92 lines
4.4 KiB
Python
92 lines
4.4 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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##############################################################################
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# Random Search for Hyper-Parameter Optimization, JMLR 2012 ##################
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##############################################################################
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# python ./exps/NATS-algos/random_wo_share.py --dataset cifar10 --search_space tss
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# python ./exps/NATS-algos/random_wo_share.py --dataset cifar100 --search_space tss
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# python ./exps/NATS-algos/random_wo_share.py --dataset ImageNet16-120 --search_space tss
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##############################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np, collections
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_search_spaces
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from nats_bench import create
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from regularized_ea import random_topology_func, random_size_func
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def main(xargs, api):
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torch.set_num_threads(4)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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logger.log('{:} use api : {:}'.format(time_string(), api))
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api.reset_time()
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search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
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if xargs.search_space == 'tss':
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random_arch = random_topology_func(search_space)
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else:
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random_arch = random_size_func(search_space)
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best_arch, best_acc, total_time_cost, history = None, -1, [], []
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current_best_index = []
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while len(total_time_cost) == 0 or total_time_cost[-1] < xargs.time_budget:
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arch = random_arch()
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accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, hp='12')
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total_time_cost.append(total_cost)
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history.append(arch)
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if best_arch is None or best_acc < accuracy:
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best_acc, best_arch = accuracy, arch
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logger.log('[{:03d}] : {:} : accuracy = {:.2f}%'.format(len(history), arch, accuracy))
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current_best_index.append(api.query_index_by_arch(best_arch))
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logger.log('{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s.'.format(time_string(), best_arch, best_acc, len(history), total_time_cost[-1]))
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info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
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logger.log('{:}'.format(info))
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logger.log('-'*100)
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logger.close()
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return logger.log_dir, current_best_index, total_time_cost
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Random NAS")
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parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
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parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).')
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parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
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# log
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parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
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parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
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args = parser.parse_args()
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api = create(None, args.search_space, fast_mode=True, verbose=False)
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args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'RANDOM')
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print('save-dir : {:}'.format(args.save_dir))
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if args.rand_seed < 0:
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save_dir, all_info = None, collections.OrderedDict()
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for i in range(args.loops_if_rand):
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print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
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args.rand_seed = random.randint(1, 100000)
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save_dir, all_archs, all_total_times = main(args, api)
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all_info[i] = {'all_archs': all_archs,
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'all_total_times': all_total_times}
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save_path = save_dir / 'results.pth'
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print('save into {:}'.format(save_path))
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torch.save(all_info, save_path)
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else:
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main(args, api)
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