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D-X-Y
2021-03-18 16:02:55 +08:00
parent ece6ac5f41
commit 63c8bb9bc8
67 changed files with 5150 additions and 1474 deletions
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287
exps/NAS-Bench-201/statistics-v2.py
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@@ -19,7 +19,9 @@ from models import CellStructure, get_cell_based_tiny_net
from nas_201_api import NASBench201API, ArchResults, ResultsCount
from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
api = NASBench201API("{:}/.torch/NAS-Bench-201-v1_0-e61699.pth".format(os.environ["HOME"]))
api = NASBench201API(
"{:}/.torch/NAS-Bench-201-v1_0-e61699.pth".format(os.environ["HOME"])
)
def create_result_count(
@@ -55,33 +57,56 @@ def create_result_count(
network.load_state_dict(xresult.get_net_param())
if "train_times" in results: # new version
xresult.update_train_info(
results["train_acc1es"], results["train_acc5es"], results["train_losses"], results["train_times"]
results["train_acc1es"],
results["train_acc5es"],
results["train_losses"],
results["train_times"],
)
xresult.update_eval(
results["valid_acc1es"], results["valid_losses"], results["valid_times"]
)
xresult.update_eval(results["valid_acc1es"], results["valid_losses"], results["valid_times"])
else:
if dataset == "cifar10-valid":
xresult.update_OLD_eval("x-valid", results["valid_acc1es"], results["valid_losses"])
xresult.update_OLD_eval(
"x-valid", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()
)
xresult.update_OLD_eval("ori-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
xresult.update_OLD_eval(
"ori-test",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
xresult.update_latency(latencies)
elif dataset == "cifar10":
xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"])
xresult.update_OLD_eval(
"ori-test", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
)
xresult.update_latency(latencies)
elif dataset == "cifar100" or dataset == "ImageNet16-120":
xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"])
xresult.update_OLD_eval(
"ori-test", results["valid_acc1es"], results["valid_losses"]
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()
)
xresult.update_OLD_eval("x-valid", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
xresult.update_OLD_eval(
"x-valid",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
loss, top1, top5, latencies = pure_evaluate(
dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
)
xresult.update_OLD_eval("x-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
xresult.update_OLD_eval(
"x-test",
{results["total_epoch"] - 1: top1},
{results["total_epoch"] - 1: loss},
)
xresult.update_latency(latencies)
else:
raise ValueError("invalid dataset name : {:}".format(dataset))
@@ -89,7 +114,11 @@ def create_result_count(
def account_one_arch(
arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text], dataloader_dict: Dict[Text, Any]
arch_index: int,
arch_str: Text,
checkpoints: List[Text],
datasets: List[Text],
dataloader_dict: Dict[Text, Any],
) -> ArchResults:
information = ArchResults(arch_index, arch_str)
@@ -99,12 +128,18 @@ def account_one_arch(
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))
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(
assert results[
"finish-train"
], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
arch_index, used_seed, dataset, checkpoint_path
)
arch_config = {
@@ -114,17 +149,22 @@ def account_one_arch(
"class_num": results["config"]["class_num"],
}
xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
xresult = create_result_count(
used_seed, dataset, arch_config, results, dataloader_dict
)
information.update(dataset, int(used_seed), xresult)
if ok_dataset == 0:
raise ValueError("{:} does not find any data".format(checkpoint_path))
return information
def correct_time_related_info(arch_index: int, arch_info_full: ArchResults, arch_info_less: ArchResults):
def correct_time_related_info(
arch_index: int, arch_info_full: ArchResults, arch_info_less: ArchResults
):
# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
cifar010_latency = (
api.get_latency(arch_index, "cifar10-valid", hp="200") + api.get_latency(arch_index, "cifar10", hp="200")
api.get_latency(arch_index, "cifar10-valid", hp="200")
+ api.get_latency(arch_index, "cifar10", hp="200")
) / 2
arch_info_full.reset_latency("cifar10-valid", None, cifar010_latency)
arch_info_full.reset_latency("cifar10", None, cifar010_latency)
@@ -139,7 +179,9 @@ def correct_time_related_info(arch_index: int, arch_info_full: ArchResults, arch
arch_info_full.reset_latency("ImageNet16-120", None, image_latency)
arch_info_less.reset_latency("ImageNet16-120", None, image_latency)
