ResNet18-50

### ResNet18 和 ResNet50 的架构对比

ResNet18 使用的是 BasicBlock 结构，而 ResNet50 则采用了 Bottleneck 结构。这两种结构的主要区别在于卷积层的数量和配置不同。

对于 **ResNet18** 而言，其网络设计较为简单，主要由两个卷积层加上一个全连接层构成，每个阶段重复应用 BasicBlock[^1]：

class BasicBlock(nn.Module):
    expansion = 1
    
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        
    def forward(self, x):
        identity = x
        
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

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

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

        return out

相比之下，**ResNet50** 更加复杂，它引入了更深层次的 Bottleneck 设计来提升性能，在每一个残差单元内部增加了更多的特征提取能力:

class Bottleneck(nn.Module):
    expansion = 4
    
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        
    def forward(self, x):
        identity = x
        
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        return out

当提到 "ResNet18-50" 这样的组合时，并不存在官方定义的标准模型。通常情况下，这可能是指希望融合两者特点的一种自定义变体版本。如果想要创建这样一个混合模型，则可以考虑在某些部分采用 ResNet18 的轻量化特性，而在其他地方利用 ResNet50 提供的强大表达力。