################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ################################################## # required to install hpbandster ################# # bash ./scripts-search/algos/BOHB.sh -1 # ################################################## import os, sys, time, glob, random, argparse import numpy as np, collections from copy import deepcopy from pathlib import Path import torch import torch.nn as nn 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 nas_102_api import NASBench102API 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_configuration_space(max_nodes, search_space): 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 config2structure_func(max_nodes): 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, nas_bench=None, time_budget=None, **kwargs): super().__init__(*args, **kwargs) self.convert_func = convert_func 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, 'cifar10-valid', None, 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, 'cifar10-valid', None, 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, nas_bench): assert torch.cuda.is_available(), 'CUDA is not available.' torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads( xargs.workers ) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10' if xargs.data_path is not None: train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1) split_Fpath = 'configs/nas-benchmark/cifar-split.txt' cifar_split = load_config(split_Fpath, None, None) train_split, valid_split = cifar_split.train, cifar_split.valid logger.log('Load split file from {:}'.format(split_Fpath)) config_path = 'configs/nas-benchmark/algos/R-EA.config' config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger) # To split data train_data_v2 = deepcopy(train_data) train_data_v2.transform = valid_data.transform valid_data = train_data_v2 search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split) # data loader train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split) , num_workers=xargs.workers, pin_memory=True) valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True) logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size)) logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config)) extra_info = {'config': config, 'train_loader': train_loader, 'valid_loader': valid_loader} else: config_path = 'configs/nas-benchmark/algos/R-EA.config' config = load_config(config_path, None, logger) logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config)) extra_info = {'config': config, 'train_loader': None, 'valid_loader': None} # nas dataset load assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset) search_space = get_search_spaces('cell', xargs.search_space_name) cs = get_configuration_space(xargs.max_nodes, search_space) config2structure = config2structure_func(xargs.max_nodes) 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 #nas_bench = AANASBenchAPI(xargs.arch_nas_dataset) #logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string())) workers = [] for i in range(num_workers): w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, 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 ) 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("Regularized Evolution Algorithm") parser.add_argument('--data_path', type=str, help='Path to dataset') parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.') # channels and number-of-cells parser.add_argument('--search_space_name', type=str, help='The search space name.') parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.') parser.add_argument('--channel', type=int, help='The number of channels.') parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.') parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).') # 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=100, type=int, nargs='?', help='number of iterations for optimization method') # log parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)') parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.') parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).') parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)') parser.add_argument('--rand_seed', type=int, help='manual seed') args = parser.parse_args() #if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000) if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset): nas_bench = None else: print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset)) nas_bench = API(args.arch_nas_dataset) if args.rand_seed < 0: save_dir, all_indexes, num, all_times = None, [], 500, [] for i in range(num): print ('{:} : {:03d}/{:03d}'.format(time_string(), i, num)) args.rand_seed = random.randint(1, 100000) save_dir, index, ctime = main(args, nas_bench) all_indexes.append( index ) all_times.append( ctime ) print ('\n average time : {:.3f} s'.format(sum(all_times)/len(all_times))) torch.save(all_indexes, save_dir / 'results.pth') else: main(args, nas_bench)