text: from .se_module import SELayer
  import torch.nn as nn
  import torch
  import math
  from collections import OrderedDict
  import torch.utils.checkpoint as cp
  __all__ = ['se_resnet50_ibn_a', 'se_resnet101_ibn_a', 'se_resnet152_ibn_a']
  
  def conv3x3(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
  
  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 SEBasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
          super(SEBasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes, 1)
          self.bn2 = nn.BatchNorm2d(planes)
          self.se = SELayer(planes, reduction)
          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.se(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class SEBottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, ibn=False, reduction=16):
          super(SEBottleneck, 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 * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.se = SELayer(planes * 4, reduction)
          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)
          out = self.se(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, last_stride,block, layers, frozen_stages=-1, num_classes=1000):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.frozen_stages = frozen_stages
          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=last_stride)
          self.avgpool = nn.AvgPool2d(7)
          self.fc = nn.Linear(512 * block.expansion, num_classes)
          
          self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
          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, stride, downsample, ibn=ibn))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, 1, None, ibn=ibn))
  
          return nn.Sequential(*layers)
  
      def _freeze_stages(self):
          if self.frozen_stages >= 0:
              self.bn1.eval()
              for m in [self.conv1, self.bn1]:
                  for param in m.parameters():
                      param.requires_grad = False
  
          for i in range(1, self.frozen_stages + 1):
              m = getattr(self, 'layer{}'.format(i))
              print('layer{}'.format(i))
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
      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.avgpool(x)
          #x = x.view(x.size(0), -1)
          #x = self.fc(x)
  
          return x
  
  
      def load_param(self, model_path):
          param_dict = torch.load(model_path)
          if 'state_dict' in param_dict:
              param_dict = param_dict['state_dict']
          for i in param_dict:
              if 'fc' in i:
                  continue
              self.state_dict()[i.replace('module.','')].copy_(param_dict[i])
  
  def se_resnet50_ibn_a(last_stride,num_classes=1000,**kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(last_stride,SEBottleneck, [3, 4, 6, 3], num_classes=num_classes,**kwargs)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def se_resnet101_ibn_a(last_stride,num_classes=1000,**kwargs):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(last_stride,SEBottleneck, [3, 4, 23, 3], num_classes=num_classes,**kwargs)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  
  
  def se_resnet152_ibn_a(last_stride,num_classes):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(last_stride,SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
      model.avgpool = nn.AdaptiveAvgPool2d(1)
      return model
  