text: import math
  import torch
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  from models.blocks import SPPLayer
  import logging
  
  
  __all__ = ['vgg19_bn', 'vgg19']
  
  
  model_urls = {
      'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
      'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
  }
  
  
  class VGG(nn.Module):
  
      def __init__(self, features, num_classes=1000, init_weights=True):
          super(VGG, self).__init__()
          self.features = features
          self.spp = SPPLayer(pool_size=[1, 2, 4], pool=nn.MaxPool2d)
          self.fc = nn.Sequential(
              nn.Linear(512 * self.spp.out_length, 1024),
              nn.ReLU(True),
              nn.Dropout(),
              nn.Linear(1024, num_classes))
  
          if init_weights:
              self._initialize_weights()
  
      def forward(self, x):
          x = self.features(x)
          x = self.spp(x)
          x = self.fc(x)
          return x
  
      def _initialize_weights(self):
          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))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  
      def get_features(self):
          return self.features
  
      def load_state_dict(self, state_dict, strict=True):
          model_dict = self.state_dict()
          pretrained_dict = {k: v for k, v in state_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
          if len(pretrained_dict) == len(state_dict):
              logging.info('%s: All params loaded' % type(self).__name__)
          else:
              logging.info('%s: Some params were not loaded:' % type(self).__name__)
              not_loaded_keys = [k for k in state_dict.keys() if k not in pretrained_dict.keys()]
              logging.info(('%s, ' * (len(not_loaded_keys) - 1) + '%s') % tuple(not_loaded_keys))
          model_dict.update(pretrained_dict)
          super(VGG, self).load_state_dict(model_dict)
  
  
  def make_layers(cfg, batch_norm=False):
      layers = []
      in_channels = 3
      for v in cfg:
          if v == 'M':
              layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
          else:
              conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
              if batch_norm:
                  layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
              else:
                  layers += [conv2d, nn.ReLU(inplace=True)]
              in_channels = v
      return nn.Sequential(*layers)
  
  
  cfg = {
      'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
      'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
      'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
      'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
  }
  
  
  def vgg19(pretrained=False, **kwargs):
      """VGG 19-layer model (configuration "E")
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      if pretrained:
          kwargs['init_weights'] = False
      model = VGG(make_layers(cfg['E']), **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
      return model
  
  
  def vgg19_bn(pretrained=False, **kwargs):
      """VGG 19-layer model (configuration 'E') with batch normalization
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      if pretrained:
          kwargs['init_weights'] = False
      model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['vgg19_bn']))
      return model
  