专栏介绍:YOLOv9改进系列 | 包含深度学习最新创新,主力高效涨点!!!
一、本文介绍
本文将以SE注意力机制为例,演示如何在YOLOv9种添加注意力机制!
《Squeeze-and-Excitation Networks》
SENet提出了一种基于"挤压和激励"(SE)的注意力模块,用于改进卷积神经网络(CNN)的性能。SE块可以适应地重新校准通道特征响应,通过建模通道之间的相互依赖关系来增强CNN的表示能力。这些块可以堆叠在一起形成SENet架构,使其在多个数据集上具有非常有效的泛化能力。
《CBAM:Convolutional Block Attention Module》
CBAM模块能够同时关注CNN的通道和空间两个维度,对输入特征图进行自适应细化。这个模块轻量级且通用,可以无缝集成到任何CNN架构中,并可以进行端到端训练。实验表明,使用CBAM可以显著提高各种模型的分类和检测性能。
《ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks》
通道注意力模块ECA,可以提升深度卷积神经网络的性能,同时不增加模型复杂性。通过改进现有的通道注意力模块,作者提出了一种无需降维的局部交互策略,并自适应选择卷积核大小。ECA模块在保持性能的同时更高效,实验表明其在多个任务上具有优势。
《SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks》
SimAM一种概念简单且非常有效的注意力模块。不同于现有的通道/空域注意力模块,该模块无需额外参数为特征图推导出3D注意力权值 。具体来说,SimAM的作者基于著名的神经科学理论提出优化能量函数
以挖掘神经元的重要性。该模块的另一个优势在于:大部分操作均基于所定义的能量函数选择,避免了过多的结构调整。
适用检测目标: YOLOv9模块通用改进
二、改进步骤
以下以SE注意力机制为例在YOLOv9中加入注意力代码,其他注意力机制同理!
2.1 复制代码
将SE的代码辅助到models包下common.py文件中。
2.2 修改yolo.py文件
在yolo.py脚本的第700行(可能因YOLOv9版本变化而变化)增加下方代码。
python
elif m in (SE,):
args.insert(0, ch[f])
2.3 创建配置文件
创建模型配置文件(yaml文件),将我们所作改进加入到配置文件中(这一步的配置文件可以复制models - > detect 下的yaml修改。)。对YOLO系列yaml文件不熟悉的同学可以看我往期的yaml详解教学!
# YOLOv9
# Powered bu https://blog.csdn.net/StopAndGoyyy
# parameters
nc: 80 # number of classes
depth_multiple: 1 # model depth multiple
width_multiple: 1 # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()
# anchors
anchors: 3
# YOLOv9 backbone
backbone:
[
[-1, 1, Silence, []],
# conv down
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 3
# avg-conv down
[-1, 1, ADown, [256]], # 4-P3/8
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 5
# avg-conv down
[-1, 1, ADown, [512]], # 6-P4/16
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 7
# avg-conv down
[-1, 1, ADown, [512]], # 8-P5/32
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 9
]
# YOLOv9 head
head:
[
# elan-spp block
[-1, 1, SPPELAN, [512, 256]], # 10
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 7], 1, Concat, [1]], # cat backbone P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 13
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 5], 1, Concat, [1]], # cat backbone P3
# elan-2 block
[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 16 (P3/8-small)
# avg-conv-down merge
[-1, 1, ADown, [256]],
[[-1, 13], 1, Concat, [1]], # cat head P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 19 (P4/16-medium)
# avg-conv-down merge
[-1, 1, ADown, [512]],
[[-1, 10], 1, Concat, [1]], # cat head P5
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 22 (P5/32-large)
# multi-level reversible auxiliary branch
# routing
[5, 1, CBLinear, [[256]]], # 23
[7, 1, CBLinear, [[256, 512]]], # 24
[9, 1, CBLinear, [[256, 512, 512]]], # 25
# conv down
[0, 1, Conv, [64, 3, 2]], # 26-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 27-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 28
# avg-conv down fuse
[-1, 1, ADown, [256]], # 29-P3/8
[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 31
# avg-conv down fuse
[-1, 1, ADown, [512]], # 32-P4/16
[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 34
# avg-conv down fuse
[-1, 1, ADown, [512]], # 35-P5/32
[[25, -1], 1, CBFuse, [[2]]], # 36
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 37
[-1, 1, SE, [16]], # 38
# detection head
# detect
[[31, 34, 38, 16, 19, 22], 1, DualDDetect, [nc]], # DualDDetect(A3, A4, A5, P3, P4, P5)
]
3.1 训练过程
最后,复制我们创建的模型配置,填入训练脚本(train_dual)中(不会训练的同学可以参考我之前的文章。),运行即可。
SE代码
python
class SE(nn.Module):
def __init__(self, channel, reduction=16):
super(SE, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
CBAM代码
python
class CBAMBlock(nn.Module):
def __init__(self, channel=512, reduction=16, kernel_size=7):
super().__init__()
self.ca = ChannelAttention(channel=channel, reduction=reduction)
self.sa = SpatialAttention(kernel_size=kernel_size)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
def forward(self, x):
b, c, _, _ = x.size()
out = x * self.ca(x)
out = out * self.sa(out)
return out
ECA代码
python
class ECAAttention(nn.Module):
def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2)
self.sigmoid = nn.Sigmoid()
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
def forward(self, x):
y = self.gap(x) # bs,c,1,1
y = y.squeeze(-1).permute(0, 2, 1) # bs,1,c
y = self.conv(y) # bs,1,c
y = self.sigmoid(y) # bs,1,c
y = y.permute(0, 2, 1).unsqueeze(-1) # bs,c,1,1
return x * y.expand_as(x)
SimAM代码
python
class SimAM(torch.nn.Module):
def __init__(self, e_lambda=1e-4):
super(SimAM, self).__init__()
self.activaton = nn.Sigmoid()
self.e_lambda = e_lambda
def __repr__(self):
s = self.__class__.__name__ + '('
s += ('lambda=%f)' % self.e_lambda)
return s
@staticmethod
def get_module_name():
return "simam"
def forward(self, x):
b, c, h, w = x.size()
n = w * h - 1
x_minus_mu_square = (x - x.mean(dim=[2, 3], keepdim=True)).pow(2)
y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2, 3], keepdim=True) / n + self.e_lambda)) + 0.5
return x * self.activaton(y)
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