text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # **************************************
  # @Time    : 2018/9/17 1:16
  # @Author  : Jiaxu Zou
  # @Lab     : nesa.zju.edu.cn
  # @File    : EAT_Test.py
  # **************************************
  import argparse
  import os
  import random
  import sys
  
  import numpy as np
  import torch
  
  sys.path.append('%s/../' % os.path.dirname(os.path.realpath(__file__)))
  from RawModels.MNISTConv import MNISTConvNet, MNIST_Training_Parameters
  from RawModels.ResNet import resnet20_cifar, CIFAR10_Training_Parameters
  from RawModels.Utils.dataset import get_mnist_train_validate_loader, get_mnist_test_loader
  from RawModels.Utils.dataset import get_cifar10_train_validate_loader, get_cifar10_test_loader
  from Defenses.DefenseMethods.EAT import EATDefense
  
  
  def main(args):
      # Device configuration
      os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_index
      device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
      # Set the random seed manually for reproducibility.
      torch.backends.cudnn.deterministic = True
      torch.backends.cudnn.benchmark = False
      torch.manual_seed(args.seed)
      if torch.cuda.is_available():
          torch.cuda.manual_seed(args.seed)
      np.random.seed(args.seed)
      random.seed(args.seed)
  
      # Get training parameters, set up model frameworks and then get the train_loader and test_loader
      dataset = args.dataset.upper()
      assert dataset == 'MNIST' or dataset == 'CIFAR10'
      if dataset == 'MNIST':
          training_parameters = MNIST_Training_Parameters
          model_framework = MNISTConvNet().to(device)
          batch_size = training_parameters['batch_size']
          train_loader, valid_loader = get_mnist_train_validate_loader(dir_name='../RawModels/MNIST/', batch_size=batch_size, valid_size=0.1,
                                                                       shuffle=True)
          test_loader = get_mnist_test_loader(dir_name='../RawModels/MNIST/', batch_size=batch_size)
      else:
          training_parameters = CIFAR10_Training_Parameters
          model_framework = resnet20_cifar().to(device)
          batch_size = training_parameters['batch_size']
          train_loader, valid_loader = get_cifar10_train_validate_loader(dir_name='../RawModels/CIFAR10/', batch_size=batch_size, valid_size=0.1,
                                                                         shuffle=True)
          test_loader = get_cifar10_test_loader(dir_name='../RawModels/CIFAR10/', batch_size=batch_size)
  
      defense_name = 'EAT'
      eat_params = {
          'eps': args.eps,
          'alpha': args.alpha
      }
  
      eat = EATDefense(model=model_framework, defense_name=defense_name, dataset=dataset, training_parameters=training_parameters, device=device,
                       **eat_params)
  
      # train the external models
      if args.train_externals:
          print('\nStart to train the external models ......\n')
          eat.train_external_model_group(train_loader=train_loader, validation_loader=valid_loader)
  
      # load the external models
      pre_train_models = eat.load_external_model_group(model_dir='../DefenseEnhancedModels/EAT/', test_loader=test_loader)
  
      eat.defense(pre_trained_models=pre_train_models, train_loader=train_loader, validation_loader=valid_loader)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description='The EAT Defenses')
      parser.add_argument('--dataset', type=str, default='MNIST', help='the dataset (MNIST or CIFAR10)')
      parser.add_argument('--gpu_index', type=str, default='0', help="gpu index to use")
      parser.add_argument('--seed', type=int, default=100, help='the default random seed for numpy and torch')
  
      # parameters for the EAT Defense
      parser.add_argument("--train_externals", type=lambda x: (str(x).lower() == 'true'), default=True,
                          help='True if required to train the external models additionally, otherwise False')
      parser.add_argument("--eps", type=float, default=0.3, help="FGSM attack scale")
      parser.add_argument("--alpha", type=float, default=0.05, help="RFGSM random perturbation scale")
      arguments = parser.parse_args()
      main(arguments)
  