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https://github.com/xmu-xiaoma666/external-attention-pytorch
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
https://github.com/xmu-xiaoma666/external-attention-pytorch
attention cbam excitation-networks linear-layers paper pytorch squeeze visual-tasks
Last synced: about 22 hours ago
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🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
- Host: GitHub
- URL: https://github.com/xmu-xiaoma666/external-attention-pytorch
- Owner: xmu-xiaoma666
- License: mit
- Created: 2021-05-08T13:11:46.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-12-06T11:35:21.000Z (25 days ago)
- Last Synced: 2024-12-23T09:22:41.037Z (8 days ago)
- Topics: attention, cbam, excitation-networks, linear-layers, paper, pytorch, squeeze, visual-tasks
- Language: Python
- Homepage:
- Size: 5.43 MB
- Stars: 11,610
- Watchers: 103
- Forks: 1,944
- Open Issues: 70
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
简体中文 | [English](./README_EN.md)
# FightingCV 代码库， 包含 [***Attention***](#attention-series),[***Backbone***](#backbone-series), [***MLP***](#mlp-series), [***Re-parameter***](#re-parameter-series), [**Convolution**](#convolution-series)
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：***
本项目致力于实现一个既能**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。
## 🌟 Star History
[](https://star-history.com/#xmu-xiaoma666/External-Attention-pytorch&Date)
## 使用
### 安装
直接通过 pip 安装
```shell
pip install fightingcv-attention
```
或克隆该仓库
```shell
git clone https://github.com/xmu-xiaoma666/External-Attention-pytorch.git
cd External-Attention-pytorch
```
### 演示
#### 使用 pip 方式
```python
import torch
from torch import nn
from torch.nn import functional as F
# 使用 pip 方式
from fightingcv_attention.attention.MobileViTv2Attention import *
if __name__ == '__main__':
input=torch.randn(50,49,512)
sa = MobileViTv2Attention(d_model=512)
output=sa(input)
print(output.shape)
```
- pip包 内置模块使用参考: [fightingcv-attention 说明文档](./README_pip.md)
#### 使用 git 方式
```python
import torch
from torch import nn
from torch.nn import functional as F
# 与 pip方式 区别在于 将 `fightingcv_attention` 替换 `model`
from model.attention.MobileViTv2Attention import *
if __name__ == '__main__':
input=torch.randn(50,49,512)
sa = MobileViTv2Attention(d_model=512)
output=sa(input)
print(output.shape)
```
-------
# 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)
- [25. ConvNeXtV2 Usage](#25-ConvNeXtV2-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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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]
```
-
### 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 model.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
#### 32.3. Usage Code
```python
from model.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 model.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 model.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 model.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 model.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 model.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)
- Pytorch implementation of [ConvNeXtV2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808)
### 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
#### 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
#### 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
#### 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
#### 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]
```
### 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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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 model.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)
```
### 25 ConvNeXtV2 Usage
#### 25.1. Paper
[ConvNeXtV2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808)
#### 25.2. Usage Code
```python
from model.backbone.convnextv2 import convnextv2_atto
import torch
from torch import nn
if __name__ == "__main__":
model = convnextv2_atto()
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.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
#### 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
#### 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
#### 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
#### 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
#### 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)
```
### 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 model.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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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
#### 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)
```
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