text: #-*-coding:utf-8-*-
  import shutil
  import argparse
  import time
  import os
  import torch
  import torch.nn.init as init
  import torch.utils.model_zoo as model_zoo
  import torch.nn as nn
  import torch.optim as optim
  import torch.backends.cudnn as cudnn
  import torchvision
  import torchvision.transforms as transforms
  import torchvision.models as models
  from torch.autograd import Variable
  
  class MobileNet(nn.Module):
      def __init__(self, classes_num):
          super(MobileNet, self).__init__()
          self.classes_num = classes_num
  
          def conv_bn(inp, oup, stride):
              return nn.Sequential(
                  nn.Conv2d(inp, oup, 3, stride, 1, bias=False),  # add zero padding=1 to maintain size unchanged
                  nn.BatchNorm2d(oup),
                  nn.ReLU(inplace=True)
              )
          def conv_dw(inp, oup, stride):
              return nn.Sequential(
                  nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                  nn.BatchNorm2d(inp),
                  nn.ReLU(inplace=True),
  
                  nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup),
                  nn.ReLU(inplace=True),
              )
  
          self.model = nn.Sequential(
              conv_bn(  3,  32, 2), 
              conv_dw( 32,  64, 1),
              conv_dw( 64, 128, 2),
              conv_dw(128, 128, 1),
              conv_dw(128, 256, 2),
              conv_dw(256, 256, 1),
              conv_dw(256, 512, 2),
              conv_dw(512, 512, 1),
              conv_dw(512, 512, 1),
              conv_dw(512, 512, 1),
              conv_dw(512, 512, 1),
              conv_dw(512, 512, 1),
              conv_dw(512, 1024, 2),
              conv_dw(1024, 1024, 1),
              nn.AvgPool2d(7),
          )
          self.fc = nn.Linear(1024, self.classes_num)
  
      def forward(self, x):
          x = self.model(x)
          x = x.view(-1, 1024)
          x = self.fc(x)
          return x
  
  
  def conv_bn(inp, oup, stride):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
          nn.BatchNorm2d(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  def conv_1x1_bn(inp, oup):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
          nn.BatchNorm2d(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  class InvertedResidual(nn.Module):
      def __init__(self, inp, oup, stride, expand_ratio):
          super(InvertedResidual, self).__init__()
          self.stride = stride
  
          self.use_res_connect = self.stride == 1 and inp == oup
  
          self.conv = nn.Sequential(
              # pw
              nn.Conv2d(inp, inp * expand_ratio, 1, 1, 0, bias=False),
              nn.BatchNorm2d(inp * expand_ratio),
              nn.ReLU6(inplace=True),
              # dw
              nn.Conv2d(inp * expand_ratio, inp * expand_ratio, 3, stride, 1, groups=inp * expand_ratio, bias=False),
              nn.BatchNorm2d(inp * expand_ratio),
              nn.ReLU6(inplace=True),
              # pw-linear
              nn.Conv2d(inp * expand_ratio, oup, 1, 1, 0, bias=False),
              nn.BatchNorm2d(oup),
          )
  
      def forward(self, x):
          if self.use_res_connect:
              return x + self.conv(x)
          else:
              return self.conv(x)
  
  class MobileNetV2(nn.Module):
      def __init__(self, n_class=20, input_size=224, width_mult=1.):
          super(MobileNetV2, self).__init__()
          # setting of inverted residual blocks
          self.interverted_residual_setting = [
              # t, c, n, s
              [1, 16, 1, 1],
              [6, 24, 2, 2],
              [6, 32, 3, 2],
              [6, 64, 4, 2],
              [6, 96, 3, 1],
              [6, 160, 3, 2],
              [6, 320, 1, 1],
          ]
  
