iicd/xautodl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Reformulate via black

Browse Source
This commit is contained in:
D-X-Y
2021-03-17 09:25:58 +00:00
parent a9093e41e1
commit f98edea22a
59 changed files with 12289 additions and 8918 deletions
Download Patch File Download Diff File Expand all files Collapse all files

428
exps/algos/BOHB.py
View File

@@ -11,14 +11,17 @@ 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))
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
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
@@ -27,209 +30,258 @@ 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
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
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 __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 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 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, 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 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)
if xargs.dataset == 'cifar10':
dataname = 'cifar10-valid'
else:
dataname = xargs.dataset
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}
if xargs.dataset == "cifar10":
dataname = "cifar10-valid"
else:
dataname = xargs.dataset
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)
# 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'
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
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, 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,
# 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,
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)
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)
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
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,
)
real_cost_time = time.time() - start_time
results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
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'] )
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
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)
real_cost_time = time.time() - start_time
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
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('--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)
if __name__ == "__main__":
parser = argparse.ArgumentParser("BOHB: Robust and Efficient Hyperparameter Optimization at Scale")
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=0.3, 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=0.33, 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)