定义ResNet18模型 class ResNet18(nn.Module): def init(self): super(ResNet18, 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 = nn.Sequential( nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(64) ) self.layer2 = nn.Sequential( nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(128), ) self.layer3 = nn.Sequential( nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(256), ) self.layer4 = nn.Sequential( nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(512), ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512, 10) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.layer1(x) + x x = self.layer2(x) + x x = self.layer3(x) + x x = self.layer4(x) + x x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x

这段代码定义了一个名为 ResNet18 的模型，它是一个继承自 nn.Module 的子类。让我逐行解读这些代码：

class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, 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 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 10)

- `ResNet18(nn.Module)`：定义了一个名为 ResNet18 的类，它继承自 nn.Module。
- `def __init__(self):`：定义类的初始化方法。
- `super(ResNet18, self).__init__()`：调用父类的初始化方法，确保父类的属性被正确初始化。
- `self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)`：定义了一个卷积层，输入通道数为3（RGB图像），输出通道数为64，卷积核大小为3x3，步长为1，填充为1，不使用偏置。
- `self.bn1 = nn.BatchNorm2d(64)`：定义了一个批归一化层，对输入的64个通道进行归一化。
- `self.relu = nn.ReLU(inplace=True)`：定义了一个 ReLU 激活函数，inplace=True 表示原地操作，节省内存。
- `self.layer1`、`self.layer2`、`self.layer3`、`self.layer4`：定义了4个残差块，每个残差块包含一系列的卷积层和批归一化层。
- `self.avgpool = nn.AdaptiveAvgPool2d((1, 1))`：定义了一个自适应平均池化层，将输入的特征图大小自适应地池化为大小为1x1。
- `self.fc = nn.Linear(512, 10)`：定义了一个全连接层，将输入特征的维度从512降至10。

def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.layer1(x) + x
    x = self.layer2(x) + x
    x = self.layer3(x) + x
    x = self.layer4(x) + x
    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.fc(x)
    return x

- `def forward(self, x):`：定义了前向传播的方法。
- `x = self.conv1(x)`：将输入 `x` 通过卷积层 `self.conv1` 进行卷积操作。
- `x = self.bn1(x)`：将卷积后的结果 `x` 通过批归一化层 `self.bn1` 进行归一化。
- `x = self.relu(x)`：将归一化后的结果 `x` 通过 ReLU 激活函数进行激活。
- `x = self.layer1(x) + x`：将 `x` 与 `self.layer1(x)` 相加，实现残差连接。
- `x = self.layer2(x) + x`、`x = self.layer3(x) + x`、`x = self.layer4(x) + x`：依次将 `x` 与 `self.layer2(x)`、`self.layer3(x)`、`self.layer4(x)` 相加，实现残差连接。
- `x = self.avgpool(x)`：将输入 `x` 通过自适应平均池化层 `self.avgpool` 进行池化操作。
- `x = x.view(x.size(0), -1)`：将池化后的结果 `x` 展平成一维向量。
- `x = self.fc(x)`：将展平后的结果 `x` 通过全连接层 `self.fc` 进行线性变换。
- `return x`：返回前向传播的结果。

这段代码定义了一个包含卷积层、批归一化层、残差块和全连接层的 ResNet18 模型，并实现了前向传播方法。