text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  
  from mmskeleton.ops.st_gcn import ConvTemporalGraphical, Graph
  
  
  def zero(x):
      return 0
  
  
  def iden(x):
      return x
  
  
  class ST_GCN_18(nn.Module):
      r"""Spatial temporal graph convolutional networks.
  
      Args:
          in_channels (int): Number of channels in the input data
          num_class (int): Number of classes for the classification task
          graph_cfg (dict): The arguments for building the graph
          edge_importance_weighting (bool): If ``True``, adds a learnable
              importance weighting to the edges of the graph
          **kwargs (optional): Other parameters for graph convolution units
  
      Shape:
          - Input: :math:`(N, in_channels, T_{in}, V_{in}, M_{in})`
          - Output: :math:`(N, num_class)` where
              :math:`N` is a batch size,
              :math:`T_{in}` is a length of input sequence, 关键帧数
              :math:`V_{in}` is the number of graph nodes, 关节点数
              :math:`M_{in}` is the number of instance in a frame. 一帧中最多两个人
      """
      def __init__(self,
                   in_channels,
                   num_class,
                   graph_cfg,
                   edge_importance_weighting=True,
                   data_bn=True,
                   **kwargs):
          super().__init__()
  
          # load graph
          self.graph = Graph(**graph_cfg)
          A = torch.tensor(self.graph.A,
                           dtype=torch.float32,
                           requires_grad=False)
          self.register_buffer('A', A)
  
          # build networks
          spatial_kernel_size = A.size(0)
          temporal_kernel_size = 9
          kernel_size = (temporal_kernel_size, spatial_kernel_size)
          # 首先进行归一化，归一化是在时间和空间维度下进行的（V x C）。也就是将一个关节在不同帧下的位置特征（x 和 y 和 acc）进行归一化
          # 标准化某节点在所有T个关键帧的特征值
          self.data_bn = nn.BatchNorm1d(in_channels *
                                        A.size(1)) if data_bn else iden
          kwargs0 = {k: v for k, v in kwargs.items() if k != 'dropout'}
  
          # 10层st-gcn构成，一个st-gcn层包含了一个GCN模块和一个TCN模块，另外，还有邻接矩阵和边权重矩阵
          self.st_gcn_networks = nn.ModuleList((
              st_gcn_block(in_channels,
                           64,
                           kernel_size,
                           1,
                           residual=False,
                           **kwargs0),
              st_gcn_block(64, 64, kernel_size, 1, **kwargs),
              st_gcn_block(64, 64, kernel_size, 1, **kwargs),
              st_gcn_block(64, 64, kernel_size, 1, **kwargs),
              st_gcn_block(64, 128, kernel_size, 2, **kwargs),
              st_gcn_block(128, 128, kernel_size, 1, **kwargs),
              st_gcn_block(128, 128, kernel_size, 1, **kwargs),
              st_gcn_block(128, 256, kernel_size, 2, **kwargs),
              st_gcn_block(256, 256, kernel_size, 1, **kwargs),
              st_gcn_block(256, 256, kernel_size, 1, **kwargs),
          ))
  
          # initialize parameters for edge importance weighting
          if edge_importance_weighting:
              self.edge_importance = nn.ParameterList([
                  nn.Parameter(torch.ones(self.A.size()))
                  for i in self.st_gcn_networks
              ])
          else:
              self.edge_importance = [1] * len(self.st_gcn_networks)
  
          # fcn for prediction（全连接层）
          self.fcn = nn.Conv2d(256, num_class, kernel_size=1)
  
      def forward(self, x):
          # data normalization
          N, C, T, V, M = x.size()  # C = 3 (X,Y,S)代表一个点的信息(位置+预测的可能性)
          x = x.permute(0, 4, 3, 1, 2).contiguous() # 改变纬度。同时将tenosr在内存中变为连续分布
          x = x.view(N * M, V * C, T)  # x：[N * M, V * C, T]
          x = self.data_bn(x)  # 输入层的batchNorm（V*C）
          x = x.view(N, M, V, C, T)
          x = x.permute(0, 1, 3, 4, 2).contiguous()
          x = x.view(N * M, C, T, V)  # 将batch和M整合在一起，batch * num_person
  
