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简体中文 | English

FightingCV 代码库, 包含 Attention,Backbone, MLP, Re-parameter, Convolution

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

Star History Chart

使用

安装

直接通过 pip 安装

pip install fightingcv-attention

或克隆该仓库

git clone https://github.com/xmu-xiaoma666/External-Attention-pytorch.git
cd External-Attention-pytorch

演示

使用 pip 方式

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

使用 git 方式

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)

Attention Series

Pytorch implementation of "Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks---arXiv 2021年05月05日"
Pytorch implementation of "Attention Is All You Need---NIPS2017"
Pytorch implementation of "Squeeze-and-Excitation Networks---CVPR2018"
Pytorch implementation of "Selective Kernel Networks---CVPR2019"
Pytorch implementation of "CBAM: Convolutional Block Attention Module---ECCV2018"
Pytorch implementation of "BAM: Bottleneck Attention Module---BMCV2018"
Pytorch implementation of "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks---CVPR2020"
Pytorch implementation of "Dual Attention Network for Scene Segmentation---CVPR2019"
Pytorch implementation of "EPSANet: An Efficient Pyramid Split Attention Block on Convolutional Neural Network---arXiv 2021年05月30日"
Pytorch implementation of "ResT: An Efficient Transformer for Visual Recognition---arXiv 2021年05月28日"
Pytorch implementation of "SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS---ICASSP 2021"
Pytorch implementation of "MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning---arXiv 2019年11月17日"
Pytorch implementation of "Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks---arXiv 2019年05月23日"
Pytorch implementation of "A2-Nets: Double Attention Networks---NIPS2018"
Pytorch implementation of "An Attention Free Transformer---ICLR2021 (Apple New Work)"
Pytorch implementation of VOLO: Vision Outlooker for Visual Recognition---arXiv 2021年06月24日" 【论文解析】
Pytorch implementation of Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition---arXiv 2021年06月23日【论文解析】
Pytorch implementation of CoAtNet: Marrying Convolution and Attention for All Data Sizes---arXiv 2021年06月09日【论文解析】
Pytorch implementation of Scaling Local Self-Attention for Parameter Efficient Visual Backbones---CVPR2021 Oral 【论文解析】
Pytorch implementation of Polarized Self-Attention: Towards High-quality Pixel-wise Regression---arXiv 2021年07月02日【论文解析】
Pytorch implementation of Contextual Transformer Networks for Visual Recognition---arXiv 2021年07月26日【论文解析】
Pytorch implementation of Residual Attention: A Simple but Effective Method for Multi-Label Recognition---ICCV2021
Pytorch implementation of S2-MLPv2: Improved Spatial-Shift MLP Architecture for Vision---arXiv 2021年08月02日【论文解析】
Pytorch implementation of Global Filter Networks for Image Classification---arXiv 2021年07月01日
Pytorch implementation of Rotate to Attend: Convolutional Triplet Attention Module---WACV 2021
Pytorch implementation of Coordinate Attention for Efficient Mobile Network Design ---CVPR 2021
Pytorch implementation of MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2021年10月05日
Pytorch implementation of Non-deep Networks---ArXiv 2021年10月20日
Pytorch implementation of UFO-ViT: High Performance Linear Vision Transformer without Softmax---ArXiv 2021年09月29日
Pytorch implementation of Separable Self-attention for Mobile Vision Transformers---ArXiv 2022年06月06日
Pytorch implementation of On the Integration of Self-Attention and Convolution---ArXiv 2022年03月14日
Pytorch implementation of CROSSFORMER: A VERSATILE VISION TRANSFORMER HINGING ON CROSS-SCALE ATTENTION---ICLR 2022
Pytorch implementation of Aggregating Global Features into Local Vision Transformer
Pytorch implementation of CCNet: Criss-Cross Attention for Semantic Segmentation
Pytorch implementation of Axial Attention in Multidimensional Transformers

1. External Attention Usage

1.1. Paper

"Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks"

1.2. Overview

1.3. Usage Code

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"

1.2. Overview

1.3. Usage Code

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

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"

4.2. Overview

4.3. Usage Code

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"

5.2. Overview

5.3. Usage Code

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"

6.2. Overview

6.3. Usage Code

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"

