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Reformulate via black

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D-X-Y
2021-03-17 09:25:58 +00:00
parent a9093e41e1
commit f98edea22a
59 changed files with 12289 additions and 8918 deletions
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exps/NATS-Bench/sss-collect.py
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@@ -16,263 +16,304 @@ from tqdm import tqdm
from pathlib import Path
from collections import defaultdict, OrderedDict
from typing import Dict, Any, Text, List
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from log_utils import AverageMeter, time_string, convert_secs2time
lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from log_utils import AverageMeter, time_string, convert_secs2time
from config_utils import dict2config
from models import CellStructure, get_cell_based_tiny_net
from nats_bench import pickle_save, pickle_load, ArchResults, ResultsCount
from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
from utils import get_md5_file
from models import CellStructure, get_cell_based_tiny_net
from nats_bench import pickle_save, pickle_load, ArchResults, ResultsCount
from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
from utils import get_md5_file
NATS_SSS_BASE_NAME = 'NATS-sss-v1_0' # 2020.08.28
NATS_SSS_BASE_NAME = "NATS-sss-v1_0" # 2020.08.28
def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text]) -> ArchResults:
information = ArchResults(arch_index, arch_str)
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
try:
checkpoint = torch.load(checkpoint_path, map_location='cpu')
except:
raise ValueError('This checkpoint failed to be loaded : {:}'.format(checkpoint_path))
used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
ok_dataset = 0
for dataset in datasets:
if dataset not in checkpoint:
print('Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path))
continue
else:
ok_dataset += 1
results = checkpoint[dataset]
assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
arch_config = {'name': 'infer.shape.tiny', 'channels': arch_str, 'arch_str': arch_str,
'genotype': results['arch_config']['genotype'],
'class_num': results['arch_config']['num_classes']}
xresult = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'],
results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
information.update(dataset, int(used_seed), xresult)
if ok_dataset < len(datasets): raise ValueError('{:} does find enought data : {:} vs {:}'.format(checkpoint_path, ok_dataset, len(datasets)))
return information
for checkpoint_path in checkpoints:
try:
checkpoint = torch.load(checkpoint_path, map_location="cpu")
except:
raise ValueError("This checkpoint failed to be loaded : {:}".format(checkpoint_path))
used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
ok_dataset = 0
for dataset in datasets:
if dataset not in checkpoint:
print("Can not find {:} in arch-{:} from {:}".format(dataset, arch_index, checkpoint_path))
continue
else:
ok_dataset += 1
results = checkpoint[dataset]
assert results["finish-train"], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
arch_index, used_seed, dataset, checkpoint_path
)
arch_config = {
"name": "infer.shape.tiny",
"channels": arch_str,
"arch_str": arch_str,
"genotype": results["arch_config"]["genotype"],
"class_num": results["arch_config"]["num_classes"],
}
xresult = ResultsCount(
dataset,
results["net_state_dict"],
results["train_acc1es"],
results["train_losses"],
results["param"],
results["flop"],
arch_config,
used_seed,
results["total_epoch"],
None,
)
xresult.update_train_info(
results["train_acc1es"], results["train_acc5es"], results["train_losses"], results["train_times"]
)
xresult.update_eval(results["valid_acc1es"], results["valid_losses"], results["valid_times"])
information.update(dataset, int(used_seed), xresult)
if ok_dataset < len(datasets):
raise ValueError(
"{:} does find enought data : {:} vs {:}".format(checkpoint_path, ok_dataset, len(datasets))
)
return information
def correct_time_related_info(hp2info: Dict[Text, ArchResults]):
# calibrate the latency based on the number of epochs = 01, since they are trained on the same machine.
x1 = hp2info['01'].get_metrics('cifar10-valid', 'x-valid')['all_time'] / 98
x2 = hp2info['01'].get_metrics('cifar10-valid', 'ori-test')['all_time'] / 40
cifar010_latency = (x1 + x2) / 2
for hp, arch_info in hp2info.items():
arch_info.reset_latency('cifar10-valid', None, cifar010_latency)
arch_info.reset_latency('cifar10', None, cifar010_latency)
