text: from __future__ import print_function, division, absolute_import
  import torch
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  import os
  import sys
  
  __all__ = ['InceptionResNetV2', 'inceptionresnetv2']
  
  pretrained_settings = {
      'inceptionresnetv2': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.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/inceptionresnetv2-520b38e4.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
          }
      }
  }
  
  def _validate(modelOutput, labels, paths=None):
      maxvalues, maxindices = torch.max(modelOutput.data, 1)
      count = 0
      Mesothelial_correct = 0
      Cancer_correct = 0
      Mesothelial_wrong = 0
      Cancer_wrong = 0
      import shutil
      for i in range(0, labels.squeeze(1).size(0)):
          if maxindices[i] == labels.squeeze(1)[i]:
              count += 1
              if maxindices[i] == 0:
                  Mesothelial_correct += 1
              elif maxindices[i] == 1:
                  Cancer_correct += 1
          else:
              if labels.squeeze(1)[i] == 0:
                  Mesothelial_wrong += 1
                  if paths is not None:
                      # print("Mesothelial_wrong: ",paths[i])
                      # shutil.copy(paths[i],"/data01/zyh/CellDet/badcase/Mesothelial_wrong/"+paths[i].split("/")[-1])
                      pass
              elif labels.squeeze(1)[i] == 1:
                  Cancer_wrong += 1
                  if paths is not None:
                      # print("Cancer_wrong: ",paths[i])
                      # shutil.copy(paths[i],"/data01/zyh/CellDet/badcase/Cancer_wrong/"+paths[i].split("/")[-1])
                      pass
  
      return count, maxindices, Mesothelial_correct, Cancer_correct, Mesothelial_wrong, Cancer_wrong
  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=False)
  
      def forward(self, x):
          x = self.conv(x)
          x = self.bn(x)
          x = self.relu(x)
          return x
  
  
  class Mixed_5b(nn.Module):
  
      def __init__(self):
          super(Mixed_5b, self).__init__()
  
          self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(192, 48, kernel_size=1, stride=1),
              BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(192, 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(192, 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 Block35(nn.Module):
  
      def __init__(self, scale=1.0):
          super(Block35, self).__init__()
  
          self.scale = scale
  
          self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(320, 32, kernel_size=1, stride=1),
              BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(320, 32, kernel_size=1, stride=1),
              BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
              BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
          )
  
          self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
          self.relu = nn.ReLU(inplace=False)
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          x2 = self.branch2(x)
          out = torch.cat((x0, x1, x2), 1)
          out = self.conv2d(out)
          out = out * self.scale + x
          out = self.relu(out)
          return out
  
  
  class Mixed_6a(nn.Module):
  
      def __init__(self):
          super(Mixed_6a, self).__init__()
  
          self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(320, 256, kernel_size=1, stride=1),
              BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
              BasicConv2d(256, 384, 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 Block17(nn.Module):
  
      def __init__(self, scale=1.0):
          super(Block17, self).__init__()
  
          self.scale = scale
  
          self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(1088, 128, kernel_size=1, stride=1),
              BasicConv2d(128, 160, kernel_size=(1,7), stride=1, padding=(0,3)),
              BasicConv2d(160, 192, kernel_size=(7,1), stride=1, padding=(3,0))
          )
  
          self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
          self.relu = nn.ReLU(inplace=False)
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          out = torch.cat((x0, x1), 1)
          out = self.conv2d(out)
          out = out * self.scale + x
          out = self.relu(out)
          return out
  
  
  class Mixed_7a(nn.Module):
  
      def __init__(self):
          super(Mixed_7a, self).__init__()
  
          self.branch0 = nn.Sequential(
              BasicConv2d(1088, 256, kernel_size=1, stride=1),
              BasicConv2d(256, 384, kernel_size=3, stride=2)
          )
  
          self.branch1 = nn.Sequential(
              BasicConv2d(1088, 256, kernel_size=1, stride=1),
              BasicConv2d(256, 288, kernel_size=3, stride=2)
          )
  
          self.branch2 = nn.Sequential(
              BasicConv2d(1088, 256, kernel_size=1, stride=1),
              BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
              BasicConv2d(288, 320, kernel_size=3, stride=2)
          )
  
          self.branch3 = nn.MaxPool2d(3, stride=2)
  
