Update NATS-Bench (sss version 1.0)
This commit is contained in:
D-X-Y
323
exps/NATS-Bench/main-tss.py
Normal file
323
exps/NATS-Bench/main-tss.py
Normal file
@@ -0,0 +1,323 @@
|
||||
##############################################################################
|
||||
# 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=meta,
|
||||
###
|
||||
##############################################################################
|
||||
# 1, generate meta data: #
|
||||
# python ./exps/NATS-Bench/main-tss.py --mode meta #
|
||||
##############################################################################
|
||||
import os, sys, time, torch, random, argparse
|
||||
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 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
|
||||
|
||||
|
||||
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)
|
||||
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':
|
||||
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)
|
||||
# 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)))
|
||||
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, 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 )
|
||||
|
||||
assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
|
||||
|
||||
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))
|
||||
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))
|
||||
|
||||
|
||||
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))
|
||||
|
||||
# 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)
|
||||
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, use_less, 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))
|
||||
# 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))
|
||||
|
||||
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))
|
||||
|
||||
"""
|
||||
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))
|
||||
"""
|
||||
|
||||
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', '90'], 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.')
|
||||
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:
|
||||
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)
|
||||
|
||||
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})
|
||||
Reference in New Issue
Block a user