text: from __future__ import print_function, division, absolute_import
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.model_zoo as model_zoo
  import os
  import sys
  
  # __all__ = ['InceptionV4', 'inceptionv4']
  
  # 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=1, padding=1)
          self.conv = BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1)
  
      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(80, 32, kernel_size=1, stride=1),
              BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch1 = nn.Sequential(
              BasicConv2d(80, 32, kernel_size=1, stride=1),
              BasicConv2d(32, 32, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(32, 32, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(32, 48, kernel_size=(3,3), stride=1, padding=(1,1))
          )
  
      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(96, 96, kernel_size=3, stride=1, padding=1)
          self.maxpool = nn.MaxPool2d(3, stride=1, padding=1)
  
      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(192, 48, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(192, 32, kernel_size=1, stride=1),
              BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(192, 32, kernel_size=1, stride=1),
              BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
              BasicConv2d(48, 48, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch3 = nn.Sequential(
              nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
              BasicConv2d(192, 48, 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(192, 192, kernel_size=3, stride=1, padding=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(192, 96, kernel_size=1, stride=1),
              BasicConv2d(96, 112, kernel_size=3, stride=1, padding=1),
              BasicConv2d(112, 128, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch2 = nn.MaxPool2d(3, stride=1, padding=1)
  
      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(512, 192, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(512, 96, kernel_size=1, stride=1),
              BasicConv2d(96, 112, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(112, 128, kernel_size=(7,1), stride=1, padding=(3,0))
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(512, 96, kernel_size=1, stride=1),
              BasicConv2d(96, 96, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(96, 112, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(112, 112, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(112, 128, 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(512, 64, 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(512, 96, kernel_size=1, stride=1),
              BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch1 = nn.Sequential(
              BasicConv2d(512, 128, kernel_size=1, stride=1),
              BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(128, 160, kernel_size=(7,1), stride=1, padding=(3,0)),
              BasicConv2d(160, 160, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch2 = nn.MaxPool2d(3, stride=1, padding=1)
  
      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(768, 128, kernel_size=1, stride=1)
  
          self.branch1_0 = BasicConv2d(768, 192, kernel_size=1, stride=1)
          self.branch1_1a = BasicConv2d(192, 128, kernel_size=(1,3), stride=1, padding=(0,1))
          self.branch1_1b = BasicConv2d(192, 128, kernel_size=(3,1), stride=1, padding=(1,0))
  
          self.branch2_0 = BasicConv2d(768, 192, kernel_size=1, stride=1)
          self.branch2_1 = BasicConv2d(192, 224, kernel_size=(3,1), stride=1, padding=(1,0))
          self.branch2_2 = BasicConv2d(224, 256, kernel_size=(1,3), stride=1, padding=(0,1))
          self.branch2_3a = BasicConv2d(256, 128, kernel_size=(1,3), stride=1, padding=(0,1))
          self.branch2_3b = BasicConv2d(256, 128, 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(768, 128, 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):
  
      def __init__(self, input_channel):
          super(InceptionV4, self).__init__()
          # # Special attributs
          # self.input_space = None
          # self.input_size = (299, 299, 3)
          # self.mean = None
          # self.std = None
          # Modules
          self.features = nn.Sequential(
              BasicConv2d(input_channel, 16, kernel_size=3, stride=1, padding=1),
              BasicConv2d(16, 16, kernel_size=3, stride=1, padding=1),
              BasicConv2d(16, 32, 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.last_linear = nn.Linear(1536, num_classes)
  
      # def logits(self, features):
      #     #Allows image of any size to be processed
      #     adaptiveAvgPoolWidth = features.shape[2]
      #     x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
      #     x = x.view(x.size(0), -1)
      #     x = self.last_linear(x)
      #     return x
  
      def forward(self, input):
          x = self.features(input)
          return x
  
  
  # def inceptionv4(num_classes=1000, pretrained='imagenet'):
  #     if pretrained:
  #         settings = pretrained_settings['inceptionv4'][pretrained]
  #         assert num_classes == settings['num_classes'], \
  #             "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
  #
  #         # both 'imagenet'&'imagenet+background' are loaded from same parameters
  #         model = InceptionV4()
  #         model_dict = model_zoo.load_url(settings['url'])
  #         model.load_state_dict(model_zoo.load_url(settings['url']))
  #
  #         if pretrained == 'imagenet':
  #             new_last_linear = nn.Linear(1536, 1000)
  #             new_last_linear.weight.data = model.last_linear.weight.data[1:]
  #             new_last_linear.bias.data = model.last_linear.bias.data[1:]
  #             model.last_linear = new_last_linear
  #
  #         model.input_space = settings['input_space']
  #         model.input_size = settings['input_size']
  #         model.input_range = settings['input_range']
  #         model.mean = settings['mean']
  #         model.std = settings['std']
  #     else:
  #         model = InceptionV4(num_classes=num_classes)
  #     return model
  
  
  '''
  TEST
  Run this code with:
  ```
  cd $HOME/pretrained-models.pytorch
  python -m pretrainedmodels.inceptionv4
  ```
  '''
  # if __name__ == '__main__':
  #
  #     assert inceptionv4(num_classes=10, pretrained=None)
  #     print('success')
  #     assert inceptionv4(num_classes=1000, pretrained='imagenet')
  #     print('success')
  #     assert inceptionv4(num_classes=1001, pretrained='imagenet+background')
  #     print('success')
  #
  #     # fail
  #     assert inceptionv4(num_classes=1001, pretrained='imagenet')