code: # ------------------------------------------------------------------------------
  # Copyright (c) Microsoft
  # Licensed under the MIT License.
  # Written by Bin Xiao (Bin.Xiao@microsoft.com)
  # Modified by Dequan Wang and Xingyi Zhou
  # ------------------------------------------------------------------------------
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import math
  import logging
  
  import torch
  import torch.nn as nn
  from .DCNv2.dcn_v2 import DCN
  import torch.utils.model_zoo as model_zoo
  
  from model.utils.misc import _sigmoid
  
  BN_MOMENTUM = 0.1
  logger = logging.getLogger(__name__)
  
  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
  
  def fill_up_weights(up):
      w = up.weight.data
      f = math.ceil(w.size(2) / 2)
      c = (2 * f - 1 - f % 2) / (2. * f)
      for i in range(w.size(2)):
          for j in range(w.size(3)):
              w[0, 0, i, j] = \
                  (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
      for c in range(1, w.size(0)):
          w[c, 0, :, :] = w[0, 0, :, :] 
  
  def fill_fc_weights(layers):
      for m in layers.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.normal_(m.weight, std=0.001)
              # torch.nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu')
              # torch.nn.init.xavier_normal_(m.weight.data)
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
  
  class PoseResNet(nn.Module):
  
      def __init__(self, block, layers, heads, head_conv):
          self.inplanes = 64
          self.heads = heads
          self.deconv_with_bias = False
  
          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)
  
          # used for deconv layers
          self.deconv_layers = self._make_deconv_layer(
              3,
              [256, 128, 64],
              [4, 4, 4],
          )
  
          for head in self.heads:
              classes = self.heads[head]
              if head_conv > 0:
                  fc = nn.Sequential(
                    nn.Conv2d(64, head_conv,
                      kernel_size=3, padding=1, bias=True),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(head_conv, classes, 
                      kernel_size=1, stride=1, 
                      padding=0, bias=True))
                  if 'hm' in head:
                      fc[-1].bias.data.fill_(-2.19)
                  else:
                      fill_fc_weights(fc)
              else:
                  fc = nn.Conv2d(64, classes, 
                    kernel_size=1, stride=1, 
                    padding=0, bias=True)
                  if 'hm' in head:
                      fc.bias.data.fill_(-2.19)
                  else:
                      fill_fc_weights(fc)
              self.__setattr__(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 _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]
              fc = DCN(self.inplanes, planes, 
                      kernel_size=(3,3), stride=1,
                      padding=1, dilation=1, deformable_groups=1)
              # fc = nn.Conv2d(self.inplanes, planes,
              #         kernel_size=3, stride=1, 
              #         padding=1, dilation=1, bias=False)
              # fill_fc_weights(fc)
              up = nn.ConvTranspose2d(
                      in_channels=planes,
                      out_channels=planes,
                      kernel_size=kernel,
                      stride=2,
                      padding=padding,
                      output_padding=output_padding,
                      bias=self.deconv_with_bias)
              fill_up_weights(up)
  
              layers.append(fc)
              layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
              layers.append(nn.ReLU(inplace=True))
              layers.append(up)
              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 = 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)
          ret['hm'] = _sigmoid(ret['hm'])
          return [ret]
  
      def init_weights(self, num_layers):
          if 1:
              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)
              print('=> init deconv weights from normal distribution')
              for name, m in self.deconv_layers.named_modules():
                  if isinstance(m, nn.BatchNorm2d):
                      nn.init.constant_(m.weight, 1)
                      nn.init.constant_(m.bias, 0)
  
  
  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 = 18
      heads = {'hm': 80, 'wh': 2, 'reg': 2}
      head_conv = 64
      block_class, layers = resnet_spec[num_layers]
  
      model = PoseResNet(block_class, layers, heads, head_conv=head_conv)
      model.init_weights(num_layers)
      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