text: ##################################################
  # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
  ###########################################################################
  # Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
  ###########################################################################
  import os, sys, time, random, argparse
  import numpy as np
  from copy import deepcopy
  import torch
  import torch.nn as nn
  from pathlib import Path
  lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
  if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
  from config_utils import load_config, dict2config
  from datasets     import get_datasets, get_nas_search_loaders
  from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
  from utils        import get_model_infos, obtain_accuracy
  from log_utils    import AverageMeter, time_string, convert_secs2time
  from models       import get_cell_based_tiny_net, get_search_spaces, get_sub_search_spaces
  from nas_102_api  import NASBench102API as API
  
  
  def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
    data_time, batch_time = AverageMeter(), AverageMeter()
    base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
    arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
    network.train()
    end = time.time()
    for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
      scheduler.update(None, 1.0 * step / len(xloader))
      base_targets = base_targets.cuda(non_blocking=True)
      arch_targets = arch_targets.cuda(non_blocking=True)
      # measure data loading time
      data_time.update(time.time() - end)
      
      # update the weights
      w_optimizer.zero_grad()
      _, logits = network(base_inputs)
      base_loss = criterion(logits, base_targets)
      base_loss.backward()
      torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
      w_optimizer.step()
      # record
      base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
      base_losses.update(base_loss.item(),  base_inputs.size(0))
      base_top1.update  (base_prec1.item(), base_inputs.size(0))
      base_top5.update  (base_prec5.item(), base_inputs.size(0))
  
      # update the architecture-weight
      a_optimizer.zero_grad()
      _, logits = network(arch_inputs)
      arch_loss = criterion(logits, arch_targets)
      arch_loss.backward()
      a_optimizer.step()
      # record
      arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
      arch_losses.update(arch_loss.item(),  arch_inputs.size(0))
      arch_top1.update  (arch_prec1.item(), arch_inputs.size(0))
      arch_top5.update  (arch_prec5.item(), arch_inputs.size(0))
  
      # measure elapsed time
      batch_time.update(time.time() - end)
      end = time.time()
  
      if step % print_freq == 0 or step + 1 == len(xloader):
        Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
        Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
        Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5)
        Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5)
        logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
    return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
  
  
  def main(xargs):
    assert torch.cuda.is_available(), 'CUDA is not available.'
    torch.backends.cudnn.enabled   = True
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True
    torch.set_num_threads( xargs.workers )
    prepare_seed(xargs.rand_seed)
    logger = prepare_logger(args)
  
    train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
    #config_path = 'configs/nas-benchmark/algos/GDAS.config'
    config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
    search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
    logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), config.batch_size))
    logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
  
    search_space = get_sub_search_spaces('cell', xargs.search_space_name)
    logger.log('search_space={}'.format(search_space))
    if xargs.model_config is None:
      model_config = dict2config({'name': 'GDAS', 'C': xargs.channel, 'N': xargs.num_cells,
                                  'max_nodes': xargs.max_nodes, 'num_classes': class_num,
                                  'space'    : search_space,
                                  'affine'   : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
    else:
      model_config = load_config(xargs.model_config, {'num_classes': class_num, 'space'    : search_space,
                                                      'affine'     : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
    search_model = get_cell_based_tiny_net(model_config)
    logger.log('search-model :\n{:}'.format(search_model))
    logger.log('model-config : {:}'.format(model_config))
    
    w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
    a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
    logger.log('w-optimizer : {:}'.format(w_optimizer))
    logger.log('a-optimizer : {:}'.format(a_optimizer))
    logger.log('w-scheduler : {:}'.format(w_scheduler))
    logger.log('criterion   : {:}'.format(criterion))
    flop, param  = get_model_infos(search_model, xshape)
    #logger.log('{:}'.format(search_model))
    logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
    logger.log('search-space [{:} ops] : {:}'.format(len(search_space), search_space))
    if xargs.arch_nas_dataset is None:
      api = None
    else:
      api = API(xargs.arch_nas_dataset)
    logger.log('{:} create API = {:} done'.format(time_string(), api))
  
    last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
    network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
  
    if last_info.exists(): # automatically resume from previous checkpoint
      logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
      last_info   = torch.load(last_info)
      start_epoch = last_info['epoch']
      checkpoint  = torch.load(last_info['last_checkpoint'])
      genotypes   = checkpoint['genotypes']
      valid_accuracies = checkpoint['valid_accuracies']
      search_model.load_state_dict( checkpoint['search_model'] )
      w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
      w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )
      a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )
      logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
    else:
      logger.log("=> do not find the last-info file : {:}".format(last_info))
      start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: search_model.genotype()}
  
