广泛使用的Residual Block

ResNet 的核心思想是引入一个所谓的「恒等快捷连接」（identity shortcut connection），直接跳过一个或多个层，如下图所示：

 

 ImageNet的一个更深层次的残差函数F。

左图：一个积木块，BasicBlock，用于ResNet-34。右图：ResNet-50/101/152的bottleneck构建块。

BasicBlock

expansion是残差结构中输出维度是输入维度的多少倍，BasicBlock没有升维，所以expansion = 1
残差结构是在求和之后才经过ReLU层

 

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

bottleneck

注意Res18、Res34用的是BasicBlock，其余用的是Bottleneck。使用Bottleneck的目的为降低通道维的数量，提高速度。可简化为“降-卷-升”，一般expansion = 4，因为Bottleneck中每个残差结构输出维度都是输入维度的4倍。

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