Update REA, REINFORCE, RANDOM, and BOHB
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@ -123,3 +123,5 @@ scripts-search/l2s-algos
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TEMP-L.sh
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.nfs00*
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*.swo
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*/*.swo
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@ -5,9 +5,9 @@
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# required to install hpbandster ##################################
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# pip install hpbandster ##################################
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###################################################################
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# python exps/algos-v2/bohb.py --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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# OMP_NUM_THREADS=4 python exps/algos-v2/bohb.py --search_space tss --dataset cifar10 --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 --rand_seed 1
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###################################################################
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import os, sys, time, random, argparse
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import os, sys, time, random, argparse, 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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@ -17,7 +17,7 @@ from config_utils import load_config
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger
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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 nas_201_api import NASBench201API, NASBench301API
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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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@ -38,7 +38,7 @@ def get_topology_config_space(search_space, max_nodes=4):
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def get_size_config_space(search_space):
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cs = ConfigSpace.ConfigurationSpace()
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import pdb; pdb.set_trace()
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import pdb; pdb.set_trace()
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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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@ -63,52 +63,21 @@ def config2topology_func(max_nodes=4):
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class MyWorker(Worker):
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def __init__(self, *args, convert_func=None, dataname=None, nas_bench=None, time_budget=None, **kwargs):
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def __init__(self, *args, convert_func=None, dataset=None, api=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.convert_func = convert_func
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self._dataname = dataname
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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, self._dataname, None, hp='200', is_random=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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self._dataset = dataset
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self._api = api
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self.total_times = []
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self.trajectory = []
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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, self._dataname, None, hp='200', is_random=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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arch = self.convert_func( config )
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accuracy, latency, time_cost, total_time = self._api.simulate_train_eval(arch, self._dataset, iepoch=int(budget)-1, hp='12')
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self.trajectory.append((accuracy, arch))
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self.total_times.append(total_time)
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return ({'loss': 100 - accuracy,
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'info': self._api.query_index_by_arch(arch)})
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def main(xargs, api):
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@ -117,12 +86,13 @@ def main(xargs, api):
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logger = prepare_logger(args)
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logger.log('{:} use api : {:}'.format(time_string(), api))
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api.reset_time()
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search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
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if xargs.search_space == 'tss':
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cs = get_topology_config_space(xargs.max_nodes, search_space)
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config2structure = config2topology_func(xargs.max_nodes)
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cs = get_topology_config_space(search_space)
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config2structure = config2topology_func()
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else:
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cs = get_size_config_space(xargs.max_nodes, search_space)
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cs = get_size_config_space(search_space)
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import pdb; pdb.set_trace()
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hb_run_id = '0'
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@ -133,41 +103,41 @@ def main(xargs, api):
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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, dataname=dataname, nas_bench=nas_bench, time_budget=xargs.time_budget, run_id=hb_run_id, id=i)
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w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, dataset=xargs.dataset, api=api, 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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bohb = BOHB(configspace=cs, run_id=hb_run_id,
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eta=3, min_budget=1, max_budget=12,
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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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# print('There are {:} runs.'.format(len(results.get_all_runs())))
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# workers[0].total_times
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# workers[0].trajectory
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current_best_index = []
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for idx in range(len(workers[0].trajectory)):
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trajectory = workers[0].trajectory[:idx+1]
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arch = max(trajectory, key=lambda x: x[0])[1]
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current_best_index.append(api.query_index_by_arch(arch))
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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, '200')
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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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best_arch = max(workers[0].trajectory, key=lambda x: x[0])[1]
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logger.log('Best found configuration: {:} within {:.3f} s'.format(best_arch, workers[0].total_times[-1]))
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info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
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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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return logger.log_dir, current_best_index, workers[0].total_times
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if __name__ == '__main__':
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@ -185,8 +155,8 @@ if __name__ == '__main__':
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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=300, type=int, nargs='?', help='number of iterations for optimization method')
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# log
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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('--rand_seed', type=int, help='manual seed')
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parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
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parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
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args = parser.parse_args()
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if args.search_space == 'tss':
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@ -43,7 +43,7 @@ def main(xargs, api):
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current_best_index = []
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while len(total_time_cost) == 0 or total_time_cost[-1] < xargs.time_budget:
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arch = random_arch()
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accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
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accuracy, _, _, total_cost = api.simulate_train_eval(arch, xargs.dataset, hp='12')
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total_time_cost.append(total_cost)
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history.append(arch)
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if best_arch is None or best_acc < accuracy:
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@ -160,7 +160,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
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while len(population) < population_size:
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model = Model()
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model.arch = random_arch()
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model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, '12')
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model.accuracy, _, _, total_cost = api.simulate_train_eval(model.arch, dataset, hp='12')
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# Append the info
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population.append(model)
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history.append((model.accuracy, model.arch))
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@ -184,7 +184,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
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# Create the child model and store it.
