xautodl/exps/NATS-Bench/main-sss.py

##############################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
##############################################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07                          #
##############################################################################
# This file is used to train (all) architecture candidate in the size search #
# space in NATS-Bench (sss) with different hyper-parameters.                 #
# When use mode=new, it will automatically detect whether the checkpoint of  #
# a trial exists, if so, it will skip this trial. When use mode=cover, it    #
# will ignore the (possible) existing checkpoint, run each trial, and save.  #
# (NOTE): the topology for all candidates in sss is fixed as:                ######################
# |nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2| #
###################################################################################################
# Please use the script of scripts/NATS-Bench/train-shapes.sh to run.        #
##############################################################################
import os, sys, time, torch, argparse
from typing import List, Text, Dict, Any
from PIL     import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
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
from utils        import split_str2indexes


def evaluate_all_datasets(channels: Text, datasets: List[Text], xpaths: List[Text],
                          splits: List[Text], config_path: Text, seed: int, workers: int, logger):
  machine_info = get_machine_info()
  all_infos = {'info': machine_info}
  all_dataset_keys = []
  # look all the dataset
  for dataset, xpath, split in zip(datasets, xpaths, splits):
    # the train and valid data
    train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
    # load the configuration
    if dataset == 'cifar10' or dataset == 'cifar100':
      split_info  = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
    elif dataset.startswith('ImageNet16'):
      split_info  = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None)
    else:
      raise ValueError('invalid dataset : {:}'.format(dataset))
    config = load_config(config_path, dict(class_num=class_num, xshape=xshape), logger)
    # check whether use the splitted validation set
    if bool(split):
      assert dataset == 'cifar10'
      ValLoaders = {'ori-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)}
      assert len(train_data) == len(split_info.train) + len(split_info.valid), 'invalid length : {:} vs {:} + {:}'.format(len(train_data), len(split_info.train), len(split_info.valid))
      train_data_v2 = deepcopy(train_data)
      train_data_v2.transform = valid_data.transform
      valid_data = train_data_v2
      # data loader
      train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train), num_workers=workers, pin_memory=True)
      valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid), num_workers=workers, pin_memory=True)
      ValLoaders['x-valid'] = valid_loader
    else:
      # data loader
      train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
      valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
      if dataset == 'cifar10':
        ValLoaders = {'ori-test': valid_loader}
      elif dataset == 'cifar100':
        cifar100_splits = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None)
        ValLoaders = {'ori-test': valid_loader,
                      'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True),
                      'x-test'  : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest ), num_workers=workers, pin_memory=True)
                     }
      elif dataset == 'ImageNet16-120':
        imagenet16_splits = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
        ValLoaders = {'ori-test': valid_loader,
                      'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xvalid), num_workers=workers, pin_memory=True),
                      'x-test'  : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xtest ), num_workers=workers, pin_memory=True)
                     }
      else:
        raise ValueError('invalid dataset : {:}'.format(dataset))

    dataset_key = '{:}'.format(dataset)
    if bool(split): dataset_key = dataset_key + '-valid'
    logger.log('Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size))
    logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config))
    for key, value in ValLoaders.items():
      logger.log('Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
    # arch-index= 9930, arch=|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|
    # this genotype is the architecture with the highest accuracy on CIFAR-100 validation set
    genotype = '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|'
    arch_config = dict2config(dict(name='infer.shape.tiny', channels=channels, genotype=genotype, num_classes=class_num), None)
    results = bench_evaluate_for_seed(arch_config, config, train_loader, ValLoaders, seed, logger)
    all_infos[dataset_key] = results
    all_dataset_keys.append( dataset_key )
  all_infos['all_dataset_keys'] = all_dataset_keys
  return all_infos


def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
         splits: List[int], seeds: List[int], nets: List[str], opt_config: Dict[Text, Any],
         to_evaluate_indexes: tuple, cover_mode: bool):

  log_dir = save_dir / 'logs'
  log_dir.mkdir(parents=True, exist_ok=True)
  logger = Logger(str(log_dir), os.getpid(), False)

  logger.log('xargs : seeds      = {:}'.format(seeds))
  logger.log('xargs : cover_mode = {:}'.format(cover_mode))
  logger.log('-' * 100)
  logger.log(
    'Start evaluating range =: {:06d} - {:06d}'.format(min(to_evaluate_indexes), max(to_evaluate_indexes))
   +'({:} in total) / {:06d} with cover-mode={:}'.format(len(to_evaluate_indexes), len(nets), cover_mode))
  for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
    logger.log(
      '--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split))
  logger.log('--->>> optimization config : {:}'.format(opt_config))

  start_time, epoch_time = time.time(), AverageMeter()
  for i, index in enumerate(to_evaluate_indexes):
    channelstr = nets[index]
    logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] {:}'.format(time_string(), i,
                       len(to_evaluate_indexes), index, len(nets), seeds, '-' * 15))
    logger.log('{:} {:} {:}'.format('-' * 15, channelstr, '-' * 15))

