ResNeXt代码复现＋超详细注释（PyTorch）

ResNeXt就是一种典型的混合模型，由基础的Inception+ResNet组合而成，本质在gruops分组卷积，核心创新点就是用一种平行堆叠相同拓扑结构的blocks代替原来 ResNet 的三层卷积的block，在不明显增加参数量级的情况下提升了模型的准确率，同时由于拓扑结构相同，超参数也减少了，便于模型移植。

关于论文更详细的解读可以看我上一篇笔记：经典神经网络论文超详细解读（八）——ResNeXt学习笔记（翻译＋精读＋代码复现）

接下来我们进行代码的复现

一、ResNeXt Block 结构

1.1 基础结构

ResNeXt是ResNet基础上的改进版本，改进的部分不多，主要将之前的残差结构换成了另外的一个Block结构，并且使用了组卷积的概念。下图是ResNeXt的一个基础Block。

左图是其基础结构，灵感来自于ResNet的BottleNeck（关于ResNet代码的详细讲解，大家可以看我之前的文章：ResNet代码复现＋超详细注释（PyTorch））。受Inception启发论文将Residual部分分成若干个支路，这个支路的数量就是cardinality的含义（Inception代码详细讲解可参考：GoogLeNet InceptionV1代码复现＋超详细注释（PyTorch））。

右图是ResNeXt提出的一个组卷积的概念：将输入通道为256的数据通过11卷积压缩成大小为4的32组，合起来也就是128通道，然后进行卷积操作后，再用11卷积扩充回32组256通道，将32组数据按对应位置相加合成一个256通道的输出。

1.2 三种等效的优化结构

（a）表示先划分，单独卷积并计算输出，最后输出相加。split-transform-merge三阶段形式

（b）表示先划分，单独卷积，然后拼接再计算输出。将各分支的最后一个1×1卷积聚合成一个卷积。

（c）就是分组卷积。将各分支的第一个1×1卷积融合成一个卷积，3×3卷积采用group（分组）卷积的形式，分组数=cardinality（基数）

以上三个Block模块在数学计算上是完全等价的。

以（c）为例：通过1×1的卷积层将输入channel从256降为128，然后利用组卷积进行处理，卷积核大小为3×3组数为32，再利用1×1的卷积层进行升维，将输出与输入相加，得到最终输出。

再看（b）模块，就是将第一层和第二层的卷积分组，将第一层卷积（卷积核大小为1×1，每个卷积核有256层）分为32组，每组4个卷积核，这样每一组输出的channel为4；将第二层卷积也分为32组对应第一层，每一组输入的channel为4，每一组4个卷积核输出channel也为4，再将输出拼接为channel为128的输出，再经过一个256个卷积核的卷积层得到最终输出。

对于（a）模块，就是对b模块的最后一层进行拆分，就是将第二层的32组的输出再经过一层（卷积核大小为1×1，每个卷积核有4层，一共有256个卷积核）卷积，再把这32组输出相加得到最终输出。

二、ResNeXt 网络结构

下图是ResNet-50和ResNeXt-50（32x4d）的对比，可以发现二者网络整体结构一致，ResNeXt替换了基本的block。32 指进入网络的第一个ResNeXt基本结构的分组数量C（即基数）为32。4d 表示depth即每一个分组的通道数为4（所以第一个基本结构输入通道数为128）

模型设计两个原则：

（1）如果输出的空间尺寸一样，那么模块的超参数（宽度和卷积核尺寸）也是一样的。

（2）每当空间分辨率/2（降采样），则卷积核的宽度*2。这样保持模块计算复杂度。

三、ResNeXt的PyTorch实现

3.1BasicBlock模块

基础Block模块，也就是对应18/34层的BasicBlock。这里实现和ResNet一样，就不再过多论述。

代码

'''-------------一、BasicBlock模块-----------------------------'''# 用于ResNet18和ResNet34基本残差结构块classBasicBlock(nn.Module):
    def__init__(self, in_channel, out_channel, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channel),
            nn.ReLU(),
            nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_channel),
            nn.downsample(downsample)
        )

    defforward(self, x):
        identity = x
        if self.downsample isnotNone:
            identity = self.downsample(x)

        out = self.left(x)  # 这是由于残差块需要保留原始输入
        out += identity  # 这是ResNet的核心，在输出上叠加了输入x
        out = F.relu(out)
        return out