7.2. Overview

7.3. Usage Code

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"

8.2. Overview

8.3. Usage Code

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"

9.2. Overview

9.3. Usage Code

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"

10.2. Overview

10.3. Usage Code

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"

11.2. Overview

11.3. Usage Code

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"

12.2. Overview

12.3. Usage Code

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"

13.2. Overview

13.3. Usage Code

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

14.2. Overview

14.3. Usage Code

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

15.2. Overview

15.3. Usage Code

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

16.2. Overview

16.3. Usage Code

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"

17.2. Overview

17.3. Usage Code

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"

18.2. Overview

18.3. Usage Code

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"

19.2. Overview

None

19.3. Usage Code

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"

20.2. Overview

20.3. Usage Code

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"

21.2. Overview

21.3. Usage Code

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年07月26日

22.2. Overview

22.3. Usage Code

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

23.2. Overview

23.3. Usage Code

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年08月02日

24.2. Overview

24.3. Usage Code

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年07月01日

25.2. Overview

25.3. Usage Code - Implemented by Wenliang Zhao (Author)

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

26.2. Overview

26.3. Usage Code - Implemented by digantamisra98

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

27.2. Overview

27.3. Usage Code - Implemented by Andrew-Qibin

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年10月05日

28.2. Overview

28.3. Usage Code

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年10月20日

29.2. Overview

29.3. Usage Code

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年09月29日

30.2. Overview

30.3. Usage Code

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

31.2. Usage Code

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年06月06日

32.2. Overview

32.3. Usage Code

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

33.2. Usage Code

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

34.2. Usage Code

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

35.2. Usage Code

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

36.2. Usage Code

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

37.2. Usage Code

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"
Pytorch implementation of "Aggregated Residual Transformations for Deep Neural Networks---CVPR2017"
Pytorch implementation of MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2020年10月05日
Pytorch implementation of Patches Are All You Need?---ICLR2022 (Under Review)
Pytorch implementation of Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer---ArXiv 2021年06月07日
Pytorch implementation of ConTNet: Why not use convolution and transformer at the same time?---ArXiv 2021年04月27日
Pytorch implementation of Vision Transformers with Hierarchical Attention---ArXiv 2022年06月15日
Pytorch implementation of Co-Scale Conv-Attentional Image Transformers---ArXiv 2021年08月26日
Pytorch implementation of Conditional Positional Encodings for Vision Transformers
Pytorch implementation of Rethinking Spatial Dimensions of Vision Transformers---ICCV 2021
Pytorch implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification---ICCV 2021
Pytorch implementation of Transformer in Transformer---NeurIPS 2021
Pytorch implementation of DeepViT: Towards Deeper Vision Transformer
Pytorch implementation of Incorporating Convolution Designs into Visual Transformers

Pytorch implementation of ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Pytorch implementation of Augmenting Convolutional networks with attention-based aggregation
Pytorch implementation of Going deeper with Image Transformers---ICCV 2021 (Oral)
Pytorch implementation of Training data-efficient image transformers & distillation through attention---ICML 2021
Pytorch implementation of LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
Pytorch implementation of VOLO: Vision Outlooker for Visual Recognition
Pytorch implementation of Container: Context Aggregation Network---NeuIPS 2021
Pytorch implementation of CMT: Convolutional Neural Networks Meet Vision Transformers---CVPR 2022
Pytorch implementation of Vision Transformer with Deformable Attention---CVPR 2022
Pytorch implementation of EfficientFormer: Vision Transformers at MobileNet Speed
Pytorch implementation of ConvNeXtV2: Co-designing and Scaling ConvNets with Masked Autoencoders

1. ResNet Usage

1.1. Paper

"Deep Residual Learning for Image Recognition---CVPR2016 Best Paper"

1.2. Overview

1.3. Usage Code

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"

2.2. Overview

2.3. Usage Code

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年10月05日

3.2. Overview

3.3. Usage Code

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)

4.2. Overview

4.3. Usage Code

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

5.2. Usage Code

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?

