text: from __future__ import absolute_import
  from __future__ import division
  
  __all__ = ['inceptionv4']
  
  import torch
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  
  
  """
  Code imported from https://github.com/Cadene/pretrained-models.pytorch
  """
  
  
  pretrained_settings = {
      'inceptionv4': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
              'input_space': 'RGB',
              'input_size': [3, 299, 299],
              'input_range': [0, 1],
              'mean': [0.5, 0.5, 0.5],
              'std': [0.5, 0.5, 0.5],
              'num_classes': 1000
          },
          'imagenet+background': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
              'input_space': 'RGB',
              'input_size': [3, 299, 299],
              'input_range': [0, 1],
              'mean': [0.5, 0.5, 0.5],
              'std': [0.5, 0.5, 0.5],
              'num_classes': 1001
          }
      }
  }
  
  
  class BasicConv2d(nn.Module):
  
      def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
          super(BasicConv2d, self).__init__()
          self.conv = nn.Conv2d(in_planes, out_planes,
                                kernel_size=kernel_size, stride=stride,
                                padding=padding, bias=False) # verify bias false
          self.bn = nn.BatchNorm2d(out_planes,
                                   eps=0.001, # value found in tensorflow
                                   momentum=0.1, # default pytorch value
                                   affine=True)
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x):
          x = self.conv(x)
          x = self.bn(x)
          x = self.relu(x)
          return x
  
  
  class Mixed_3a(nn.Module):
  
      def __init__(self):
          super(Mixed_3a, self).__init__()
          self.maxpool = nn.MaxPool2d(3, stride=2)
          self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
  
      def forward(self, x):
          x0 = self.maxpool(x)
          x1 = self.conv(x)
          out = torch.cat((x0, x1), 1)
          return out
  
  
  class Mixed_4a(nn.Module):
  
      def __init__(self):
          super(Mixed_4a, self).__init__()
  
          self.branch0 = nn.Sequential(
              BasicConv2d(160, 64, kernel_size=1, stride=1),
              BasicConv2d(64, 96, kernel_size=3, stride=1)
          )
  
          self.branch1 = nn.Sequential(
              BasicConv2d(160, 64, kernel_size=1, stride=1),
              BasicConv2d(64, 64, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(64, 64, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(64, 96, kernel_size=(3,3), stride=1)
          )
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          out = torch.cat((x0, x1), 1)
          return out
  
  
  class Mixed_5a(nn.Module):
  
      def __init__(self):
          super(Mixed_5a, self).__init__()
          self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
          self.maxpool = nn.MaxPool2d(3, stride=2)
  
      def forward(self, x):
          x0 = self.conv(x)
          x1 = self.maxpool(x)
          out = torch.cat((x0, x1), 1)
          return out
  
  
  class Inception_A(nn.Module):
  
      def __init__(self):
          super(Inception_A, self).__init__()
          self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(384, 64, kernel_size=1, stride=1),
              BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(384, 64, kernel_size=1, stride=1),
              BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
              BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch3 = nn.Sequential(
              nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
              BasicConv2d(384, 96, kernel_size=1, stride=1)
          )
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          x2 = self.branch2(x)
          x3 = self.branch3(x)
          out = torch.cat((x0, x1, x2, x3), 1)
          return out
  
  
  class Reduction_A(nn.Module):
  
      def __init__(self):
          super(Reduction_A, self).__init__()
          self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(384, 192, kernel_size=1, stride=1),
              BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
              BasicConv2d(224, 256, kernel_size=3, stride=2)
          )
          
          self.branch2 = nn.MaxPool2d(3, stride=2)
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          x2 = self.branch2(x)
          out = torch.cat((x0, x1, x2), 1)
          return out
  
  
  class Inception_B(nn.Module):
  
      def __init__(self):
          super(Inception_B, self).__init__()
          self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
          
          self.branch1 = nn.Sequential(
              BasicConv2d(1024, 192, kernel_size=1, stride=1),
              BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(224, 256, kernel_size=(7,1), stride=1, padding=(3,0))
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(1024, 192, kernel_size=1, stride=1),
              BasicConv2d(192, 192, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(224, 224, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(224, 256, kernel_size=(1,7), stride=1, padding=(0,3))
          )
  
          self.branch3 = nn.Sequential(
              nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
              BasicConv2d(1024, 128, kernel_size=1, stride=1)
          )
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          x2 = self.branch2(x)
          x3 = self.branch3(x)
          out = torch.cat((x0, x1, x2, x3), 1)
          return out
  
