YOLOv5改进---注意力机制：SKAttention注意力，SENet进阶版本
原创YOLOv5改进---注意力机制：SKAttention注意力，SENet进阶版本
原创
AI小怪兽
发布于 2023-11-30 15:44:31
发布于 2023-11-30 15:44:31
文章被收录于专栏：YOLO大作战
摘要：SKAttention注意力助力YOLOv5，即插即用，性能优于SENet
1. SKAttention
论文：https://arxiv.org/pdf/1903.06586.pdf
多个 SK 块的堆叠得到 SKNet，这个名字也是为了致敬 SENet。
SKNet 在 ImageNet、CIFAR 数据集上都取得了 SOTA。
详细的实验分析表明，SKNet 中的神经元可以捕获具有不同比例的目标对象，实验验证了神经元根据输入自适应地调整其感受野大小的能力。
本文的方法分为三个部分：Split,Fuse,Select。Split就是一个multi-branch的操作，用不同的卷积核进行卷积得到不同的特征；Fuse部分就是用SE的结构获取通道注意力的矩阵(N个卷积核就可以得到N个注意力矩阵，这步操作对所有的特征参数共享)，这样就可以得到不同kernel经过SE之后的特征；Select操作就是将这几个特征进行相加。
1.1 加入 common.py中
###################### SKAttention #### start by AI&CV ###############################
from torch.nn import init
from collections import OrderedDict
class SKAttention(nn.Module):
def __init__(self, channel=512, kernels=[1, 3, 5, 7], reduction=16, group=1, L=32):
super().__init__()
self.d = max(L, channel // reduction)
self.convs = nn.ModuleList([])
for k in kernels:
self.convs.append(
nn.Sequential(OrderedDict([
('conv', nn.Conv2d(channel, channel, kernel_size=k, padding=k // 2, groups=group)),
('bn', nn.BatchNorm2d(channel)),
('relu', nn.ReLU())
]))
)
self.fc = nn.Linear(channel, self.d)
self.fcs = nn.ModuleList([])
for i in range(len(kernels)):
self.fcs.append(nn.Linear(self.d, channel))
self.softmax = nn.Softmax(dim=0)
def forward(self, x):
bs, c, _, _ = x.size()
conv_outs = []
### split
for conv in self.convs:
conv_outs.append(conv(x))
feats = torch.stack(conv_outs, 0) # k,bs,channel,h,w
### fuse
U = sum(conv_outs) # bs,c,h,w
### reduction channel
S = U.mean(-1).mean(-1) # bs,c
Z = self.fc(S) # bs,d
### calculate attention weight
weights = []
for fc in self.fcs:
weight = fc(Z)
weights.append(weight.view(bs, c, 1, 1)) # bs,channel
attention_weughts = torch.stack(weights, 0) # k,bs,channel,1,1
attention_weughts = self.softmax(attention_weughts) # k,bs,channel,1,1
### fuse
V = (attention_weughts * feats).sum(0)
return V
###################### SKAttention #### end by AI&CV ###############################
1.2 加入yolo.py中：
elif m is SKAttention:
c1, c2 = ch[f], args[0]
if c2 != nc:
c2 = make_divisible(min(c2, max_channels) * width, 8)
args = [c1, *args[1:]]1.3 yolov5s_SKAttention.yaml
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 1 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[-1, 1, SKAttention, [1024]], # 24
[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
by CSDN AI小怪兽 https://blog.csdn.net/m0_63774211/article/details/131375247
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
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