text: import torchvision
  import torchvision.transforms as transforms
  import torch
  import torch.utils.data
  import resnet
  from torch import nn
  from tqdm.auto import tqdm
  import time
  from datetime import timedelta
  import torchattacks
  import os
  import numpy as np
  import argparse
  import dataloader
  import wrn
  import vgg
  import mobilenetv2
  import attacks
  from torch.utils.data import TensorDataset, DataLoader
  from logging import getLogger
  import logging
  import random
  from torch.optim.lr_scheduler import CosineAnnealingLR,MultiStepLR
  import torchvision.transforms.functional as TF
  class LogFormatter:
  	def __init__(self):
  		self.start_time = time.time()
  
  	def format(self, record):
  		elapsed_seconds = round(record.created - self.start_time)
  
  		prefix = "%s - %s - %s" % (
  			record.levelname,
  			time.strftime("%x %X"),
  			timedelta(seconds=elapsed_seconds),
  		)
  		message = record.getMessage()
  		message = message.replace("\n", "\n" + " " * (len(prefix) + 3))
  		return "%s - %s" % (prefix, message) if message else ""
  def create_logger(filepath, rank):
  	# create log formatter
  	log_formatter = LogFormatter()
  
  	# create file handler and set level to debug
  	if filepath is not None:
  		if rank > 0:
  			filepath = "%s-%i" % (filepath, rank)
  		file_handler = logging.FileHandler(filepath, "a")
  		file_handler.setLevel(logging.DEBUG)
  		file_handler.setFormatter(log_formatter)
  
  	# create console handler and set level to info
  	console_handler = logging.StreamHandler()
  	console_handler.setLevel(logging.INFO)
  	console_handler.setFormatter(log_formatter)
  
  	# create logger and set level to debug
  	logger = logging.getLogger()
  	logger.handlers = []
  	logger.setLevel(logging.DEBUG)
  	logger.propagate = False
  	if filepath is not None:
  		logger.addHandler(file_handler)
  	logger.addHandler(console_handler)
  
  	# reset logger elapsed time
  	def reset_time():
  		log_formatter.start_time = time.time()
  
  	logger.reset_time = reset_time
  
  	return logger
  def setup_seed(seed):
  	torch.manual_seed(seed)
  	torch.cuda.manual_seed_all(seed)
  	random.seed(seed)
  	torch.backends.cudnn.deterministic = True
  	np.random.seed(seed)
  parser = argparse.ArgumentParser()
  parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
  parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet',
  					help='model architecture')
  parser.add_argument('--dataset', default='tiny', type=str,
  					help='which dataset used to train')
  parser.add_argument('--path', default='./', type=str,
  					help='path to your dataset')
  parser.add_argument('--epochs', default=200, type=int, metavar='N',
  					help='number of total epochs to run')
  parser.add_argument('-b', '--batch_size', default=128, type=int,
  					metavar='N',
  					help='mini-batch size (default: 256), this is the total '
  						 'batch size of all GPUs on the current node when '
  						 'using Data Parallel or Distributed Data Parallel')
  parser.add_argument('--lr', '--learning-rate', default=1e-1, type=float,
  					metavar='LR', help='initial learning rate', dest='lr')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
  					help='momentum of SGD solver')
  parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
  					metavar='W', help='weight decay (default: 1e-4)',
  					dest='wd')
  parser.add_argument('--seed', default=0, type=int,
  					help='seed for initializing training. ')
  parser.add_argument('--class_num', default=200, type=int,
  					help='num of classes')
  parser.add_argument('--save', default='MN.pkl', type=str,
  					help='model save name')
  parser.add_argument('--exp', default='exp_test_grad', type=str,
  					help='exp name')
  
  parser.add_argument('--aug', default=1, type=int,
  					help='whether use data augmentation')
  parser.add_argument('--gamma', type=float, default=0.1,
  					help='LR is multiplied by gamma on schedule.')
  
  
  args = parser.parse_args()
  logger = getLogger()
  if not os.path.exists('./pretrained/'+args.dataset+'/'+args.exp):
  	os.makedirs('./pretrained/'+args.dataset+'/'+args.exp)
  logger = create_logger(
  	os.path.join('./pretrained/' + args.dataset + '/' + args.exp + '/', args.exp + ".log"), rank=0
  )
  logger.info("============ Initialized logger ============")
  logger.info(
  	"\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items()))
  )
  args.save = './pretrained/' + args.dataset + '/' + args.exp + '/' +  args.save
  os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
  setup_seed(args.seed)
  wd=args.wd
  learning_rate=args.lr
  epochs=args.epochs
  batch_size=args.batch_size
  torch.backends.cudnn.benchmark = True
  
  if args.arch == 'resnet':
  	n = resnet.resnet18(args.dataset).cuda()
  elif args.arch == 'wrn':
  	n = wrn.WideResNet(num_classes=200).cuda()
  elif args.arch == 'vgg':
  	n = vgg.vgg19_bn(200).cuda()
  elif args.arch == 'mobilenet':
  	n = mobilenetv2.mobilenetv2(200).cuda()
  
