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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)
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)
目录
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
一个基于 PyTorch 的代码集合，收录了论文中注意力机制、骨干网络、MLP、重参数化和卷积的实现，帮助你通过阅读代码来理解论文中的机制。