yolov8添加cbam
时间: 2023-08-27 21:04:52 浏览: 138
要在YOLOv8中添加CBAM(Convolutional Block Attention Module),首先需要导入相关的库,然后在YOLOv8的网络结构中添加CBAM模块。以下是一个简单的示例:
1. 导入相关库:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
```
2. 定义CBAM模块:
```python
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 = avg_out + max_out
return self.sigmoid(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 = nn.Conv2d(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)
return self.sigmoid(x)
```
3. 在YOLOv8的网络结构中添加CBAM模块:
```python
class YOLOv8(nn.Module):
def __init__(self):
super(YOLOv8, self).__init__()
# ... 其他层 ...
self.cbam1 = ChannelAttention()
self.cbam2 = ChannelAttention()
# ... 其他层 ...
```
4. 在前向传播过程中使用CBAM模块:
```python
def forward(self, x):
x = self.conv1(x) # C_{in} x H_{in/2} x W_{in/2} -> C_{out} x H_{out} x W_{out}
x = self.relu1(self.bn1(x)) # C_{out} x H_{out} x W_{out} -> C_{out} x H_{out} x W_{out}
x = self.cbam1(x) # C_{out} x H_{out} x W_{out} -> C_{out} x H_{out} x W_{out} with C_{out}/2 channels after attention and before residual connection
x = self.residual(x) # C_{out} x H_{out} x W_{out} -> C_{out} x H_{out} x W_{out} with skip connection and residual connection
x = self.relu2(self.bn2(x)) # C_{out} x H_{out} x W_{out} -> C_{out} x H_{out} x W_{out} with a shortcut connection to the skip connection path of the residual block above it (the identity mapping is applied here). This is used to reduce computational complexity when training deep networks with large numbers of parameters (e.g., ResNet-50). The shortcut connection is also known as a "skip connection" because the output of the layer isirectly passed (without
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