写一段7分类的深度残差收缩网络pytorch代码

以下是一个简单的7分类深度残差收缩网络的 PyTorch 代码示例：

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

class ResNet(nn.Module):
    def __init__(self, num_classes=7):
        super(ResNet, self).__init__()
        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.layer1 = self._make_layer(64, 2)
        self.layer2 = self._make_layer(128, 2, stride=2)
        self.layer3 = self._make_layer(256, 2, stride=2)
        self.layer4 = self._make_layer(512, 2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or planes != 64:
            downsample = nn.Sequential(
                nn.Conv2d(64, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes),
            )

        layers = []
        layers.append(ResidualBlock(self.conv1, self.bn1, planes, stride, downsample))
        for i in range(1, blocks):
            layers.append(ResidualBlock(None, None, planes))

        return nn.Sequential(*layers)

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

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

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

        return x

class ResidualBlock(nn.Module):
    def __init__(self, conv, bn, planes, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = conv if conv is not None else nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = bn if bn is not None else nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

在上面的代码中，我们定义了一个 `ResNet` 类，它包含了多个残差块，每个块都包含两个卷积层和一个跳跃连接。我们还定义了一个 `ResidualBlock` 类，它包含了两个卷积层和一个跳跃连接。最后，我们使用全局平均池化层和全连接层对特征进行分类。