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Update NATS-Bench (tss version 0.8)

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This commit is contained in:
D-X-Y
2020-08-28 08:31:53 +00:00
parent c68458f66c
commit 2c86d6aa67
5 changed files with 172 additions and 111 deletions
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22
exps/NATS-Bench/main-sss.py
View File

@@ -27,6 +27,7 @@ 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],
@@ -107,7 +108,6 @@ def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
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))
@@ -115,7 +115,6 @@ def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
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):
@@ -136,7 +135,9 @@ def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
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)
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))
@@ -224,20 +225,7 @@ if __name__ == '__main__':
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)
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))

189
exps/NATS-Bench/main-tss.py
View File

@@ -5,13 +5,20 @@
##############################################################################
# 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=meta,
###
# 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. #
##############################################################################
# 1, generate meta data: #
# 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
@@ -19,16 +26,18 @@ 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 load_config
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, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger):
machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
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
@@ -37,19 +46,12 @@ def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_c
train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
# load the configuration
if dataset == 'cifar10' or dataset == 'cifar100':
if use_less: config_path = 'configs/nas-benchmark/LESS.config'
else : config_path = 'configs/nas-benchmark/CIFAR.config'
split_info = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
elif dataset.startswith('ImageNet16'):
if use_less: config_path = 'configs/nas-benchmark/LESS.config'
else : config_path = 'configs/nas-benchmark/ImageNet-16.config'
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, \
{'class_num': class_num,
'xshape' : xshape}, \
logger)
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'
@@ -89,6 +91,8 @@ def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_c
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 )
@@ -96,71 +100,59 @@ def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_c
return all_infos
def main(save_dir, workers, datasets, xpaths, splits, use_less, srange, arch_index, seeds, cover_mode, meta_info, arch_config):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
#torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
torch.set_num_threads( workers )
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]):
assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
log_dir = save_dir / 'logs'
log_dir.mkdir(parents=True, exist_ok=True)
logger = Logger(str(log_dir), os.getpid(), False)
if use_less:
sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}-LESS'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
else:
sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
logger = Logger(str(sub_dir), 0, False)
all_archs = meta_info['archs']
assert srange[1] < meta_info['total'], 'invalid range : {:}-{:} vs. {:}'.format(srange[0], srange[1], meta_info['total'])
assert arch_index == -1 or srange[0] <= arch_index <= srange[1], 'invalid range : {:} vs. {:} vs. {:}'.format(srange[0], arch_index, srange[1])
if arch_index == -1:
to_evaluate_indexes = list(range(srange[0], srange[1]+1))
else:
to_evaluate_indexes = [arch_index]
logger.log('xargs : seeds = {:}'.format(seeds))
logger.log('xargs : arch_index = {:}'.format(arch_index))
logger.log('xargs : cover_mode = {:}'.format(cover_mode))
logger.log('-'*100)
logger.log('Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}'.format(srange[0], arch_index, srange[1], meta_info['total'], 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('--->>> architecture config : {:}'.format(arch_config))
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 = all_archs[index]
logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seeds, '-'*15))
#logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
logger.log('{:} {:} {:}'.format('-'*15, arch, '-'*15))
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 = sub_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
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 :
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, use_less, seed, \
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('{:} --evaluate-- {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seed, 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, meta_info['total'], need_time), '*'*10))
logger.log('{:}'.format('*'*100))
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()
@@ -256,28 +248,34 @@ def generate_meta_info(save_dir, max_node, divide=40):
torch.save(info, save_name)
print ('save the meta file into {:}'.format(save_name))
"""
script_name_full = save_dir / 'BENCH-201-N{:}.opt-full.script'.format(max_node)
script_name_less = save_dir / 'BENCH-201-N{:}.opt-less.script'.format(max_node)
full_file = open(str(script_name_full), 'w')
less_file = open(str(script_name_less), 'w')
gaps = total_arch // divide
for start in range(0, total_arch, gaps):
xend = min(start+gaps, total_arch)
full_file.write('bash ./scripts-search/NAS-Bench-201/train-models.sh 0 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
less_file.write('bash ./scripts-search/NAS-Bench-201/train-models.sh 1 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
print ('save the training script into {:} and {:}'.format(script_name_full, script_name_less))
full_file.close()
less_file.close()
script_name = save_dir / 'meta-node-{:}.cal-script.txt'.format(max_node)
macro = 'OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0'
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/NAS-Bench-201/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))
"""
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()]
@@ -291,11 +289,12 @@ if __name__ == '__main__':
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('--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)
@@ -308,16 +307,28 @@ if __name__ == '__main__':
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:
meta_path = Path(args.save_dir) / 'meta-node-{:}.pth'.format(args.max_node)
assert meta_path.exists(), '{:} does not exist.'.format(meta_path)
meta_info = torch.load( meta_path )
# check whether args is ok
assert len(args.srange) == 2 and args.srange[0] <= args.srange[1], 'invalid length of srange args: {:}'.format(args.srange)
assert len(args.seeds) > 0, 'invalid length of seeds args: {:}'.format(args.seeds)
assert len(args.datasets) == len(args.xpaths) == len(args.splits), 'invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))
assert args.workers > 0, 'invalid number of workers : {:}'.format(args.workers)
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))
main(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, args.hyper, \
tuple(args.srange), args.arch_index, tuple(args.seeds), \
args.mode == 'cover', meta_info, \
{'channel': args.channel, 'num_cells': args.num_cells})
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})