  transform_test=transforms.Compose([torchvision.transforms.Resize((64,64)),
  									   transforms.ToTensor(),
  									   ])
  dl = dataloader.Data(args.dataset, args.path)
  trainloader, testloader = dl.data_loader(transform_test, transform_test, batch_size)
  def data_aug(image):
  	image = TF.center_crop(image, [int(64.0 * random.uniform(0.95, 1.0)), int(64.0 * random.uniform(0.95, 1.0))])
  	image = TF.resize(image, [64, 64])
  	noise = torch.randn_like(image).cuda() * 0.001
  	image = torch.clamp(image + noise, 0.0, 1.0)
  	if random.uniform(0, 1) > 0.5:
  		image = TF.vflip(image)
  	if random.uniform(0, 1) > 0.5:
  		image = TF.hflip(image)
  	angles=[-15, 0, 15]
  	angle = random.choice(angles)
  	image = TF.rotate(image, angle)
  	return image
  
  def mixup_data(x, y, alpha=1.0, use_cuda=True):
  	'''Returns mixed inputs, pairs of targets, and lambda'''
  	if alpha > 0:
  		lam = np.random.beta(alpha, alpha)
  	else:
  		lam = 1
  
  	batch_size = x.size()[0]
  	if use_cuda:
  		index = torch.randperm(batch_size).cuda()
  	else:
  		index = torch.randperm(batch_size)
  
  	mixed_x = lam * x + (1 - lam) * x[index, :]
  	y_a, y_b = y, y[index]
  	return mixed_x, y_a, y_b, lam
  
  
  def mixup_criterion(criterion, pred, y_a, y_b, lam):
  	return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
  
  Loss = nn.CrossEntropyLoss().cuda()
  
  optimizer = torch.optim.SGD(n.parameters() ,momentum=args.momentum,
  							lr=learning_rate,weight_decay=wd)#+ optimize_parameters
  milestones = [int(args.epochs * 0.5), int(args.epochs * 0.75)]
  scheduler = MultiStepLR(optimizer,milestones=milestones,gamma=args.gamma)
  best_eval_acc = 0.0
  
  for epoch in range(epochs):
  	loadertrain = tqdm(trainloader, desc='{} E{:03d}'.format('train', epoch), ncols=0, total=len(trainloader))
  	epoch_loss = 0.0
  	clean_acc = 0.0
  	adv_acc = 0.0
  	total=0.0
  	for x_train, y_train in loadertrain:
  		x_train, y_train = x_train.cuda(), y_train.cuda()
  		if args.aug == 1:
  			x_train = data_aug(x_train)
  		n.train()
  		inputs, targets_a, targets_b, lam = mixup_data(x_train, y_train,
  													   1.0, True)
  		inputs, targets_a, targets_b = map(torch.autograd.Variable, (inputs,
  													  targets_a, targets_b))
  		outputs = n(inputs)
  		loss = mixup_criterion(Loss, outputs, targets_a, targets_b, lam)
  		optimizer.zero_grad()
  		loss.backward()
  		optimizer.step()
  		epoch_loss += loss.data.item()
  		_, predicted = torch.max(outputs.data, 1)
  		clean_acc  += predicted.eq(y_train.data).cuda().sum()
  		total += y_train.size(0)
  		fmt = '{:.4f}'.format
  		loadertrain.set_postfix(loss=fmt(loss.data.item()),
  								clean_acc=fmt(clean_acc.item() / total * 100))
  	scheduler.step()
  	if (epoch) % 1 == 0:
  		test_loss_cl = 0.0
  		test_loss_adv = 0.0
  		correct_cl = 0.0
  		correct_adv = 0.0
  		total = 0.0
  		n.eval()
  		loadertest = tqdm(testloader, desc='{} E{:03d}'.format('test', epoch), ncols=0)
  		with torch.enable_grad():
  			for x_test, y_test in loadertest:
  				x_test, y_test = x_test.cuda(), y_test.cuda()
  				n.eval()
  				y_pre = n(x_test)
  				loss_cl = Loss(y_pre, y_test)
  				test_loss_cl += loss_cl.data.item()
  				_, predicted = torch.max(y_pre.data, 1)
  				total += y_test.size(0)
  				correct_cl += predicted.eq(y_test.data).cuda().sum()
  				fmt = '{:.4f}'.format
  				loadertest.set_postfix(loss_cl=fmt(loss_cl.data.item()),
  									   acc_cl=fmt(correct_cl.item() / total * 100))
  		if correct_cl.item() / total * 100 > best_eval_acc:
  			best_eval_acc = correct_cl.item() / total * 100
  			checkpoint = {
  				'state_dict': n.state_dict(),
  				'epoch': epoch
  			}
  			torch.save(checkpoint, args.save)
  
  
  
  