  
  class Reduction_B(nn.Module):
  
      def __init__(self):
          super(Reduction_B, self).__init__()
  
          self.branch0 = nn.Sequential(
              BasicConv2d(1024, 192, kernel_size=1, stride=1),
              BasicConv2d(192, 192, kernel_size=3, stride=2)
          )
  
          self.branch1 = nn.Sequential(
              BasicConv2d(1024, 256, kernel_size=1, stride=1),
              BasicConv2d(256, 256, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(256, 320, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(320, 320, kernel_size=3, stride=2)
          )
  
          self.branch2 = nn.MaxPool2d(3, stride=2)
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          x2 = self.branch2(x)
          out = torch.cat((x0, x1, x2), 1)
          return out
  
  
  class Inception_C(nn.Module):
  
      def __init__(self):
          super(Inception_C, self).__init__()
  
          self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
          
          self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
          self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1,3), stride=1, padding=(0,1))
          self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3,1), stride=1, padding=(1,0))
          
          self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
          self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3,1), stride=1, padding=(1,0))
          self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1,3), stride=1, padding=(0,1))
          self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1,3), stride=1, padding=(0,1))
          self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3,1), stride=1, padding=(1,0))
          
          self.branch3 = nn.Sequential(
              nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
              BasicConv2d(1536, 256, kernel_size=1, stride=1)
          )
  
      def forward(self, x):
          x0 = self.branch0(x)
          
          x1_0 = self.branch1_0(x)
          x1_1a = self.branch1_1a(x1_0)
          x1_1b = self.branch1_1b(x1_0)
          x1 = torch.cat((x1_1a, x1_1b), 1)
  
          x2_0 = self.branch2_0(x)
          x2_1 = self.branch2_1(x2_0)
          x2_2 = self.branch2_2(x2_1)
          x2_3a = self.branch2_3a(x2_2)
          x2_3b = self.branch2_3b(x2_2)
          x2 = torch.cat((x2_3a, x2_3b), 1)
  
          x3 = self.branch3(x)
  
          out = torch.cat((x0, x1, x2, x3), 1)
          return out
  
  
  class InceptionV4(nn.Module):
      """Inception-v4.
  
      Reference:
          Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
          Connections on Learning. AAAI 2017.
  
      Public keys:
          - ``inceptionv4``: InceptionV4.
      """
      
      def __init__(self, num_classes, loss, **kwargs):
          super(InceptionV4, self).__init__()
          self.loss = loss
  
          self.features = nn.Sequential(
              BasicConv2d(3, 32, kernel_size=3, stride=2),
              BasicConv2d(32, 32, kernel_size=3, stride=1),
              BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
              Mixed_3a(),
              Mixed_4a(),
              Mixed_5a(),
              Inception_A(),
              Inception_A(),
              Inception_A(),
              Inception_A(),
              Reduction_A(), # Mixed_6a
              Inception_B(),
              Inception_B(),
              Inception_B(),
              Inception_B(),
              Inception_B(),
              Inception_B(),
              Inception_B(),
              Reduction_B(), # Mixed_7a
              Inception_C(),
              Inception_C(),
              Inception_C()
          )
          self.global_avgpool = nn.AdaptiveAvgPool2d(1)
          self.classifier = nn.Linear(1536, num_classes)
  
      def forward(self, x):
          f = self.features(x)
          v = self.global_avgpool(f)
          v = v.view(v.size(0), -1)
  
          if not self.training:
              return v
  
          y = self.classifier(v)
  
          if self.loss == 'softmax':
              return y
          elif self.loss == 'triplet':
              return y, v
          else:
              raise KeyError('Unsupported loss: {}'.format(self.loss))
  
  
  def init_pretrained_weights(model, model_url):
      """Initializes model with pretrained weights.
      
      Layers that don't match with pretrained layers in name or size are kept unchanged.
      """
      pretrain_dict = model_zoo.load_url(model_url)
      model_dict = model.state_dict()
      pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
      model_dict.update(pretrain_dict)
      model.load_state_dict(model_dict)
  
  
  def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):
      model = InceptionV4(num_classes, loss, **kwargs)
      if pretrained:
          model_url = pretrained_settings['inceptionv4']['imagenet']['url']
          init_pretrained_weights(model, model_url)
      return model
  