code: # coding: utf-8
  import math
  import numpy as np
  
  
  import torch
  import torch.nn as nn
  from torch.autograd import Variable
  import torch.nn.functional as F
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=1, bias=False)
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=1)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, se=False):
          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
          self.se = se
          
          if(self.se):
              self.gap = nn.AdaptiveAvgPool2d(1)
              self.conv3 = conv1x1(planes, planes//16)
              self.conv4 = conv1x1(planes//16, planes)
  
      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)
              
          if(self.se):
              w = self.gap(out)
              w = self.conv3(w)
              w = self.relu(w)
              w = self.conv4(w).sigmoid()
              
              out = out * w
          
          out = out + residual
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(self, block, layers, se=False):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.se = se
          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)
          
          
          self.avgpool = nn.AdaptiveAvgPool2d(1)
          
          self.bn = nn.BatchNorm1d(512)
          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_()
              elif isinstance(m, nn.BatchNorm1d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, 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, se=self.se))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, se=self.se))
  
          return nn.Sequential(*layers)
  
      def forward(self, 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.bn(x)
          return x        
  
  
  class VideoCNN(nn.Module):
      def __init__(self, se=False):
          super(VideoCNN, self).__init__()
          
          # frontend3D
          self.frontend3D = nn.Sequential(
                  nn.Conv3d(1, 64, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3), bias=False),
                  nn.BatchNorm3d(64),
                  nn.ReLU(True),
                  nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1))
                  )
          # resnet
          self.resnet18 = ResNet(BasicBlock, [2, 2, 2, 2], se=se)
          self.dropout = nn.Dropout(p=0.5)
  
          # backend_gru
          # initialize
          self._initialize_weights()
      
      def visual_frontend_forward(self, x):
          x = x.transpose(1, 2)
          x = self.frontend3D(x)
          x = x.transpose(1, 2)
          x = x.contiguous()
          x = x.view(-1, 64, x.size(3), x.size(4))
          x = self.resnet18(x)
          return x        
      
      def forward(self, x):
          b, t = x.size()[:2]
  
          x = self.visual_frontend_forward(x)
          
          #x = self.dropout(x)
          feat = x.view(b, -1, 512)
  
          x = x.view(b, -1, 512)       
          return x
  
      def _initialize_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv3d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.kernel_size[2] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
  
              elif 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))
                  if m.bias is not None:
                      m.bias.data.zero_()
  
              elif isinstance(m, nn.Conv1d):
                  n = m.kernel_size[0] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
  
              elif isinstance(m, nn.BatchNorm3d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
              elif isinstance(m, nn.BatchNorm1d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
apis: torch.nn.BatchNorm2d torch.nn.MaxPool3d torch.nn.BatchNorm3d torch.nn.BatchNorm1d torch.nn.AdaptiveAvgPool2d torch.nn.Conv2d torch.nn.Sequential torch.nn.Conv3d torch.nn.ReLU torch.nn.Dropout