train_per_epoch_time = list(arch_info_less.query("cifar10-valid", 777).train_times.values())
train_per_epoch_time = list(
arch_info_less.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 arch_info_less.query("cifar10-valid", 777).eval_times.items():
@@ -149,7 +191,9 @@ def correct_time_related_info(arch_index: int, arch_info_full: ArchResults, arch
eval_x_valid_time.append(value)
else:
raise ValueError("-- {:} --".format(key))
eval_ori_test_time, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(np.mean(eval_x_valid_time))
eval_ori_test_time, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(
np.mean(eval_x_valid_time)
)
nums = {
"ImageNet16-120-train": 151700,
"ImageNet16-120-valid": 3000,
@@ -162,36 +206,72 @@ def correct_time_related_info(arch_index: int, arch_info_full: ArchResults, arch
"cifar100-test": 10000,
"cifar100-valid": 5000,
}
eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (nums["cifar10-valid-valid"] + nums["cifar10-test"])
eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (
nums["cifar10-valid-valid"] + nums["cifar10-test"]
)
for arch_info in [arch_info_less, arch_info_full]:
arch_info.reset_pseudo_train_times(
"cifar10-valid", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-valid-train"]
"cifar10-valid",
None,
train_per_epoch_time
/ nums["cifar10-valid-train"]
* nums["cifar10-valid-train"],
)
arch_info.reset_pseudo_train_times(
"cifar10", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-train"]
"cifar10",
None,
train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-train"],
)
arch_info.reset_pseudo_train_times(
"cifar100", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar100-train"]
"cifar100",
None,
train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar100-train"],
)
arch_info.reset_pseudo_train_times(
"ImageNet16-120", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["ImageNet16-120-train"]
"ImageNet16-120",
None,
train_per_epoch_time
/ nums["cifar10-valid-train"]
* nums["ImageNet16-120-train"],
)
arch_info.reset_pseudo_eval_times(
"cifar10-valid", None, "x-valid", eval_per_sample * nums["cifar10-valid-valid"]
)
arch_info.reset_pseudo_eval_times("cifar10-valid", None, "ori-test", eval_per_sample * nums["cifar10-test"])
arch_info.reset_pseudo_eval_times("cifar10", None, "ori-test", eval_per_sample * nums["cifar10-test"])
arch_info.reset_pseudo_eval_times("cifar100", None, "x-valid", eval_per_sample * nums["cifar100-valid"])
arch_info.reset_pseudo_eval_times("cifar100", None, "x-test", eval_per_sample * nums["cifar100-valid"])
arch_info.reset_pseudo_eval_times("cifar100", None, "ori-test", eval_per_sample * nums["cifar100-test"])
arch_info.reset_pseudo_eval_times(
"ImageNet16-120", None, "x-valid", eval_per_sample * nums["ImageNet16-120-valid"]
"cifar10-valid",
None,
"x-valid",
eval_per_sample * nums["cifar10-valid-valid"],
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120", None, "x-test", eval_per_sample * nums["ImageNet16-120-valid"]
"cifar10-valid", None, "ori-test", eval_per_sample * nums["cifar10-test"]
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120", None, "ori-test", eval_per_sample * nums["ImageNet16-120-test"]
"cifar10", None, "ori-test", eval_per_sample * nums["cifar10-test"]
)
arch_info.reset_pseudo_eval_times(
"cifar100", None, "x-valid", eval_per_sample * nums["cifar100-valid"]
)
arch_info.reset_pseudo_eval_times(
"cifar100", None, "x-test", eval_per_sample * nums["cifar100-valid"]
)
arch_info.reset_pseudo_eval_times(
"cifar100", None, "ori-test", eval_per_sample * nums["cifar100-test"]
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120",
None,
"x-valid",
eval_per_sample * nums["ImageNet16-120-valid"],
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120",
None,
"x-test",
eval_per_sample * nums["ImageNet16-120-valid"],
)
arch_info.reset_pseudo_eval_times(
"ImageNet16-120",
None,
"ori-test",
eval_per_sample * nums["ImageNet16-120-test"],
)
# arch_info_full.debug_test()
# arch_info_less.debug_test()
@@ -202,12 +282,16 @@ def simplify(save_dir, meta_file, basestr, target_dir):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"] # a list of architecture strings
meta_num_archs = meta_infos["total"]
assert meta_num_archs == len(meta_archs), "invalid number of archs : {:} vs {:}".format(
meta_num_archs, len(meta_archs)
)
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print("{:} find {:} directories used to save checkpoints".format(time_string(), len(sub_model_dirs)))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
num_seeds = defaultdict(lambda: 0)
@@ -217,14 +301,18 @@ def simplify(save_dir, meta_file, basestr, target_dir):
