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re-organize NATS-Bench

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D-X-Y
2020-08-18 06:29:30 +00:00
parent ffd23a6cbd
commit 3529b993ff
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exps/NATS-Bench/main-sss.py Normal file
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##############################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
##############################################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
##############################################################################
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
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))
#to_evaluate_indexes = list(range(srange[0], srange[1] + 1))
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)
if not isinstance(args.srange, str):
raise ValueError('Invalid scheme for {:}'.format(args.srange))
srangestr = "".join(args.srange.split())
to_evaluate_indexes = set()
for srange in srangestr.split(','):
srange = srange.split('-')
if len(srange) != 2:
raise ValueError('invalid srange : {:}'.format(srange))
assert len(srange[0]) == len(srange[1]) == 5, 'invalid srange : {:}'.format(srange)
srange = (int(srange[0]), int(srange[1]))
if not (0 <= srange[0] <= srange[1] < args.check_N):
raise ValueError('{:} vs {:} vs {:}'.format(srange[0], srange[1], args.check_N))
for i in range(srange[0], srange[1]+1):
to_evaluate_indexes.add(i)
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')

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exps/NATS-Bench/sss-file-manager.py Normal file
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##############################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
##############################################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
##############################################################################
# Usage: python exps/NATS-Bench/sss-file-manager.py --mode check #
##############################################################################
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))