用pytorch写Resnet

以下是使用PyTorch实现ResNet的代码示例：

import torch
import torch.nn as nn

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, 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.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != self.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * out_channels)
            )

    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = nn.ReLU()(out)
        return out

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, self.expansion * out_channels, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion * out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != self.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * out_channels)
            )

    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))
        out = nn.ReLU()(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = nn.ReLU()(out)
        return out

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.layer1 = self.make_layer(block, 64, layers[0], stride=1)
        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 * block.expansion, 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 * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
            self.in_channels = out_channels * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

def ResNet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])

def ResNet34():
    return ResNet(BasicBlock, [3, 4, 6, 3])

def ResNet50():
    return ResNet(Bottleneck, [3, 4, 6, 3])

def ResNet101():
    return ResNet(Bottleneck, [3, 4, 23, 3])

def ResNet152():
    return ResNet(Bottleneck, [3, 8, 36, 3])

ResNet被定义为一个nn.Module子类，它由多个BasicBlock或Bottleneck组成。这里我们定义了两个块类：BasicBlock和Bottleneck。BasicBlock由两个3x3卷积层组成，而Bottleneck由1x1，3x3和1x1卷积层组成。这些块都包含一个shortcut分支，该分支将输入x添加到块输出中。

ResNet类定义了网络结构，由四个层组成，每个层都包含多个块。对于每个层，第一个块的步幅可能为2，以实现下采样。每个层的输入通道数是前一个层的输出通道数的四倍，这是因为Bottleneck块的存在。

在ResNet类的前向方法中，我们首先对输入进行一次卷积和Batch Normalization，然后通过每个层和块。在最后一个块之后，我们对输出进行平均池化，然后将其展平并通过全连接层进行分类。

最后，我们定义了几个较小的ResNet模型：ResNet18，ResNet34，ResNet50，ResNet101和ResNet152。这些模型具有不同的块数，并且ResNet152是最大的。