CBAM完整python代码
时间: 2023-12-08 19:38:15 浏览: 58
CBAM(Convolutional Block Attention Module)是一种注意力模块,用于增强卷积神经网络中的特征表示。下面是一个使用Python和PyTorch实现的CBAM的完整代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class CBAM(nn.Module):
def __init__(self, in_channels, out_channels):
super(CBAM, self).__init__()
self.conv_query = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv_key = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.conv_value = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self, x):
query = self.conv_query(x)
query = self.bn(query)
query = self.relu(query)
key = self.conv_key(x)
key = self.bn(key)
value = self.conv_value(x)
# Calculate dot product attention
energy = torch.matmul(query, key.transpose(-1, -2))
attention = F.softmax(energy, dim=-1)
out = torch.matmul(attention, value)
out = out * key.size(-1)
out = out + x
out = self.relu(out)
return out
```
这段代码定义了一个CBAM模块,它包含一个查询卷积层、一个键卷积层、一个值卷积层和一个批量归一化层。在forward函数中,首先通过查询、键和值卷积层提取特征,然后通过计算点积注意力分数来对特征进行加权,最后将加权后的特征与原始输入进行拼接并应用ReLU激活函数。这个模块可以嵌入到任何卷积神经网络中,以提高特征表示的性能。