This repository contains an implementation of many attention mechanism models.
Project description
Attention-Mechanism-Pytorch
This repository contains an implementation of many attention mechanism models.
Change Log
- Published Initial Attention Models, 2024-8-12.
目录
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
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
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks (ICML 2021)
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
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
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]
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 2022.06.06
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)
38. Frequency Channel Attention Usage
38.1. Paper
FcaNet: Frequency Channel Attention Networks (ICCV 2021)
38.2. Overview
38.3. Usage Code
from model.attention.FCA import MultiSpectralAttentionLayer
import torch
if __name__ == "__main__":
input = torch.randn(32, 128, 64, 64) # (b, c, h, w)
fca_layer = MultiSpectralAttentionLayer(channel = 128, dct_h = 64, dct_w = 64, reduction = 16, freq_sel_method = 'top16')
output = fca_layer(input)
print(output.shape)
39. Attention Augmented Convolutional Networks Usage
39.1. Paper
Attention Augmented Convolutional Networks (ICCV 2019)
39.2. Overview
39.3. Usage Code
from model.attention.AAAttention import AugmentedConv
import torch
if __name__ == "__main__":
input = torch.randn((16, 3, 32, 32))
augmented_conv = AugmentedConv(in_channels=3, out_channels=64, kernel_size=3, dk=40, dv=4, Nh=4, relative=True, stride=2, shape=16)
output = augmented_conv(input)
print(output.shape)
40. Global Context Attention Usage
40.1. Paper
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond (ICCVW 2019 Best Paper)
Global Context Networks (TPAMI 2020)
40.2. Overview
40.3. Usage Code
from model.attention.GCAttention import GCModule
import torch
if __name__ == "__main__":
input = torch.randn(16, 64, 32, 32)
gc_layer = GCModule(64)
output = gc_layer(input)
print(output.shape)
41. Linear Context Transform Attention Usage
41.1. Paper
Linear Context Transform Block (AAAI 2020)
41.2. Overview
41.3. Usage Code
from model.attention.LCTAttention import LCT
import torch
if __name__ == "__main__":
x = torch.randn(16, 64, 32, 32)
attn = LCT(64, 8)
y = attn(x)
print(y.shape)
42. Gated Channel Transformation Usage
42.1. Paper
Gated Channel Transformation for Visual Recognition (CVPR 2020)
42.2. Overview
42.3. Usage Code
from model.attention.GCTAttention import GCT
import torch
if __name__ == "__main__":
input = torch.randn(16, 64, 32, 32)
gct_layer = GCT(64)
output = gct_layer(input)
print(output.shape)
43. Gaussian Context Attention Usage
43.1. Paper
Gaussian Context Transformer (CVPR 2021)
43.2. Overview
43.3. Usage Code
from model.attention.GaussianAttention import GCA
import torch
if __name__ == "__main__":
input = torch.randn(16, 64, 32, 32)
gca_layer = GCA(64)
output = gca_layer(input)
print(output.shape)
Acknowledgements
During the development of this project, the following open-source projects provided significant help and support. We hereby express our sincere gratitude:
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