    # test this arch on different datasets with different seeds
    has_continue = False
    for seed in seeds:
      to_save_name = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
      if to_save_name.exists():
        if cover_mode:
          logger.log('Find existing file : {:}, remove it before evaluation'.format(to_save_name))
          os.remove(str(to_save_name))
        else:
          logger.log('Find existing file : {:}, skip this evaluation'.format(to_save_name))
          has_continue = True
          continue
      results = evaluate_all_datasets(channelstr,
                                      datasets, xpaths, splits, opt_config, seed,
                                       workers, logger)
      torch.save(results, to_save_name)
      logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}'.format(time_string(), i,
                  len(to_evaluate_indexes), index, len(nets), seeds, to_save_name))
    # measure elapsed time
    if not has_continue: epoch_time.update(time.time() - start_time)
    start_time = time.time()
    need_time = 'Time Left: {:}'.format(convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True))
    logger.log('This arch costs : {:}'.format(convert_secs2time(epoch_time.val, True)))
    logger.log('{:}'.format('*' * 100))
    logger.log('{:}   {:74s}   {:}'.format('*' * 10, '{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}'.format(i, len(
      to_evaluate_indexes), index, len(nets), need_time), '*' * 10))
    logger.log('{:}'.format('*' * 100))

  logger.close()


def traverse_net(candidates: List[int], N: int):
  nets = ['']
  for i in range(N):
    new_nets = []
    for net in nets:
      for C in candidates:
        new_nets.append(str(C) if net == '' else "{:}:{:}".format(net,C))
    nets = new_nets
  return nets


def filter_indexes(xlist, mode, save_dir, seeds):
  all_indexes = []
  for index in xlist:
    if mode == 'cover':
      all_indexes.append(index)
    else:
      for seed in seeds:
        temp_path = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
        if not temp_path.exists():
          all_indexes.append(index)
          break
  print('{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total'.format(time_string(), len(all_indexes), len(xlist)))

  SLURM_PROCID, SLURM_NTASKS = 'SLURM_PROCID', 'SLURM_NTASKS'
  if SLURM_PROCID in os.environ and  SLURM_NTASKS in os.environ:  # run on the slurm
    proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS])
    assert 0 <= proc_id < ntasks, 'invalid proc_id {:} vs ntasks {:}'.format(proc_id, ntasks)
    scales = [int(float(i)/ntasks*len(all_indexes)) for i in range(ntasks)] + [len(all_indexes)]
    per_job = []
    for i in range(ntasks):
      xs, xe = min(max(scales[i],0), len(all_indexes)-1), min(max(scales[i+1]-1,0), len(all_indexes)-1)
      per_job.append((xs, xe))
    for i, srange in enumerate(per_job):
      print('  -->> {:2d}/{:02d} : {:}'.format(i, ntasks, srange))
    current_range = per_job[proc_id]
    all_indexes = [all_indexes[i] for i in range(current_range[0], current_range[1]+1)]
    # set the device id
    device = proc_id % torch.cuda.device_count()
    torch.cuda.set_device(device)
    print('  set the device id = {:}'.format(device))
  print('{:} [FILTER-INDEXES] : after filtering there are {:} architectures in total'.format(time_string(), len(all_indexes)))
  return all_indexes


if __name__ == '__main__':
  parser = argparse.ArgumentParser(description='NATS-Bench (size search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('--mode',        type=str, required=True, choices=['new', 'cover'], 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('--candidateC',  type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
  parser.add_argument('--num_layers',  type=int, default=5,      help='The number of layers in a network.')
  parser.add_argument('--check_N',     type=int, default=32768,  help='For safety.')
  # use for train the model
  parser.add_argument('--workers',     type=int, default=8,      help='The number of data loading workers (default: 2)')
  parser.add_argument('--srange' ,     type=str, required=True,  help='The range of models to be evaluated')
  parser.add_argument('--datasets',    type=str, nargs='+',      help='The applied datasets.')
  parser.add_argument('--xpaths',      type=str, nargs='+',      help='The root path for this dataset.')
  parser.add_argument('--splits',      type=int, nargs='+',      help='The root path for this dataset.')
  parser.add_argument('--hyper',       type=str, default='12', choices=['01', '12', '90'], help='The tag for hyper-parameters.')
  parser.add_argument('--seeds'  ,     type=int, nargs='+',      help='The range of models to be evaluated')
  args = parser.parse_args()

  nets = traverse_net(args.candidateC, args.num_layers)
  if len(nets) != args.check_N:
    raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))

  opt_config = './configs/nas-benchmark/hyper-opts/{:}E.config'.format(args.hyper)
  if not os.path.isfile(opt_config):
    raise ValueError('{:} is not a file.'.format(opt_config))
  save_dir = Path(args.save_dir) / 'raw-data-{:}'.format(args.hyper)
  save_dir.mkdir(parents=True, exist_ok=True)
  to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)

  if not len(args.seeds):
    raise ValueError('invalid length of seeds args: {:}'.format(args.seeds))
  if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
    raise ValueError('invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits)))
  if args.workers <= 0:
    raise ValueError('invalid number of workers : {:}'.format(args.workers))

  target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
  
  assert torch.cuda.is_available(), 'CUDA is not available.'
  torch.backends.cudnn.enabled = True
  torch.backends.cudnn.deterministic = True
  torch.set_num_threads(args.workers)

  main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config, target_indexes, args.mode == 'cover')