resnet18与resnet50

全栈程序员-站长 • 2022年5月9日上午10:00 • 未分类 • 阅读 321

大家好，又见面了，我是你们的朋友全栈君。

ResNet18的18层代表的是带有权重的 18层，包括卷积层和全连接层，不包括池化层和BN层。

Resnet论文给出的结构图

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结构解析：

首先是第一层卷积使用7∗77∗7大小的模板，步长为2，padding为3。之后进行BN，ReLU和maxpool。这些构成了第一部分卷积模块conv1。

然后是四个stage，代码中用make_layer()来生成stage，每个stage中有多个模块，每个模块叫做building block，resnet18= [2,2,2,2]，就有8个building block。注意到他有两种模块BasicBlock和Bottleneck。resnet18和resnet34用的是BasicBlock，resnet50及以上用的是Bottleneck。无论BasicBlock还是Bottleneck模块，都用到了shortcut connection连接方式：

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BasicBlock架构中，主要使用了两个3×3的卷积，然后进行BN，之后的out += residual这一句在输出上叠加了输入xx（注意到一开始定义了residual = x）

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):
        residual = 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:
            residual = self.downsample(x)

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

        return out

Bottleneck模块使用1×1，3×3，1×1的卷积模板，使用Bottleneck结构可以减少网络参数数量。他首先用1×1卷积将通道数缩减为一半，3×3卷积维持通道数不变，1×1卷积将通道数放大为4倍。则总体来看，经过这个模块后通道数变为两倍。

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

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

        return out