3.2 Bottleneck模块

从表中可以看出，ResNeXt网络每一个convx的第一层和第二层卷积的卷积核个数是ResNet网络的两倍，在代码实现时，需要注意在代码中增加一下两个参数groups和width_per_group（即为group数和conv2中组卷积每个group的卷积核个数）并且根据这两个参数计算出第一层卷积的输出（为ResNet网络的两倍）。

代码

'''-------------二、Bottleneck模块-----------------------------'''classBottleneck(nn.Module):

    expansion = 4# 这里相对于RseNet，在代码中增加一下两个参数groups和width_per_group（即为group数和conv2中组卷积每个group的卷积核个数）# 默认值就是正常的ResNetdef__init__(self, in_channel, out_channel, stride=1, downsample=None,
                 groups=1, width_per_group=64):
        super(Bottleneck, self).__init__()
        # 这里也可以自动计算中间的通道数，也就是3x3卷积后的通道数，如果不改变就是out_channels# 如果groups=32,with_per_group=4,out_channels就翻倍了
        width = int(out_channel * (width_per_group / 64.)) * groups

        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,
                               kernel_size=1, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width)
        # -----------------------------------------# 组卷积的数，需要传入参数
        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,
                               kernel_size=3, stride=stride, bias=False, padding=1)
        self.bn2 = nn.BatchNorm2d(width)
        # -----------------------------------------
        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel * self.expansion,
                               kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
        # -----------------------------------------
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    defforward(self, x):
        identity = x
        if self.downsample isnotNone:
            identity = self.downsample(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)

        out += identity  # 残差连接
        out = self.relu(out)

        return out

3.3搭建ResNeXt网络结构

（1）网络整体结构

根据（c）模块，首先通过1x1的卷积层将输入特征矩阵的channel从256降维到128；再通过3x3的32组group卷积对其进行处理；再通过1x1的卷积层进行将特征矩阵的channel从128升维到256；最后主分支与短路连接的输出进行相加得到最终输出。

代码

'''-------------三、搭建ResNeXt结构-----------------------------'''
class ResNeXt(nn.Module):
    def __init__(self,
                 block,  # 表示block的类型
                 blocks_num,  # 表示的是每一层block的个数
                 num_classes=1000,  # 表示类别
                 include_top=True,  # 表示是否含有分类层(可做迁移学习)
                 groups=1,  # 表示组卷积的数
                 width_per_group=64):
        super(ResNeXt, self).__init__()
        self.include_top = include_top
        self.in_channel = 64

        self.groups = groups
        self.width_per_group = width_per_group

        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
                               padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channel)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, blocks_num[0])           # 64 -> 128
        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)# 128 -> 256
        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)# 256 -> 512
        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) # 512 -> 1024
        if self.include_top:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
            self.fc = nn.Linear(512 * block.expansion, num_classes)

    # 形成单个Stage的网络结构
    def _make_layer(self, block, channel, block_num, stride=1):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(channel * block.expansion))
        # 该部分是将每个blocks的第一个残差结构保存在layers列表中。
        layers = []
        layers.append(block(self.in_channel,
                            channel,
                            downsample=downsample,
                            stride=stride,
                            groups=self.groups,
                            width_per_group=self.width_per_group))
        self.in_channel = channel * block.expansion  # 得到最后的输出

        # 该部分是将每个blocks的剩下残差结构保存在layers列表中，这样就完成了一个blocks的构造。
        for _ in range(1, block_num):
            layers.append(block(self.in_channel,
                                channel,
                                groups=self.groups,
                                width_per_group=self.width_per_group))

         # 返回Conv Block和Identity Block的集合，形成一个Stage的网络结构
        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)

        if self.include_top:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x

（2）搭建网络模型

使用时直接调用每种不同层的结构对应的残差块作为参数传入。除了残差块不同以外，每个残差块重复的次数也不同，所以也作为参数。每个不同的模型只需往ResNet模型中传入不同参数即可。

代码

def ResNet34(num_classes=1000, include_top=True):

    return ResNeXt(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)

def ResNet50(num_classes=1000, include_top=True):

    return ResNeXt(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)

def ResNet101(num_classes=1000, include_top=True):

    return ResNeXt(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)

# 论文中的ResNeXt50_32x4d
def ResNeXt50_32x4d(num_classes=1000, include_top=True):

    groups = 32
    width_per_group = 4
    return ResNeXt(Bottleneck, [3, 4, 6, 3],
                  num_classes=num_classes,
                  include_top=include_top,
                  groups=groups,
                  width_per_group=width_per_group)

def ResNeXt101_32x8d(num_classes=1000, include_top=True):

    groups = 32
    width_per_group = 8
    return ResNeXt(Bottleneck, [3, 4, 23, 3],
                  num_classes=num_classes,
                  include_top=include_top,
                  groups=groups,
                  width_per_group=width_per_group)

