def generate_meta_info(save_dir, max_node, divide=40):
aa_nas_bench_ss = get_search_spaces("cell", "nas-bench-201")
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)))
random.seed(88) # please do not change this line for reproducibility
random.shuffle(archs)
# to test fixed-random shuffle
# print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
# print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
assert (
archs[0].tostr() ==
"|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|"
), "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|"
), "please check the 123-th architecture : {:}".format(archs[123])
total_arch = len(archs)
num = 50000
indexes_5W = list(range(num))
random.seed(1021)
random.shuffle(indexes_5W)
train_split = sorted(list(set(indexes_5W[:num // 2])))
valid_split = sorted(list(set(indexes_5W[num // 2:])))
assert len(train_split) + len(valid_split) == num
assert (train_split[0] == 0 and train_split[10] == 26
and train_split[111] == 203 and valid_split[0] == 1
and valid_split[10] == 18
and valid_split[111] == 242), "{:} {:} {:} - {:} {:} {:}".format(
train_split[0],
train_split[10],
train_split[111],
valid_split[0],
valid_split[10],
valid_split[111],
)
splits = {num: {"train": train_split, "valid": valid_split}}
info = {
"archs": [x.tostr() for x in archs],
"total": total_arch,
"max_node": max_node,
"splits": splits,
}
save_dir = Path(save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
save_name = save_dir / "meta-node-{:}.pth".format(max_node)
assert not save_name.exists(), "{:} already exist".format(save_name)
torch.save(info, save_name)
print("save the meta file into {:}".format(save_name))
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)))
random.seed(88) # please do not change this line for reproducibility
random.shuffle(archs)
assert (
archs[0].tostr() ==
"|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|"
), "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|"
), "please check the 123-th architecture : {:}".format(archs[123])
return [x.tostr() for x in archs]
def generate_meta_info(save_dir, max_node, divide=40):
aa_nas_bench_ss = get_search_spaces("cell", "nas-bench-201")
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)))
random.seed(88) # please do not change this line for reproducibility
random.shuffle(archs)
# to test fixed-random shuffle
# print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
# print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
assert (
archs[0].tostr() ==
"|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|"
), "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|"
), "please check the 123-th architecture : {:}".format(archs[123])
total_arch = len(archs)
num = 50000
indexes_5W = list(range(num))
random.seed(1021)
random.shuffle(indexes_5W)
train_split = sorted(list(set(indexes_5W[:num // 2])))
valid_split = sorted(list(set(indexes_5W[num // 2:])))
assert len(train_split) + len(valid_split) == num
assert (train_split[0] == 0 and train_split[10] == 26
and train_split[111] == 203 and valid_split[0] == 1
and valid_split[10] == 18
and valid_split[111] == 242), "{:} {:} {:} - {:} {:} {:}".format(
train_split[0],
train_split[10],
train_split[111],
valid_split[0],
valid_split[10],
valid_split[111],
)
splits = {num: {"train": train_split, "valid": valid_split}}
info = {
"archs": [x.tostr() for x in archs],
"total": total_arch,
"max_node": max_node,
"splits": splits,
}
save_dir = Path(save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
save_name = save_dir / "meta-node-{:}.pth".format(max_node)
assert not save_name.exists(), "{:} already exist".format(save_name)
torch.save(info, save_name)
print("save the meta file into {:}".format(save_name))
script_name_full = save_dir / "BENCH-201-N{:}.opt-full.script".format(
max_node)
script_name_less = save_dir / "BENCH-201-N{:}.opt-less.script".format(
max_node)
full_file = open(str(script_name_full), "w")
less_file = open(str(script_name_less), "w")
gaps = total_arch // divide
for start in range(0, total_arch, gaps):
xend = min(start + gaps, total_arch)
full_file.write(
"bash ./scripts-search/NAS-Bench-201/train-models.sh 0 {:5d} {:5d} -1 '777 888 999'\n"
.format(start, xend - 1))
less_file.write(
"bash ./scripts-search/NAS-Bench-201/train-models.sh 1 {:5d} {:5d} -1 '777 888 999'\n"
.format(start, xend - 1))
print("save the training script into {:} and {:}".format(
script_name_full, script_name_less))
full_file.close()
less_file.close()
script_name = save_dir / "meta-node-{:}.cal-script.txt".format(max_node)
macro = "OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0"
with open(str(script_name), "w") as cfile:
for start in range(0, total_arch, gaps):
xend = min(start + gaps, total_arch)
cfile.write(
"{:} python exps/NAS-Bench-201/statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n"
.format(macro, start, xend - 1))
print("save the post-processing script into {:}".format(script_name))