233 lines
11 KiB
Python
233 lines
11 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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###################################################################
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
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# required to install hpbandster ##################################
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# bash ./scripts-search/algos/BOHB.sh -1 ##################
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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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from pathlib import Path
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import torch
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import torch.nn as nn
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: 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 prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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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 nas_201_api import NASBench201API as API
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from models import CellStructure, get_search_spaces
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
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import ConfigSpace
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from hpbandster.optimizers.bohb import BOHB
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import hpbandster.core.nameserver as hpns
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from hpbandster.core.worker import Worker
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def get_configuration_space(max_nodes, search_space):
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cs = ConfigSpace.ConfigurationSpace()
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#edge2index = {}
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space))
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return cs
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def config2structure_func(max_nodes):
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def config2structure(config):
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genotypes = []
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for i in range(1, max_nodes):
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xlist = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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op_name = config[node_str]
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xlist.append((op_name, j))
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genotypes.append( tuple(xlist) )
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return CellStructure( genotypes )
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return config2structure
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class MyWorker(Worker):
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def __init__(self, *args, convert_func=None, nas_bench=None, time_budget=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.convert_func = convert_func
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self.nas_bench = nas_bench
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self.time_budget = time_budget
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self.seen_archs = []
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self.sim_cost_time = 0
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self.real_cost_time = 0
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self.is_end = False
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def get_the_best(self):
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assert len(self.seen_archs) > 0
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best_index, best_acc = -1, None
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for arch_index in self.seen_archs:
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info = self.nas_bench.get_more_info(arch_index, 'cifar10-valid', None, True)
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vacc = info['valid-accuracy']
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if best_acc is None or best_acc < vacc:
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best_acc = vacc
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best_index = arch_index
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assert best_index != -1
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return best_index
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def compute(self, config, budget, **kwargs):
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start_time = time.time()
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structure = self.convert_func( config )
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arch_index = self.nas_bench.query_index_by_arch( structure )
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info = self.nas_bench.get_more_info(arch_index, 'cifar10-valid', None, True)
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cur_time = info['train-all-time'] + info['valid-per-time']
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cur_vacc = info['valid-accuracy']
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self.real_cost_time += (time.time() - start_time)
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if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
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self.sim_cost_time += cur_time
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self.seen_archs.append( arch_index )
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return ({'loss': 100 - float(cur_vacc),
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'info': {'seen-arch' : len(self.seen_archs),
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'sim-test-time' : self.sim_cost_time,
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'current-arch' : arch_index}
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})
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else:
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self.is_end = True
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return ({'loss': 100,
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'info': {'seen-arch' : len(self.seen_archs),
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'sim-test-time' : self.sim_cost_time,
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'current-arch' : None}
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})
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def main(xargs, nas_bench):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads( xargs.workers )
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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config_path = 'configs/nas-benchmark/algos/R-EA.config'
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config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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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)
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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)
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logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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extra_info = {'config': config, 'train_loader': train_loader, 'valid_loader': valid_loader}
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else:
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config_path = 'configs/nas-benchmark/algos/R-EA.config'
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config = load_config(config_path, None, logger)
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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extra_info = {'config': config, 'train_loader': None, 'valid_loader': None}
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# nas dataset load
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assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset)
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search_space = get_search_spaces('cell', xargs.search_space_name)
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cs = get_configuration_space(xargs.max_nodes, search_space)
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config2structure = config2structure_func(xargs.max_nodes)
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hb_run_id = '0'
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NS = hpns.NameServer(run_id=hb_run_id, host='localhost', port=0)
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ns_host, ns_port = NS.start()
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num_workers = 1
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#nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
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#logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
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workers = []
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for i in range(num_workers):
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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)
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w.run(background=True)
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workers.append(w)
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start_time = time.time()
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bohb = BOHB(configspace=cs,
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run_id=hb_run_id,
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eta=3, min_budget=12, max_budget=200,
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nameserver=ns_host,
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nameserver_port=ns_port,
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num_samples=xargs.num_samples,
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random_fraction=xargs.random_fraction, bandwidth_factor=xargs.bandwidth_factor,
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ping_interval=10, min_bandwidth=xargs.min_bandwidth)
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results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
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bohb.shutdown(shutdown_workers=True)
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NS.shutdown()
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real_cost_time = time.time() - start_time
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id2config = results.get_id2config_mapping()
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incumbent = results.get_incumbent_id()
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logger.log('Best found configuration: {:} within {:.3f} s'.format(id2config[incumbent]['config'], real_cost_time))
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best_arch = config2structure( id2config[incumbent]['config'] )
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info = nas_bench.query_by_arch( best_arch )
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if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
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else : logger.log('{:}'.format(info))
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logger.log('-'*100)
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logger.log('workers : {:.1f}s with {:} archs'.format(workers[0].time_budget, len(workers[0].seen_archs)))
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch( best_arch ), real_cost_time
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Regularized Evolution Algorithm")
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parser.add_argument('--data_path', type=str, help='Path to dataset')
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parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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# channels and number-of-cells
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parser.add_argument('--search_space_name', type=str, help='The search space name.')
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parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
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parser.add_argument('--channel', type=int, help='The number of channels.')
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parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
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parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
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# BOHB
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parser.add_argument('--strategy', default="sampling", type=str, nargs='?', help='optimization strategy for the acquisition function')
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parser.add_argument('--min_bandwidth', default=.3, type=float, nargs='?', help='minimum bandwidth for KDE')
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parser.add_argument('--num_samples', default=64, type=int, nargs='?', help='number of samples for the acquisition function')
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parser.add_argument('--random_fraction', default=.33, type=float, nargs='?', help='fraction of random configurations')
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parser.add_argument('--bandwidth_factor', default=3, type=int, nargs='?', help='factor multiplied to the bandwidth')
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parser.add_argument('--n_iters', default=100, type=int, nargs='?', help='number of iterations for optimization method')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')
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parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
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parser.add_argument('--rand_seed', type=int, help='manual seed')
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args = parser.parse_args()
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#if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
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nas_bench = None
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else:
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print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
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nas_bench = API(args.arch_nas_dataset)
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if args.rand_seed < 0:
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save_dir, all_indexes, num, all_times = None, [], 500, []
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for i in range(num):
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print ('{:} : {:03d}/{:03d}'.format(time_string(), i, num))
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args.rand_seed = random.randint(1, 100000)
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save_dir, index, ctime = main(args, nas_bench)
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all_indexes.append( index )
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all_times.append( ctime )
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print ('\n average time : {:.3f} s'.format(sum(all_times)/len(all_times)))
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torch.save(all_indexes, save_dir / 'results.pth')
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else:
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main(args, nas_bench)
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