CenterNet网络模型代码_1-msra_resnet.py

小咪咪 2022-12-23 00:43 192阅读 0赞

##### 这里开始进行阅读CenterNet源码模型中的**models\\networks**的部分的网络代码，从简单的 Resnet 模型开始，这里先给出卷积的输入输出的计算公式，以及反卷积的输入输出计算公式： #####

> 1 2维卷积操作(Conv2d)：  
> **nn.Conv2d(in\_channels, out\_channels, kernel\_size, stride=1, padding=0, dilation=1, groups=1, bias=True))**  
> 参数解析：  
> in\_channel:　输入数据的通道数，例RGB图片通道数为3；out\_channel: 输出数据的通道数，这个根据模型调整；kennel\_size: 卷积核大小，可以是int，或tuple；kennel\_size=2,意味着卷积大小(2,2)， kennel\_size=（2,3），意味着卷积大小（2，3）即非正方形卷积；  
> stride：步长，默认为1，与kennel\_size类似，stride=2,意味着步长上下左右扫描皆为2， stride=（2,3），左右扫描步长为2，上下为3；  
> padding：　零填充  
> dilation:空洞卷积参数  
> groups : 组卷积，该参数必须能被输入通道整除
> 
> 假设输入图像大小为 (H,W), 卷积核大小为 K，strides步长为S, Padding 填充为 P，则经过该卷积后，其输出的计算公式为：  
> **1) H’=(H-K+2P)/S+1**  
> **2) W’=(w-K+2P)/S+1**  
> 2 反卷积操作(ConvTranspose2d)：  
> 其参数为**ConvTranspose2d(in\_channels,out\_channels,kernel\_size,stride=1,padding=0,output\_padding=0， groups=1,bias=True,dilation=1)**  
> 参数解析：  
> in\_channels(int) —输入信号的通道数  
> out\_channels(int) ----卷积产生的通道数  
> kernel\_size(int or tuple) —卷积核的大小  
> stride(int or tuple,optional) —卷积步长，即要将输入扩大的倍数  
> padding(int or tuple,optional) —输入的每一条边补充0的层数，高宽都增加2*padding  
> output\_padding(int or tuple,optional) —输出边补充0的层数，高宽都增加2*padding  
> groups(int ,optional) —从输入通道到输出通道的阻塞连接数  
> bias(bool, optional) —若bias=True，添加偏置  
> dilation（int or tuple,optional）—卷积核元素之间的间距  
> 其中  
> 假设输入图像大小为 (H,W), 卷积核大小为 K，strides步长为S, Padding 填充为 P，则经过该卷积后，其输出的计算公式为：  
> ![在这里插入图片描述][20201121113314197.png_pic_center]  
> 一般来说，我们希望得到输入特征图大小/输出特征图大小 = stride的话，代入上面的式子能够得到结果：  
> padding = (kernel\_size - stride + output\_padding )/2  
> 所以为了让padding = (kernel\_size - 1)/2，则output\_padding应该取值为stride - 1，这样就能够满足输入特征图大小/输出特征图大小 = stride

##### 二话不说，上代码： #####

# BachNorm2d函数的动量参数
    BN_MOMENTUM = 0.1
    #预训练模型链接
    model_urls = { 
        'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
        'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
        'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
        'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
        'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    }
    # 带Padding的3*3的卷积模块
    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)
    
    # 堆叠的 两层 3*3 的网络
    class BasicBlock(nn.Module):
        expansion = 1
    
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(BasicBlock, self).__init__()
            # 两个3*3的卷积模块
            self.conv1 = conv3x3(inplanes, planes, stride)
            self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
            self.relu = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(planes, planes)
            self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
            # 是否存在下采样模块
            self.downsample = downsample
            self.stride = stride
         # 网络前向传播
        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)
            # 当下采样的时候
            if self.downsample is not None:
                residual = self.downsample(x)
    
            out += residual
            out = self.relu(out)
    
            return out
    
    # 瓶颈层 标准版的
    class Bottleneck(nn.Module):
        expansion = 4# 输入和输出的扩展率，一般为4 即 输入64通道 输出 256通道 
        #初始化 模型模块
        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, momentum=BN_MOMENTUM)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                   padding=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
            self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
                                   bias=False)
            self.bn3 = nn.BatchNorm2d(planes * self.expansion,
                                      momentum=BN_MOMENTUM)
            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
    