# hp2info['01'].get_latency('cifar10')
# calibrate the latency based on the number of epochs = 01, since they are trained on the same machine.
x1 = hp2info["01"].get_metrics("cifar10-valid", "x-valid")["all_time"] / 98
x2 = hp2info["01"].get_metrics("cifar10-valid", "ori-test")["all_time"] / 40
cifar010_latency = (x1 + x2) / 2
for hp, arch_info in hp2info.items():
arch_info.reset_latency("cifar10-valid", None, cifar010_latency)
arch_info.reset_latency("cifar10", None, cifar010_latency)
# hp2info['01'].get_latency('cifar10')
x1 = hp2info['01'].get_metrics('cifar100', 'ori-test')['all_time'] / 40
x2 = hp2info['01'].get_metrics('cifar100', 'x-test')['all_time'] / 20
x3 = hp2info['01'].get_metrics('cifar100', 'x-valid')['all_time'] / 20
cifar100_latency = (x1 + x2 + x3) / 3
for hp, arch_info in hp2info.items():
arch_info.reset_latency('cifar100', None, cifar100_latency)
x1 = hp2info["01"].get_metrics("cifar100", "ori-test")["all_time"] / 40
x2 = hp2info["01"].get_metrics("cifar100", "x-test")["all_time"] / 20
x3 = hp2info["01"].get_metrics("cifar100", "x-valid")["all_time"] / 20
cifar100_latency = (x1 + x2 + x3) / 3
for hp, arch_info in hp2info.items():
arch_info.reset_latency("cifar100", None, cifar100_latency)
x1 = hp2info['01'].get_metrics('ImageNet16-120', 'ori-test')['all_time'] / 24
x2 = hp2info['01'].get_metrics('ImageNet16-120', 'x-test')['all_time'] / 12
x3 = hp2info['01'].get_metrics('ImageNet16-120', 'x-valid')['all_time'] / 12
image_latency = (x1 + x2 + x3) / 3
for hp, arch_info in hp2info.items():
arch_info.reset_latency('ImageNet16-120', None, image_latency)
x1 = hp2info["01"].get_metrics("ImageNet16-120", "ori-test")["all_time"] / 24
x2 = hp2info["01"].get_metrics("ImageNet16-120", "x-test")["all_time"] / 12
x3 = hp2info["01"].get_metrics("ImageNet16-120", "x-valid")["all_time"] / 12
image_latency = (x1 + x2 + x3) / 3
for hp, arch_info in hp2info.items():
arch_info.reset_latency("ImageNet16-120", None, image_latency)
# CIFAR10 VALID
train_per_epoch_time = list(hp2info['01'].query('cifar10-valid', 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time = [], []
for key, value in hp2info['01'].query('cifar10-valid', 777).eval_times.items():
if key.startswith('ori-test@'):
eval_ori_test_time.append(value)
elif key.startswith('x-valid@'):
eval_x_valid_time.append(value)
else: raise ValueError('-- {:} --'.format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times('cifar10-valid', None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'x-valid', eval_x_valid_time)
arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'ori-test', eval_ori_test_time)
# CIFAR10 VALID
train_per_epoch_time = list(hp2info["01"].query("cifar10-valid", 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time = [], []
for key, value in hp2info["01"].query("cifar10-valid", 777).eval_times.items():
if key.startswith("ori-test@"):
eval_ori_test_time.append(value)
elif key.startswith("x-valid@"):
eval_x_valid_time.append(value)
else:
raise ValueError("-- {:} --".format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times("cifar10-valid", None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times("cifar10-valid", None, "x-valid", eval_x_valid_time)
arch_info.reset_pseudo_eval_times("cifar10-valid", None, "ori-test", eval_ori_test_time)
# CIFAR10
train_per_epoch_time = list(hp2info['01'].query('cifar10', 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time = []
for key, value in hp2info['01'].query('cifar10', 777).eval_times.items():
if key.startswith('ori-test@'):
eval_ori_test_time.append(value)
else: raise ValueError('-- {:} --'.format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times('cifar10', None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times('cifar10', None, 'ori-test', eval_ori_test_time)
# CIFAR10
train_per_epoch_time = list(hp2info["01"].query("cifar10", 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time = []
for key, value in hp2info["01"].query("cifar10", 777).eval_times.items():
if key.startswith("ori-test@"):
eval_ori_test_time.append(value)
else:
