如何使用pytorch创建残差网络？

在PyTorch中，创建一个残差网络（Residual Network，简称ResNet）通常涉及以下几个步骤：

1. 导入必要的库：

import torch
import torch.nn as nn
import torch.nn.functional as F

2. 定义基本块（Basic Block）或更深的模块（如Bottleneck Block），这是ResNet的核心部分：
- 对于基础块，你可以这样做：

   class BasicBlock(nn.Module):
       def __init__(self, in_channels, out_channels, stride=1):
           super(BasicBlock, self).__init__()
           self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride)
           self.bn1 = nn.BatchNorm2d(out_channels)
           self.relu = nn.ReLU(inplace=True)
           self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
           self.bn2 = nn.BatchNorm2d(out_channels)

           if stride != 1 or in_channels != out_channels:
               self.shortcut = nn.Sequential(
                   nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride),
                   nn.BatchNorm2d(out_channels)
               )
           else:
               self.shortcut = None

       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)

           if self.shortcut is not None:
               shortcut = self.shortcut(residual)
           else:
               shortcut = residual

           return out + shortcut

- 如果你需要更复杂的Bottleneck Block，可以在每个卷积层之间添加更多的内核和BN层。

3. 构建网络结构，比如常见的ResNet-50有4个阶段：

   num_blocks_per_stage = [3, 4, 6, 3]
   layers = [2, 2, 2, 2] # 每个stage包含的blocks数

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

       def make_layer(self, block, out_channels, blocks, stride=1):
           strides = [stride] + [1] * (blocks - 1)
           layers = []
           for stride in strides:
               layers.append(block(self.in_channels, out_channels, stride))
               self.in_channels = out_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)

           x = self.avg_pool(x)
           x = x.view(x.size(0), -1)
           x = self.fc(x)

           return x

4. 创建并初始化模型实例：

model = ResNet(BasicBlock, layers)
# 可能还需要设置优化器和损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = nn.CrossEntropyLoss()

现在，你已经准备好了一个基本的ResNet模型。训练时记得调整学习率策略和其他超参数。