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update GDAS

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This commit is contained in:
D-X-Y
2019-11-19 11:58:04 +11:00
parent c3672648d7
commit 09d68c6375
20 changed files with 1176 additions and 90 deletions
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31
exps/algos/BOHB.py
View File

@@ -69,7 +69,7 @@ class MyWorker(Worker):
'info': None})
def main(xargs):
def main(xargs, nas_bench):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
@@ -111,7 +111,7 @@ def main(xargs):
ns_host, ns_port = NS.start()
num_workers = 1
nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
#nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
logger.log('{:} Create AA-NAS-BENCH-API DONE'.format(time_string()))
workers = []
for i in range(num_workers):
@@ -140,15 +140,14 @@ def main(xargs):
logger.log('Best found configuration: {:}'.format(id2config[incumbent]['config']))
best_arch = config2structure( id2config[incumbent]['config'] )
if nas_bench is not None:
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))
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 : {:}'.format(workers[0].test_time))
logger.close()
return logger.log_dir, nas_bench.query_index_by_arch( best_arch )
@@ -175,5 +174,19 @@ if __name__ == '__main__':
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)
main(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 = AANASBenchAPI(args.arch_nas_dataset)
if args.rand_seed < 0:
save_dir, all_indexes, num = 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 = main(args, nas_bench)
all_indexes.append( index )
torch.save(all_indexes, save_dir / 'results.pth')
else:
main(args, nas_bench)

35
exps/algos/RANDOM.py
View File

@@ -19,7 +19,7 @@ from aa_nas_api import AANASBenchAPI
from R_EA import train_and_eval, random_architecture_func
def main(xargs):
def main(xargs, nas_bench):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
@@ -51,12 +51,6 @@ def main(xargs):
search_space = get_search_spaces('cell', xargs.search_space_name)
random_arch = random_architecture_func(xargs.max_nodes, search_space)
#x =random_arch() ; y = mutate_arch(x)
if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))
nas_bench = None
else:
logger.log('{:} build NAS-Benchmark-API from {:}'.format(time_string(), xargs.arch_nas_dataset))
nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
best_arch, best_acc = None, -1
for idx in range(xargs.random_num):
@@ -67,13 +61,12 @@ def main(xargs):
logger.log('[{:03d}/{:03d}] : {:} : accuracy = {:.2f}%'.format(idx, xargs.random_num, arch, accuracy))
logger.log('{:} best arch is {:}, accuracy = {:.2f}%'.format(time_string(), best_arch, best_acc))
if nas_bench is not None:
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))
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.close()
return logger.log_dir, nas_bench.query_index_by_arch( best_arch )
@@ -94,5 +87,19 @@ if __name__ == '__main__':
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)
main(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 = AANASBenchAPI(args.arch_nas_dataset)
if args.rand_seed < 0:
save_dir, all_indexes, num = 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 = main(args, nas_bench)
all_indexes.append( index )
torch.save(all_indexes, save_dir / 'results.pth')
else:
main(args, nas_bench)

41
exps/algos/R_EA.py
View File

@@ -60,7 +60,8 @@ def train_and_eval(arch, nas_bench, extra_info):
arch_index = nas_bench.query_index_by_arch( arch )
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
info = nas_bench.arch2infos[ arch_index ]
_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25) # use the validation accuracy after 25 training epochs
_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
#import pdb; pdb.set_trace()
else:
# train a model from scratch.
raise ValueError('NOT IMPLEMENT YET')
@@ -153,7 +154,7 @@ def regularized_evolution(cycles, population_size, sample_size, random_arch, mut
return history
def main(xargs):
def main(xargs, nas_bench):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
@@ -186,12 +187,6 @@ def main(xargs):
random_arch = random_architecture_func(xargs.max_nodes, search_space)
mutate_arch = mutate_arch_func(search_space)
#x =random_arch() ; y = mutate_arch(x)
if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))
nas_bench = None
else:
logger.log('{:} build NAS-Benchmark-API from {:}'.format(time_string(), xargs.arch_nas_dataset))
nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
history = regularized_evolution(xargs.ea_cycles, xargs.ea_population, xargs.ea_sample_size, random_arch, mutate_arch, nas_bench if args.ea_fast_by_api else None, extra_info)
logger.log('{:} regularized_evolution finish with history of {:} arch.'.format(time_string(), len(history)))
@@ -199,13 +194,12 @@ def main(xargs):
best_arch = best_arch.arch
logger.log('{:} best arch is {:}'.format(time_string(), best_arch))
if nas_bench is not None:
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))
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.close()
return logger.log_dir, nas_bench.query_index_by_arch( best_arch )
@@ -227,8 +221,23 @@ if __name__ == '__main__':
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')
parser.add_argument('--rand_seed', type=int, default=-1, 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.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
args.ea_fast_by_api = args.ea_fast_by_api > 0
main(args)
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 = AANASBenchAPI(args.arch_nas_dataset)
if args.rand_seed < 0:
save_dir, all_indexes, num = 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 = main(args, nas_bench)
all_indexes.append( index )
torch.save(all_indexes, save_dir / 'results.pth')
else:
main(args, nas_bench)

37
exps/algos/reinforce.py
View File

@@ -89,7 +89,7 @@ def select_action(policy):
return m.log_prob(action), action.cpu().tolist()
def main(xargs):
def main(xargs, nas_bench):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
@@ -128,12 +128,6 @@ def main(xargs):
logger.log('eps : {:}'.format(eps))
# nas dataset load
if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))
nas_bench = None
else:
logger.log('{:} build NAS-Benchmark-API from {:}'.format(time_string(), xargs.arch_nas_dataset))
nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
# REINFORCE
@@ -156,13 +150,12 @@ def main(xargs):
best_arch = policy.genotype()
if nas_bench is not None:
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))
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.close()
return logger.log_dir, nas_bench.query_index_by_arch( best_arch )
@@ -183,7 +176,21 @@ if __name__ == '__main__':
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')
parser.add_argument('--rand_seed', type=int, default=-1, 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)
main(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 = AANASBenchAPI(args.arch_nas_dataset)
if args.rand_seed < 0:
save_dir, all_indexes, num = 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 = main(args, nas_bench)
all_indexes.append( index )
torch.save(all_indexes, save_dir / 'results.pth')
else:
main(args, nas_bench)