##############################################################################
# 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 topology #
# search space in NATS-Bench (tss) 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. #
##############################################################################
# Please use the script of scripts/NATS-Bench/train-topology.sh to run. #
# bash scripts/NATS-Bench/train-topology.sh 00000-15624 12 777 #
# bash scripts/NATS-Bench/train-topology.sh 00000-15624 200 '777 888 999' #
# #
################ #
# [Deprecated Function: Generate the meta information] #
# python ./exps/NATS-Bench/main-tss.py --mode meta #
##############################################################################
import os, sys, time, torch, random, 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 models import CellStructure, CellArchitectures, get_search_spaces
from utils import split_str2indexes
def evaluate_all_datasets(arch: Text, datasets: List[Text], xpaths: List[Text],
splits: List[Text], config_path: Text, seed: int, raw_arch_config, workers, logger):
machine_info, raw_arch_config = get_machine_info(), deepcopy(raw_arch_config)
all_infos = {'info': machine_info}
all_dataset_keys = []
# look all the datasets
for dataset, xpath, split in zip(datasets, xpaths, splits):
# train 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 splited 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_config = dict2config(dict(name='infer.tiny', C=raw_arch_config['channel'], N=raw_arch_config['num_cells'],
genotype=arch, num_classes=config.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, arch_config: Dict[Text, Any]):
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):
arch = 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, arch, '-' * 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(CellStructure.str2structure(arch),
datasets, xpaths, splits, opt_config, seed,
arch_config, 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 train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
#torch.backends.cudnn.benchmark = True
torch.set_num_threads( workers )
save_dir = Path(save_dir) / 'specifics' / '{:}-{:}-{:}-{:}'.format('LESS' if use_less else 'FULL', model_str, arch_config['channel'], arch_config['num_cells'])
logger = Logger(str(save_dir), 0, False)
if model_str in CellArchitectures:
arch = CellArchitectures[model_str]
logger.log('The model string is found in pre-defined architecture dict : {:}'.format(model_str))
else:
try:
arch = CellStructure.str2structure(model_str)
except:
raise ValueError('Invalid model string : {:}. It can not be found or parsed.'.format(model_str))
assert arch.check_valid_op(get_search_spaces('cell', 'full')), '{:} has the invalid op.'.format(arch)
logger.log('Start train-evaluate {:}'.format(arch.tostr()))
logger.log('arch_config : {:}'.format(arch_config))
start_time, seed_time = time.time(), AverageMeter()
for _is, seed in enumerate(seeds):
logger.log('\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------'.format(_is, len(seeds), seed))
to_save_name = save_dir / 'seed-{:04d}.pth'.format(seed)
if to_save_name.exists():
logger.log('Find the existing file {:}, directly load!'.format(to_save_name))
checkpoint = torch.load(to_save_name)
else:
logger.log('Does not find the existing file {:}, train and evaluate!'.format(to_save_name))
checkpoint = evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger)
torch.save(checkpoint, to_save_name)
# log information
logger.log('{:}'.format(checkpoint['info']))
all_dataset_keys = checkpoint['all_dataset_keys']
for dataset_key in all_dataset_keys:
logger.log('\n{:} dataset : {:} {:}'.format('-'*15, dataset_key, '-'*15))
dataset_info = checkpoint[dataset_key]
#logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
logger.log('Flops = {:} MB, Params = {:} MB'.format(dataset_info['flop'], dataset_info['param']))
logger.log('config : {:}'.format(dataset_info['config']))
logger.log('Training State (finish) = {:}'.format(dataset_info['finish-train']))
last_epoch = dataset_info['total_epoch'] - 1
train_acc1es, train_acc5es = dataset_info['train_acc1es'], dataset_info['train_acc5es']
valid_acc1es, valid_acc5es = dataset_info['valid_acc1es'], dataset_info['valid_acc5es']
logger.log('Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%'.format(train_acc1es[last_epoch], train_acc5es[last_epoch], 100-train_acc1es[last_epoch], valid_acc1es[last_epoch], valid_acc5es[last_epoch], 100-valid_acc1es[last_epoch]))
# measure elapsed time
seed_time.update(time.time() - start_time)
start_time = time.time()
need_time = 'Time Left: {:}'.format( convert_secs2time(seed_time.avg * (len(seeds)-_is-1), True) )
logger.log('\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}'.format(_is, len(seeds), seed, need_time))
logger.close()
def generate_meta_info(save_dir, max_node, divide=40):
aa_nas_bench_ss = get_search_spaces('cell', 'nas-bench-201')
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
random.seed( 88 ) # please do not change this line for reproducibility
random.shuffle( archs )
# to test fixed-random shuffle
#print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
#print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
assert archs[0 ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
assert archs[9 ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
total_arch = len(archs)
num = 50000
indexes_5W = list(range(num))
random.seed( 1021 )
random.shuffle( indexes_5W )
train_split = sorted( list(set(indexes_5W[:num//2])) )
valid_split = sorted( list(set(indexes_5W[num//2:])) )
assert len(train_split) + len(valid_split) == num
assert train_split[0] == 0 and train_split[10] == 26 and train_split[111] == 203 and valid_split[0] == 1 and valid_split[10] == 18 and valid_split[111] == 242, '{:} {:} {:} - {:} {:} {:}'.format(train_split[0], train_split[10], train_split[111], valid_split[0], valid_split[10], valid_split[111])
splits = {num: {'train': train_split, 'valid': valid_split} }
info = {'archs' : [x.tostr() for x in archs],
'total' : total_arch,
'max_node' : max_node,
'splits': splits}
save_dir = Path(save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
save_name = save_dir / 'meta-node-{:}.pth'.format(max_node)
assert not save_name.exists(), '{:} already exist'.format(save_name)
torch.save(info, save_name)
print ('save the meta file into {:}'.format(save_name))
def traverse_net(max_node):
aa_nas_bench_ss = get_search_spaces('cell', 'nats-bench')
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
random.seed( 88 ) # please do not change this line for reproducibility
random.shuffle( archs )
assert archs[0 ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
assert archs[9 ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
return [x.tostr() for x in archs]
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)))
return all_indexes
if __name__ == '__main__':
# mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
parser = argparse.ArgumentParser(description='NATS-Bench (topology search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mode' , type=str, required=True, help='The script mode.')
parser.add_argument('--save_dir', type=str, default='output/NATS-Bench-topology', help='Folder to save checkpoints and log.')
parser.add_argument('--max_node', type=int, default=4, help='The maximum node in a cell (please do not change it).')
# use for train the model
parser.add_argument('--workers', type=int, default=8, help='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', '200'], help='The tag for hyper-parameters.')
parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated')
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.')
parser.add_argument('--check_N', type=int, default=15625, help='For safety.')
args = parser.parse_args()
assert args.mode in ['meta', 'new', 'cover'] or args.mode.startswith('specific-'), 'invalid mode : {:}'.format(args.mode)
if args.mode == 'meta':
generate_meta_info(args.save_dir, args.max_node)
elif args.mode.startswith('specific'):
assert len(args.mode.split('-')) == 2, 'invalid mode : {:}'.format(args.mode)
model_str = args.mode.split('-')[1]
train_single_model(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, args.use_less>0, \
tuple(args.seeds), model_str, {'channel': args.channel, 'num_cells': args.num_cells})
else:
nets = traverse_net(args.max_node)
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', \
{'name': 'infer.tiny', 'channel': args.channel, 'num_cells': args.num_cells})