          # building first layer
          input_channel = int(32 * width_mult)
          self.last_channel = int(1280 * width_mult) if width_mult > 1.0 else 1280
          self.features = [conv_bn(3, input_channel, 2)]
          # building inverted residual blocks
          for t, c, n, s in self.interverted_residual_setting:
              output_channel = int(c * width_mult)
              for i in range(n):
                  if i == 0:
                      self.features.append(InvertedResidual(input_channel, output_channel, s, t))
                  else:
                      self.features.append(InvertedResidual(input_channel, output_channel, 1, t))
                  input_channel = output_channel
          # building last several layers
          self.features.append(conv_1x1_bn(input_channel, self.last_channel))
          self.features.append(nn.AvgPool2d(input_size/32))
          # make it nn.Sequential
          self.features = nn.Sequential(*self.features)
  
          # building classifier
          self.classifier = nn.Sequential(
              nn.Dropout(),
              nn.Linear(self.last_channel, n_class),
          )
  
  
      def forward(self, x):
          x = self.features(x)
          x = x.view(-1, self.last_channel)
          x = self.classifier(x)
          return x
  
  
  __all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
  
  
  model_urls = {
      'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
      'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',
  }
  
  
  class Fire(nn.Module):
  
      def __init__(self, inplanes, squeeze_planes,
                   expand1x1_planes, expand3x3_planes):
          super(Fire, self).__init__()
          self.inplanes = inplanes
          self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
          self.squeeze_activation = nn.ReLU(inplace=True)
          self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
                                     kernel_size=1)
          self.expand1x1_activation = nn.ReLU(inplace=True)
          self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
                                     kernel_size=3, padding=1)
          self.expand3x3_activation = nn.ReLU(inplace=True)
  
      def forward(self, x):
          x = self.squeeze_activation(self.squeeze(x))
          return torch.cat([
              self.expand1x1_activation(self.expand1x1(x)),
              self.expand3x3_activation(self.expand3x3(x))
          ], 1)
  
  
  class SqueezeNet(nn.Module):
  
      def __init__(self, num_classes=1000, version=1.0):
          super(SqueezeNet, self).__init__()
          if version not in [1.0, 1.1]:
              raise ValueError("Unsupported SqueezeNet version {version}:"
                               "1.0 or 1.1 expected".format(version=version))
          self.num_classes = num_classes
          if version == 1.0:
              self.features = nn.Sequential(
                  nn.Conv2d(3, 96, kernel_size=7, stride=2),
                  nn.ReLU(inplace=True),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
                  Fire(96, 16, 64, 64),
                  Fire(128, 16, 64, 64),
                  Fire(128, 32, 128, 128),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
                  Fire(256, 32, 128, 128),
                  Fire(256, 48, 192, 192),
                  Fire(384, 48, 192, 192),
                  Fire(384, 64, 256, 256),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
                  Fire(512, 64, 256, 256),
              )
          else:
              self.features = nn.Sequential(
                  nn.Conv2d(3, 64, kernel_size=3, stride=2),
                  nn.ReLU(inplace=True),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
                  Fire(64, 16, 64, 64),
                  Fire(128, 16, 64, 64),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
                  Fire(128, 32, 128, 128),
                  Fire(256, 32, 128, 128),
                  nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
                  Fire(256, 48, 192, 192),
                  Fire(384, 48, 192, 192),
                  Fire(384, 64, 256, 256),
                  Fire(512, 64, 256, 256),
              )
          # Final convolution is initialized differently form the rest
          final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
          self.newclassifier = nn.Sequential(
              nn.Dropout(p=0.5),
              final_conv,
              nn.ReLU(inplace=True),
              nn.AvgPool2d(13)
          )
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  if m is final_conv:
                      init.normal(m.weight.data, mean=0.0, std=0.01)
                  else:
                      init.kaiming_uniform(m.weight.data)
                  if m.bias is not None:
                      m.bias.data.zero_()
  
      def forward(self, x):
          x = self.features(x)
          x = self.newclassifier(x)
          return x.view(x.size(0), self.num_classes)
  
  
  def squeezenet1_1(num_classes, pretrained=False, version=1.1):
      r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
      <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
      SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
      than SqueezeNet 1.0, without sacrificing accuracy.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = SqueezeNet(num_classes=num_classes, version=1.1)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_1']), strict=False)
      return model
  