    # 构建 backbone 模型
    class PoseResNet(nn.Module):
    
        def __init__(self, block, layers, heads, head_conv, **kwargs):
            self.inplanes = 64 # 第一层卷积的输入通道数
            self.deconv_with_bias = False# 不使用可变形卷积
            self.heads = heads# 头部
            super(PoseResNet, self).__init__()
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,bias=False) # 预处理模块
            self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)# 进行BN
            self.relu = nn.ReLU(inplace=True)# 进行Relu
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)# MaxPool # 以上共下采样4倍
            # 组合backbone
            self.layer1 = self._make_layer(block, 64, layers[0])
            self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    
            # used for deconv layers
            self.deconv_layers = self._make_deconv_layer(
                3,
                [256, 256, 256],
                [4, 4, 4],
            )
            # self.final_layer = []
    
            for head in sorted(self.heads):
              num_output = self.heads[head]
              if head_conv > 0:
                fc = nn.Sequential(
                    nn.Conv2d(256, head_conv,
                      kernel_size=3, padding=1, bias=True),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(head_conv, num_output, 
                      kernel_size=1, stride=1, padding=0))
              else:
                fc = nn.Conv2d(
                  in_channels=256,
                  out_channels=num_output,
                  kernel_size=1,
                  stride=1,
                  padding=0
              )
              self.__setattr__(head, fc)
    
            # self.final_layer = nn.ModuleList(self.final_layer)
        # 构建卷积层
        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None # 下采样模型
            # 判断是否使用下采样 当stride!=1 或者 输入和输出扩展率不一致时运用
            if stride != 1 or self.inplanes != planes * block.expansion:
                # 下采样模块为 1*1 的卷积模块
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion,
                              kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
                )
    
            layers = []# 层元素列表
            layers.append(block(self.inplanes, planes, stride, downsample))# 第一个残差块 
            self.inplanes = planes * block.expansion # 输出的通道
            for i in range(1, blocks):# 进行堆叠残差块
                layers.append(block(self.inplanes, planes))
    
            return nn.Sequential(*layers)# 组成网络序列
    
        def _get_deconv_cfg(self, deconv_kernel, index):
            if deconv_kernel == 4:
                padding = 1
                output_padding = 0
            elif deconv_kernel == 3:
                padding = 1
                output_padding = 1
            elif deconv_kernel == 2:
                padding = 0
                output_padding = 0
    
            return deconv_kernel, padding, output_padding
        # 构建可变性卷积
        def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
            # 进行初步验证
            assert num_layers == len(num_filters), \
                'ERROR: num_deconv_layers is different len(num_deconv_filters)'
            assert num_layers == len(num_kernels), \
                'ERROR: num_deconv_layers is different len(num_deconv_filters)'
            # 层列表 num_layers=3
            layers = []
            for i in range(num_layers):
                # 得到卷积的一些配置参数
                #kernel：4 padding：1 output_padding ：0
                kernel, padding, output_padding = \
                    self._get_deconv_cfg(num_kernels[i], i)
                # 256
                planes = num_filters[i]
                # 追加反卷积操作 self.inplanes 512
                # planes 256 
                # 原始输入2048 即resnet第5阶段的输出
                layers.append(
                    nn.ConvTranspose2d(
                        in_channels=self.inplanes,
                        out_channels=planes,
                        kernel_size=kernel,
                        stride=2,
                        padding=padding,
                        output_padding=output_padding,
                        bias=self.deconv_with_bias))
                # 追加 BN
                layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
                # 追加 Relu
                layers.append(nn.ReLU(inplace=True))
                self.inplanes = planes# 更新 inplanes
    
