135 lines
4.8 KiB
Python
135 lines
4.8 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:
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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 (
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prepare_seed,
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prepare_logger,
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save_checkpoint,
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copy_checkpoint,
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get_optim_scheduler,
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)
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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, "nats-bench")
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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(
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arch, xargs.dataset, hp="12"
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)
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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(
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"[{:03d}] : {:} : accuracy = {:.2f}%".format(len(history), arch, accuracy)
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)
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current_best_index.append(api.query_index_by_arch(best_arch))
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logger.log(
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"{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s.".format(
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time_string(), best_arch, best_acc, len(history), total_time_cost[-1]
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)
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)
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info = api.query_info_str_by_arch(
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best_arch, "200" if xargs.search_space == "tss" else "90"
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)
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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(
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"--dataset",
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type=str,
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choices=["cifar10", "cifar100", "ImageNet16-120"],
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help="Choose between Cifar10/100 and ImageNet-16.",
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)
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parser.add_argument(
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"--search_space",
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type=str,
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choices=["tss", "sss"],
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help="Choose the search space.",
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)
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parser.add_argument(
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"--time_budget",
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type=int,
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default=20000,
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help="The total time cost budge for searching (in seconds).",
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)
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parser.add_argument(
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"--loops_if_rand", type=int, default=500, help="The total runs for evaluation."
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)
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# log
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./output/search",
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help="Folder to save checkpoints and log.",
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)
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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(
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"{:}-{:}".format(args.save_dir, args.search_space),
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"{:}-T{:}".format(args.dataset, args.time_budget),
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"RANDOM",
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)
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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, "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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