在PyTorch框架下,是如何实现残差连接的
时间: 2023-05-15 09:07:06 浏览: 44
在 PyTorch 框架下,可以通过使用 nn.ModuleList 和 nn.Identity() 来实现残差连接。具体实现方式如下:
1. 定义一个 ResidualBlock 类,其中包含两个卷积层和一个残差连接:
```
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.stride = stride
self.identity = nn.Identity()
def forward(self, x):
identity = self.identity(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out
```
2. 定义一个 ResNet 类,其中包含多个 ResidualBlock:
```
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
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, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
layers = []
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels
for i in range(1, blocks):
layers.append(block(out_channels, out_channels))
return nn.ModuleList(layers)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.maxpool(out)
for layer in self.layer1:
out = layer(out)
for layer in self.layer2:
out = layer(out)
for layer in self.layer3:
out = layer(out)
for layer in self.layer4:
out = layer(out)
out = self.avgpool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
```
在 ResidualBlock 中,使用 nn.Identity() 来实现残差连接。在 ResNet 中,使用 nn.ModuleList() 来存储多个 ResidualBlock。这样就可以实现在 PyTorch 框架下的残差连接。
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