yolov5怎么添加注意力机制
时间: 2023-06-29 19:15:52 浏览: 61
在YOLOv5中添加注意力机制,可以通过在骨干网络的某些层上添加注意力模块来实现。常用的注意力模块有SE模块、CBAM模块等。
以SE模块为例,可以在骨干网络的某些卷积层后添加SE模块,具体步骤如下:
1. 定义SE模块的类:
```python
import torch.nn as nn
import torch.nn.functional as F
class SEModule(nn.Module):
def __init__(self, in_channels, reduction=16):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(in_channels, in_channels // reduction, kernel_size=1, stride=1, padding=0)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(in_channels // reduction, in_channels, kernel_size=1, stride=1, padding=0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c, 1, 1)
y = self.fc1(y)
y = self.relu(y)
y = self.fc2(y)
y = self.sigmoid(y)
return x * y
```
其中,in_channels是输入通道数,reduction是SE模块中的压缩比例,一般取16。
2. 在骨干网络的某些卷积层后添加SE模块:
```python
import torch
import torch.nn as nn
from models.common import SEModule
class YOLOv5Backbone(nn.Module):
def __init__(self, backbone, num_features=3, feature_channels=[256, 512, 1024], attention=False):
super(YOLOv5Backbone, self).__init__()
self.backbone = backbone
self.num_features = num_features
self.feature_channels = feature_channels
self.attention = attention
if self.attention:
self.se1 = SEModule(feature_channels[0])
self.se2 = SEModule(feature_channels[1])
self.se3 = SEModule(feature_channels[2])
def forward(self, x):
x = self.backbone.conv1(x)
x = self.backbone.bn1(x)
x = self.backbone.act1(x)
x = self.backbone.maxpool(x)
x = self.backbone.layer1(x)
if self.attention:
x = self.se1(x)
x = self.backbone.layer2(x)
if self.attention:
x = self.se2(x)
x = self.backbone.layer3(x)
if self.attention:
x = self.se3(x)
x = self.backbone.layer4(x)
return x
```
其中,attention为True时,会在骨干网络的layer1、layer2和layer3后分别添加SE模块。
3. 在训练时,设置attention为True即可启用注意力机制。