text: from __future__ import absolute_import
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import math
  import torch.utils.model_zoo as model_zoo
  from .pooling import GeneralizedMeanPoolingP
  
  
  __all__ = ['ResNet', 'resnet50_ibn_a', 'resnet101_ibn_a']
  
  
  model_urls = {
      'ibn_resnet50a': './logs/pretrained/resnet50_ibn_a.pth.tar',
      'ibn_resnet101a': './logs/pretrained/resnet101_ibn_a.pth.tar',
  }
  
  def weights_init_kaiming(m):
      classname = m.__class__.__name__
      if classname.find('Linear') != -1:
          nn.init.kaiming_normal_(m.weight, a=0, mode='fan_out')
          nn.init.constant_(m.bias, 0.0)
      elif classname.find('Conv') != -1:
          nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')
          if m.bias is not None:
              nn.init.constant_(m.bias, 0.0)
      elif classname.find('BatchNorm') != -1:
          if m.affine:
              nn.init.constant_(m.weight, 1.0)
              nn.init.constant_(m.bias, 0.0)
  
  
  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)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          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 IBN(nn.Module):
      def __init__(self, planes):
          super(IBN, self).__init__()
          half1 = int(planes/2)
          self.half = half1
          half2 = planes - half1
          self.IN = nn.InstanceNorm2d(half1, affine=True)
          self.BN = nn.BatchNorm2d(half2)
  
      def forward(self, x):
          split = torch.split(x, self.half, 1)
          out1 = self.IN(split[0].contiguous())
          out2 = self.BN(split[1].contiguous())
          out = torch.cat((out1, out2), 1)
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          if ibn:
              self.bn1 = IBN(planes)
          else:
              self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * self.expansion)
          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 ResNet(nn.Module):
  
      def __init__(self, block, layers, num_classes=1000, pool_type='avgpool'):
          scale = 64
          self.inplanes = scale
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, scale, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(scale)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, scale, layers[0])
          self.layer2 = self._make_layer(block, scale*2, layers[1], stride=2)
          self.layer3 = self._make_layer(block, scale*4, layers[2], stride=2)
          self.layer4 = self._make_layer(block, scale*8, layers[3], stride=2)
  
          self.layer4[0].conv2.stride = (1,1)  # new add by wml
          self.layer4[0].downsample[0].stride = (1,1)  # new add by wml
  
          #self.avgpool = nn.AvgPool2d(7)
          #self.avgpool = nn.AdaptiveAvgPool2d(1)
          if pool_type == 'avgpool':
              self.global_pool = nn.AdaptiveAvgPool2d(1)
          if pool_type == 'gempool':
              self.global_pool = GeneralizedMeanPoolingP()  # default initial norm=3
  
          self.num_classes = num_classes
          if self.num_classes > 0:
              self.fc = nn.Linear(scale * 8 * block.expansion, num_classes)
  
          self.bottleneck = nn.BatchNorm1d(2048)
          self.bottleneck.bias.requires_grad_(False)
          self.bottleneck.apply(weights_init_kaiming)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.InstanceNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      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),
              )
  
          layers = []
          ibn = True
          if planes == 512:
              ibn = False
          layers.append(block(self.inplanes, planes, ibn, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, ibn))
  
          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)
          
          features = self.global_pool(x)
          features = features.view(features.size(0), -1)
  
          bn = self.bottleneck(features)
  
          bn = F.normalize(bn, p=2, dim=1)
  
          if not self.training:
              return bn
  
          if self.num_classes == 0:
              return features, bn
  
          cls_score = self.fc(bn)
          return features, cls_score
  
  
  def resnet50_ibn_a(pretrained=False, **kwargs):
      """Constructs a ResNet-50 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          state_dict = torch.load(model_urls['ibn_resnet50a'], map_location=torch.device('cpu'))['state_dict']
          state_dict = remove_module_key(state_dict)
          # remove classifier weight
          filtered_trained_dict = {k: v for k, v in state_dict.items() if (not k.startswith('module.fc')) and (not k.startswith('fc'))}
          model_dict = model.state_dict()
          model_dict.update(filtered_trained_dict)
          model.load_state_dict(model_dict)
  
          #model.load_state_dict(state_dict)
      return model
  
  
  def resnet101_ibn_a(pretrained=False, **kwargs):
      """Constructs a ResNet-101 model.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          state_dict = torch.load(model_urls['ibn_resnet101a'], map_location=torch.device('cpu'))['state_dict']
          state_dict = remove_module_key(state_dict)
          model.load_state_dict(state_dict)
      return model
  
  
  def remove_module_key(state_dict):
      for key in list(state_dict.keys()):
          if 'module' in key:
              state_dict[key.replace('module.','')] = state_dict.pop(key)
      return state_dict
  