text: from __future__ import print_function
  #halo
  import argparse
  import os
  import shutil
  import time
  import random
  
  import torch
  import torch.nn as nn
  import torch.nn.parallel
  import torch.backends.cudnn as cudnn
  import torch.optim as optim
  import torch.utils.data as data
  import transforms
  import torchvision.datasets as datasets
  import models.cifar as models
  
  from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig
  
  
  model_names = sorted(name for name in models.__dict__
      if name.islower() and not name.startswith("__")
      and callable(models.__dict__[name]))
  
  parser = argparse.ArgumentParser(description='PyTorch CIFAR10 and 100 Training')
  # Datasets
  parser.add_argument('-d', '--dataset', default='cifar10', type=str)
  parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                      help='number of data loading workers (default: 4)')
  # Optimization options
  parser.add_argument('--epochs', default=300, type=int, metavar='N',
                      help='number of total epochs to run')
  parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                      help='manual epoch number (useful on restarts)')
  parser.add_argument('--train-batch', default=128, type=int, metavar='N',
                      help='train batchsize')
  parser.add_argument('--test-batch', default=100, type=int, metavar='N',
                      help='test batchsize')
  parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                      metavar='LR', help='initial learning rate')
  parser.add_argument('--drop', '--dropout', default=0, type=float,
                      metavar='Dropout', help='Dropout ratio')
  parser.add_argument('--schedule', type=int, nargs='+', default=[150, 225],
                          help='Decrease learning rate at these epochs.')
  parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                      metavar='W', help='weight decay (default: 1e-4)')
  # Checkpoints
  parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
                      help='path to save checkpoint (default: checkpoint)')
  parser.add_argument('--resume', default='', type=str, metavar='PATH',
                      help='path to latest checkpoint (default: none)')
  # Architecture
  parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet',
                      choices=model_names,
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet20)')
  parser.add_argument('--depth', type=int, default=20, help='Model depth.')
  parser.add_argument('--widen-factor', type=int, default=10, help='Widen factor. 10')
  parser.add_argument('--growthRate', type=int, default=12, help='Growth rate for DenseNet.')
  parser.add_argument('--compressionRate', type=int, default=2, help='Compression Rate (theta) for DenseNet.')
  # Miscs
  parser.add_argument('--manualSeed', type=int, help='manual seed')
  parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                      help='evaluate model on validation set')
  
  # Random Erasing
  parser.add_argument('--p', default=0, type=float, help='Random Erasing probability')
  parser.add_argument('--sh', default=0.4, type=float, help='max erasing area')
  parser.add_argument('--r1', default=0.3, type=float, help='aspect of erasing area')
  
  args = parser.parse_args()
  state = {k: v for k, v in args._get_kwargs()}
  
  # Validate dataset
  assert args.dataset == 'cifar10' or args.dataset == 'cifar100', 'Dataset can only be cifar10 or cifar100.'
  
  # Use CUDA
  use_cuda = torch.cuda.is_available()
  
  # Random seed
  if args.manualSeed is None:
      args.manualSeed = random.randint(1, 10000)
  random.seed(args.manualSeed)
  torch.manual_seed(args.manualSeed)
  if use_cuda:
      torch.cuda.manual_seed_all(args.manualSeed)
  
  best_acc = 0  # best test accuracy
  
  def main():
      global best_acc
      start_epoch = args.start_epoch  # start from epoch 0 or last checkpoint epoch
  
      if not os.path.isdir(args.checkpoint):
          mkdir_p(args.checkpoint)
  
  
  
      # Data
      print('==> Preparing dataset %s' % args.dataset)
      transform_train = transforms.Compose([
          transforms.RandomCrop(32, padding=4),
          transforms.RandomHorizontalFlip(),
          transforms.ToTensor(),
          transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
          transforms.RandomErasing(probability = args.p, sh = args.sh, r1 = args.r1, ),
      ])
  
      transform_test = transforms.Compose([
          transforms.ToTensor(),
          transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
      ])
      if args.dataset == 'cifar10':
          dataloader = datasets.CIFAR10
          num_classes = 10
      else:
          dataloader = datasets.CIFAR100
          num_classes = 100
  
  
      trainset = dataloader(root='./data', train=True, download=True, transform=transform_train)
      trainloader = data.DataLoader(trainset, batch_size=args.train_batch, shuffle=True, num_workers=args.workers)
  
      testset = dataloader(root='./data', train=False, download=False, transform=transform_test)
      testloader = data.DataLoader(testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
  