    # start training
    start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
    for epoch in range(start_epoch, total_epoch):
      w_scheduler.update(epoch, 0.0)
      need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
      epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
      search_model.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
      logger.log('\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}'.format(epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr())))
  
      search_w_loss, search_w_top1, search_w_top5, valid_a_loss , valid_a_top1 , valid_a_top5 \
                = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
      search_time.update(time.time() - start_time)
      logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
      logger.log('[{:}] evaluate  : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss , valid_a_top1 , valid_a_top5 ))
      # check the best accuracy
      valid_accuracies[epoch] = valid_a_top1
      if valid_a_top1 > valid_accuracies['best']:
        valid_accuracies['best'] = valid_a_top1
        genotypes['best']        = search_model.genotype()
        find_best = True
      else: find_best = False
  
      genotypes[epoch] = search_model.genotype()
      logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
      # save checkpoint
      save_path = save_checkpoint({'epoch' : epoch + 1,
                  'args'  : deepcopy(xargs),
                  'search_model': search_model.state_dict(),
                  'w_optimizer' : w_optimizer.state_dict(),
                  'a_optimizer' : a_optimizer.state_dict(),
                  'w_scheduler' : w_scheduler.state_dict(),
                  'genotypes'   : genotypes,
                  'valid_accuracies' : valid_accuracies},
                  model_base_path, logger)
      last_info = save_checkpoint({
            'epoch': epoch + 1,
            'args' : deepcopy(args),
            'last_checkpoint': save_path,
            }, logger.path('info'), logger)
      # if find_best:
      #   logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
      #   copy_checkpoint(model_base_path, model_best_path, logger)
      with torch.no_grad():
        logger.log('{:}'.format(search_model.show_alphas()))
      if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] )))
      # measure elapsed time
      epoch_time.update(time.time() - start_time)
      start_time = time.time()
  
    logger.log('\n' + '-'*100)
    # check the performance from the architecture dataset
    logger.log('GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotypes[total_epoch-1]))
    if api is not None: logger.log('{:}'.format( api.query_by_arch(genotypes[total_epoch-1]) ))
    logger.close()
    
  
  
  if __name__ == '__main__':
    parser = argparse.ArgumentParser("GDAS")
    parser.add_argument('--data_path',          type=str,   help='Path to dataset')
    parser.add_argument('--dataset',            type=str,   choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
    # channels and number-of-cells
    parser.add_argument('--search_space_name',  type=str,   help='The search space name.')
    parser.add_argument('--max_nodes',          type=int,   help='The maximum number of nodes.')
    parser.add_argument('--channel',            type=int,   help='The number of channels.')
    parser.add_argument('--num_cells',          type=int,   help='The number of cells in one stage.')
    parser.add_argument('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
    parser.add_argument('--config_path',        type=str,   help='The path of the configuration.')
    parser.add_argument('--model_config',       type=str,   help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.')
    # architecture leraning rate
    parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
    parser.add_argument('--arch_weight_decay',  type=float, default=1e-3, help='weight decay for arch encoding')
    parser.add_argument('--tau_min',            type=float,               help='The minimum tau for Gumbel')
    parser.add_argument('--tau_max',            type=float,               help='The maximum tau for Gumbel')
    # log
    parser.add_argument('--workers',            type=int,   default=2,    help='number of data loading workers (default: 2)')
    parser.add_argument('--save_dir',           type=str,   help='Folder to save checkpoints and log.')
    parser.add_argument('--arch_nas_dataset',   type=str,   help='The path to load the architecture dataset (tiny-nas-benchmark).')
    parser.add_argument('--print_freq',         type=int,   help='print frequency (default: 200)')
    parser.add_argument('--rand_seed',          type=int,   help='manual seed')
    args = parser.parse_args()
    if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
    main(args)
  