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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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275
exps/NAS-Bench-201/statistics.py
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@@ -48,33 +48,56 @@ def create_result_count(used_seed, dataset, arch_config, results, dataloader_dic
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))
@@ -88,11 +111,15 @@ def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dic
checkpoint = torch.load(checkpoint_path, map_location="cpu")
used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
for dataset in datasets:
assert dataset in checkpoint, "Can not find {:} in arch-{:} from {:}".format(
assert (
dataset in checkpoint
), "Can not find {:} in arch-{:} from {:}".format(
dataset, arch_index, checkpoint_path
)
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 = {
@@ -102,7 +129,9 @@ def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dic
"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)
return information
@@ -118,14 +147,29 @@ def GET_DataLoaders(workers):
cifar_config = load_config(cifar_config_path, None, None)
print("{:} Create data-loader for all datasets".format(time_string()))
print("-" * 200)
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets("cifar10", str(torch_dir / "cifar.python"), -1)
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets(
"cifar10", str(torch_dir / "cifar.python"), -1
)
print(
"original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
)
)
cifar10_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None)
assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [
cifar10_splits = load_config(
root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None
)
assert cifar10_splits.train[:10] == [
0,
5,
7,
11,
13,
15,
16,
17,
20,
24,
] and cifar10_splits.valid[:10] == [
1,
2,
3,
@@ -141,7 +185,11 @@ def GET_DataLoaders(workers):
temp_dataset.transform = VALID_CIFAR10.transform
# data loader
trainval_cifar10_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
TRAIN_CIFAR10,
batch_size=cifar_config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
train_cifar10_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR10,
@@ -158,7 +206,11 @@ def GET_DataLoaders(workers):
pin_memory=True,
)
test__cifar10_loader = torch.utils.data.DataLoader(
VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
VALID_CIFAR10,
batch_size=cifar_config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
print(
"CIFAR-10 : trval-loader has {:3d} batch with {:} per batch".format(
@@ -182,14 +234,29 @@ def GET_DataLoaders(workers):
)
print("-" * 200)
# CIFAR-100
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets("cifar100", str(torch_dir / "cifar.python"), -1)
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets(
"cifar100", str(torch_dir / "cifar.python"), -1
)
print(
"original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
)
)
cifar100_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None)
assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [
cifar100_splits = load_config(
root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None
)
assert cifar100_splits.xvalid[:10] == [
1,
3,
4,
5,
8,
10,
13,
14,
15,
16,
] and cifar100_splits.xtest[:10] == [
0,
2,
6,
@@ -202,7 +269,11 @@ def GET_DataLoaders(workers):
24,
]
train_cifar100_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
TRAIN_CIFAR100,
batch_size=cifar_config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
valid_cifar100_loader = torch.utils.data.DataLoader(
VALID_CIFAR100,
@@ -218,9 +289,15 @@ def GET_DataLoaders(workers):
num_workers=workers,
pin_memory=True,
)
print("CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader)))
print("CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader)))
print("CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader)))
print(
"CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader))
)
print(
"CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader))
)
print(
"CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader))
)
print("-" * 200)
imagenet16_config_path = "configs/nas-benchmark/ImageNet-16.config"
@@ -233,8 +310,23 @@ def GET_DataLoaders(workers):
len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
)
)
imagenet_splits = load_config(root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt", None, None)
assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [
imagenet_splits = load_config(
root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt",
None,
None,
)
assert imagenet_splits.xvalid[:10] == [
1,
2,
3,
6,
7,
8,
9,
12,
16,
18,
] and imagenet_splits.xtest[:10] == [
0,
4,
5,
@@ -304,12 +396,16 @@ def simplify(save_dir, meta_file, basestr, target_dir):
meta_archs = meta_infos["archs"] # a list of architecture strings
meta_num_archs = meta_infos["total"]
meta_max_node = meta_infos["max_node"]
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)
@@ -319,14 +415,18 @@ 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
@@ -334,7 +434,9 @@ 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_DataLoaders(6)
@@ -346,8 +448,15 @@ 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_directory = save_dir / target_dir
target_less_dir = save_dir / "{:}-LESS".format(target_dir)
@@ -356,24 +465,36 @@ 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_directory.glob("arch-{:}-seed-*.pth".format(arch_index)))
checkpoints = list(
target_directory.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, ["cifar10-valid"], dataloader_dict
arch_index,
meta_archs[int(arch_index)],
ckps_less,
["cifar10-valid"],
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
@@ -390,14 +511,19 @@ 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,
@@ -409,7 +535,9 @@ 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
)
)
@@ -418,12 +546,16 @@ def merge_all(save_dir, meta_file, basestr):
meta_archs = meta_infos["archs"]
meta_num_archs = meta_infos["total"]
meta_max_node = meta_infos["max_node"]
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(
@@ -437,11 +569,16 @@ 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(),
@@ -458,7 +595,11 @@ 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():
@@ -472,16 +613,24 @@ 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,
@@ -489,16 +638,26 @@ 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":