resnet-18添加注意力机制
时间: 2024-02-15 07:00:24 浏览: 49
在ResNet-18中添加注意力机制可以通过引入SE(Squeeze-and-Excitation)注意力模块来实现。SE注意力模块是一种传统的通道注意力机制,可以有效地改进网络的分类精度。
以下是在ResNet-18中添加SE注意力模块的示例代码:
```python
import torch
import torch.nn as nn
# 定义SE注意力模块
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
# 定义ResNet-18网络结构
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
# 添加SE注意力模块
self.se = SELayer(512 * block.expansion)
def _make_layer(self, block, channels, blocks, stride=1):
layers = []
layers.append(block(self.in_channels, channels, stride))
self.in_channels = channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# 应用SE注意力模块
x = self.se(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
# 创建ResNet-18模型并添加SE注意力模块
model = ResNet(block, [2, 2, 2, 2])
```
在上述代码中,我们定义了一个SE注意力模块`SELayer`,并在ResNet-18的每个残差块的输出上应用了该注意力模块。通过这种方式,我们可以在ResNet-18中添加注意力机制,从而改进网络的分类精度。