6.2. Usage Code

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

7.2. Usage Code

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

8.2. Usage Code

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

9.2. Usage Code

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

10.2. Usage Code

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

11.2. Usage Code

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

12.2. Usage Code

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

13.2. Usage Code

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

14.2. Usage Code

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

15.2. Usage Code

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

16.2. Usage Code

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

17.2. Usage Code

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

18.2. Usage Code

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

19.2. Usage Code

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

20.2. Usage Code

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

21.2. Usage Code

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

22.2. Usage Code

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

23.2. Usage Code

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

24.2. Usage Code

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

25.2. Usage Code

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年05月05日"
Pytorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision---arXiv 2021年05月17日"
Pytorch implementation of "ResMLP: Feedforward networks for image classification with data-efficient training---arXiv 2021年05月07日"
Pytorch implementation of "Pay Attention to MLPs---arXiv 2021年05月17日"
Pytorch implementation of "Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?---arXiv 2021年09月12日"

1. RepMLP Usage

1.1. Paper

"RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition"

1.2. Overview

1.3. Usage Code

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"

2.2. Overview

2.3. Usage Code

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"

3.2. Overview

3.3. Usage Code

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"

4.2. Overview

4.3. Usage Code

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?"

5.2. Overview

5.3. Usage Code

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"

6.2. Usage Code

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"
Pytorch implementation of "ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks---ICCV2019"
Pytorch implementation of "Diverse Branch Block: Building a Convolution as an Inception-like Unit---CVPR2021"

1. RepVGG Usage

1.1. Paper

"RepVGG: Making VGG-style ConvNets Great Again"

1.2. Overview

1.3. Usage Code

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"

2.2. Overview

2.3. Usage Code

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"

2.2. Overview

2.3. Usage Code

2.3.1 Transform I

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

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

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

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

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

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"
Pytorch implementation of "Efficientnet: Rethinking model scaling for convolutional neural networks---PMLR2019"
Pytorch implementation of "Involution: Inverting the Inherence of Convolution for Visual Recognition---CVPR2021"
Pytorch implementation of "Dynamic Convolution: Attention over Convolution Kernels---CVPR2020 Oral"
Pytorch implementation of "CondConv: Conditionally Parameterized Convolutions for Efficient Inference---NeurIPS2019"

1. Depthwise Separable Convolution Usage

1.1. Paper

"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"

1.2. Overview

1.3. Usage Code

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"

2.2. Overview

2.3. Usage Code

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"

3.2. Overview

3.3. Usage Code

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"

4.2. Overview

4.3. Usage Code

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"

5.2. Overview

5.3. Usage Code

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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FightingCV 代码库, 包含 Attention,Backbone, MLP, Re-parameter, Convolution

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使用

安装

演示

使用 pip 方式

使用 git 方式

目录

Attention Series

1. External Attention Usage

1.1. Paper

1.2. Overview

1.3. Usage Code

2. Self Attention Usage

2.1. Paper

1.2. Overview

1.3. Usage Code

3. Simplified Self Attention Usage

3.1. Paper

3.2. Overview

3.3. Usage Code

4. Squeeze-and-Excitation Attention Usage

4.1. Paper

4.2. Overview

4.3. Usage Code

5. SK Attention Usage

5.1. Paper

5.2. Overview

5.3. Usage Code

6. CBAM Attention Usage

6.1. Paper

6.2. Overview

6.3. Usage Code

7. BAM Attention Usage

7.1. Paper

7.2. Overview

7.3. Usage Code

8. ECA Attention Usage

8.1. Paper

8.2. Overview

8.3. Usage Code

9. DANet Attention Usage

9.1. Paper

9.2. Overview

9.3. Usage Code

10. Pyramid Split Attention Usage

10.1. Paper

10.2. Overview

10.3. Usage Code

11. Efficient Multi-Head Self-Attention Usage

11.1. Paper

11.2. Overview

11.3. Usage Code

12. Shuffle Attention Usage

12.1. Paper

12.2. Overview

12.3. Usage Code

13. MUSE Attention Usage

13.1. Paper

13.2. Overview

13.3. Usage Code

14. SGE Attention Usage

14.1. Paper

14.2. Overview

14.3. Usage Code

15. A2 Attention Usage

15.1. Paper

15.2. Overview

15.3. Usage Code

16. AFT Attention Usage

16.1. Paper

16.2. Overview

16.3. Usage Code

17. Outlook Attention Usage