SE模块如何插入到resnet18中
时间: 2023-08-11 15:04:08 浏览: 165
SE模块是一种轻量级的注意力机制,可以帮助提高网络的性能。将SE模块插入到ResNet18中的方法如下:
1. 导入SE模块的代码,例如:
```
import torch.nn as nn
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):
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
```
2. 在ResNet18中的每个残差块中添加SE模块,例如:
```
import torch.nn as nn
import torchvision.models as models
class ResNet18(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet18, self).__init__()
self.model = models.resnet18(pretrained=True)
# Add SE module to every residual block
for name, module in self.model.named_modules():
if isinstance(module, nn.Sequential) and len(module) == 2:
module.add_module("SE", SEModule(module[0].conv2.out_channels, reduction=16))
# Replace the last fully connected layer
self.model.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.model(x)
return x
```
这样就将SE模块成功插入到了ResNet18中的每个残差块中,可以提高网络的性能。