YOLOv5添加CBAM 机制
时间: 2023-07-19 13:04:12 浏览: 111
要在YOLOv5中添加CBAM机制,需要进行以下步骤:
1. 导入必要的库和模块:
```
import torch
import torch.nn as nn
from models.common import Conv
```
2. 定义CBAM模块中的两个核心操作:通道注意力和空间注意力。
```
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
out = self.sigmoid(avg_out + max_out)
return out
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = Conv(2, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
out = self.sigmoid(x)
return out
```
3. 在YOLOv5的网络结构中,添加CBAM模块。
```
class CBAM(nn.Module):
def __init__(self, in_planes, ratio=16, kernel_size=7):
super(CBAM, self).__init__()
self.ca = ChannelAttention(in_planes, ratio)
self.sa = SpatialAttention(kernel_size)
def forward(self, x):
out = self.ca(x) * x
out = self.sa(out) * out
return out
```
4. 在YOLOv5的主干网络中调用CBAM模块。
```
class CSPDarknet53(nn.Module):
def __init__(self, num_classes=80):
super(CSPDarknet53, self).__init__()
self.num_classes = num_classes
self.downsample = nn.Sequential(
Conv(3, 32, 3, stride=2),
Conv(32, 64, 3, stride=2),
ResLayer(64, 64, 1),
Conv(64, 128, 3, stride=2),
ResLayer(128, 128, 2),
ResLayer(128, 128, 1),
Conv(128, 256, 3, stride=2),
ResLayer(256, 256, 8),
ResLayer(256, 256, 1),
Conv(256, 512, 3, stride=2),
ResLayer(512, 512, 8),
ResLayer(512, 512, 1),
Conv(512, 1024, 3, stride=2),
ResLayer(1024, 1024, 4),
)
self.conv = nn.Sequential(
Conv(1024, 512, 1),
Conv(512, 1024, 3),
Conv(1024, 512, 1),
CBAM(512),
Conv(512, 1024, 3),
Conv(1024, 512, 1),
CBAM(512),
Conv(512, 1024, 3),
)
self.head = nn.Sequential(
Conv(512, 256, 1),
nn.Upsample(scale_factor=2),
Conv(256 + 512, 256, 1),
Conv(256, 512, 3),
Conv(512, 256, 1),
Conv(256, 512, 3),
Conv(512, 256, 1),
Conv(256, 512, 3),
Conv(512, 512, 1),
nn.Upsample(scale_factor=2),
Conv(512 + 256, 256, 1),
Conv(256, 512, 3),
Conv(512, 256, 1),
Conv(256, 512, 3),
Conv(512, 256, 1),
Conv(256, 512, 3),
Conv(512, self.num_classes, 1),
)
def forward(self, x):
x = self.downsample(x)
x = self.conv(x)
x = self.head(x)
return x
```
通过以上步骤,就可以在YOLOv5中添加CBAM机制了。