text: 
  
  import os, sys
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from pytorchocr.modeling.common import Activation
  
  class ConvBNLayer(nn.Module):
      def __init__(self,
                   in_channels,
                   out_channels,
                   kernel_size,
                   stride=1,
                   groups=1,
                   is_vd_mode=False,
                   act=None,
                   name=None, ):
          super(ConvBNLayer, self).__init__()
  
          self.is_vd_mode = is_vd_mode
          self.act = act
          self._pool2d_avg = nn.AvgPool2d(
              kernel_size=2, stride=2, padding=0, ceil_mode=True)
          self._conv = nn.Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=(kernel_size - 1) // 2,
              groups=groups,
              bias=False)
          if name == "conv1":
              bn_name = "bn_" + name
          else:
              bn_name = "bn" + name[3:]
          self._batch_norm = nn.BatchNorm2d(
              out_channels,
              track_running_stats=True,
              )
  
          if act is not None:
              self._act = Activation(act_type=act, inplace=True)
  
  
      def forward(self, inputs):
          if self.is_vd_mode:
              inputs = self._pool2d_avg(inputs)
          y = self._conv(inputs)
          y = self._batch_norm(y)
          if self.act is not None:
              y = self._act(y)
          return y
  
  
  class BottleneckBlock(nn.Module):
      def __init__(self,
                   in_channels,
                   out_channels,
                   stride,
                   shortcut=True,
                   if_first=False,
                   name=None):
          super(BottleneckBlock, self).__init__()
  
          self.conv0 = ConvBNLayer(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=1,
              act='relu',
              name=name + "_branch2a")
          self.conv1 = ConvBNLayer(
              in_channels=out_channels,
              out_channels=out_channels,
              kernel_size=3,
              stride=stride,
              act='relu',
              name=name + "_branch2b")
          self.conv2 = ConvBNLayer(
              in_channels=out_channels,
              out_channels=out_channels * 4,
              kernel_size=1,
              act=None,
              name=name + "_branch2c")
  
          if not shortcut:
              self.short = ConvBNLayer(
                  in_channels=in_channels,
                  out_channels=out_channels * 4,
                  kernel_size=1,
                  stride=1,
                  is_vd_mode=False if if_first else True,
                  name=name + "_branch1")
  
          self.shortcut = shortcut
  
      def forward(self, inputs):
          y = self.conv0(inputs)
          conv1 = self.conv1(y)
          conv2 = self.conv2(conv1)
  
          if self.shortcut:
              short = inputs
          else:
              short = self.short(inputs)
          y = torch.add(short, conv2)
          y = F.relu(y)
          return y
  
  
  class BasicBlock(nn.Module):
      def __init__(self,
                   in_channels,
                   out_channels,
                   stride,
                   shortcut=True,
                   if_first=False,
                   name=None):
          super(BasicBlock, self).__init__()
          self.stride = stride
          self.conv0 = ConvBNLayer(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=3,
              stride=stride,
              act='relu',
              name=name + "_branch2a")
          self.conv1 = ConvBNLayer(
              in_channels=out_channels,
              out_channels=out_channels,
              kernel_size=3,
              act=None,
              name=name + "_branch2b")
  
          if not shortcut:
              self.short = ConvBNLayer(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  kernel_size=1,
                  stride=1,
                  is_vd_mode=False if if_first else True,
                  name=name + "_branch1")
  
          self.shortcut = shortcut
  
      def forward(self, inputs):
          y = self.conv0(inputs)
          conv1 = self.conv1(y)
  
          if self.shortcut:
              short = inputs
          else:
              short = self.short(inputs)
          y = short + conv1
          y = F.relu(y)
          return y
  
  
  class ResNet(nn.Module):
      def __init__(self, in_channels=3, layers=50, **kwargs):
          super(ResNet, self).__init__()
  
          self.layers = layers
          supported_layers = [18, 34, 50, 101, 152, 200]
          assert layers in supported_layers, \
              "supported layers are {} but input layer is {}".format(
                  supported_layers, layers)
  
          if layers == 18:
              depth = [2, 2, 2, 2]
          elif layers == 34 or layers == 50:
              depth = [3, 4, 6, 3]
          elif layers == 101:
              depth = [3, 4, 23, 3]
          elif layers == 152:
              depth = [3, 8, 36, 3]
          elif layers == 200:
              depth = [3, 12, 48, 3]
          num_channels = [64, 256, 512,
                          1024] if layers >= 50 else [64, 64, 128, 256]
          num_filters = [64, 128, 256, 512]
  
          self.conv1_1 = ConvBNLayer(
              in_channels=in_channels,
              out_channels=32,
              kernel_size=3,
              stride=2,
              act='relu',
              name="conv1_1")
          self.conv1_2 = ConvBNLayer(
              in_channels=32,
              out_channels=32,
              kernel_size=3,
              stride=1,
              act='relu',
              name="conv1_2")
          self.conv1_3 = ConvBNLayer(
              in_channels=32,
              out_channels=64,
              kernel_size=3,
              stride=1,
              act='relu',
              name="conv1_3")
          self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
          self.stages = nn.ModuleList()
          self.out_channels = []
          if layers >= 50:
              for block in range(len(depth)):
                  # block_list = []
                  block_list = nn.Sequential()
                  shortcut = False
                  for i in range(depth[block]):
                      if layers in [101, 152] and block == 2:
                          if i == 0:
                              conv_name = "res" + str(block + 2) + "a"
                          else:
                              conv_name = "res" + str(block + 2) + "b" + str(i)
                      else:
                          conv_name = "res" + str(block + 2) + chr(97 + i)
                      bottleneck_block = BottleneckBlock(
                              in_channels=num_channels[block]
                              if i == 0 else num_filters[block] * 4,
                              out_channels=num_filters[block],
                              stride=2 if i == 0 and block != 0 else 1,
                              shortcut=shortcut,
                              if_first=block == i == 0,
                              name=conv_name)
  
                      shortcut = True
                      block_list.add_module('bb_%d_%d' % (block, i), bottleneck_block)
                  self.out_channels.append(num_filters[block] * 4)
                  # self.stages.append(nn.Sequential(*block_list))
                  self.stages.append(block_list)
          else:
              for block in range(len(depth)):
                  # block_list = []
                  block_list = nn.Sequential()
                  shortcut = False
                  for i in range(depth[block]):
                      conv_name = "res" + str(block + 2) + chr(97 + i)
                      basic_block = BasicBlock(
                              in_channels=num_channels[block]
                              if i == 0 else num_filters[block],
                              out_channels=num_filters[block],
                              stride=2 if i == 0 and block != 0 else 1,
                              shortcut=shortcut,
                              if_first=block == i == 0,
                              name=conv_name)
  
                      shortcut = True
                      block_list.add_module('bb_%d_%d' % (block, i), basic_block)
                      # block_list.append(basic_block)
                  self.out_channels.append(num_filters[block])
                  self.stages.append(block_list)
  
                  # self.stages.append(nn.Sequential(*block_list))
  
  
      def forward(self, inputs):
          y = self.conv1_1(inputs)
          y = self.conv1_2(y)
          y = self.conv1_3(y)
          y = self.pool2d_max(y)
          out = []
          for block in self.stages:
              y = block(y)
              out.append(y)
          return out