raise ValueError("-- {:} --".format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times("cifar10", None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times("cifar10", None, "ori-test", eval_ori_test_time)
# CIFAR100
train_per_epoch_time = list(hp2info['01'].query('cifar100', 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
for key, value in hp2info['01'].query('cifar100', 777).eval_times.items():
if key.startswith('ori-test@'):
eval_ori_test_time.append(value)
elif key.startswith('x-valid@'):
eval_x_valid_time.append(value)
elif key.startswith('x-test@'):
eval_x_test_time.append(value)
else: raise ValueError('-- {:} --'.format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times('cifar100', None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times('cifar100', None, 'x-valid', eval_x_valid_time)
arch_info.reset_pseudo_eval_times('cifar100', None, 'x-test', eval_x_test_time)
arch_info.reset_pseudo_eval_times('cifar100', None, 'ori-test', eval_ori_test_time)
# CIFAR100
train_per_epoch_time = list(hp2info["01"].query("cifar100", 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
for key, value in hp2info["01"].query("cifar100", 777).eval_times.items():
if key.startswith("ori-test@"):
eval_ori_test_time.append(value)
elif key.startswith("x-valid@"):
eval_x_valid_time.append(value)
elif key.startswith("x-test@"):
eval_x_test_time.append(value)
else:
raise ValueError("-- {:} --".format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times("cifar100", None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times("cifar100", None, "x-valid", eval_x_valid_time)
arch_info.reset_pseudo_eval_times("cifar100", None, "x-test", eval_x_test_time)
arch_info.reset_pseudo_eval_times("cifar100", None, "ori-test", eval_ori_test_time)
# ImageNet16-120
train_per_epoch_time = list(hp2info['01'].query('ImageNet16-120', 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
for key, value in hp2info['01'].query('ImageNet16-120', 777).eval_times.items():
if key.startswith('ori-test@'):
eval_ori_test_time.append(value)
elif key.startswith('x-valid@'):
eval_x_valid_time.append(value)
elif key.startswith('x-test@'):
eval_x_test_time.append(value)
else: raise ValueError('-- {:} --'.format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times('ImageNet16-120', None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-valid', eval_x_valid_time)
arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-test', eval_x_test_time)
arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'ori-test', eval_ori_test_time)
return hp2info
# ImageNet16-120
train_per_epoch_time = list(hp2info["01"].query("ImageNet16-120", 777).train_times.values())
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
for key, value in hp2info["01"].query("ImageNet16-120", 777).eval_times.items():
if key.startswith("ori-test@"):
eval_ori_test_time.append(value)
elif key.startswith("x-valid@"):
eval_x_valid_time.append(value)
elif key.startswith("x-test@"):
eval_x_test_time.append(value)
else:
raise ValueError("-- {:} --".format(key))
eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
for hp, arch_info in hp2info.items():
arch_info.reset_pseudo_train_times("ImageNet16-120", None, train_per_epoch_time)
arch_info.reset_pseudo_eval_times("ImageNet16-120", None, "x-valid", eval_x_valid_time)
arch_info.reset_pseudo_eval_times("ImageNet16-120", None, "x-test", eval_x_test_time)
arch_info.reset_pseudo_eval_times("ImageNet16-120", None, "ori-test", eval_ori_test_time)
return hp2info
def simplify(save_dir, save_name, nets, total):
hps, seeds = ['01', '12', '90'], set()
for hp in hps:
sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
ckps = sorted(list(sub_save_dir.glob('arch-*-seed-*.pth')))
seed2names = defaultdict(list)
for ckp in ckps:
parts = re.split('-|\.', ckp.name)
seed2names[parts[3]].append(ckp.name)
print('DIR : {:}'.format(sub_save_dir))
nums = []
for seed, xlist in seed2names.items():
seeds.add(seed)
nums.append(len(xlist))
print(' [seed={:}] there are {:} checkpoints.'.format(seed, len(xlist)))
assert len(nets) == total == max(nums), 'there are some missed files : {:} vs {:}'.format(max(nums), total)
print('{:} start simplify the checkpoint.'.format(time_string()))