3.4测试网络模型

（1）网络模型测试并打印论文中的ResNeXt50_32x4d

if __name__ == '__main__':
    model = ResNeXt50_32x4d()
    print(model)
    input = torch.randn(1, 3, 224, 224)
    out = model(input)
    print(out.shape)
# test()

打印模型如下

ResNeXt(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=2048, out_features=1000, bias=True)
)
torch.Size([1, 1000])

Process finished with exit code 0

（2）使用torchsummary打印每个网络模型的详细信息

from torchsummary import summary

if __name__ == '__main__':
    net = ResNeXt50_32x4d().cuda()
    summary(net, (3, 224, 224))

打印模型如下

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 112, 112]           9,408
       BatchNorm2d-2         [-1, 64, 112, 112]             128
              ReLU-3         [-1, 64, 112, 112]               0
         MaxPool2d-4           [-1, 64, 56, 56]               0
            Conv2d-5          [-1, 256, 56, 56]          16,384
       BatchNorm2d-6          [-1, 256, 56, 56]             512
            Conv2d-7          [-1, 128, 56, 56]           8,192
       BatchNorm2d-8          [-1, 128, 56, 56]             256
              ReLU-9          [-1, 128, 56, 56]               0
           Conv2d-10          [-1, 128, 56, 56]           4,608
      BatchNorm2d-11          [-1, 128, 56, 56]             256
             ReLU-12          [-1, 128, 56, 56]               0
           Conv2d-13          [-1, 256, 56, 56]          32,768
      BatchNorm2d-14          [-1, 256, 56, 56]             512
             ReLU-15          [-1, 256, 56, 56]               0
       Bottleneck-16          [-1, 256, 56, 56]               0
           Conv2d-17          [-1, 128, 56, 56]          32,768
      BatchNorm2d-18          [-1, 128, 56, 56]             256
             ReLU-19          [-1, 128, 56, 56]               0
           Conv2d-20          [-1, 128, 56, 56]           4,608
      BatchNorm2d-21          [-1, 128, 56, 56]             256
             ReLU-22          [-1, 128, 56, 56]               0
           Conv2d-23          [-1, 256, 56, 56]          32,768
      BatchNorm2d-24          [-1, 256, 56, 56]             512
             ReLU-25          [-1, 256, 56, 56]               0
       Bottleneck-26          [-1, 256, 56, 56]               0
           Conv2d-27          [-1, 128, 56, 56]          32,768
      BatchNorm2d-28          [-1, 128, 56, 56]             256
             ReLU-29          [-1, 128, 56, 56]               0
           Conv2d-30          [-1, 128, 56, 56]           4,608
      BatchNorm2d-31          [-1, 128, 56, 56]             256
             ReLU-32          [-1, 128, 56, 56]               0
           Conv2d-33          [-1, 256, 56, 56]          32,768
      BatchNorm2d-34          [-1, 256, 56, 56]             512
             ReLU-35          [-1, 256, 56, 56]               0
       Bottleneck-36          [-1, 256, 56, 56]               0
           Conv2d-37          [-1, 512, 28, 28]         131,072
      BatchNorm2d-38          [-1, 512, 28, 28]           1,024
           Conv2d-39          [-1, 256, 56, 56]          65,536
      BatchNorm2d-40          [-1, 256, 56, 56]             512
             ReLU-41          [-1, 256, 56, 56]               0
           Conv2d-42          [-1, 256, 28, 28]          18,432
      BatchNorm2d-43          [-1, 256, 28, 28]             512
             ReLU-44          [-1, 256, 28, 28]               0
           Conv2d-45          [-1, 512, 28, 28]         131,072
      BatchNorm2d-46          [-1, 512, 28, 28]           1,024
             ReLU-47          [-1, 512, 28, 28]               0
       Bottleneck-48          [-1, 512, 28, 28]               0
           Conv2d-49          [-1, 256, 28, 28]         131,072
      BatchNorm2d-50          [-1, 256, 28, 28]             512
             ReLU-51          [-1, 256, 28, 28]               0
           Conv2d-52          [-1, 256, 28, 28]          18,432
      BatchNorm2d-53          [-1, 256, 28, 28]             512
             ReLU-54          [-1, 256, 28, 28]               0
