之前的文章讲过ResNet网络的基本架构，其本质就是让网络的学习目的从学习 转为学习 ，也就是学习输入和输出之间的残差信息，从而缓解了梯度消失和网络退化问题。

本文讲下ResNet网络的改进版：ResNeXt。

架构

下面是ResNet和ResNeXt的架构对比：

从上图中便可以看到ResNeXt的核心：用了类似于Inception的思想，把残差块的一条路径变成多条路径。

如果看原文也可以发现，其实作者最主要的对标就是Inception的模块。在Inception中，作者设计了4个不同的路径来从数据中提取信息，然后在输出的通道维进行合并，但是其弊端是这种拓扑结构（卷积核的个数和大小）需要精心设计才能够取得理想的效果，更增加了深度学习的黑箱特性。

在ResNeXt中，作者沿用了VGG和ResNet的两个主流思想：

• 层数堆叠
  
• split-transform-merge思想（就是分割-转换-合并，对于于上图）
  

但是在进行transform的时候，ResNeXt对于每一个路径的转换都是相同的，比如每条路径的输入和输出通道、卷积核大小都是相同的，这样子就避免了像Inception那样还要花费心思来设计路径数量，本文作者直接把这个通道数量设计成了一个参数，叫做cardinality。

如果与ResNet对比，则如下图所示：

值得注意的是，作者在原文中说道，增加cardinality比增加深度和宽度更有效！

代码

有了架构图，代码按照搭积木原则来写就行，这里直接放上公布的代码（具体的可以看参考链接2）：

import torch.nn as nnimport math

__all__ = ['ResNeXt', 'resnext18', 'resnext34', 'resnext50', 'resnext101',           'resnext152']

def conv3x3(in_planes, out_planes, stride=1):    """3x3 convolution with padding"""    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,                     padding=1, bias=False)

class BasicBlock(nn.Module):    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, num_group=32):        super(BasicBlock, self).__init__()        self.conv1 = conv3x3(inplanes, planes*2, stride)        self.bn1 = nn.BatchNorm2d(planes*2)        self.relu = nn.ReLU(inplace=True)        self.conv2 = conv3x3(planes*2, planes*2, groups=num_group)        self.bn2 = nn.BatchNorm2d(planes*2)        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

class Bottleneck(nn.Module):    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, num_group=32):        super(Bottleneck, self).__init__()        self.conv1 = nn.Conv2d(inplanes, planes*2, kernel_size=1, bias=False)        self.bn1 = nn.BatchNorm2d(planes*2)        self.conv2 = nn.Conv2d(planes*2, planes*2, kernel_size=3, stride=stride,                               padding=1, bias=False, groups=num_group)        self.bn2 = nn.BatchNorm2d(planes*2)        self.conv3 = nn.Conv2d(planes*2, 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

class ResNeXt(nn.Module):

    def __init__(self, block, layers, num_classes=1000, num_group=32):        self.inplanes = 64        super(ResNeXt, self).__init__()        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,                               bias=False)        self.bn1 = nn.BatchNorm2d(64)        self.relu = nn.ReLU(inplace=True)        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)        self.layer1 = self._make_layer(block, 64, layers[0], num_group)        self.layer2 = self._make_layer(block, 128, layers[1], num_group, stride=2)        self.layer3 = self._make_layer(block, 256, layers[2], num_group, stride=2)        self.layer4 = self._make_layer(block, 512, layers[3], num_group, stride=2)        self.avgpool = nn.AvgPool2d(7, stride=1)        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():            if isinstance(m, nn.Conv2d):                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels                m.weight.data.normal_(0, math.sqrt(2. / n))            elif isinstance(m, nn.BatchNorm2d):                m.weight.data.fill_(1)                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, num_group, stride=1):        downsample = None        if stride != 1 or self.inplanes != planes * block.expansion:            downsample = nn.Sequential(                nn.Conv2d(self.inplanes, planes * block.expansion,                          kernel_size=1, stride=stride, bias=False),                nn.BatchNorm2d(planes * block.expansion),            )

        layers = []        layers.append(block(self.inplanes, planes, stride, downsample, num_group=num_group))        self.inplanes = planes * block.expansion        for i in range(1, blocks):            layers.append(block(self.inplanes, planes, num_group=num_group))

        return nn.Sequential(*layers)

    def forward(self, x):        x = self.conv1(x)        x = self.bn1(x)        x = self.relu(x)        x = self.maxpool(x)

        x = self.layer1(x)        x = self.layer2(x)        x = self.layer3(x)        x = self.layer4(x)

        x = self.avgpool(x)        x = x.view(x.size(0), -1)        x = self.fc(x)

        return x

def resnext18( **kwargs):    """Constructs a ResNeXt-18 model.    """    model = ResNeXt(BasicBlock, [2, 2, 2, 2], **kwargs)    return model

知识点速记

结合ResNet和Inception的思想，ResNeXt把每个模块的路径数设置为一个超参数，称为cardinality。

另外要注意，下面这三种方式是严格等价的：

参考

【1】XIE S, GIRSHICK R, DOLLáR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2017:1492-1500.
【2】https://github.com/miraclewkf/ResNeXt-PyTorch