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