code: ##################################################
  # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
  ##################################################
  import os, sys, time, argparse, collections
  from copy import deepcopy
  import torch
  import torch.nn as nn
  from pathlib import Path
  from collections import defaultdict
  lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
  if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
  from log_utils    import AverageMeter, time_string, convert_secs2time
  from config_utils import load_config, dict2config
  from datasets     import get_datasets
  # NAS-Bench-201 related module or function
  from models       import CellStructure, get_cell_based_tiny_net
  from nas_201_api  import ArchResults, ResultsCount
  from functions    import pure_evaluate
  
  
  
  def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
    xresult     = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'], \
                                 results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
  
    net_config = dict2config({'name': 'infer.tiny', 'C': arch_config['channel'], 'N': arch_config['num_cells'], 'genotype': CellStructure.str2structure(arch_config['arch_str']), 'num_classes':arch_config['class_num']}, None)
    network = get_cell_based_tiny_net(net_config)
    network.load_state_dict(xresult.get_net_param())
    if 'train_times' in results: # new version
      xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
      xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
    else:
      if dataset == 'cifar10-valid':
        xresult.update_OLD_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
        loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format('cifar10', 'test')], network.cuda())
        xresult.update_OLD_eval('ori-test', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
        xresult.update_latency(latencies)
      elif dataset == 'cifar10':
        xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
        loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
        xresult.update_latency(latencies)
      elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
        xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
        loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network.cuda())
        xresult.update_OLD_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
        loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset,  'test')], network.cuda())
        xresult.update_OLD_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
        xresult.update_latency(latencies)
      else:
        raise ValueError('invalid dataset name : {:}'.format(dataset))
    return xresult
    
  
  
  def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
    information = ArchResults(arch_index, arch_str)
  
    for checkpoint_path in checkpoints:
      checkpoint = torch.load(checkpoint_path, map_location='cpu')
      used_seed  = checkpoint_path.name.split('-')[-1].split('.')[0]
      for dataset in datasets:
        assert dataset in checkpoint, 'Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path)
        results     = checkpoint[dataset]
        assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
        arch_config = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
        
        xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
        information.update(dataset, int(used_seed), xresult)
    return information
  
  
  
  def GET_DataLoaders(workers):
  
    torch.set_num_threads(workers)
  
    root_dir  = (Path(__file__).parent / '..' / '..').resolve()
    torch_dir = Path(os.environ['TORCH_HOME'])
    # cifar
    cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
    cifar_config = load_config(cifar_config_path, None, None)
    print ('{:} Create data-loader for all datasets'.format(time_string()))
    print ('-'*200)
    TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
    print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
    cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
    assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
    temp_dataset = deepcopy(TRAIN_CIFAR10)
    temp_dataset.transform = VALID_CIFAR10.transform
    # data loader
    trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
    train_cifar10_loader    = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
    valid_cifar10_loader    = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
    test__cifar10_loader    = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
    print ('CIFAR-10  : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
    print ('CIFAR-10  : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
    print ('CIFAR-10  : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
    print ('CIFAR-10  : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
    print ('-'*200)
    # CIFAR-100
    TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
    print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
    cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
    assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
    train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
    valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
    test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
    print ('CIFAR-100  : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
    print ('CIFAR-100  : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
    print ('CIFAR-100  : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
    print ('-'*200)
  
    imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
    imagenet16_config = load_config(imagenet16_config_path, None, None)
    TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
    print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
    imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
    assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
    train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
    valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
    test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
    print ('ImageNet-16-120  : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
    print ('ImageNet-16-120  : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
    print ('ImageNet-16-120  : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))
  
    # 'cifar10', 'cifar100', 'ImageNet16-120'
    loaders = {'cifar10@trainval': trainval_cifar10_loader,
               'cifar10@train'   : train_cifar10_loader,
               'cifar10@valid'   : valid_cifar10_loader,
               'cifar10@test'    : test__cifar10_loader,
               'cifar100@train'  : train_cifar100_loader,
               'cifar100@valid'  : valid_cifar100_loader,
               'cifar100@test'   : test__cifar100_loader,
               'ImageNet16-120@train': train_imagenet_loader,
               'ImageNet16-120@valid': valid_imagenet_loader,
               'ImageNet16-120@test' : test__imagenet_loader}
    return loaders
  
  
  
  def simplify(save_dir, meta_file, basestr, target_dir):
    meta_infos     = torch.load(meta_file, map_location='cpu')
    meta_archs     = meta_infos['archs'] # a list of architecture strings
    meta_num_archs = meta_infos['total']
    meta_max_node  = meta_infos['max_node']
    assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
  
    sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
    print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
    
    subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
    num_seeds = defaultdict(lambda: 0)
    for index, sub_dir in enumerate(sub_model_dirs):
      xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
      arch_indexes = set()
      for checkpoint in xcheckpoints:
        temp_names = checkpoint.name.split('-')
        assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
        arch_indexes.add( temp_names[1] )
      subdir2archs[sub_dir] = sorted(list(arch_indexes))
      num_evaluated_arch   += len(arch_indexes)
      # count number of seeds for each architecture
      for arch_index in arch_indexes:
        num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
    print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
    for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))
  
