可以给我一个这样的CBAM代码吗
时间: 2024-02-26 12:59:26 浏览: 12
以下是一个使用PyTorch实现的VGG网络加入CBAM模块的代码示例,保持输出特征的尺寸和维度不变:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class CBAM(nn.Module):
def __init__(self, in_channels):
super(CBAM, self).__init__()
self.in_channels = in_channels
# 通道注意力模块
self.channel_att = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, in_channels // 16, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels // 16, in_channels, kernel_size=1),
nn.Sigmoid()
)
# 空间注意力模块
self.spatial_att = nn.Sequential(
nn.Conv2d(in_channels, in_channels // 16, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels // 16, in_channels // 16, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels // 16, 1, kernel_size=1),
nn.Sigmoid()
)
def forward(self, x):
# 通道注意力
channel_att = self.channel_att(x)
x = x * channel_att
# 空间注意力
spatial_att = self.spatial_att(x)
x = x * spatial_att
return x
class VGG_CBAM(nn.Module):
def __init__(self, num_classes=1000):
super(VGG_CBAM, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.cbam = CBAM(512)
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, num_classes)
)
def forward(self, x):
x = self.features(x)
x = self.cbam(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
```
以上代码中,首先定义了CBAM模块,包括通道注意力模块和空间注意力模块。然后定义了一个VGG_CBAM类,继承自nn.Module,包含了VGG网络的全部结构,并在最后加入了CBAM模块和全局平均池化层。在前向传播时,先通过VGG网络的卷积层和池化层提取特征,然后经过CBAM模块和全局平均池化层,最后通过全连接层输出分类结果。通过CBAM模块的加入,可以提高VGG网络的性能,而且输出特征的尺寸和维度不变。