datasets = ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
# Create the directory to save the processed data
# full_save_dir contains all benchmark files with trained weights.
# simplify_save_dir contains all benchmark files without trained weights.
full_save_dir = save_dir / (save_name + '-FULL')
simple_save_dir = save_dir / (save_name + '-SIMPLIFY')
full_save_dir.mkdir(parents=True, exist_ok=True)
simple_save_dir.mkdir(parents=True, exist_ok=True)
# all data in memory
arch2infos, evaluated_indexes = dict(), set()
end_time, arch_time = time.time(), AverageMeter()
for index in tqdm(range(total)):
arch_str = nets[index]
hp2info = OrderedDict()
full_save_path = full_save_dir / '{:06d}.pickle'.format(index)
simple_save_path = simple_save_dir / '{:06d}.pickle'.format(index)
hps, seeds = ["01", "12", "90"], set()
for hp in hps:
sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
ckps = [sub_save_dir / 'arch-{:06d}-seed-{:}.pth'.format(index, seed) for seed in seeds]
ckps = [x for x in ckps if x.exists()]
if len(ckps) == 0:
raise ValueError('Invalid data : index={:}, hp={:}'.format(index, hp))
sub_save_dir = save_dir / "raw-data-{:}".format(hp)
ckps = sorted(list(sub_save_dir.glob("arch-*-seed-*.pth")))
seed2names = defaultdict(list)
for ckp in ckps:
parts = re.split("-|\.", ckp.name)
seed2names[parts[3]].append(ckp.name)
print("DIR : {:}".format(sub_save_dir))
nums = []
for seed, xlist in seed2names.items():
seeds.add(seed)
nums.append(len(xlist))
print(" [seed={:}] there are {:} checkpoints.".format(seed, len(xlist)))
assert len(nets) == total == max(nums), "there are some missed files : {:} vs {:}".format(max(nums), total)
print("{:} start simplify the checkpoint.".format(time_string()))
arch_info = account_one_arch(index, arch_str, ckps, datasets)
hp2info[hp] = arch_info
hp2info = correct_time_related_info(hp2info)
evaluated_indexes.add(index)
datasets = ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
hp2info['01'].clear_params() # to save some spaces...
to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
'12': hp2info['12'].state_dict(),
'90': hp2info['90'].state_dict()})
pickle_save(to_save_data, str(full_save_path))
for hp in hps: hp2info[hp].clear_params()
to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
'12': hp2info['12'].state_dict(),
'90': hp2info['90'].state_dict()})
pickle_save(to_save_data, str(simple_save_path))
arch2infos[index] = to_save_data
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = '{:}'.format(convert_secs2time(arch_time.avg * (total-index-1), True))
# print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
print('{:} {:} done.'.format(time_string(), save_name))
final_infos = {'meta_archs' : nets,
'total_archs': total,
'arch2infos' : arch2infos,
'evaluated_indexes': evaluated_indexes}
save_file_name = save_dir / '{:}.pickle'.format(save_name)
pickle_save(final_infos, str(save_file_name))
# move the benchmark file to a new path
hd5sum = get_md5_file(str(save_file_name) + '.pbz2')
hd5_file_name = save_dir / '{:}-{:}.pickle.pbz2'.format(NATS_SSS_BASE_NAME, hd5sum)
shutil.move(str(save_file_name) + '.pbz2', hd5_file_name)
print('Save {:} / {:} architecture results into {:} -> {:}.'.format(len(evaluated_indexes), total, save_file_name, hd5_file_name))
# move the directory to a new path
hd5_full_save_dir = save_dir / '{:}-{:}-full'.format(NATS_SSS_BASE_NAME, hd5sum)
hd5_simple_save_dir = save_dir / '{:}-{:}-simple'.format(NATS_SSS_BASE_NAME, hd5sum)
shutil.move(full_save_dir, hd5_full_save_dir)
shutil.move(simple_save_dir, hd5_simple_save_dir)
# save the meta information for simple and full
final_infos['arch2infos'] = None
final_infos['evaluated_indexes'] = set()
pickle_save(final_infos, str(hd5_full_save_dir / 'meta.pickle'))
pickle_save(final_infos, str(hd5_simple_save_dir / 'meta.pickle'))
# Create the directory to save the processed data
# full_save_dir contains all benchmark files with trained weights.
# simplify_save_dir contains all benchmark files without trained weights.
full_save_dir = save_dir / (save_name + "-FULL")
simple_save_dir = save_dir / (save_name + "-SIMPLIFY")
full_save_dir.mkdir(parents=True, exist_ok=True)
simple_save_dir.mkdir(parents=True, exist_ok=True)
# all data in memory
arch2infos, evaluated_indexes = dict(), set()
end_time, arch_time = time.time(), AverageMeter()
for index in tqdm(range(total)):
arch_str = nets[index]
hp2info = OrderedDict()
full_save_path = full_save_dir / "{:06d}.pickle".format(index)
simple_save_path = simple_save_dir / "{:06d}.pickle".format(index)
for hp in hps:
sub_save_dir = save_dir / "raw-data-{:}".format(hp)
ckps = [sub_save_dir / "arch-{:06d}-seed-{:}.pth".format(index, seed) for seed in seeds]
ckps = [x for x in ckps if x.exists()]
if len(ckps) == 0:
raise ValueError("Invalid data : index={:}, hp={:}".format(index, hp))
arch_info = account_one_arch(index, arch_str, ckps, datasets)
hp2info[hp] = arch_info
hp2info = correct_time_related_info(hp2info)
evaluated_indexes.add(index)
hp2info["01"].clear_params() # to save some spaces...
