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Add int search space

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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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203
exps/NATS-Bench/tss-collect.py
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@@ -32,7 +32,9 @@ from utils import get_md5_file
from nas_201_api import NASBench201API
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"])
)
NATS_TSS_BASE_NAME = "NATS-tss-v1_0" # 2020.08.28
@@ -68,35 +70,58 @@ def create_result_count(
)
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:
network = get_cell_based_tiny_net(net_config)
network.load_state_dict(xresult.get_net_param())
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))
@@ -112,12 +137,18 @@ def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dic
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 = {
@@ -127,7 +158,9 @@ 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)
if ok_dataset == 0:
raise ValueError("{:} does not find any data".format(checkpoint_path))
@@ -137,7 +170,8 @@ def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dic
def correct_time_related_info(arch_index: int, arch_infos: Dict[Text, 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
cifar100_latency = api.get_latency(arch_index, "cifar100", hp="200")
image_latency = api.get_latency(arch_index, "ImageNet16-120", hp="200")
@@ -147,7 +181,9 @@ def correct_time_related_info(arch_index: int, arch_infos: Dict[Text, ArchResult
arch_info.reset_latency("cifar100", None, cifar100_latency)
arch_info.reset_latency("ImageNet16-120", None, image_latency)
train_per_epoch_time = list(arch_infos["12"].query("cifar10-valid", 777).train_times.values())
train_per_epoch_time = list(
arch_infos["12"].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_infos["12"].query("cifar10-valid", 777).eval_times.items():
@@ -157,7 +193,9 @@ def correct_time_related_info(arch_index: int, arch_infos: Dict[Text, ArchResult
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,
@@ -170,36 +208,72 @@ def correct_time_related_info(arch_index: int, arch_infos: Dict[Text, ArchResult
"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 hp, arch_info in arch_infos.items():
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"],
)
return arch_infos
@@ -220,7 +294,9 @@ def simplify(save_dir, save_name, nets, total, sup_config):
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)
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")
@@ -236,7 +312,12 @@ def simplify(save_dir, save_name, nets, total, sup_config):
arch2infos, evaluated_indexes = dict(), set()
end_time, arch_time = time.time(), AverageMeter()
# save the meta information
temp_final_infos = {"meta_archs": nets, "total_archs": total, "arch2infos": None, "evaluated_indexes": set()}
temp_final_infos = {
"meta_archs": nets,
"total_archs": total,
"arch2infos": None,
"evaluated_indexes": set(),
}
pickle_save(temp_final_infos, str(full_save_dir / "meta.pickle"))
pickle_save(temp_final_infos, str(simple_save_dir / "meta.pickle"))
@@ -248,29 +329,40 @@ def simplify(save_dir, save_name, nets, total, sup_config):
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 = [
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, dataloader_dict)
arch_info = account_one_arch(
index, arch_str, ckps, datasets, dataloader_dict
)
hp2info[hp] = arch_info
hp2info = correct_time_related_info(index, hp2info)
evaluated_indexes.add(index)
to_save_data = OrderedDict({"12": hp2info["12"].state_dict(), "200": hp2info["200"].state_dict()})
to_save_data = OrderedDict(
{"12": hp2info["12"].state_dict(), "200": hp2info["200"].state_dict()}
)
pickle_save(to_save_data, str(full_save_path))
for hp in hps:
hp2info[hp].clear_params()
to_save_data = OrderedDict({"12": hp2info["12"].state_dict(), "200": hp2info["200"].state_dict()})
to_save_data = OrderedDict(
{"12": hp2info["12"].state_dict(), "200": hp2info["200"].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))
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 = {
@@ -303,7 +395,11 @@ def simplify(save_dir, save_name, nets, total, sup_config):
def traverse_net(max_node):
aa_nas_bench_ss = get_search_spaces("cell", "nats-bench")
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
print("There are {:} archs vs {:}.".format(len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)))
print(
"There are {:} archs vs {:}.".format(
len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)
)
)
random.seed(88) # please do not change this line for reproducibility
random.shuffle(archs)
@@ -312,10 +408,12 @@ def traverse_net(max_node):
== "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
), "please check the 0-th architecture : {:}".format(archs[0])
assert (
archs[9].tostr() == "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
archs[9].tostr()
== "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
), "please check the 9-th architecture : {:}".format(archs[9])
assert (
archs[123].tostr() == "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
archs[123].tostr()
== "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
), "please check the 123-th architecture : {:}".format(archs[123])
return [x.tostr() for x in archs]
@@ -323,7 +421,8 @@ def traverse_net(max_node):
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NATS-Bench (topology search space)", formatter_class=argparse.ArgumentDefaultsHelpFormatter
description="NATS-Bench (topology search space)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--base_save_dir",
@@ -331,16 +430,26 @@ if __name__ == "__main__":
default="./output/NATS-Bench-topology",
help="The base-name of folder to save checkpoints and log.",
)
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."
)
parser.add_argument("--check_N", type=int, default=15625, help="For safety.")
parser.add_argument("--save_name", type=str, default="process", help="The save directory.")
parser.add_argument(
"--save_name", type=str, default="process", help="The save directory."
)
args = parser.parse_args()
nets = traverse_net(args.max_node)
if len(nets) != args.check_N:
raise ValueError("Pre-num-check failed : {:} vs {:}".format(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(