            return nn.Sequential(*layers)# 这是模型序列
        # 前向传播
        def forward(self, x):
            # 预处理
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
            x = self.maxpool(x)
            # 各个阶段的输出
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            # 反卷积
            x = self.deconv_layers(x)
            ret = { }
            # 输出头 得到输出头的结果字典
            for head in self.heads:
                ret[head] = self.__getattr__(head)(x)
            return [ret]
        # 初始化权重
        def init_weights(self, num_layers, pretrained=True):
            # 默认加载预训练权重
            if pretrained:
                # print('=> init resnet deconv weights from normal distribution')
                for _, m in self.deconv_layers.named_modules():
                    # 初始化反卷积模块
                    if isinstance(m, nn.ConvTranspose2d):
                        # print('=> init {}.weight as normal(0, 0.001)'.format(name))
                        # print('=> init {}.bias as 0'.format(name))
                        nn.init.normal_(m.weight, std=0.001)
                        if self.deconv_with_bias:
                            nn.init.constant_(m.bias, 0)
                    # 初始化 BN 参数
                    elif isinstance(m, nn.BatchNorm2d):
                        # print('=> init {}.weight as 1'.format(name))
                        # print('=> init {}.bias as 0'.format(name))
                        nn.init.constant_(m.weight, 1)
                        nn.init.constant_(m.bias, 0)
                # print('=> init final conv weights from normal distribution')
                # 遍历初始化结果部分
                for head in self.heads:
                  # 得到对应头部的模型
                  final_layer = self.__getattr__(head)
                  # 遍历头部模块
                  for i, m in enumerate(final_layer.modules()):
                      # 初始化 卷积部分
                      if isinstance(m, nn.Conv2d):
                          # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                          # print('=> init {}.weight as normal(0, 0.001)'.format(name))
                          # print('=> init {}.bias as 0'.format(name))
                          if m.weight.shape[0] == self.heads[head]:
                              if 'hm' in head:
                                  nn.init.constant_(m.bias, -2.19)
                              else:
                                  nn.init.normal_(m.weight, std=0.001)
                                  nn.init.constant_(m.bias, 0)
                #pretrained_state_dict = torch.load(pretrained)
                # 获取预训练的连接
                url = model_urls['resnet{}'.format(num_layers)]
                pretrained_state_dict = model_zoo.load_url(url)#加载预训练模型
                print('=> loading pretrained model {}'.format(url))
                # 加载模型
                self.load_state_dict(pretrained_state_dict, strict=False)
            else:
                print('=> imagenet pretrained model dose not exist')
                print('=> please download it first')
                raise ValueError('imagenet pretrained model does not exist')
    
    # 不同深度的 resnet 模块
    resnet_spec = { 18: (BasicBlock, [2, 2, 2, 2]),
                   34: (BasicBlock, [3, 4, 6, 3]),
                   50: (Bottleneck, [3, 4, 6, 3]),
                   101: (Bottleneck, [3, 4, 23, 3]),
                   152: (Bottleneck, [3, 8, 36, 3])}
    
    # 生成网络模型
    def get_pose_net(num_layers, heads, head_conv):
      block_class, layers = resnet_spec[num_layers]# 选择特定深度的模型
      # 生成 backbone
      model = PoseResNet(block_class, layers, heads, head_conv=head_conv)
      model.init_weights(num_layers, pretrained=True)# 初始化权重
      return model

以上就是CenterNet源码中 backbone 为 resnet 的代码说明，如果有问题，其中不好理解的部分其实是反卷积的操作，这个最好把网络模型整体打印出来，就能知道哪些模块到底在做什么！ 欢迎指正！

[20201121113314197.png_pic_center]: /images/20221120/5e6c6150a2874fe5ac63a14f2535fbd8.png