code: # ------------------------------------------------------------------------------
  # Copyright (c) Microsoft
  # Licensed under the MIT License.
  # Written by Bin Xiao (Bin.Xiao@microsoft.com)
  # Modified by Xingyi Zhou
  # ------------------------------------------------------------------------------
  
  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
  
  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(1, 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)
  
          # used for deconv layers
          self.deconv_layers = self._make_deconv_layer(
              3,
              [256, 256, 256],
              [4, 4, 4],
          )
          # self.final_layer = []
  
          for head in sorted(self.heads):
            num_output = self.heads[head]
            if head_conv > 0:
              fc = nn.Sequential(
                  nn.Conv2d(256, 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=256,
                out_channels=num_output,
                kernel_size=1,
                stride=1,
                padding=0)
            self.__setattr__(head, fc)
  
          # self.final_layer = nn.ModuleList(self.final_layer)
  
      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 _get_deconv_cfg(self, deconv_kernel, index):
          if deconv_kernel == 4:
              padding = 1
              output_padding = 0
          elif deconv_kernel == 3:
              padding = 1
              output_padding = 1
          elif deconv_kernel == 2:
              padding = 0
              output_padding = 0
  
          return deconv_kernel, padding, output_padding
  
      def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
          assert num_layers == len(num_filters), \
              'ERROR: num_deconv_layers is different len(num_deconv_filters)'
          assert num_layers == len(num_kernels), \
              'ERROR: num_deconv_layers is different len(num_deconv_filters)'
  
          layers = []
          for i in range(num_layers):
              kernel, padding, output_padding = \
                  self._get_deconv_cfg(num_kernels[i], i)
  
              planes = num_filters[i]
              layers.append(
                  nn.ConvTranspose2d(
                      in_channels=self.inplanes,
                      out_channels=planes,
                      kernel_size=kernel,
                      stride=2,
                      padding=padding,
                      output_padding=output_padding,
                      bias=self.deconv_with_bias))
              layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
              layers.append(nn.ReLU(inplace=True))
              self.inplanes = planes
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = x.float()
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          x = self.deconv_layers(x)
          ret = {}
          for head in self.heads:
              ret[head] = self.__getattr__(head)(x)
          return [ret]
  
      def init_weights(self, num_layers, pretrained=True):
          if pretrained:
              # print('=> init resnet deconv weights from normal distribution')
              for _, m in self.deconv_layers.named_modules():
                  if isinstance(m, nn.ConvTranspose2d):
                      # print('=> init {}.weight as normal(0, 0.001)'.format(name))
                      # print('=> init {}.bias as 0'.format(name))
                      nn.init.normal_(m.weight, std=0.001)
                      if self.deconv_with_bias:
                          nn.init.constant_(m.bias, 0)
                  elif isinstance(m, nn.BatchNorm2d):
                      # print('=> init {}.weight as 1'.format(name))
                      # print('=> init {}.bias as 0'.format(name))
                      nn.init.constant_(m.weight, 1)
                      nn.init.constant_(m.bias, 0)
              # print('=> init final conv weights from normal distribution')
              for head in self.heads:
                final_layer = self.__getattr__(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)
          else:
              print('=> imagenet pretrained model dose not exist')
              print('=> please download it first')
              raise ValueError('imagenet pretrained model does not exist')
  
  
  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):
    block_class, layers = resnet_spec[num_layers]
  
    model = PoseResNet(block_class, layers, heads, head_conv=head_conv)
    #model.init_weights(num_layers, pretrained=True)
    return model
  
apis: torch.nn.MaxPool2d torch.nn.BatchNorm2d torch.nn.init.constant_ torch.nn.init.normal_ torch.nn.Conv2d torch.nn.Sequential torch.utils.model_zoo.load_url torch.nn.ReLU torch.nn.ConvTranspose2d