FightingCV Codebase For Attention,Backbone, MLP, Re-parameter, Convolution
Project description
目录
Attention Series
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Pytorch implementation of "Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks---arXiv 2021.05.05"
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Pytorch implementation of "Attention Is All You Need---NIPS2017"
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Pytorch implementation of "Squeeze-and-Excitation Networks---CVPR2018"
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Pytorch implementation of "Selective Kernel Networks---CVPR2019"
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Pytorch implementation of "CBAM: Convolutional Block Attention Module---ECCV2018"
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Pytorch implementation of "BAM: Bottleneck Attention Module---BMCV2018"
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Pytorch implementation of "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks---CVPR2020"
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Pytorch implementation of "Dual Attention Network for Scene Segmentation---CVPR2019"
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Pytorch implementation of "EPSANet: An Efficient Pyramid Split Attention Block on Convolutional Neural Network---arXiv 2021.05.30"
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Pytorch implementation of "ResT: An Efficient Transformer for Visual Recognition---arXiv 2021.05.28"
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Pytorch implementation of "SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS---ICASSP 2021"
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Pytorch implementation of "MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning---arXiv 2019.11.17"
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Pytorch implementation of "Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks---arXiv 2019.05.23"
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Pytorch implementation of "A2-Nets: Double Attention Networks---NIPS2018"
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Pytorch implementation of "An Attention Free Transformer---ICLR2021 (Apple New Work)"
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Pytorch implementation of VOLO: Vision Outlooker for Visual Recognition---arXiv 2021.06.24" 【论文解析】
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Pytorch implementation of Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition---arXiv 2021.06.23 【论文解析】
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Pytorch implementation of CoAtNet: Marrying Convolution and Attention for All Data Sizes---arXiv 2021.06.09 【论文解析】
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Pytorch implementation of Scaling Local Self-Attention for Parameter Efficient Visual Backbones---CVPR2021 Oral 【论文解析】
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Pytorch implementation of Polarized Self-Attention: Towards High-quality Pixel-wise Regression---arXiv 2021.07.02 【论文解析】
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Pytorch implementation of Contextual Transformer Networks for Visual Recognition---arXiv 2021.07.26 【论文解析】
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Pytorch implementation of Residual Attention: A Simple but Effective Method for Multi-Label Recognition---ICCV2021
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Pytorch implementation of S²-MLPv2: Improved Spatial-Shift MLP Architecture for Vision---arXiv 2021.08.02 【论文解析】
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Pytorch implementation of Global Filter Networks for Image Classification---arXiv 2021.07.01
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Pytorch implementation of Rotate to Attend: Convolutional Triplet Attention Module---WACV 2021
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Pytorch implementation of Coordinate Attention for Efficient Mobile Network Design ---CVPR 2021
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Pytorch implementation of MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2021.10.05
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Pytorch implementation of Non-deep Networks---ArXiv 2021.10.20
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Pytorch implementation of UFO-ViT: High Performance Linear Vision Transformer without Softmax---ArXiv 2021.09.29
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Pytorch implementation of Separable Self-attention for Mobile Vision Transformers---ArXiv 2022.06.06
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Pytorch implementation of On the Integration of Self-Attention and Convolution---ArXiv 2022.03.14
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Pytorch implementation of CROSSFORMER: A VERSATILE VISION TRANSFORMER HINGING ON CROSS-SCALE ATTENTION---ICLR 2022
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Pytorch implementation of Aggregating Global Features into Local Vision Transformer
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Pytorch implementation of CCNet: Criss-Cross Attention for Semantic Segmentation
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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 fightingcv_attention.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 fightingcv_attention.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
3.2. Overview
3.3. Usage Code
from fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 2020.10.05
-
Pytorch implementation of Patches Are All You Need?---ICLR2022 (Under Review)
-
Pytorch implementation of Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer---ArXiv 2021.06.07
-
Pytorch implementation of ConTNet: Why not use convolution and transformer at the same time?---ArXiv 2021.04.27
-
Pytorch implementation of Vision Transformers with Hierarchical Attention---ArXiv 2022.06.15
-
Pytorch implementation of Co-Scale Conv-Attentional Image Transformers---ArXiv 2021.08.26
-
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
1. ResNet Usage
1.1. Paper
"Deep Residual Learning for Image Recognition---CVPR2016 Best Paper"
1.2. Overview
1.3. Usage Code
from fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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
13.2. Usage Code
from fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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)
MLP Series
-
Pytorch implementation of "RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition---arXiv 2021.05.05"
-
Pytorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision---arXiv 2021.05.17"
-
Pytorch implementation of "ResMLP: Feedforward networks for image classification with data-efficient training---arXiv 2021.05.07"
-
Pytorch implementation of "Pay Attention to MLPs---arXiv 2021.05.17"
-
Pytorch implementation of "Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?---arXiv 2021.09.12"
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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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
4.2. Overview
4.3. Usage Code
from fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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 fightingcv_attention.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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