code: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  
  
  class VGG_FeatureExtractor(nn.Module):
      def __init__(self, input_channel, output_channel=512):
          super(VGG_FeatureExtractor, self).__init__()
          self.output_channel = [int(output_channel / 8), int(output_channel / 4),
                                 int(output_channel / 2), output_channel]  # [64, 128, 256, 512]
          self.ConvNet = nn.Sequential(
              nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True),
              nn.MaxPool2d(2, 2),  # 64x16x50
              nn.Conv2d(self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True),
              nn.MaxPool2d(2, 2),  # 128x8x25
              nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True),  # 256x8x25
              nn.Conv2d(self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True),
              nn.MaxPool2d((2, 1), (2, 1)),  # 256x4x25
              nn.Conv2d(self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False),
              nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True),  # 512x4x25
              nn.Conv2d(self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False),
              nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True),
              nn.MaxPool2d((2, 1), (2, 1)),  # 512x2x25
              nn.Conv2d(self.output_channel[3], self.output_channel[3], 2, 1, 0), nn.ReLU(True))  # 512x1x24
  
      def forward(self, input):
          return self.ConvNet(input)
  
  
  class RCNN_FeatureExtractor(nn.Module):
      def __init__(self, input_channel, output_channel=512):
          super(RCNN_FeatureExtractor, self).__init__()
          self.output_channel = [int(output_channel / 8), int(output_channel / 4),
                                 int(output_channel / 2), output_channel]  # [64, 128, 256, 512]
          self.ConvNet = nn.Sequential(
              nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True),
              nn.MaxPool2d(2, 2),  # 64 x 16 x 50
              GRCL(self.output_channel[0], self.output_channel[0], num_iteration=5, kernel_size=3, pad=1),
              nn.MaxPool2d(2, 2),  # 64 x 8 x 25
              GRCL(self.output_channel[0], self.output_channel[1], num_iteration=5, kernel_size=3, pad=1),
              nn.MaxPool2d(2, (2, 1), (0, 1)),  # 128 x 4 x 26
              GRCL(self.output_channel[1], self.output_channel[2], num_iteration=5, kernel_size=3, pad=1),
              nn.MaxPool2d(2, (2, 1), (0, 1)),  # 256 x 2 x 27
              nn.Conv2d(self.output_channel[2], self.output_channel[3], 2, 1, 0, bias=False),
              nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True))  # 512 x 1 x 26
  
      def forward(self, input):
          return self.ConvNet(input)
  
  
  class ResNet_FeatureExtractor(nn.Module):
      def __init__(self, input_channel, output_channel=512):
          super(ResNet_FeatureExtractor, self).__init__()
          self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3])
  
      def forward(self, input):
          return self.ConvNet(input)
  
  
  class GRCL(nn.Module):
  
      def __init__(self, input_channel, output_channel, num_iteration, kernel_size, pad):
          super(GRCL, self).__init__()
          self.wgf_u = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=False)
          self.wgr_x = nn.Conv2d(output_channel, output_channel, 1, 1, 0, bias=False)
          self.wf_u = nn.Conv2d(input_channel, output_channel, kernel_size, 1, pad, bias=False)
          self.wr_x = nn.Conv2d(output_channel, output_channel, kernel_size, 1, pad, bias=False)
  
          self.BN_x_init = nn.BatchNorm2d(output_channel)
  
          self.num_iteration = num_iteration
          self.GRCL = [GRCL_unit(output_channel) for _ in range(num_iteration)]
          self.GRCL = nn.Sequential(*self.GRCL)
  
      def forward(self, input):
          """ The input of GRCL is consistant over time t, which is denoted by u(0)
          thus wgf_u / wf_u is also consistant over time t.
          """
          wgf_u = self.wgf_u(input)
          wf_u = self.wf_u(input)
          x = F.relu(self.BN_x_init(wf_u))
  
          for i in range(self.num_iteration):
              x = self.GRCL[i](wgf_u, self.wgr_x(x), wf_u, self.wr_x(x))
  
          return x
  
  
  class GRCL_unit(nn.Module):
  
      def __init__(self, output_channel):
          super(GRCL_unit, self).__init__()
          self.BN_gfu = nn.BatchNorm2d(output_channel)
          self.BN_grx = nn.BatchNorm2d(output_channel)
          self.BN_fu = nn.BatchNorm2d(output_channel)
          self.BN_rx = nn.BatchNorm2d(output_channel)
          self.BN_Gx = nn.BatchNorm2d(output_channel)
  
      def forward(self, wgf_u, wgr_x, wf_u, wr_x):
          G_first_term = self.BN_gfu(wgf_u)
          G_second_term = self.BN_grx(wgr_x)
          G = F.sigmoid(G_first_term + G_second_term)
  
          x_first_term = self.BN_fu(wf_u)
          x_second_term = self.BN_Gx(self.BN_rx(wr_x) * G)
          x = F.relu(x_first_term + x_second_term)
  
          return x
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = self._conv3x3(inplanes, planes)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = self._conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def _conv3x3(self, in_planes, out_planes, stride=1):
          return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                           padding=1, bias=False)
  
      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 ResNet(nn.Module):
  
      def __init__(self, input_channel, output_channel, block, layers):
          super(ResNet, self).__init__()
  
          self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel]
  
          self.inplanes = int(output_channel / 8)
          self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16),
                                   kernel_size=3, stride=1, padding=1, bias=False)
          self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16))
          self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes,
                                   kernel_size=3, stride=1, padding=1, bias=False)
          self.bn0_2 = nn.BatchNorm2d(self.inplanes)
          self.relu = nn.ReLU(inplace=True)
  
          self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
          self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0])
          self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[
                                 0], kernel_size=3, stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(self.output_channel_block[0])
  
          self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
          self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1)
          self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[
                                 1], kernel_size=3, stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(self.output_channel_block[1])
  
          self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1))
          self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1)
          self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[
                                 2], kernel_size=3, stride=1, padding=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.output_channel_block[2])
  
          self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1)
          self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
                                   3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False)
          self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3])
          self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[
                                   3], kernel_size=2, stride=1, padding=0, bias=False)
          self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3])
  
      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))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv0_1(x)
          x = self.bn0_1(x)
          x = self.relu(x)
          x = self.conv0_2(x)
          x = self.bn0_2(x)
          x = self.relu(x)
  
          x = self.maxpool1(x)
          x = self.layer1(x)
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          x = self.maxpool2(x)
          x = self.layer2(x)
          x = self.conv2(x)
          x = self.bn2(x)
          x = self.relu(x)
  
          x = self.maxpool3(x)
          x = self.layer3(x)
          x = self.conv3(x)
          x = self.bn3(x)
          x = self.relu(x)
  
          x = self.layer4(x)
          x = self.conv4_1(x)
          x = self.bn4_1(x)
          x = self.relu(x)
          x = self.conv4_2(x)
          x = self.bn4_2(x)
          x = self.relu(x)
  
          return x
  
apis: torch.nn.BatchNorm2d torch.nn.MaxPool2d torch.nn.functional.sigmoid torch.nn.functional.relu torch.cuda.is_available torch.nn.Conv2d torch.nn.Sequential torch.nn.ReLU