           Conv2d-55          [-1, 512, 28, 28]         131,072
      BatchNorm2d-56          [-1, 512, 28, 28]           1,024
             ReLU-57          [-1, 512, 28, 28]               0
       Bottleneck-58          [-1, 512, 28, 28]               0
           Conv2d-59          [-1, 256, 28, 28]         131,072
      BatchNorm2d-60          [-1, 256, 28, 28]             512
             ReLU-61          [-1, 256, 28, 28]               0
           Conv2d-62          [-1, 256, 28, 28]          18,432
      BatchNorm2d-63          [-1, 256, 28, 28]             512
             ReLU-64          [-1, 256, 28, 28]               0
           Conv2d-65          [-1, 512, 28, 28]         131,072
      BatchNorm2d-66          [-1, 512, 28, 28]           1,024
             ReLU-67          [-1, 512, 28, 28]               0
       Bottleneck-68          [-1, 512, 28, 28]               0
           Conv2d-69          [-1, 256, 28, 28]         131,072
      BatchNorm2d-70          [-1, 256, 28, 28]             512
             ReLU-71          [-1, 256, 28, 28]               0
           Conv2d-72          [-1, 256, 28, 28]          18,432
      BatchNorm2d-73          [-1, 256, 28, 28]             512
             ReLU-74          [-1, 256, 28, 28]               0
           Conv2d-75          [-1, 512, 28, 28]         131,072
      BatchNorm2d-76          [-1, 512, 28, 28]           1,024
             ReLU-77          [-1, 512, 28, 28]               0
       Bottleneck-78          [-1, 512, 28, 28]               0
           Conv2d-79         [-1, 1024, 14, 14]         524,288
      BatchNorm2d-80         [-1, 1024, 14, 14]           2,048
           Conv2d-81          [-1, 512, 28, 28]         262,144
      BatchNorm2d-82          [-1, 512, 28, 28]           1,024
             ReLU-83          [-1, 512, 28, 28]               0
           Conv2d-84          [-1, 512, 14, 14]          73,728
      BatchNorm2d-85          [-1, 512, 14, 14]           1,024
             ReLU-86          [-1, 512, 14, 14]               0
           Conv2d-87         [-1, 1024, 14, 14]         524,288
      BatchNorm2d-88         [-1, 1024, 14, 14]           2,048
             ReLU-89         [-1, 1024, 14, 14]               0
       Bottleneck-90         [-1, 1024, 14, 14]               0
           Conv2d-91          [-1, 512, 14, 14]         524,288
      BatchNorm2d-92          [-1, 512, 14, 14]           1,024
             ReLU-93          [-1, 512, 14, 14]               0
           Conv2d-94          [-1, 512, 14, 14]          73,728
      BatchNorm2d-95          [-1, 512, 14, 14]           1,024
             ReLU-96          [-1, 512, 14, 14]               0
           Conv2d-97         [-1, 1024, 14, 14]         524,288
      BatchNorm2d-98         [-1, 1024, 14, 14]           2,048
             ReLU-99         [-1, 1024, 14, 14]               0
      Bottleneck-100         [-1, 1024, 14, 14]               0
          Conv2d-101          [-1, 512, 14, 14]         524,288
     BatchNorm2d-102          [-1, 512, 14, 14]           1,024
            ReLU-103          [-1, 512, 14, 14]               0
          Conv2d-104          [-1, 512, 14, 14]          73,728
     BatchNorm2d-105          [-1, 512, 14, 14]           1,024
            ReLU-106          [-1, 512, 14, 14]               0
          Conv2d-107         [-1, 1024, 14, 14]         524,288
     BatchNorm2d-108         [-1, 1024, 14, 14]           2,048
            ReLU-109         [-1, 1024, 14, 14]               0
      Bottleneck-110         [-1, 1024, 14, 14]               0
          Conv2d-111          [-1, 512, 14, 14]         524,288
     BatchNorm2d-112          [-1, 512, 14, 14]           1,024
            ReLU-113          [-1, 512, 14, 14]               0
          Conv2d-114          [-1, 512, 14, 14]          73,728
     BatchNorm2d-115          [-1, 512, 14, 14]           1,024
            ReLU-116          [-1, 512, 14, 14]               0
