class RestNet18(nn.Module): def __init__(self): super(RestNet18, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = nn.Sequential(RestNetBasicBlock(64, 64, 1), RestNetBasicBlock(64, 64, 1)) self.layer2 = nn.Sequential(RestNetDownBlock(64, 128, [2, 1]), RestNetBasicBlock(128, 128, 1)) self.layer3 = nn.Sequential(RestNetDownBlock(128, 256, [2, 1]), RestNetBasicBlock(256, 256, 1)) self.layer4 = nn.Sequential(RestNetDownBlock(256, 512, [2, 1]), RestNetBasicBlock(512, 512, 1)) self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) self.fc = nn.Linear(512, 10) def forward(self, x): out = self.conv1(x) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) out = out.reshape(x.shape[0], -1) out = self.fc(out) return out ———————————————— 逐行解释以上代码

这段代码定义了一个名为`RestNet18`的类，它是一个使用ResNet的18层网络模型。下面是对代码的逐行解释：

class RestNet18(nn.Module):
    def __init__(self):
        super(RestNet18, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

首先定义了一个名为`RestNet18`的类，它继承自`nn.Module`类。在构造函数`__init__`中，定义了卷积层`conv1`，输入通道数为3，输出通道数为64，卷积核大小为7x7，步长为2，填充为3。同时定义了批归一化层`bn1`和最大池化层`maxpool`，池化核大小为3x3，步长为2，填充为1。

        self.layer1 = nn.Sequential(RestNetBasicBlock(64, 64, 1),
                                    RestNetBasicBlock(64, 64, 1))

接下来定义了`layer1`，它是一个包含两个`RestNetBasicBlock`的序列模块。每个`RestNetBasicBlock`的输入通道数和输出通道数都是64，步长为1。

        self.layer2 = nn.Sequential(RestNetDownBlock(64, 128, [2, 1]),
                                    RestNetBasicBlock(128, 128, 1))

然后定义了`layer2`，它是一个包含一个`RestNetDownBlock`和一个`RestNetBasicBlock`的序列模块。`RestNetDownBlock`的输入通道数为64，输出通道数为128，步长为[2, 1]，而`RestNetBasicBlock`的输入通道数和输出通道数都是128，步长为1。

        self.layer3 = nn.Sequential(RestNetDownBlock(128, 256, [2, 1]),
                                    RestNetBasicBlock(256, 256, 1))

接下来定义了`layer3`，它是一个包含一个`RestNetDownBlock`和一个`RestNetBasicBlock`的序列模块。`RestNetDownBlock`的输入通道数为128，输出通道数为256，步长为[2, 1]，而`RestNetBasicBlock`的输入通道数和输出通道数都是256，步长为1。

        self.layer4 = nn.Sequential(RestNetDownBlock(256, 512, [2, 1]),
                                    RestNetBasicBlock(512, 512, 1))

然后定义了`layer4`，它是一个包含一个`RestNetDownBlock`和一个`RestNetBasicBlock`的序列模块。`RestNetDownBlock`的输入通道数为256，输出通道数为512，步长为[2, 1]，而`RestNetBasicBlock`的输入通道数和输出通道数都是512，步长为1。

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))

接下来定义了自适应平均池化层`avgpool`，它将输入特征图的大小自适应地池化为大小为1x1。

        self.fc = nn.Linear(512, 10)

最后定义了全连接层`fc`，将输入特征的维度512映射到10个输出类别。

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = out.reshape(x.shape[0], -1)
        out = self.fc(out)
        return out

`forward`方法定义了正向传播过程。给定输入`x`，首先通过卷积层`conv1`进行卷积操作，然后依次经过`layer1`，`layer2`，`layer3`和`layer4`模块进行特征提取。

接下来通过自适应平均池化层`avgpool`对特征图进行池化操作。然后将池化后的特征图展平为一维张量，并通过全连接层`fc`进行分类预测。

最后返回预测结果。

这个网络模型基于ResNet架构，通过堆叠不同的残差块和其他层来构建深层网络，并在最后使用全连接层进行分类预测。它可以用于图像分类任务。