for checkpoint in xcheckpoints:
temp_names = checkpoint.name.split("-")
assert (
len(temp_names) == 4 and temp_names[0] == "arch" and temp_names[2] == "seed"
len(temp_names) == 4
and temp_names[0] == "arch"
and temp_names[2] == "seed"
), "invalid checkpoint name : {:}".format(checkpoint.name)
arch_indexes.add(temp_names[1])
subdir2archs[sub_dir] = sorted(list(arch_indexes))
num_evaluated_arch += len(arch_indexes)
# count number of seeds for each architecture
for arch_index in arch_indexes:
num_seeds[len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))] += 1
num_seeds[
len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))
] += 1
print(
"{:} There are {:5d} architectures that have been evaluated ({:} in total).".format(
time_string(), num_evaluated_arch, meta_num_archs
@@ -232,7 +320,9 @@ def simplify(save_dir, meta_file, basestr, target_dir):
)
for key in sorted(list(num_seeds.keys())):
print(
"{:} There are {:5d} architectures that are evaluated {:} times.".format(time_string(), num_seeds[key], key)
"{:} There are {:5d} architectures that are evaluated {:} times.".format(
time_string(), num_seeds[key], key
)
)
dataloader_dict = get_nas_bench_loaders(6)
@@ -243,8 +333,15 @@ def simplify(save_dir, meta_file, basestr, target_dir):
if not to_save_allarc.exists():
to_save_allarc.mkdir(parents=True, exist_ok=True)
assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format(target_dir)
arch2infos, datasets = {}, ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format(
target_dir
)
arch2infos, datasets = {}, (
"cifar10-valid",
"cifar10",
"cifar100",
"ImageNet16-120",
)
evaluated_indexes = set()
target_full_dir = save_dir / target_dir
target_less_dir = save_dir / "{:}-LESS".format(target_dir)
@@ -253,30 +350,46 @@ def simplify(save_dir, meta_file, basestr, target_dir):
end_time = time.time()
arch_time = AverageMeter()
for idx, arch_index in enumerate(arch_indexes):
checkpoints = list(target_full_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))
checkpoints = list(
target_full_dir.glob("arch-{:}-seed-*.pth".format(arch_index))
)
ckps_less = list(target_less_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))
# create the arch info for each architecture
try:
arch_info_full = account_one_arch(
arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict
arch_index,
meta_archs[int(arch_index)],
checkpoints,
datasets,
dataloader_dict,
)
arch_info_less = account_one_arch(
arch_index, meta_archs[int(arch_index)], ckps_less, datasets, dataloader_dict
arch_index,
meta_archs[int(arch_index)],
ckps_less,
datasets,
dataloader_dict,
)
num_seeds[len(checkpoints)] += 1
except:
print("Loading {:} failed, : {:}".format(arch_index, checkpoints))
continue
assert int(arch_index) not in evaluated_indexes, "conflict arch-index : {:}".format(arch_index)
assert 0 <= int(arch_index) < len(meta_archs), "invalid arch-index {:} (not found in meta_archs)".format(
arch_index
)
assert (
int(arch_index) not in evaluated_indexes
), "conflict arch-index : {:}".format(arch_index)
assert (
0 <= int(arch_index) < len(meta_archs)
), "invalid arch-index {:} (not found in meta_archs)".format(arch_index)
arch_info = {"full": arch_info_full, "less": arch_info_less}
evaluated_indexes.add(int(arch_index))
arch2infos[int(arch_index)] = arch_info
# to correct the latency and training_time info.
arch_info_full, arch_info_less = correct_time_related_info(int(arch_index), arch_info_full, arch_info_less)
to_save_data = OrderedDict(full=arch_info_full.state_dict(), less=arch_info_less.state_dict())
arch_info_full, arch_info_less = correct_time_related_info(
int(arch_index), arch_info_full, arch_info_less
)
to_save_data = OrderedDict(
full=arch_info_full.state_dict(), less=arch_info_less.state_dict()
)
torch.save(to_save_data, to_save_allarc / "{:}-FULL.pth".format(arch_index))
arch_info["full"].clear_params()
arch_info["less"].clear_params()
@@ -284,14 +397,19 @@ def simplify(save_dir, meta_file, basestr, target_dir):
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = "{:}".format(convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True))
need_time = "{:}".format(
convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True)
)
print(
"{:} {:} [{:03d}/{:03d}] : {:} still need {:}".format(
time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time
)
)
# measure time
xstrs = ["{:}:{:03d}".format(key, num_seeds[key]) for key in sorted(list(num_seeds.keys()))]
xstrs = [
"{:}:{:03d}".format(key, num_seeds[key])
for key in sorted(list(num_seeds.keys()))
]
print("{:} {:} done : {:}".format(time_string(), target_dir, xstrs))
final_infos = {
"meta_archs": meta_archs,