      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 Block8(nn.Module):
  
      def __init__(self, scale=1.0, noReLU=False):
          super(Block8, self).__init__()
  
          self.scale = scale
          self.noReLU = noReLU
  
          self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
  
          self.branch1 = nn.Sequential(
              BasicConv2d(2080, 192, kernel_size=1, stride=1),
              BasicConv2d(192, 224, kernel_size=(1,3), stride=1, padding=(0,1)),
              BasicConv2d(224, 256, kernel_size=(3,1), stride=1, padding=(1,0))
          )
  
          self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
          if not self.noReLU:
              self.relu = nn.ReLU(inplace=False)
  
      def forward(self, x):
          x0 = self.branch0(x)
          x1 = self.branch1(x)
          out = torch.cat((x0, x1), 1)
          out = self.conv2d(out)
          out = out * self.scale + x
          if not self.noReLU:
              out = self.relu(out)
          return out
  
  
  class InceptionResNetV2(nn.Module):
  
      def __init__(self, num_classes=1001):
          super(InceptionResNetV2, self).__init__()
          # Special attributs
          self.input_space = None
          self.input_size = (299, 299, 3)
          self.mean = None
          self.std = None
          # Modules
          self.conv2d_1a = BasicConv2d(1, 32, kernel_size=3, stride=2)
          self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
          self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
          self.maxpool_3a = nn.MaxPool2d(3, stride=2)
          self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
          self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
          self.maxpool_5a = nn.MaxPool2d(3, stride=2)
          self.mixed_5b = Mixed_5b()
          self.repeat = nn.Sequential(
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17),
              Block35(scale=0.17)
          )
          self.mixed_6a = Mixed_6a()
          self.repeat_1 = nn.Sequential(
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10),
              Block17(scale=0.10)
          )
          self.mixed_7a = Mixed_7a()
          self.repeat_2 = nn.Sequential(
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20),
              Block8(scale=0.20)
          )
          self.block8 = Block8(noReLU=True)
          self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
          self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)
          self.last_linear = nn.Linear(1536, num_classes)
          self.loss = nn.CrossEntropyLoss()
          self.validator = _validate
  
      def features(self, input):
          x = self.conv2d_1a(input)
          x = self.conv2d_2a(x)
          x = self.conv2d_2b(x)
          x = self.maxpool_3a(x)
          x = self.conv2d_3b(x)
          x = self.conv2d_4a(x)
          x = self.maxpool_5a(x)
          x = self.mixed_5b(x)
          x = self.repeat(x)
          x = self.mixed_6a(x)
          x = self.repeat_1(x)
          x = self.mixed_7a(x)
          x = self.repeat_2(x)
          x = self.block8(x)
          x = self.conv2d_7b(x)
          return x
  
      def logits(self, features):
          x = self.avgpool_1a(features)
          x = x.view(x.size(0), -1)
          x = self.last_linear(x)
          return x
  
      def forward(self, input):
          x = self.features(input)
          x = self.logits(x)
          return x
  
  def inceptionresnetv2(num_classes=1000, pretrained='imagenet'):
      r"""InceptionResNetV2 model architecture from the
      `"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
      """
      if pretrained:
          settings = pretrained_settings['inceptionresnetv2'][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 = InceptionResNetV2(num_classes=1001)
          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 = InceptionResNetV2(num_classes=num_classes)
      return model
  
  '''
  TEST
  Run this code with:
  ```
  cd $HOME/pretrained-models.pytorch
  python -m pretrainedmodels.inceptionresnetv2
  ```
  '''
  if __name__ == '__main__':
  
      model = inceptionresnetv2(num_classes=2,pretrained=None)
      img=torch.rand(1,1,500,500)
      output=model(img)
      print(output.size()) # torch.Size([1, 2])
  