SENet注意力机制代码
时间: 2023-06-28 13:08:36 浏览: 111
SENet是一种基于注意力机制的神经网络模型,它可以自适应地调整每个通道的权重,以提高模型的表现能力。下面是SENet的一个简单实现代码,包括SE模块和SENet的主体结构:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
batch_size, channels, _, _ = x.size()
y = self.avg_pool(x).view(batch_size, channels)
y = self.fc1(y)
y = self.relu(y)
y = self.fc2(y)
y = self.sigmoid(y).view(batch_size, channels, 1, 1)
return x * y
class SENet(nn.Module):
def __init__(self, num_classes=1000, channels=64, reduction=16):
super(SENet, self).__init__()
self.conv1 = nn.Conv2d(3, channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(channels)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(SEModule, channels, reduction, 3)
self.layer2 = self._make_layer(SEModule, channels*2, reduction, 4, stride=2)
self.layer3 = self._make_layer(SEModule, channels*4, reduction, 6, stride=2)
self.layer4 = self._make_layer(SEModule, channels*8, reduction, 3, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(channels*8, num_classes)
def _make_layer(self, block, channels, reduction, blocks, stride=1):
layers = []
layers.append(block(channels, reduction))
for i in range(1, blocks):
layers.append(block(channels, reduction))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
在上面的代码中,SEModule是SE模块,它通过自适应平均池化和两个全连接层实现通道注意力,SENet则是SE模型的主体框架,它采用四个卷积层和四个SE模块构建。
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