psa注意力机制代码
时间: 2023-11-20 15:54:49 浏览: 45
是使用PyTorch实现的PSA极化自注意力机制的代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class PSA(nn.Module):
def __init__(self, in_channels, reduction=8):
super(PSA, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, kernel_size=1, stride=1, padding=0)
self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, kernel_size=1, stride=1, padding=0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x)
y = self.conv1(y)
y = F.relu(y)
y = self.conv2(y)
y = self.sigmoid(y)
return x * y.expand_as(x)
```
其中,`in_channels`表示输入的通道数,`reduction`表示压缩比例,即将输入通道数压缩为原来的1/reduction。在`forward`函数中,首先使用自适应平均池化层对输入进行全局平均池化,然后通过两个卷积层和一个Sigmoid函数得到注意力权重,最后将输入和注意力权重相乘得到输出。