to_save_data = OrderedDict(
{"01": hp2info["01"].state_dict(), "12": hp2info["12"].state_dict(), "90": hp2info["90"].state_dict()}
)
pickle_save(to_save_data, str(full_save_path))
for hp in hps:
hp2info[hp].clear_params()
to_save_data = OrderedDict(
{"01": hp2info["01"].state_dict(), "12": hp2info["12"].state_dict(), "90": hp2info["90"].state_dict()}
)
pickle_save(to_save_data, str(simple_save_path))
arch2infos[index] = to_save_data
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = "{:}".format(convert_secs2time(arch_time.avg * (total - index - 1), True))
# print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
print("{:} {:} done.".format(time_string(), save_name))
final_infos = {
"meta_archs": nets,
"total_archs": total,
"arch2infos": arch2infos,
"evaluated_indexes": evaluated_indexes,
}
save_file_name = save_dir / "{:}.pickle".format(save_name)
pickle_save(final_infos, str(save_file_name))
# move the benchmark file to a new path
hd5sum = get_md5_file(str(save_file_name) + ".pbz2")
hd5_file_name = save_dir / "{:}-{:}.pickle.pbz2".format(NATS_SSS_BASE_NAME, hd5sum)
shutil.move(str(save_file_name) + ".pbz2", hd5_file_name)
print(
"Save {:} / {:} architecture results into {:} -> {:}.".format(
len(evaluated_indexes), total, save_file_name, hd5_file_name
)
)
# move the directory to a new path
hd5_full_save_dir = save_dir / "{:}-{:}-full".format(NATS_SSS_BASE_NAME, hd5sum)
hd5_simple_save_dir = save_dir / "{:}-{:}-simple".format(NATS_SSS_BASE_NAME, hd5sum)
shutil.move(full_save_dir, hd5_full_save_dir)
shutil.move(simple_save_dir, hd5_simple_save_dir)
# save the meta information for simple and full
final_infos["arch2infos"] = None
final_infos["evaluated_indexes"] = set()
pickle_save(final_infos, str(hd5_full_save_dir / "meta.pickle"))
pickle_save(final_infos, str(hd5_simple_save_dir / "meta.pickle"))
def traverse_net(candidates: List[int], N: int):
nets = ['']
for i in range(N):
new_nets = []
for net in nets:
for C in candidates:
new_nets.append(str(C) if net == '' else "{:}:{:}".format(net,C))
nets = new_nets
return nets
nets = [""]
for i in range(N):
new_nets = []
for net in nets:
for C in candidates:
new_nets.append(str(C) if net == "" else "{:}:{:}".format(net, C))
nets = new_nets
return nets
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='NATS-Bench (size search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--base_save_dir', type=str, default='./output/NATS-Bench-size', help='The base-name of folder to save checkpoints and log.')
parser.add_argument('--candidateC' , type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
parser.add_argument('--num_layers' , type=int, default=5, help='The number of layers in a network.')
parser.add_argument('--check_N' , type=int, default=32768, help='For safety.')
parser.add_argument('--save_name' , type=str, default='process', help='The save directory.')
args = parser.parse_args()
nets = traverse_net(args.candidateC, args.num_layers)
if len(nets) != args.check_N:
raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NATS-Bench (size search space)", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--base_save_dir",
type=str,
default="./output/NATS-Bench-size",
help="The base-name of folder to save checkpoints and log.",
)
parser.add_argument("--candidateC", type=int, nargs="+", default=[8, 16, 24, 32, 40, 48, 56, 64], help=".")
parser.add_argument("--num_layers", type=int, default=5, help="The number of layers in a network.")
parser.add_argument("--check_N", type=int, default=32768, help="For safety.")
parser.add_argument("--save_name", type=str, default="process", help="The save directory.")
args = parser.parse_args()
save_dir = Path(args.base_save_dir)
simplify(save_dir, args.save_name, nets, args.check_N)
nets = traverse_net(args.candidateC, args.num_layers)
if len(nets) != args.check_N:
raise ValueError("Pre-num-check failed : {:} vs {:}".format(len(nets), args.check_N))
save_dir = Path(args.base_save_dir)
simplify(save_dir, args.save_name, nets, args.check_N)