      # Model   
      print("==> creating model '{}'".format(args.arch))
      if args.arch.startswith('wrn'):
          model = models.__dict__[args.arch](
                      num_classes=num_classes,
                      depth=args.depth,
                      widen_factor=args.widen_factor,
                      dropRate=args.drop,
                  )
      elif args.arch.endswith('resnet'):
          model = models.__dict__[args.arch](
                      num_classes=num_classes,
                      depth=args.depth,
                  )
  
      model = torch.nn.DataParallel(model).cuda()
      cudnn.benchmark = True
      print('    Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
  
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
  
      # Resume
      title = 'cifar-10-' + args.arch
      if args.resume:
          # Load checkpoint.
          print('==> Resuming from checkpoint..')
          assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
          args.checkpoint = os.path.dirname(args.resume)
          checkpoint = torch.load(args.resume)
          best_acc = checkpoint['best_acc']
          start_epoch = checkpoint['epoch']
          model.load_state_dict(checkpoint['state_dict'])
          optimizer.load_state_dict(checkpoint['optimizer'])
          logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
      else:
          logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
          logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
  
  
      if args.evaluate:
          print('\nEvaluation only')
          test_loss, test_acc = test(testloader, model, criterion, start_epoch, use_cuda)
          print(' Test Loss:  %.8f, Test Acc:  %.2f' % (test_loss, test_acc))
          return
  
      # Train and val
      for epoch in range(start_epoch, args.epochs):
          adjust_learning_rate(optimizer, epoch)
  
          print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))
  
          train_loss, train_acc = train(trainloader, model, criterion, optimizer, epoch, use_cuda)
          test_loss, test_acc = test(testloader, model, criterion, epoch, use_cuda)
  
          # append logger file
          logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc])
  
          # save model
          is_best = test_acc > best_acc
          best_acc = max(test_acc, best_acc)
          save_checkpoint({
                  'epoch': epoch + 1,
                  'state_dict': model.state_dict(),
                  'acc': test_acc,
                  'best_acc': best_acc,
                  'optimizer' : optimizer.state_dict(),
              }, is_best, checkpoint=args.checkpoint)
  
      logger.close()
      logger.plot()
      savefig(os.path.join(args.checkpoint, 'log.eps'))
  
      print('Best acc:')
      print(best_acc)
  
  def train(trainloader, model, criterion, optimizer, epoch, use_cuda):
      # switch to train mode
      model.train()
  
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
      end = time.time()
  
      bar = Bar('Processing', max=len(trainloader))
      for batch_idx, (inputs, targets) in enumerate(trainloader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if use_cuda:
              inputs, targets = inputs.cuda(), targets.cuda(async=True)
          inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
  
          # compute output
          outputs = model(inputs)
          loss = criterion(outputs, targets)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
          losses.update(loss.item(), inputs.size(0))
          top1.update(prec1.item(), inputs.size(0))
          top5.update(prec5.item(), inputs.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          # plot progress
          bar.suffix  = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
                      batch=batch_idx + 1,
                      size=len(trainloader),
                      data=data_time.avg,
                      bt=batch_time.avg,
                      total=bar.elapsed_td,
                      eta=bar.eta_td,
                      loss=losses.avg,
                      top1=top1.avg,
                      top5=top5.avg,
                      )
          bar.next()
      bar.finish()
      return (losses.avg, top1.avg)
  
  def test(testloader, model, criterion, epoch, use_cuda):
      global best_acc
  
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      end = time.time()
      bar = Bar('Processing', max=len(testloader))
      for batch_idx, (inputs, targets) in enumerate(testloader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if use_cuda:
              inputs, targets = inputs.cuda(), targets.cuda()
          inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
  
          # compute output
          outputs = model(inputs)
          loss = criterion(outputs, targets)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
          losses.update(loss.item(), inputs.size(0))
          top1.update(prec1.item(), inputs.size(0))
          top5.update(prec5.item(), inputs.size(0))
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          # plot progress
          bar.suffix  = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
                      batch=batch_idx + 1,
                      size=len(testloader),
                      data=data_time.avg,
                      bt=batch_time.avg,
                      total=bar.elapsed_td,
                      eta=bar.eta_td,
                      loss=losses.avg,
                      top1=top1.avg,
                      top5=top5.avg,
                      )
          bar.next()
      bar.finish()
      return (losses.avg, top1.avg)
  
  def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'):
      filepath = os.path.join(checkpoint, filename)
      torch.save(state, filepath)
      if is_best:
          shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))
  
  def adjust_learning_rate(optimizer, epoch):
      global state
      if epoch in args.schedule:
          state['lr'] *= args.gamma
          for param_group in optimizer.param_groups:
              param_group['lr'] = state['lr']
  
  if __name__ == '__main__':
      main()
  