text: """添加了senet模块,去除transition的senet模块,仅仅在denseblock中添加senet模块"""
  import sys
  sys.path.append("F:/car_classify_abnormal")
  
  import re
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.model_zoo as model_zoo
  from collections import OrderedDict
  from core.se_module import SELayer
  
  
  __all__ = ['SEDenseNet', 'se_densenet121', 'se_densenet169', 'se_densenet201', 'se_densenet161']
  
  
  model_urls = {
      'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
      'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
      'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
      'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
  }
  
  
  def se_densenet121(pretrained=False, is_strict=False, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SEDenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                       **kwargs)
      if pretrained:
          # '.'s are no longer allowed in module names, but pervious _DenseLayer
          # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
          # They are also in the checkpoints in model_urls. This pattern is used
          # to find such keys.
          pattern = re.compile(
              r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
          state_dict = model_zoo.load_url(model_urls['densenet121'])
          for key in list(state_dict.keys()):
              res = pattern.match(key)
              if res:
                  new_key = res.group(1) + res.group(2)
                  state_dict[new_key] = state_dict[key]
                  del state_dict[key]
          model.load_state_dict(state_dict, strict=is_strict)
      return model
  
  
  def se_densenet169(pretrained=False, **kwargs):
      r"""Densenet-169 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SEDenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
                       **kwargs)
      if pretrained:
          # '.'s are no longer allowed in module names, but pervious _DenseLayer
          # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
          # They are also in the checkpoints in model_urls. This pattern is used
          # to find such keys.
          pattern = re.compile(
              r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
          state_dict = model_zoo.load_url(model_urls['densenet169'])
          for key in list(state_dict.keys()):
              res = pattern.match(key)
              if res:
                  new_key = res.group(1) + res.group(2)
                  state_dict[new_key] = state_dict[key]
                  del state_dict[key]
          model.load_state_dict(state_dict, strict=False)
      return model
  
  
  def se_densenet201(pretrained=False, **kwargs):
      r"""Densenet-201 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SEDenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
                       **kwargs)
      if pretrained:
          # '.'s are no longer allowed in module names, but pervious _DenseLayer
          # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
          # They are also in the checkpoints in model_urls. This pattern is used
          # to find such keys.
          pattern = re.compile(
              r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
          state_dict = model_zoo.load_url(model_urls['densenet201'])
          for key in list(state_dict.keys()):
              res = pattern.match(key)
              if res:
                  new_key = res.group(1) + res.group(2)
                  state_dict[new_key] = state_dict[key]
                  del state_dict[key]
          model.load_state_dict(state_dict, strict=False)
      return model
  
  
  def se_densenet161(pretrained=False, **kwargs):
      r"""Densenet-161 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SEDenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
                       **kwargs)
      if pretrained:
          # '.'s are no longer allowed in module names, but pervious _DenseLayer
          # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
          # They are also in the checkpoints in model_urls. This pattern is used
          # to find such keys.
          pattern = re.compile(
              r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
          state_dict = model_zoo.load_url(model_urls['densenet161'])
          for key in list(state_dict.keys()):
              res = pattern.match(key)
              if res:
                  new_key = res.group(1) + res.group(2)
                  state_dict[new_key] = state_dict[key]
                  del state_dict[key]
          model.load_state_dict(state_dict, strict=False)
      return model
  
  
  class _DenseLayer(nn.Sequential):
      def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
          super(_DenseLayer, self).__init__()
          # Add SELayer at here, like SE-PRE block in original paper illustrates
          self.add_module("selayer", SELayer(channel=num_input_features)),
  
          self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
          self.add_module('relu1', nn.ReLU(inplace=True)),
          self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                          growth_rate, kernel_size=1, stride=1, bias=False)),
          self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
          self.add_module('relu2', nn.ReLU(inplace=True)),
          self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                          kernel_size=3, stride=1, padding=1, bias=False)),        
          self.drop_rate = drop_rate
  
      def forward(self, x):
          new_features = super(_DenseLayer, self).forward(x)
          if self.drop_rate > 0:
              new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
          return torch.cat([x, new_features], 1)
  
  
  class _DenseBlock(nn.Sequential):
      def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
          super(_DenseBlock, self).__init__()
          for i in range(num_layers):
              layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
              self.add_module('denselayer%d' % (i + 1), layer)
  
  
  class _Transition(nn.Sequential):
      def __init__(self, num_input_features, num_output_features):
          super(_Transition, self).__init__()
          self.add_module('norm', nn.BatchNorm2d(num_input_features))
          self.add_module('relu', nn.ReLU(inplace=True))
          self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                            kernel_size=1, stride=1, bias=False))
          self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
  
  
  class SEDenseNet(nn.Module):
      r"""Densenet-BC model class, based on
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
  
      Args:
          growth_rate (int) - how many filters to add each layer (`k` in paper)
          block_config (list of 4 ints) - how many layers in each pooling block
          num_init_features (int) - the number of filters to learn in the first convolution layer
          bn_size (int) - multiplicative factor for number of bottle neck layers
            (i.e. bn_size * k features in the bottleneck layer)
          drop_rate (float) - dropout rate after each dense layer
          num_classes (int) - number of classification classes
      """
  
      def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                   num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000):
  
          super(SEDenseNet, self).__init__()
  
          # First convolution
          self.features = nn.Sequential(OrderedDict([
              ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
              ('norm0', nn.BatchNorm2d(num_init_features)),
              ('relu0', nn.ReLU(inplace=True)),
              ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
          ]))
  
          # Add SELayer at first convolution
          # self.features.add_module("SELayer_0a", SELayer(channel=num_init_features))
  
          # Each denseblock
          num_features = num_init_features
          for i, num_layers in enumerate(block_config):
              # Add a SELayer 
              # self.features.add_module("SELayer_%da" % (i + 1), SELayer(channel=num_features))
  
              block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
                                  bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
              self.features.add_module('denseblock%d' % (i + 1), block)
  
              num_features = num_features + num_layers * growth_rate
  
              if i != len(block_config) - 1:
                  # Add a SELayer behind each transition block
                  # self.features.add_module("SELayer_%db" % (i + 1), SELayer(channel=num_features))
  
                  trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
                  self.features.add_module('transition%d' % (i + 1), trans)
                  num_features = num_features // 2
  
          # Final batch norm
          self.features.add_module('norm5', nn.BatchNorm2d(num_features))
  
          # Add SELayer
          # self.features.add_module("SELayer_0b", SELayer(channel=num_features))
  
          # Linear layer
          self.classifier = nn.Linear(num_features, num_classes)
  
          # Official init from torch repo.
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.Linear):
                  nn.init.constant_(m.bias, 0)
  
      def forward(self, x):
          features = self.features(x)
          out = F.relu(features, inplace=True)
          out = F.avg_pool2d(out, kernel_size=7, stride=1).view(features.size(0), -1)
          out = self.classifier(out)
          return out
  
  
  def test_se_densenet(pretrained=False):
      X = torch.Tensor(32, 3, 224, 224)
  
      if pretrained:
          model = se_densenet121(pretrained=pretrained)
          net_state_dict = {key: value for key, value in model_zoo.load_url("https://download.pytorch.org/models/densenet121-a639ec97.pth").items()}
          model.load_state_dict(net_state_dict, strict=False)
  
      else:
          model = se_densenet121(pretrained=pretrained)
  
      print(model)
      if torch.cuda.is_available():
          X = X.cuda()
          model = model.cuda()
      model.eval()
      with torch.no_grad():
          output = model(X)
          print(output.shape)
  
  if __name__ == "__main__":
      test_se_densenet()