cbam代码pytorch
时间: 2023-07-03 20:30:47 浏览: 45
以下是一个使用PyTorch实现CBAM的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
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)
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
```
这里定义了一个包含Channel Attention和Spatial Attention的CBAM模块,可以在卷积神经网络中使用。在模块中,Channel Attention用于学习通道间的相互关系,Spatial Attention用于学习空间上的相互关系。这两个Attention模块的输出会相乘,得到最终的CBAM特征。