text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from utils.config import opt
  from model.faster_rcnn.faster_rcnn import _fasterRCNN
  import torch
  import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
         'resnet152']
  
  
  model_urls = {
      'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
      'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',
      'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
      'resnet101': 'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',
      'resnet152': 'https://s3.amazonaws.com/pytorch/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)
          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 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, stride=stride, bias=False)  # change
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,  # change
                       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.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):
          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=0, ceil_mode=True) # change
          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)
          # it is slightly better whereas slower to set stride = 1
          # self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
          self.avgpool = nn.AvgPool2d(7)
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          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_()
  
      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 = []
          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 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 resnet18(pretrained=False):
      """Constructs a ResNet-18 model.
      Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [2, 2, 2, 2])
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
      return model
  
  
  def resnet34(pretrained=False):
      """Constructs a ResNet-34 model.
      Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [3, 4, 6, 3])
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
      return model
  
  
  def resnet50(pretrained=False):
      """Constructs a ResNet-50 model.
      Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3])
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
      return model
  
  
  def resnet101(pretrained=False):
      """Constructs a ResNet-101 model.
      Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3])
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
      return model
  
  
  def resnet152(pretrained=False):
      """Constructs a ResNet-152 model.
      Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 8, 36, 3])
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
      return model
  
  
  class resnet(_fasterRCNN):
      def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False):
          self.model_path = 'weights/resnet101_caffe.pth'
          self.dout_base_model = 1024
          self.pretrained = pretrained
          self.class_agnostic = class_agnostic
  
          _fasterRCNN.__init__(self, classes, class_agnostic)
  
      def _init_modules(self):
          resnet = resnet101()
  
          if self.pretrained:
              print("Loading pretrained weights from {}".format(self.model_path))
              state_dict = torch.load(self.model_path)
              resnet.load_state_dict({k:v for k,v in state_dict.items() if k in resnet.state_dict()})
  
          # Build resnet.
          self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,resnet.maxpool, resnet.layer1,
                                         resnet.layer2, resnet.layer3)
  
          self.RCNN_top = nn.Sequential(resnet.layer4)
  
          self.RCNN_cls_score = nn.Linear(2048, self.n_classes)
          if self.class_agnostic:
              self.RCNN_bbox_pred = nn.Linear(2048, 4)
          else:
              self.RCNN_bbox_pred = nn.Linear(2048, 4 * self.n_classes)
  
          # Fix blocks
          for p in self.RCNN_base[0].parameters(): p.requires_grad = False
          for p in self.RCNN_base[1].parameters(): p.requires_grad = False
  
          assert (0 <= opt.RESNET_FIXED_BLOCKS < 4)
          if opt.RESNET_FIXED_BLOCKS >= 3:
              for p in self.RCNN_base[6].parameters(): p.requires_grad = False
          if opt.RESNET_FIXED_BLOCKS >= 2:
              for p in self.RCNN_base[5].parameters(): p.requires_grad = False
          if opt.RESNET_FIXED_BLOCKS >= 1:
              for p in self.RCNN_base[4].parameters(): p.requires_grad = False
  
          def set_bn_fix(m):
              classname = m.__class__.__name__
              if classname.find('BatchNorm') != -1:
                  for p in m.parameters(): p.requires_grad = False
  
          self.RCNN_base.apply(set_bn_fix)
          self.RCNN_top.apply(set_bn_fix)
  
      def train(self, mode=True):
          # Override train so that the training mode is set as we want
          nn.Module.train(self, mode)
          if mode:
              # Set fixed blocks to be in eval mode
              self.RCNN_base.eval()
              self.RCNN_base[5].train()
              self.RCNN_base[6].train()
  
          def set_bn_eval(m):
              classname = m.__class__.__name__
              if classname.find('BatchNorm') != -1:
                  m.eval()
  
          self.RCNN_base.apply(set_bn_eval)
          self.RCNN_top.apply(set_bn_eval)
  
      def _head_to_tail(self, pool5):
          fc7 = self.RCNN_top(pool5).mean(3).mean(2)
          return fc7
  