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🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

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FightingCV Codebase For Attention,Backbone, MLP, Re-parameter, Convolution


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Hello,大家好,我是小马🚀🚀🚀

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

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Contents


Attention Series


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 2021.07.26

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 2021.08.02

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 2021.07.01

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 2021.10.05

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 2021.10.20

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 2021.09.29

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]
    

Backbone Series

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 2020.10.05

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]

MLP Series

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)

Re-Parameter Series


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


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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🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

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