    dataloader_dict = GET_DataLoaders( 6 )
  
    to_save_simply = save_dir / 'simplifies'
    to_save_allarc = save_dir / 'simplifies' / 'architectures'
    if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
    if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)
  
    assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
    arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
    evaluated_indexes    = set()
    target_directory     = save_dir / target_dir
    target_less_dir      = save_dir / '{:}-LESS'.format(target_dir)
    arch_indexes         = subdir2archs[ target_directory ]
    num_seeds            = defaultdict(lambda: 0)
    end_time             = time.time()
    arch_time            = AverageMeter()
    for idx, arch_index in enumerate(arch_indexes):
      checkpoints = list(target_directory.glob('arch-{:}-seed-*.pth'.format(arch_index)))
      ckps_less   = list(target_less_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
      # create the arch info for each architecture
      try:
        arch_info_full = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
        arch_info_less = account_one_arch(arch_index, meta_archs[int(arch_index)], ckps_less, ['cifar10-valid'], dataloader_dict)
        num_seeds[ len(checkpoints) ] += 1
      except:
        print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
        continue
      assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
      assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
      arch_info = {'full': arch_info_full, 'less': arch_info_less}
      evaluated_indexes.add( int(arch_index) )
      arch2infos[int(arch_index)] = arch_info
      torch.save({'full': arch_info_full.state_dict(),
                  'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
      arch_info['full'].clear_params()
      arch_info['less'].clear_params()
      torch.save({'full': arch_info_full.state_dict(),
                  'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
      # measure elapsed time
      arch_time.update(time.time() - end_time)
      end_time  = time.time()
      need_time = '{:}'.format( convert_secs2time(arch_time.avg * (len(arch_indexes)-idx-1), True) )
      print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
    # measure time
    xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
    print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
    final_infos = {'meta_archs' : meta_archs,
                   'total_archs': meta_num_archs,
                   'basestr'    : basestr,
                   'arch2infos' : arch2infos,
                   'evaluated_indexes': evaluated_indexes}
    save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
    torch.save(final_infos, save_file_name)
    print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
  
  
  
  def merge_all(save_dir, meta_file, basestr):
    meta_infos     = torch.load(meta_file, map_location='cpu')
    meta_archs     = meta_infos['archs']
    meta_num_archs = meta_infos['total']
    meta_max_node  = meta_infos['max_node']
    assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
  
    sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
    print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
    for index, sub_dir in enumerate(sub_model_dirs):
      arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
      print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
    
    arch2infos, evaluated_indexes = dict(), set()
    for IDX, sub_dir in enumerate(sub_model_dirs):
      ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
      if ckp_path.exists():
        sub_ckps = torch.load(ckp_path, map_location='cpu')
        assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
        xarch2infos = sub_ckps['arch2infos']
        xevalindexs = sub_ckps['evaluated_indexes']
        for eval_index in xevalindexs:
          assert eval_index not in evaluated_indexes and eval_index not in arch2infos
          #arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
          arch2infos[eval_index] = {'full': xarch2infos[eval_index]['full'].state_dict(),
                                    'less': xarch2infos[eval_index]['less'].state_dict()}
          evaluated_indexes.add( eval_index )
        print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)))
      else:
        raise ValueError('Can not find {:}'.format(ckp_path))
        #print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
  
    evaluated_indexes = sorted( list( evaluated_indexes ) )
    print ('Finally, there are {:} architectures that have been trained and evaluated.'.format(len(evaluated_indexes)))
  
    to_save_simply = save_dir / 'simplifies'
    if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
    final_infos = {'meta_archs' : meta_archs,
                   'total_archs': meta_num_archs,
                   'arch2infos' : arch2infos,
                   'evaluated_indexes': evaluated_indexes}
    save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
    torch.save(final_infos, save_file_name)
    print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
  
  
  
  if __name__ == '__main__':
  
    parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--mode'         ,  type=str, choices=['cal', 'merge'],            help='The running mode for this script.')
    parser.add_argument('--base_save_dir',  type=str, default='./output/NAS-BENCH-201-4',  help='The base-name of folder to save checkpoints and log.')
    parser.add_argument('--target_dir'   ,  type=str,                                      help='The target directory.')
    parser.add_argument('--max_node'     ,  type=int, default=4,                           help='The maximum node in a cell.')
    parser.add_argument('--channel'      ,  type=int, default=16,                          help='The number of channels.')
    parser.add_argument('--num_cells'    ,  type=int, default=5,                           help='The number of cells in one stage.')
    args = parser.parse_args()
    
    save_dir  = Path( args.base_save_dir )
    meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
    assert save_dir.exists(),  'invalid save dir path : {:}'.format(save_dir)
    assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
    print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
    basestr   = 'C{:}-N{:}'.format(args.channel, args.num_cells)
    
    if args.mode == 'cal':
      simplify(save_dir, meta_path, basestr, args.target_dir)
    elif args.mode == 'merge':
      merge_all(save_dir, meta_path, basestr)
    else:
      raise ValueError('invalid mode : {:}'.format(args.mode))
  
apis: torch.utils.data.DataLoader torch.load torch.save torch.set_num_threads torch.utils.data.sampler.SubsetRandomSampler