text: """
  # ---------------------------------------------------------------------------------
  # -*- coding: utf-8 -*-
  -----------------------------------------------------------------------------------
  # Copyright (c) Microsoft
  # Licensed under the MIT License.
  # Written by Bin Xiao (Bin.Xiao@microsoft.com)
  # Modified by Xingyi Zhou
  # Refer from: https://github.com/xingyizhou/CenterNet
  
  # Modifier: Nguyen Mau Dung (2020.08.09)
  # ------------------------------------------------------------------------------
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  import torch
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  import torch.nn.functional as F
  
  BN_MOMENTUM = 0.1
  
  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',
  }
  
  
  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)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          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)
          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)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      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
  
  
  class PoseResNet(nn.Module):
  
      def __init__(self, block, layers, heads, head_conv, **kwargs):
          self.inplanes = 64
          self.deconv_with_bias = False
          self.heads = heads
  
          super(PoseResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          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.conv_up_level1 = nn.Conv2d(768, 256, kernel_size=1, stride=1, padding=0)
          self.conv_up_level2 = nn.Conv2d(384, 128, kernel_size=1, stride=1, padding=0)
          self.conv_up_level3 = nn.Conv2d(192, 64, kernel_size=1, stride=1, padding=0)
  
          fpn_channels = [256, 128, 64]
          for fpn_idx, fpn_c in enumerate(fpn_channels):
              for head in sorted(self.heads):
                  num_output = self.heads[head]
                  if head_conv > 0:
                      fc = nn.Sequential(
                          nn.Conv2d(fpn_c, 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=fpn_c, out_channels=num_output, kernel_size=1, stride=1, padding=0)
  
                  self.__setattr__('fpn{}_{}'.format(fpn_idx, head), fc)
  
      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, momentum=BN_MOMENTUM),
              )
  
          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):
          _, _, input_h, input_w = x.size()
          hm_h, hm_w = input_h // 4, input_w // 4
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          out_layer1 = self.layer1(x)
          out_layer2 = self.layer2(out_layer1)
  
          out_layer3 = self.layer3(out_layer2)
  
          out_layer4 = self.layer4(out_layer3)
  
          # up_level1: torch.Size([b, 512, 14, 14])
          up_level1 = F.interpolate(out_layer4, scale_factor=2, mode='bilinear', align_corners=True)
  
          concat_level1 = torch.cat((up_level1, out_layer3), dim=1)
          # up_level2: torch.Size([b, 256, 28, 28])
          up_level2 = F.interpolate(self.conv_up_level1(concat_level1), scale_factor=2, mode='bilinear',
                                    align_corners=True)
  
          concat_level2 = torch.cat((up_level2, out_layer2), dim=1)
          # up_level3: torch.Size([b, 128, 56, 56]),
          up_level3 = F.interpolate(self.conv_up_level2(concat_level2), scale_factor=2, mode='bilinear',
                                    align_corners=True)
          # up_level4: torch.Size([b, 64, 56, 56])
          up_level4 = self.conv_up_level3(torch.cat((up_level3, out_layer1), dim=1))
  
          ret = {}
          for head in self.heads:
              temp_outs = []
              for fpn_idx, fdn_input in enumerate([up_level2, up_level3, up_level4]):
                  fpn_out = self.__getattr__('fpn{}_{}'.format(fpn_idx, head))(fdn_input)
                  _, _, fpn_out_h, fpn_out_w = fpn_out.size()
                  # Make sure the added features having same size of heatmap output
                  if (fpn_out_w != hm_w) or (fpn_out_h != hm_h):
                      fpn_out = F.interpolate(fpn_out, size=(hm_h, hm_w))
                  temp_outs.append(fpn_out)
              # Take the softmax in the keypoint feature pyramid network
              final_out = self.apply_kfpn(temp_outs)
  
              ret[head] = final_out
  
          return ret
  
      def apply_kfpn(self, outs):
          outs = torch.cat([out.unsqueeze(-1) for out in outs], dim=-1)
          softmax_outs = F.softmax(outs, dim=-1)
          ret_outs = (outs * softmax_outs).sum(dim=-1)
          return ret_outs
  
      def init_weights(self, num_layers, pretrained=True):
          if pretrained:
              # TODO: Check initial weights for head later
              for fpn_idx in [0, 1, 2]:  # 3 FPN layers
                  for head in self.heads:
                      final_layer = self.__getattr__('fpn{}_{}'.format(fpn_idx, head))
                      for i, m in enumerate(final_layer.modules()):
                          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)
              url = model_urls['resnet{}'.format(num_layers)]
              pretrained_state_dict = model_zoo.load_url(url)
              print('=> loading pretrained model {}'.format(url))
              self.load_state_dict(pretrained_state_dict, strict=False)
  
  
  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])}
  
  
  def get_pose_net(num_layers, heads, head_conv, imagenet_pretrained):
      block_class, layers = resnet_spec[num_layers]
  
      model = PoseResNet(block_class, layers, heads, head_conv=head_conv)
      model.init_weights(num_layers, pretrained=imagenet_pretrained)
      return model