xautodl/exps/NATS-algos/random_wo_share.py

157 lines
5.4 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 xautodl.config_utils import load_config, dict2config, configure2str
from xautodl.datasets import get_datasets, SearchDataset
from xautodl.procedures import (
prepare_seed,
prepare_logger,
save_checkpoint,
copy_checkpoint,
get_optim_scheduler,
)
from xautodl.utils import get_model_infos, obtain_accuracy
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.models import CellStructure, get_search_spaces
from nats_bench import create
def random_topology_func(op_names, max_nodes=4):
# Return a random architecture
def random_architecture():
genotypes = []
for i in range(1, max_nodes):
xlist = []
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
op_name = random.choice(op_names)
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return CellStructure(genotypes)
return random_architecture
def random_size_func(info):
# Return a random architecture
def random_architecture():
channels = []
for i in range(info["numbers"]):
channels.append(str(random.choice(info["candidates"])))
return ":".join(channels)
return random_architecture
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)