@@ -303,7 +421,9 @@ def simplify(save_dir, meta_file, basestr, target_dir):
save_file_name = to_save_simply / "{:}.pth".format(target_dir)
torch.save(final_infos, save_file_name)
print(
"Save {:} / {:} architecture results into {:}.".format(len(evaluated_indexes), meta_num_archs, save_file_name)
"Save {:} / {:} architecture results into {:}.".format(
len(evaluated_indexes), meta_num_archs, save_file_name
)
)
@@ -311,12 +431,16 @@ def merge_all(save_dir, meta_file, basestr):
meta_infos = torch.load(meta_file, map_location="cpu")
meta_archs = meta_infos["archs"]
meta_num_archs = meta_infos["total"]
assert meta_num_archs == len(meta_archs), "invalid number of archs : {:} vs {:}".format(
meta_num_archs, len(meta_archs)
)
assert meta_num_archs == len(
meta_archs
), "invalid number of archs : {:} vs {:}".format(meta_num_archs, len(meta_archs))
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
print("{:} find {:} directories used to save checkpoints".format(time_string(), len(sub_model_dirs)))
print(
"{:} find {:} directories used to save checkpoints".format(
time_string(), len(sub_model_dirs)
)
)
for index, sub_dir in enumerate(sub_model_dirs):
arch_info_files = sorted(list(sub_dir.glob("arch-*-seed-*.pth")))
print(
@@ -330,11 +454,16 @@ def merge_all(save_dir, meta_file, basestr):
ckp_path = sub_dir.parent / "simplifies" / "{:}.pth".format(sub_dir.name)
if ckp_path.exists():
sub_ckps = torch.load(ckp_path, map_location="cpu")
assert sub_ckps["total_archs"] == meta_num_archs and sub_ckps["basestr"] == basestr
assert (
sub_ckps["total_archs"] == meta_num_archs
and sub_ckps["basestr"] == basestr
)
xarch2infos = sub_ckps["arch2infos"]
xevalindexs = sub_ckps["evaluated_indexes"]
for eval_index in xevalindexs:
assert eval_index not in evaluated_indexes and eval_index not in arch2infos
assert (
eval_index not in evaluated_indexes and eval_index not in arch2infos
)
# arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
arch2infos[eval_index] = {
"full": xarch2infos[eval_index]["full"].state_dict(),
@@ -351,7 +480,11 @@ def merge_all(save_dir, meta_file, basestr):
# print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
evaluated_indexes = sorted(list(evaluated_indexes))
print("Finally, there are {:} architectures that have been trained and evaluated.".format(len(evaluated_indexes)))
print(
"Finally, there are {:} architectures that have been trained and evaluated.".format(
len(evaluated_indexes)
)
)
to_save_simply = save_dir / "simplifies"
if not to_save_simply.exists():
@@ -365,16 +498,24 @@ def merge_all(save_dir, meta_file, basestr):
save_file_name = to_save_simply / "{:}-final-infos.pth".format(basestr)
torch.save(final_infos, save_file_name)
print(
"Save {:} / {:} architecture results into {:}.".format(len(evaluated_indexes), meta_num_archs, save_file_name)
"Save {:} / {:} architecture results into {:}.".format(
len(evaluated_indexes), meta_num_archs, save_file_name
)
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NAS-BENCH-201", formatter_class=argparse.ArgumentDefaultsHelpFormatter
description="NAS-BENCH-201",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--mode",
type=str,
choices=["cal", "merge"],
help="The running mode for this script.",
)
parser.add_argument("--mode", type=str, choices=["cal", "merge"], help="The running mode for this script.")
parser.add_argument(
"--base_save_dir",
type=str,
@@ -382,16 +523,26 @@ if __name__ == "__main__":
help="The base-name of folder to save checkpoints and log.",
)
parser.add_argument("--target_dir", type=str, help="The target directory.")
parser.add_argument("--max_node", type=int, default=4, help="The maximum node in a cell.")
parser.add_argument("--channel", type=int, default=16, help="The number of channels.")
parser.add_argument("--num_cells", type=int, default=5, help="The number of cells in one stage.")
parser.add_argument(
"--max_node", type=int, default=4, help="The maximum node in a cell."
)
parser.add_argument(
"--channel", type=int, default=16, help="The number of channels."
)
parser.add_argument(
"--num_cells", type=int, default=5, help="The number of cells in one stage."
)
args = parser.parse_args()
save_dir = Path(args.base_save_dir)
meta_path = save_dir / "meta-node-{:}.pth".format(args.max_node)
assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir)
assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path)
print("start the statistics of our nas-benchmark from {:} using {:}.".format(save_dir, args.target_dir))
print(
"start the statistics of our nas-benchmark from {:} using {:}.".format(
save_dir, args.target_dir
)
)
basestr = "C{:}-N{:}".format(args.channel, args.num_cells)
if args.mode == "cal":