text: import os, sys, random
  
  import torch
  import torch.nn as nn
  
  import torchvision.models as models
  
  class MyResNeXt(models.resnet.ResNet):
      def __init__(self, training=True):
          super(MyResNeXt, self).__init__(block=models.resnet.Bottleneck,
                                          layers=[3, 4, 6, 3],
                                          groups=32,
                                          width_per_group=4)
  
          # self.load_state_dict(checkpoint)
  
          # Override the existing FC layer with a new one.
          self.fc = nn.Sequential(nn.Linear(2048, 1),
                                   nn.Sigmoid())
  
  def MyResNetX():
      model = MyResNeXt()
      return model
  def resnext50(pretrained=True):
      model = models.resnext50_32x4d(pretrained=pretrained)
      model.fc = nn.Sequential(nn.Linear(2048, 1),
                                   nn.Sigmoid())
      return model
  
  def mnasnet(pretrained=True):
      model = models.mnasnet1_0(pretrained=pretrained)
      model.classifier = nn.Sequential(nn.Linear(1280, 1),
                                       nn.Sigmoid())
      return model
  
  def resnext101(pretrained=True):
      model = models.resnext101_32x8d(pretrained=pretrained)
      model.fc = nn.Sequential(nn.Linear(2048, 1),
                               nn.Sigmoid())
      return model
  class Meso4(nn.Module):
  
      """
  	Pytorch Implemention of Meso4
  	Autor: Honggu Liu
  	Date: July 4, 2019
  	"""
      def __init__(self, num_classes=1,image_size=256):
          super(Meso4, self).__init__()
          self.num_classes = num_classes
          self.conv1 = nn.Conv2d(3, 8, 3, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(8)
          self.relu = nn.ReLU(inplace=True)
          self.leakyrelu = nn.LeakyReLU(0.1)
  
          self.conv2 = nn.Conv2d(8, 8, 5, padding=2, bias=False)
          self.bn2 = nn.BatchNorm2d(16)
          self.conv3 = nn.Conv2d(8, 16, 5, padding=2, bias=False)
          self.conv4 = nn.Conv2d(16, 16, 5, padding=2, bias=False)
          self.maxpooling1 = nn.MaxPool2d(kernel_size=(2, 2))
          self.maxpooling2 = nn.MaxPool2d(kernel_size=(4, 4))
          #flatten: x = x.view(x.size(0), -1)
          self.dropout = nn.Dropout2d(0.5)
          self.fc1 = nn.Linear(16*int(image_size/32)*int(image_size/32), 16)
          self.fc2 = nn.Linear(16, num_classes)
  
      def forward(self, input):
          x = self.conv1(input) #(8, 256, 256)
          x = self.relu(x)
          x = self.bn1(x)
          x = self.maxpooling1(x)  # (8, 128, 128)
          x = self.conv2(x)  # (8, 128, 128)
          x = self.relu(x)
          x = self.bn1(x)
          x = self.maxpooling1(x)  # (8, 64, 64)
  
          x = self.conv3(x)  # (16, 64, 64)
          x = self.relu(x)
          x = self.bn2(x)
          x = self.maxpooling1(x)  # (16, 32, 32)
  
          x = self.conv4(x)  # (16, 32, 32)
          x = self.relu(x)
          x = self.bn2(x)
          x = self.maxpooling2(x)  # (16, 8, 8)
  
          x = x.view(x.size(0), -1)  # (Batch, 16*8*8)
          x = self.dropout(x)
          x = self.fc1(x)  # (Batch, 16)
          x = self.leakyrelu(x)
          x = self.dropout(x)
          x = self.fc2(x)
          x = nn.Sigmoid()(x)
          return x
  def mesonet(image_size=256):
      model = Meso4(image_size=image_size)
      return model
  if __name__ == '__main__':
      # model = resnext50(False)
      image_size = 128
      model = mesonet(image_size=image_size)
      import torchsummary
      torchsummary.summary(model, (3, image_size, image_size))