        num_heads=6, 
        mlp_ratio=4, 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6)
        )
    output=model(input)
    print(output[0].shape)

```

### 20 LeViT Usage
#### 20.1. Paper
[LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference](https://arxiv.org/abs/2104.01136)

#### 20.2. Usage Code
```python

from fightingcv_attention.backbone.LeViT import *
import torch
from torch import nn

if __name__ == '__main__':
    for name in specification:
        input=torch.randn(1,3,224,224)
        model = globals()[name](fuse=True, pretrained=False)
        model.eval()
        output = model(input)
        print(output.shape)

```

### 21 VOLO Usage
#### 21.1. Paper
[VOLO: Vision Outlooker for Visual Recognition](https://arxiv.org/abs/2106.13112)

#### 21.2. Usage Code
```python

from fightingcv_attention.backbone.VOLO import VOLO
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = VOLO([4, 4, 8, 2],
                 embed_dims=[192, 384, 384, 384],
                 num_heads=[6, 12, 12, 12],
                 mlp_ratios=[3, 3, 3, 3],
                 downsamples=[True, False, False, False],
                 outlook_attention=[True, False, False, False ],
                 post_layers=['ca', 'ca'],
                 )
    output=model(input)
    print(output[0].shape)

```

### 22 Container Usage
#### 22.1. Paper
[Container: Context Aggregation Network](https://arxiv.org/abs/2106.01401)

#### 22.2. Usage Code
```python

from fightingcv_attention.backbone.Container import VisionTransformer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = VisionTransformer(
        img_size=[224, 56, 28, 14], 
        patch_size=[4, 2, 2, 2], 
        embed_dim=[64, 128, 320, 512], 
        depth=[3, 4, 8, 3], 
        num_heads=16, 
        mlp_ratio=[8, 8, 4, 4], 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6))
    output=model(input)
    print(output.shape)

```

### 23 CMT Usage
#### 23.1. Paper
[CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/abs/2107.06263)

#### 23.2. Usage Code
```python

from fightingcv_attention.backbone.CMT import CMT_Tiny
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = CMT_Tiny()
    output=model(input)
    print(output[0].shape)

```

### 24 EfficientFormer Usage
#### 24.1. Paper
[EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)

#### 24.2. Usage Code
```python

from fightingcv_attention.backbone.EfficientFormer import EfficientFormer
import torch
from torch import nn

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = EfficientFormer(
        layers=EfficientFormer_depth['l1'],
        embed_dims=EfficientFormer_width['l1'],
        downsamples=[True, True, True, True],
        vit_num=1,
    )
    output=model(input)
    print(output[0].shape)

```






# 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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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)
```

### 6. vip-mlp Usage
#### 6.1. Paper
["Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition"](https://arxiv.org/abs/2106.12368)

#### 6.2. Usage Code
```python
from fightingcv_attention.mlp.vip-mlp import VisionPermutator
import torch
from torch import nn
from torch.nn import functional as F

if __name__ == '__main__':
    input=torch.randn(1,3,224,224)
    model = VisionPermutator(
        layers=[4, 3, 8, 3], 
        embed_dims=[384, 384, 384, 384], 
        patch_size=14, 
        transitions=[False, False, False, False],
        segment_dim=[16, 16, 16, 16], 
        mlp_ratios=[3, 3, 3, 3], 
        mlp_fn=WeightedPermuteMLP
    )
    output=model(input)
    print(output.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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)

```