text: # ------------------------------------------------------------------------------
  # Copyright (c) Microsoft
  # Licensed under the MIT License.
  # Written by Bin Xiao (Bin.Xiao@microsoft.com)
  # ------------------------------------------------------------------------------
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import logging
  
  import torch
  import torch.nn as nn
  from collections import OrderedDict
  
  
  BN_MOMENTUM = 0.1
  logger = logging.getLogger(__name__)
  
  
  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 Bottleneck_CAFFE(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(Bottleneck_CAFFE, self).__init__()
          # add stride to conv1x1
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
          self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
                                 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, cfg, **kwargs):
          self.inplanes = 64
          extra = cfg.MODEL.EXTRA
          self.deconv_with_bias = extra.DECONV_WITH_BIAS
  
          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_list = self._make_deconv_layer(
              extra.NUM_DECONV_LAYERS,
              extra.NUM_DECONV_FILTERS,
              extra.NUM_DECONV_KERNELS)
  
          self.deconv_layers = nn.Sequential(*self.deconv_layers_list)
  
          self.final_layer1 = nn.Conv2d(
              in_channels=extra.NUM_DECONV_FILTERS[-1],
              out_channels=cfg.MODEL.NUM_JOINTS,
              kernel_size=extra.FINAL_CONV_KERNEL,
              stride=1,
              padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0
          )
          self.final_layer2 = nn.Conv2d(
              in_channels=extra.NUM_DECONV_FILTERS[-1],
              out_channels=cfg.MODEL.NUM_JOINTS,
              kernel_size=extra.FINAL_CONV_KERNEL,
              stride=1,
              padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0
          )
          
  
      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]
              for _ in range(2 if i == num_layers-1 or i == num_layers-2 else 1):
                  layers.append(
                      nn.Sequential(
                          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),
                          nn.BatchNorm2d(planes, momentum=BN_MOMENTUM),
                          nn.ReLU(inplace=True)
                      )
                  )
              self.inplanes = planes
              
  
          return 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[0](x)
          x1 = self.deconv_layers[1](x)
          x2 = self.deconv_layers[2](x)
          x = x1 + x2
          x1 = self.deconv_layers[3](x)
          x2 = self.deconv_layers[4](x)
          x1 = self.final_layer1(x1)
          x2 = self.final_layer2(x2)
          y = torch.cat([x1, x2], 1)
          return y
  
      def init_weights(self, pretrained=''):
          if os.path.isfile(pretrained):
              logger.info('=> init deconv weights from normal distribution')
              for name, m in self.deconv_layers.named_modules():
                  if isinstance(m, nn.ConvTranspose2d):
                      logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
                      logger.info('=> 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):
                      logger.info('=> init {}.weight as 1'.format(name))
                      logger.info('=> init {}.bias as 0'.format(name))
                      nn.init.constant_(m.weight, 1)
                      nn.init.constant_(m.bias, 0)
              logger.info('=> init final conv weights from normal distribution')
              for m in self.final_layer1.modules():
                  if isinstance(m, nn.Conv2d):
                      # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                      logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
                      logger.info('=> init {}.bias as 0'.format(name))
                      nn.init.normal_(m.weight, std=0.001)
                      nn.init.constant_(m.bias, 0)
              for m in self.final_layer2.modules():
                  if isinstance(m, nn.Conv2d):
                      # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                      logger.info('=> init {}.weight as normal(0, 0.001)'.format(name))
                      logger.info('=> init {}.bias as 0'.format(name))
                      nn.init.normal_(m.weight, std=0.001)
                      nn.init.constant_(m.bias, 0)
  
              # pretrained_state_dict = torch.load(pretrained)
              logger.info('=> loading pretrained model {}'.format(pretrained))
              # self.load_state_dict(pretrained_state_dict, strict=False)
              checkpoint = torch.load(pretrained)
              if isinstance(checkpoint, OrderedDict):
                  state_dict = checkpoint
              elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
                  state_dict_old = checkpoint['state_dict']
                  state_dict = OrderedDict()
                  # delete 'module.' because it is saved from DataParallel module
                  for key in state_dict_old.keys():
                      if key.startswith('module.'):
                          # state_dict[key[7:]] = state_dict[key]
                          # state_dict.pop(key)
                          state_dict[key[7:]] = state_dict_old[key]
                      else:
                          state_dict[key] = state_dict_old[key]
              else:
                  raise RuntimeError(
                      'No state_dict found in checkpoint file {}'.format(pretrained))
              self.load_state_dict(state_dict, strict=False)
          else:
              logger.error('=> imagenet pretrained model dose not exist')
              logger.error('=> 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(cfg, is_train, **kwargs):
      num_layers = cfg.MODEL.EXTRA.NUM_LAYERS
      style = cfg.MODEL.STYLE
  
      block_class, layers = resnet_spec[num_layers]
  
      if style == 'caffe':
          block_class = Bottleneck_CAFFE
  
      model = PoseResNet(block_class, layers, cfg, **kwargs)
  
      if is_train and cfg.MODEL.INIT_WEIGHTS:
          model.init_weights(cfg.MODEL.PRETRAINED)
  
      return model
  