update for NAS-Bench-102

exps/NAS-Bench-102/functions.py

@@ -10,7 +10,36 @@ from models       import get_cell_based_tiny_net
 
 
 
-__all__ = ['evaluate_for_seed']
+__all__ = ['evaluate_for_seed', 'pure_evaluate']
+
+
+
+def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
+  data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
+  losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
+  latencies = []
+  network.eval()
+  with torch.no_grad():
+    end = time.time()
+    for i, (inputs, targets) in enumerate(xloader):
+      targets = targets.cuda(non_blocking=True)
+      inputs  = inputs.cuda(non_blocking=True)
+      data_time.update(time.time() - end)
+      # forward
+      features, logits = network(inputs)
+      loss             = criterion(logits, targets)
+      batch_time.update(time.time() - end)
+      if batch is None or batch == inputs.size(0):
+        batch = inputs.size(0)
+        latencies.append( batch_time.val - data_time.val )
+      # record loss and accuracy
+      prec1, prec5 = obtain_accuracy(logits.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))
+      end = time.time()
+  if len(latencies) > 2: latencies = latencies[1:]
+  return losses.avg, top1.avg, top5.avg, latencies
 
 
 

exps/NAS-Bench-102/main.py

@@ -1,4 +1,6 @@
 ##################################################
+# NAS-Bench-102 ##################################
+##################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
 import os, sys, time, torch, random, argparse
@@ -265,13 +267,14 @@ def generate_meta_info(save_dir, max_node, divide=40):
   with open(str(script_name), 'w') as cfile:
     for start in range(0, total_arch, gaps):
       xend = min(start+gaps, total_arch)
-      cfile.write('{:} python exps/AA-NAS-statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n'.format(macro, start, xend-1))
+      cfile.write('{:} python exps/NAS-Bench-102/statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n'.format(macro, start, xend-1))
   print ('save the post-processing script into {:}'.format(script_name))
 
 
 if __name__ == '__main__':
   #mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
-  parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  #parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
   parser.add_argument('--mode'   ,     type=str,   required=True,  help='The script mode.')
   parser.add_argument('--save_dir',    type=str,                   help='Folder to save checkpoints and log.')
   parser.add_argument('--max_node',    type=int,                   help='The maximum node in a cell.')

exps/NAS-Bench-102/statistics.py

@@ -0,0 +1,295 @@
+##################################################
+# 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-102 related module or function
+from models       import CellStructure, get_cell_based_tiny_net
+from nas_102_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-102', 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-102-4',  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))

exps/algos/BOHB.py

@@ -17,7 +17,7 @@ from datasets     import get_datasets, SearchDataset
 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 aa_nas_api   import AANASBenchAPI
+from nas_102_api  import NASBench102API as API
 from models       import CellStructure, get_search_spaces
 from R_EA import train_and_eval
 # BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
@@ -112,7 +112,7 @@ def main(xargs, nas_bench):
   num_workers = 1
 
   #nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
-  logger.log('{:} Create AA-NAS-BENCH-API DONE'.format(time_string()))
+  #logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
   workers = []
   for i in range(num_workers):
     w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, nas_bench=nas_bench, run_id=hb_run_id, id=i)
@@ -179,7 +179,7 @@ if __name__ == '__main__':
     nas_bench = None
   else:
     print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
-    nas_bench = AANASBenchAPI(args.arch_nas_dataset)
+    nas_bench = API(args.arch_nas_dataset)
   if args.rand_seed < 0:
     save_dir, all_indexes, num = None, [], 500
     for i in range(num):

exps/algos/RANDOM.py

@@ -15,7 +15,7 @@ from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_che
 from utils        import get_model_infos, obtain_accuracy
 from log_utils    import AverageMeter, time_string, convert_secs2time
 from models       import get_search_spaces
-from aa_nas_api   import AANASBenchAPI
+from nas_102_api  import NASBench102API as API
 from R_EA         import train_and_eval, random_architecture_func
 
 
@@ -92,7 +92,7 @@ if __name__ == '__main__':
     nas_bench = None
   else:
     print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
-    nas_bench = AANASBenchAPI(args.arch_nas_dataset)
+    nas_bench = API(args.arch_nas_dataset)
   if args.rand_seed < 0:
     save_dir, all_indexes, num = None, [], 500
     for i in range(num):

exps/algos/R_EA.py

@@ -16,7 +16,7 @@ from datasets     import get_datasets, SearchDataset
 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 aa_nas_api   import AANASBenchAPI
+from nas_102_api  import NASBench102API as API
 from models       import CellStructure, get_search_spaces
 
 
@@ -230,7 +230,7 @@ if __name__ == '__main__':
     nas_bench = None
   else:
     print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
-    nas_bench = AANASBenchAPI(args.arch_nas_dataset)
+    nas_bench = API(args.arch_nas_dataset)
   if args.rand_seed < 0:
     save_dir, all_indexes, num = None, [], 500
     for i in range(num):

exps/algos/reinforce.py

@@ -17,7 +17,7 @@ from datasets     import get_datasets, SearchDataset
 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 aa_nas_api   import AANASBenchAPI
+from nas_102_api  import NASBench102API
 from models       import CellStructure, get_search_spaces
 from R_EA import train_and_eval
 

exps/vis/test.py

@@ -0,0 +1,27 @@
+# python ./exps/vis/test.py
+import os, sys
+from pathlib import Path
+import torch
+import numpy as np
+from collections import OrderedDict
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+
+
+def test_nas_api():
+  from nas_102_api import ArchResults
+  xdata   = torch.load('/home/dxy/FOR-RELEASE/NAS-Projects/output/NAS-BENCH-102-4/simplifies/architectures/000157-FULL.pth')
+  for key in ['full', 'less']:
+    print ('\n------------------------- {:} -------------------------'.format(key))
+    archRes = ArchResults.create_from_state_dict(xdata[key])
+    print(archRes)
+    print(archRes.arch_idx_str())
+    print(archRes.get_dataset_names())
+    print(archRes.get_comput_costs('cifar10-valid'))
+    # get the metrics
+    print(archRes.get_metrics('cifar10-valid', 'x-valid', None, False))
+    print(archRes.get_metrics('cifar10-valid', 'x-valid', None,  True))
+    print(archRes.query('cifar10-valid', 777))
+
+if __name__ == '__main__':
+  test_nas_api()