pytorch残差网络构建

要构建一个PyTorch残差网络，你可以按照以下步骤进行操作：

1. 首先，导入所需的PyTorch库：

   import torch
   import torch.nn as nn

2. 定义一个基本的残差块（Residual Block）：

   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)

           # 如果输入和输出通道数不相同，则使用 1x1 卷积进行维度匹配
           if stride != 1 or in_channels != out_channels:
               self.shortcut = nn.Sequential(
                   nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                   nn.BatchNorm2d(out_channels)
               )
           else:
               self.shortcut = nn.Identity()

       def forward(self, x):
           residual = x

           out = self.conv1(x)
           out = self.bn1(out)
           out = self.relu(out)

           out = self.conv2(out)
           out = self.bn2(out)

           out += self.shortcut(residual)
           out = self.relu(out)

           return out

3. 构建整个残差网络（ResNet）：

   class ResNet(nn.Module):
       def __init__(self, num_classes=10):
           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(64, 3, stride=1)
           self.layer2 = self._make_layer(128, 4, stride=2)
           self.layer3 = self._make_layer(256, 6, stride=2)
           self.layer4 = self._make_layer(512, 3, stride=2)

           self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
           self.fc = nn.Linear(512, num_classes)

       def _make_layer(self, out_channels, num_blocks, stride):
           layers = []
           layers.append(ResidualBlock(self.in_channels, out_channels, stride))
           self.in_channels = out_channels

           for _ in range(1, num_blocks):
               layers.append(ResidualBlock(self.in_channels, out_channels))

           return nn.Sequential(*layers)

       def forward(self, x):
           out = self.conv1(x)
           out = self.bn1(out)
           out = self.relu(out)
           out = self.maxpool(out)

           out = self.layer1(out)
           out = self.layer2(out)
           out = self.layer3(out)
           out = self.layer4(out)

           out = self.avgpool(out)
           out = out.view(out.size(0), -1)
           out = self.fc(out)

           return out

4. 使用定义好的残差网络进行训练和测试：

   model = ResNet(num_classes=10)
   criterion = nn.CrossEntropyLoss()
   optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

   # 在训练集上进行训练...
   # 在测试集上进行测试...

这是一个简化的ResNet实现示例，你可以根据自己的需求进行修改和扩展。希望对你有帮助！