xautodl/exps/NATS-Bench/sss-file-manager.py

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# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.08                          #
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# Usage: python exps/NATS-Bench/sss-file-manager.py --mode check             #
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import os, sys, time, torch, argparse
from typing import List, Text, Dict, Any
from shutil import copyfile
from collections import defaultdict
from copy    import deepcopy
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 dict2config, load_config
from procedures   import bench_evaluate_for_seed
from procedures   import get_machine_info
from datasets     import get_datasets
from log_utils    import Logger, AverageMeter, time_string, convert_secs2time


def obtain_valid_ckp(save_dir: Text, total: int):
  possible_seeds = [777, 888, 999]
  seed2ckps = defaultdict(list)
  miss2ckps = defaultdict(list)
  for i in range(total):
    for seed in possible_seeds:
      path = os.path.join(save_dir, 'arch-{:06d}-seed-{:04d}.pth'.format(i, seed))
      if os.path.exists(path):
        seed2ckps[seed].append(i)
      else:
        miss2ckps[seed].append(i)
  for seed, xlist in seed2ckps.items():
    print('[{:}] [seed={:}] has {:5d}/{:5d} | miss {:5d}/{:5d}'.format(save_dir, seed, len(xlist), total, total-len(xlist), total))
  return dict(seed2ckps), dict(miss2ckps)
    

def copy_data(source_dir, target_dir, meta_path):
  target_dir = Path(target_dir)
  target_dir.mkdir(parents=True, exist_ok=True)
  miss2ckps = torch.load(meta_path)['miss2ckps']
  s2t = {}
  for seed, xlist in miss2ckps.items():
    for i in xlist:
      file_name = 'arch-{:06d}-seed-{:04d}.pth'.format(i, seed)
      source_path = os.path.join(source_dir, file_name)
      target_path = os.path.join(target_dir, file_name)
      if os.path.exists(source_path):
        s2t[source_path] = target_path
  print('Map from {:} to {:}, find {:} missed ckps.'.format(source_dir, target_dir, len(s2t)))
  for s, t in s2t.items():
    copyfile(s, t)


if __name__ == '__main__':
  parser = argparse.ArgumentParser(description='NATS-Bench (size search space) file manager.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('--mode',        type=str, required=True, choices=['check', 'copy'], help='The script mode.')
  parser.add_argument('--save_dir',    type=str, default='output/NATS-Bench-size', help='Folder to save checkpoints and log.')
  parser.add_argument('--check_N',     type=int, default=32768,  help='For safety.')
  # use for train the model
  args = parser.parse_args()
  possible_configs = ['01', '12', '90']
  if args.mode == 'check':
    for config in possible_configs:
      cur_save_dir = '{:}/raw-data-{:}'.format(args.save_dir, config)
      seed2ckps, miss2ckps = obtain_valid_ckp(cur_save_dir, args.check_N)
      torch.save(dict(seed2ckps=seed2ckps, miss2ckps=miss2ckps), '{:}/meta-{:}.pth'.format(args.save_dir, config))
  elif args.mode == 'copy':
    for config in possible_configs:
      cur_save_dir = '{:}/raw-data-{:}'.format(args.save_dir, config)
      cur_copy_dir = '{:}/copy-{:}'.format(args.save_dir, config)
      cur_meta_path = '{:}/meta-{:}.pth'.format(args.save_dir, config)
      if os.path.exists(cur_meta_path):
        copy_data(cur_save_dir, cur_copy_dir, cur_meta_path)
      else:
        print('Do not find : {:}'.format(cur_meta_path))
  else:
    raise ValueError('invalid mode : {:}'.format(args.mode))