          Conv2d-117         [-1, 1024, 14, 14]         524,288
     BatchNorm2d-118         [-1, 1024, 14, 14]           2,048
            ReLU-119         [-1, 1024, 14, 14]               0
      Bottleneck-120         [-1, 1024, 14, 14]               0
          Conv2d-121          [-1, 512, 14, 14]         524,288
     BatchNorm2d-122          [-1, 512, 14, 14]           1,024
            ReLU-123          [-1, 512, 14, 14]               0
          Conv2d-124          [-1, 512, 14, 14]          73,728
     BatchNorm2d-125          [-1, 512, 14, 14]           1,024
            ReLU-126          [-1, 512, 14, 14]               0
          Conv2d-127         [-1, 1024, 14, 14]         524,288
     BatchNorm2d-128         [-1, 1024, 14, 14]           2,048
            ReLU-129         [-1, 1024, 14, 14]               0
      Bottleneck-130         [-1, 1024, 14, 14]               0
          Conv2d-131          [-1, 512, 14, 14]         524,288
     BatchNorm2d-132          [-1, 512, 14, 14]           1,024
            ReLU-133          [-1, 512, 14, 14]               0
          Conv2d-134          [-1, 512, 14, 14]          73,728
     BatchNorm2d-135          [-1, 512, 14, 14]           1,024
            ReLU-136          [-1, 512, 14, 14]               0
          Conv2d-137         [-1, 1024, 14, 14]         524,288
     BatchNorm2d-138         [-1, 1024, 14, 14]           2,048
            ReLU-139         [-1, 1024, 14, 14]               0
      Bottleneck-140         [-1, 1024, 14, 14]               0
          Conv2d-141           [-1, 2048, 7, 7]       2,097,152
     BatchNorm2d-142           [-1, 2048, 7, 7]           4,096
          Conv2d-143         [-1, 1024, 14, 14]       1,048,576
     BatchNorm2d-144         [-1, 1024, 14, 14]           2,048
            ReLU-145         [-1, 1024, 14, 14]               0
          Conv2d-146           [-1, 1024, 7, 7]         294,912
     BatchNorm2d-147           [-1, 1024, 7, 7]           2,048
            ReLU-148           [-1, 1024, 7, 7]               0
          Conv2d-149           [-1, 2048, 7, 7]       2,097,152
     BatchNorm2d-150           [-1, 2048, 7, 7]           4,096
            ReLU-151           [-1, 2048, 7, 7]               0
      Bottleneck-152           [-1, 2048, 7, 7]               0
          Conv2d-153           [-1, 1024, 7, 7]       2,097,152
     BatchNorm2d-154           [-1, 1024, 7, 7]           2,048
            ReLU-155           [-1, 1024, 7, 7]               0
          Conv2d-156           [-1, 1024, 7, 7]         294,912
     BatchNorm2d-157           [-1, 1024, 7, 7]           2,048
            ReLU-158           [-1, 1024, 7, 7]               0
          Conv2d-159           [-1, 2048, 7, 7]       2,097,152
     BatchNorm2d-160           [-1, 2048, 7, 7]           4,096
            ReLU-161           [-1, 2048, 7, 7]               0
      Bottleneck-162           [-1, 2048, 7, 7]               0
          Conv2d-163           [-1, 1024, 7, 7]       2,097,152
     BatchNorm2d-164           [-1, 1024, 7, 7]           2,048
            ReLU-165           [-1, 1024, 7, 7]               0
          Conv2d-166           [-1, 1024, 7, 7]         294,912
     BatchNorm2d-167           [-1, 1024, 7, 7]           2,048
            ReLU-168           [-1, 1024, 7, 7]               0
          Conv2d-169           [-1, 2048, 7, 7]       2,097,152
     BatchNorm2d-170           [-1, 2048, 7, 7]           4,096
            ReLU-171           [-1, 2048, 7, 7]               0
      Bottleneck-172           [-1, 2048, 7, 7]               0
AdaptiveAvgPool2d-173           [-1, 2048, 1, 1]               0
          Linear-174                 [-1, 1000]       2,049,000
================================================================
Total params: 25,028,904
Trainable params: 25,028,904
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 361.78
Params size (MB): 95.48
Estimated Total Size (MB): 457.83
----------------------------------------------------------------

