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Update REA, REINFORCE, and RANDOM

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D-X-Y
2020-07-14 06:10:34 +00:00
parent ebad9197f7
commit 168b08d9e6
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exps/algos-v2/bohb.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
###################################################################
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
# required to install hpbandster ##################################
# pip install hpbandster ##################################
###################################################################
# python exps/algos-v2/bohb.py --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
###################################################################
import os, sys, time, random, argparse
from copy import deepcopy
from pathlib import Path
import torch
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
from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API as API
from models import CellStructure, get_search_spaces
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
import ConfigSpace
from hpbandster.optimizers.bohb import BOHB
import hpbandster.core.nameserver as hpns
from hpbandster.core.worker import Worker
def get_topology_config_space(search_space, max_nodes=4):
cs = ConfigSpace.ConfigurationSpace()
#edge2index = {}
for i in range(1, max_nodes):
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space))
return cs
def get_size_config_space(search_space):
cs = ConfigSpace.ConfigurationSpace()
import pdb; pdb.set_trace()
#edge2index = {}
for i in range(1, max_nodes):
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space))
return cs
def config2topology_func(max_nodes=4):
def config2structure(config):
genotypes = []
for i in range(1, max_nodes):
xlist = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
op_name = config[node_str]
xlist.append((op_name, j))
genotypes.append( tuple(xlist) )
return CellStructure( genotypes )
return config2structure
class MyWorker(Worker):
def __init__(self, *args, convert_func=None, dataname=None, nas_bench=None, time_budget=None, **kwargs):
super().__init__(*args, **kwargs)
self.convert_func = convert_func
self._dataname = dataname
self._nas_bench = nas_bench
self.time_budget = time_budget
self.seen_archs = []
self.sim_cost_time = 0
self.real_cost_time = 0
self.is_end = False
def get_the_best(self):
assert len(self.seen_archs) > 0
best_index, best_acc = -1, None
for arch_index in self.seen_archs:
info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp='200', is_random=True)
vacc = info['valid-accuracy']
if best_acc is None or best_acc < vacc:
best_acc = vacc
best_index = arch_index
assert best_index != -1
return best_index
def compute(self, config, budget, **kwargs):
start_time = time.time()
structure = self.convert_func( config )
arch_index = self._nas_bench.query_index_by_arch( structure )
info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp='200', is_random=True)
cur_time = info['train-all-time'] + info['valid-per-time']
cur_vacc = info['valid-accuracy']
self.real_cost_time += (time.time() - start_time)
if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
self.sim_cost_time += cur_time
self.seen_archs.append( arch_index )
return ({'loss': 100 - float(cur_vacc),
'info': {'seen-arch' : len(self.seen_archs),
'sim-test-time' : self.sim_cost_time,
'current-arch' : arch_index}
})
else:
self.is_end = True
return ({'loss': 100,
'info': {'seen-arch' : len(self.seen_archs),
'sim-test-time' : self.sim_cost_time,
'current-arch' : None}
})
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))
search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
if xargs.search_space == 'tss':
cs = get_topology_config_space(xargs.max_nodes, search_space)
config2structure = config2topology_func(xargs.max_nodes)
else:
cs = get_size_config_space(xargs.max_nodes, search_space)
import pdb; pdb.set_trace()
hb_run_id = '0'
NS = hpns.NameServer(run_id=hb_run_id, host='localhost', port=0)
ns_host, ns_port = NS.start()
num_workers = 1
workers = []
for i in range(num_workers):
w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, dataname=dataname, nas_bench=nas_bench, time_budget=xargs.time_budget, run_id=hb_run_id, id=i)
w.run(background=True)
workers.append(w)
start_time = time.time()
bohb = BOHB(configspace=cs,
run_id=hb_run_id,
eta=3, min_budget=12, max_budget=200,
nameserver=ns_host,
nameserver_port=ns_port,
num_samples=xargs.num_samples,
random_fraction=xargs.random_fraction, bandwidth_factor=xargs.bandwidth_factor,
ping_interval=10, min_bandwidth=xargs.min_bandwidth)
results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
real_cost_time = time.time() - start_time
id2config = results.get_id2config_mapping()
incumbent = results.get_incumbent_id()
logger.log('Best found configuration: {:} within {:.3f} s'.format(id2config[incumbent]['config'], real_cost_time))
best_arch = config2structure( id2config[incumbent]['config'] )
info = nas_bench.query_by_arch(best_arch, '200')
if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
else : logger.log('{:}'.format(info))
logger.log('-'*100)
logger.log('workers : {:.1f}s with {:} archs'.format(workers[0].time_budget, len(workers[0].seen_archs)))
logger.close()
return logger.log_dir, nas_bench.query_index_by_arch( best_arch ), real_cost_time
if __name__ == '__main__':
parser = argparse.ArgumentParser("BOHB: Robust and Efficient Hyperparameter Optimization at Scale")
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
# general arg
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.')
# BOHB
parser.add_argument('--strategy', default="sampling", type=str, nargs='?', help='optimization strategy for the acquisition function')
parser.add_argument('--min_bandwidth', default=.3, type=float, nargs='?', help='minimum bandwidth for KDE')
parser.add_argument('--num_samples', default=64, type=int, nargs='?', help='number of samples for the acquisition function')
parser.add_argument('--random_fraction', default=.33, type=float, nargs='?', help='fraction of random configurations')
parser.add_argument('--bandwidth_factor', default=3, type=int, nargs='?', help='factor multiplied to the bandwidth')
parser.add_argument('--n_iters', default=300, type=int, nargs='?', help='number of iterations for optimization method')
# log
parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
parser.add_argument('--rand_seed', type=int, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'BOHB')
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)

4
exps/algos-v2/regularized_ea.py
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@@ -214,8 +214,7 @@ def main(xargs, api):
logger.log('-'*30 + ' start searching with the time budget of {:} s'.format(xargs.time_budget))
history, current_best_index, total_times = regularized_evolution(xargs.ea_cycles, xargs.ea_population, xargs.ea_sample_size, xargs.time_budget, random_arch, mutate_arch, api, xargs.dataset)
logger.log('{:} regularized_evolution finish with history of {:} arch with {:.1f} s (real-cost={:.2f} s).'.format(time_string(), len(history), total_times[-1], time.time()-x_start_time))
best_arch = max(history, key=lambda i: i.accuracy)
best_arch = best_arch.arch
best_arch = max(history, key=lambda x: x[0])[1]
logger.log('{:} best arch is {:}'.format(time_string(), best_arch))
info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
@@ -249,6 +248,7 @@ if __name__ == '__main__':
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'R-EA-SS{:}'.format(args.ea_sample_size))
print('save-dir : {:}'.format(args.save_dir))
print('xargs : {:}'.format(args))
if args.rand_seed < 0:
save_dir, all_info = None, collections.OrderedDict()

4
exps/algos-v2/run-all.sh
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@@ -11,8 +11,8 @@ for dataset in ${datasets}
do
for search_space in ${search_spaces}
do
python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
# python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
# python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
done
done