          # forward (for gcn，importance应该改为for st-gcn， importance)
          # ST-GCN与可学习的权重矩阵不断重复与堆叠
          for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
              x, _ = gcn(x, self.A * importance)
  
          # 全局平均池化以及全卷积层输出预测分支
          # global pooling
          x = F.avg_pool2d(x, x.size()[2:])
          x = x.view(N, M, -1, 1, 1).mean(dim=1)
  
          # prediction
          x = self.fcn(x)
          x = x.view(x.size(0), -1)  # 将batch_size个多维tensor变为batch_size个一维tensor
  
          return x
  
      def extract_feature(self, x):
  
          # data normalization
          N, C, T, V, M = x.size()  # C = 3 (X,Y,S)代表一个点的信息(位置+预测的可能性)
          x = x.permute(0, 4, 3, 1, 2).contiguous()
          x = x.view(N * M, V * C, T)  # x：[N * M, V * C, T]
          x = self.data_bn(x) # 输入层的batchNorm（V*C）
          x = x.view(N, M, V, C, T)
          x = x.permute(0, 1, 3, 4, 2).contiguous()
          x = x.view(N * M, C, T, V) # 将batch和M整合在一起，batch * num_person
  
          # forwad
          for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
              x, _ = gcn(x, self.A * importance)
  
          _, c, t, v = x.size()
          feature = x.view(N, M, c, t, v).permute(0, 2, 3, 4, 1)
  
          # prediction
          x = self.fcn(x)
          output = x.view(N, M, -1, t, v).permute(0, 2, 3, 4, 1)
  
          return output, feature
  
  
  class st_gcn_block(nn.Module):
      r"""Applies a spatial temporal graph convolution over an input graph sequence.
  
      Args:
          in_channels (int): Number of channels in the input sequence data
          out_channels (int): Number of channels produced by the convolution
          kernel_size (tuple): Size of the temporal convolving kernel and graph convolving kernel
          stride (int, optional): Stride of the temporal convolution. Default: 1
          dropout (int, optional): Dropout rate of the final output. Default: 0
          residual (bool, optional): If ``True``, applies a residual mechanism. Default: ``True``
  
      Shape:
          - Input[0]: Input graph sequence in :math:`(N, in_channels, T_{in}, V)` format
          - Input[1]: Input graph adjacency matrix in :math:`(K, V, V)` format
          - Output[0]: Outpu graph sequence in :math:`(N, out_channels, T_{out}, V)` format
          - Output[1]: Graph adjacency matrix for output data in :math:`(K, V, V)` format
  
          where
              :math:`N` is a batch size,
              :math:`K` is the spatial kernel size, as :math:`K == kernel_size[1]`,
              :math:`T_{in}/T_{out}` is a length of input/output sequence,
              :math:`V` is the number of graph nodes.
  
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   kernel_size,
                   stride=1,
                   dropout=0,
                   residual=True):
          super().__init__()
  
          assert len(kernel_size) == 2
          assert kernel_size[0] % 2 == 1
          padding = ((kernel_size[0] - 1) // 2, 0)
  
          self.gcn = ConvTemporalGraphical(in_channels, out_channels,
                                           kernel_size[1])
  
          # tcn模块让网络在时域中进行特征的提取
          # tcn是用(temporal_kernel_size, 1)的卷积核对t维度进行卷积运算, 相当于同一个节点在不同t下的特征的卷积
          self.tcn = nn.Sequential(
              nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(
                  out_channels,
                  out_channels, # 不改变channel值
                  (kernel_size[0], 1),
                  (stride, 1), # stride可以控制t域的缩小，可当做poolling操作
                  padding,
              ),
              nn.BatchNorm2d(out_channels),
              nn.Dropout(dropout, inplace=True),
          )
  
          if not residual:
              self.residual = zero
  
          elif (in_channels == out_channels) and (stride == 1):
              self.residual = iden
  
          # 残差结构：Conv2d + BN2d
          else:
              self.residual = nn.Sequential(
                  nn.Conv2d(in_channels,
                            out_channels,
                            kernel_size=1,
                            stride=(stride, 1)),
                  nn.BatchNorm2d(out_channels),
              )
  
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x, A):
  
          res = self.residual(x)
          x, A = self.gcn(x, A)
          x = self.tcn(x) + res
  
          return self.relu(x), A