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child = Model()
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child.arch = mutate_arch(parent.arch)
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child.accuracy, _, _, total_cost = api.simulate_train_eval(child.arch, dataset, '12')
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child.accuracy, _, _, total_cost = api.simulate_train_eval(child.arch, dataset, hp='12')
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# Append the info
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population.append(child)
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history.append((child.accuracy, child.arch))
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@ -150,7 +150,7 @@ def main(xargs, api):
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start_time = time.time()
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log_prob, action = select_action( policy )
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arch = policy.generate_arch( action )
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reward, _, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, '12')
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reward, _, _, current_total_cost = api.simulate_train_eval(arch, xargs.dataset, hp='12')
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trace.append((reward, arch))
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total_costs.append(current_total_cost)
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@ -1,18 +1,19 @@
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#!/bin/bash
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# bash ./exps/algos-v2/run-all.sh
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set -e
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echo script name: $0
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echo $# arguments
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datasets="cifar10 cifar100 ImageNet16-120"
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search_spaces="tss sss"
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for dataset in ${datasets}
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do
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for search_space in ${search_spaces}
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do
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# python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
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python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
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python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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# python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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python exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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done
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done
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@ -5,7 +5,7 @@
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###############################################################
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# Usage: python exps/experimental/vis-bench-algos.py #
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###############################################################
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import os, sys, time, torch, argparse
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import os, gc, sys, time, torch, argparse
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import numpy as np
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from typing import List, Text, Dict, Any
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from shutil import copyfile
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@ -31,6 +31,7 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
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alg2name['REA'] = 'R-EA-SS3'
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alg2name['REINFORCE'] = 'REINFORCE-0.001'
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alg2name['RANDOM'] = 'RANDOM'
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alg2name['BOHB'] = 'BOHB'
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for alg, name in alg2name.items():
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alg2path[alg] = os.path.join(ss_dir, dataset, name, 'results.pth')
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assert os.path.isfile(alg2path[alg]), 'invalid path : {:}'.format(alg2path[alg])
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@ -58,14 +59,27 @@ def query_performance(api, data, dataset, ticket):
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results.append(interplate)
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return sum(results) / len(results)
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y_min_s = {('cifar10', 'tss'): 90,
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('cifar10', 'sss'): 92,
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('cifar100', 'tss'): 65,
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('cifar100', 'sss'): 65,
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('ImageNet16-120', 'tss'): 36,
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('ImageNet16-120', 'sss'): 40}
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y_max_s = {('cifar10', 'tss'): 94.5,
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('cifar10', 'sss'): 93.3,
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('cifar100', 'tss'): 72,
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('cifar100', 'sss'): 70,
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('ImageNet16-120', 'tss'): 44,
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('ImageNet16-120', 'sss'): 46}
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def visualize_curve(api, vis_save_dir, search_space, max_time):
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vis_save_dir = vis_save_dir.resolve()
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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dpi, width, height = 250, 5100, 1500
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dpi, width, height = 250, 5200, 1400
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 14, 14
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LabelSize, LegendFontsize = 16, 16
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def sub_plot_fn(ax, dataset):
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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@ -73,6 +87,8 @@ def visualize_curve(api, vis_save_dir, search_space, max_time):
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total_tickets = 150
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time_tickets = [float(i) / total_tickets * max_time for i in range(total_tickets)]
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colors = ['b', 'g', 'c', 'm', 'y']
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ax.set_xlim(0, 200)
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ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
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for idx, (alg, data) in enumerate(alg2data.items()):
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print('plot alg : {:}'.format(alg))
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accuracies = []
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@ -107,5 +123,7 @@ if __name__ == '__main__':
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api201 = NASBench201API(verbose=False)
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visualize_curve(api201, save_dir, 'tss', args.max_time)
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del api201
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gc.collect()
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api301 = NASBench301API(verbose=False)
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visualize_curve(api301, save_dir, 'sss', args.max_time)
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@ -68,14 +68,14 @@ class NASBenchMetaAPI(metaclass=abc.ABCMeta):
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def reset_time(self):
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self._used_time = 0
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def simulate_train_eval(self, arch, dataset, hp='12', account_time=True):
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def simulate_train_eval(self, arch, dataset, iepoch=None, hp='12', account_time=True):
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index = self.query_index_by_arch(arch)
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all_names = ('cifar10', 'cifar100', 'ImageNet16-120')
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assert dataset in all_names, 'Invalid dataset name : {:} vs {:}'.format(dataset, all_names)
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if dataset == 'cifar10':
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info = self.get_more_info(index, 'cifar10-valid', iepoch=None, hp=hp, is_random=True)
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info = self.get_more_info(index, 'cifar10-valid', iepoch=iepoch, hp=hp, is_random=True)
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else:
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info = self.get_more_info(index, dataset, iepoch=None, hp=hp, is_random=True)
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info = self.get_more_info(index, dataset, iepoch=iepoch, hp=hp, is_random=True)
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valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
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latency = self.get_latency(index, dataset)
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if account_time:
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