1
lib/utils/__init__.py
View File

@@ -4,3 +4,4 @@ from .flop_benchmark import get_model_infos, count_parameters_in_MB
from .affine_utils import normalize_points, denormalize_points
from .affine_utils import identity2affine, solve2theta, affine2image
from .hash_utils import get_md5_file
from .str_utils import split_str2indexes

18
lib/utils/str_utils.py Normal file
View File

@@ -0,0 +1,18 @@
def split_str2indexes(string: str, max_check: int, length_limit=5):
if not isinstance(string, str):
raise ValueError('Invalid scheme for {:}'.format(string))
srangestr = "".join(string.split())
indexes = set()
for srange in srangestr.split(','):
srange = srange.split('-')
if len(srange) != 2:
raise ValueError('invalid srange : {:}'.format(srange))
if length_limit is not None:
assert len(srange[0]) == len(srange[1]) == length_limit, 'invalid srange : {:}'.format(srange)
srange = (int(srange[0]), int(srange[1]))
if not (0 <= srange[0] <= srange[1] < max_check):
raise ValueError('{:} vs {:} vs {:}'.format(srange[0], srange[1], max_check))
for i in range(srange[0], srange[1]+1):
indexes.add(i)
return indexes

43
scripts/NATS-Bench/train-topology.sh Normal file
View File

@@ -0,0 +1,43 @@
#!/bin/bash
##############################################################################
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
##############################################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
##############################################################################
# CUDA_VISIBLE_DEVICES=0 bash scripts/NATS-Bench/train-topology.sh 00000-05000 12 777
# bash ./scripts/NATS-Bench/train-topology.sh 05001-10000 12 777
# bash ./scripts/NATS-Bench/train-topology.sh 10001-14500 12 777
# bash ./scripts/NATS-Bench/train-topology.sh 14501-15624 12 777
#
##############################################################################
echo script name: $0
echo $# arguments
if [ "$#" -ne 3 ] ;then
echo "Input illegal number of parameters " $#
echo "Need 3 parameters for start-and-end, hyper-parameters-opt-file, and seeds"
exit 1
fi
if [ "$TORCH_HOME" = "" ]; then
echo "Must set TORCH_HOME envoriment variable for data dir saving"
exit 1
else
echo "TORCH_HOME : $TORCH_HOME"
fi
srange=$1
opt=$2
all_seeds=$3
cpus=4
save_dir=./output/NATS-Bench-topology/
OMP_NUM_THREADS=${cpus} python exps/NATS-Bench/main-tss.py \
--mode new --srange ${srange} --hyper ${opt} --save_dir ${save_dir} \
--datasets cifar10 cifar10 cifar100 ImageNet16-120 \
--splits 1 0 0 0 \
--xpaths $TORCH_HOME/cifar.python \
$TORCH_HOME/cifar.python \
$TORCH_HOME/cifar.python \
$TORCH_HOME/cifar.python/ImageNet16 \
--workers ${cpus} \
--seeds ${all_seeds}