Process finished with exit code 0

3.5完整代码

import torch
import torch.nn as nn
import torch.nn.functional as F

'''-------------一、BasicBlock模块-----------------------------'''
# 用于ResNet18和ResNet34基本残差结构块
class BasicBlock(nn.Module):
    def __init__(self, in_channel, out_channel, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channel),
            nn.ReLU(),
            nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_channel),
            nn.downsample(downsample)
        )

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

        out = self.left(x)  # 这是由于残差块需要保留原始输入
        out += identity  # 这是ResNet的核心，在输出上叠加了输入x
        out = F.relu(out)
        return out

'''-------------二、Bottleneck模块-----------------------------'''
class Bottleneck(nn.Module):

    expansion = 4

    # 这里相对于RseNet，在代码中增加一下两个参数groups和width_per_group（即为group数和conv2中组卷积每个group的卷积核个数）
    # 默认值就是正常的ResNet
    def __init__(self, in_channel, out_channel, stride=1, downsample=None,
                 groups=1, width_per_group=64):
        super(Bottleneck, self).__init__()
        # 这里也可以自动计算中间的通道数，也就是3x3卷积后的通道数，如果不改变就是out_channels
        # 如果groups=32,with_per_group=4,out_channels就翻倍了
        width = int(out_channel * (width_per_group / 64.)) * groups

        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,
                               kernel_size=1, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width)
        # -----------------------------------------
        # 组卷积的数，需要传入参数
        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,
                               kernel_size=3, stride=stride, bias=False, padding=1)
        self.bn2 = nn.BatchNorm2d(width)
        # -----------------------------------------
        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel * self.expansion,
                               kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
        # -----------------------------------------
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

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

        out += identity  # 残差连接
        out = self.relu(out)

        return out

'''-------------三、搭建ResNeXt结构-----------------------------'''
class ResNeXt(nn.Module):
    def __init__(self,
                 block,  # 表示block的类型
                 blocks_num,  # 表示的是每一层block的个数
                 num_classes=1000,  # 表示类别
                 include_top=True,  # 表示是否含有分类层(可做迁移学习)
                 groups=1,  # 表示组卷积的数
                 width_per_group=64):
        super(ResNeXt, self).__init__()
        self.include_top = include_top
        self.in_channel = 64

        self.groups = groups
        self.width_per_group = width_per_group

        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
                               padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channel)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, blocks_num[0])           # 64 -> 128
        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)# 128 -> 256
        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)# 256 -> 512
        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) # 512 -> 1024
        if self.include_top:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
            self.fc = nn.Linear(512 * block.expansion, num_classes)

    # 形成单个Stage的网络结构
    def _make_layer(self, block, channel, block_num, stride=1):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(channel * block.expansion))
        # 该部分是将每个blocks的第一个残差结构保存在layers列表中。
        layers = []
        layers.append(block(self.in_channel,
                            channel,
                            downsample=downsample,
                            stride=stride,
                            groups=self.groups,
                            width_per_group=self.width_per_group))
        self.in_channel = channel * block.expansion  # 得到最后的输出

        # 该部分是将每个blocks的剩下残差结构保存在layers列表中，这样就完成了一个blocks的构造。
        for _ in range(1, block_num):
            layers.append(block(self.in_channel,
                                channel,
                                groups=self.groups,
                                width_per_group=self.width_per_group))

         # 返回Conv Block和Identity Block的集合，形成一个Stage的网络结构
        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)

        if self.include_top:
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x

def ResNet34(num_classes=1000, include_top=True):

    return ResNeXt(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)

def ResNet50(num_classes=1000, include_top=True):

    return ResNeXt(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)

def ResNet101(num_classes=1000, include_top=True):

    return ResNeXt(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)

# 论文中的ResNeXt50_32x4d
def ResNeXt50_32x4d(num_classes=1000, include_top=True):

    groups = 32
    width_per_group = 4
    return ResNeXt(Bottleneck, [3, 4, 6, 3],
                  num_classes=num_classes,
                  include_top=include_top,
                  groups=groups,
                  width_per_group=width_per_group)

def ResNeXt101_32x8d(num_classes=1000, include_top=True):

    groups = 32
    width_per_group = 8
    return ResNeXt(Bottleneck, [3, 4, 23, 3],
                  num_classes=num_classes,
                  include_top=include_top,
                  groups=groups,
                  width_per_group=width_per_group)

'''
if __name__ == '__main__':
    model = ResNeXt50_32x4d()
    print(model)
    input = torch.randn(1, 3, 224, 224)
    out = model(input)
    print(out.shape)
# test()
'''
from torchsummary import summary

if __name__ == '__main__':
    net = ResNeXt50_32x4d().cuda()
    summary(net, (3, 224, 224))