autodl-projects/exps/NATS-algos/random_wo_share.py
2021-03-17 09:25:58 +00:00

106 lines
4.6 KiB
Python

##################################################
# 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)