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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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116
exps/algos/R_EA.py
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@@ -15,7 +15,13 @@ if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from procedures import (
prepare_seed,
prepare_logger,
save_checkpoint,
copy_checkpoint,
get_optim_scheduler,
)
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API as API
@@ -38,13 +44,20 @@ class Model(object):
# In this case, the LR schedular is converged.
# For use_012_epoch_training = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
#
def train_and_eval(arch, nas_bench, extra_info, dataname="cifar10-valid", use_012_epoch_training=True):
def train_and_eval(
arch, nas_bench, extra_info, dataname="cifar10-valid", use_012_epoch_training=True
):
if use_012_epoch_training and nas_bench is not None:
arch_index = nas_bench.query_index_by_arch(arch)
assert arch_index >= 0, "can not find this arch : {:}".format(arch)
info = nas_bench.get_more_info(arch_index, dataname, iepoch=None, hp="12", is_random=True)
valid_acc, time_cost = info["valid-accuracy"], info["train-all-time"] + info["valid-per-time"]
info = nas_bench.get_more_info(
arch_index, dataname, iepoch=None, hp="12", is_random=True
)
valid_acc, time_cost = (
info["valid-accuracy"],
info["train-all-time"] + info["valid-per-time"],
)
# _, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
elif not use_012_epoch_training and nas_bench is not None:
# Please contact me if you want to use the following logic, because it has some potential issues.
@@ -52,7 +65,9 @@ def train_and_eval(arch, nas_bench, extra_info, dataname="cifar10-valid", use_01
# It did return values for cifar100 and ImageNet16-120, but it has some potential issues. (Please email me for more details)
arch_index, nepoch = nas_bench.query_index_by_arch(arch), 25
assert arch_index >= 0, "can not find this arch : {:}".format(arch)
xoinfo = nas_bench.get_more_info(arch_index, "cifar10-valid", iepoch=None, hp="12")
xoinfo = nas_bench.get_more_info(
arch_index, "cifar10-valid", iepoch=None, hp="12"
)
xocost = nas_bench.get_cost_info(arch_index, "cifar10-valid", hp="200")
info = nas_bench.get_more_info(
arch_index, dataname, nepoch, hp="200", is_random=True
@@ -85,9 +100,15 @@ def train_and_eval(arch, nas_bench, extra_info, dataname="cifar10-valid", use_01
* cost["latency"]
)
try:
valid_acc, time_cost = info["valid-accuracy"], estimated_train_cost + estimated_valid_cost
valid_acc, time_cost = (
info["valid-accuracy"],
estimated_train_cost + estimated_valid_cost,
)
except:
valid_acc, time_cost = info["valtest-accuracy"], estimated_train_cost + estimated_valid_cost
valid_acc, time_cost = (
info["valtest-accuracy"],
estimated_train_cost + estimated_valid_cost,
)
else:
# train a model from scratch.
raise ValueError("NOT IMPLEMENT YET")
@@ -131,7 +152,15 @@ def mutate_arch_func(op_names):
def regularized_evolution(
cycles, population_size, sample_size, time_budget, random_arch, mutate_arch, nas_bench, extra_info, dataname
cycles,
population_size,
sample_size,
time_budget,
random_arch,
mutate_arch,
nas_bench,
extra_info,
dataname,
):
"""Algorithm for regularized evolution (i.e. aging evolution).
@@ -149,13 +178,18 @@ def regularized_evolution(
during the evolution experiment.
"""
population = collections.deque()
history, total_time_cost = [], 0 # Not used by the algorithm, only used to report results.
history, total_time_cost = (
[],
0,
) # Not used by the algorithm, only used to report results.
# Initialize the population with random models.
while len(population) < population_size:
model = Model()
model.arch = random_arch()
model.accuracy, time_cost = train_and_eval(model.arch, nas_bench, extra_info, dataname)
model.accuracy, time_cost = train_and_eval(
model.arch, nas_bench, extra_info, dataname
)
population.append(model)
history.append(model)
total_time_cost += time_cost
@@ -180,7 +214,9 @@ def regularized_evolution(
child = Model()
child.arch = mutate_arch(parent.arch)
total_time_cost += time.time() - start_time
child.accuracy, time_cost = train_and_eval(child.arch, nas_bench, extra_info, dataname)
child.accuracy, time_cost = train_and_eval(
child.arch, nas_bench, extra_info, dataname
)
if total_time_cost + time_cost > time_budget: # return
return history, total_time_cost
else:
@@ -207,13 +243,17 @@ def main(xargs, nas_bench):
else:
dataname = xargs.dataset
if xargs.data_path is not None:
train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, xargs.data_path, -1
)
split_Fpath = "configs/nas-benchmark/cifar-split.txt"
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log("Load split file from {:}".format(split_Fpath))
config_path = "configs/nas-benchmark/algos/R-EA.config"
config = load_config(config_path, {"class_num": class_num, "xshape": xshape}, logger)
config = load_config(
config_path, {"class_num": class_num, "xshape": xshape}, logger
)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
@@ -240,7 +280,11 @@ def main(xargs, nas_bench):
)
)
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
extra_info = {"config": config, "train_loader": train_loader, "valid_loader": valid_loader}
extra_info = {
"config": config,
"train_loader": train_loader,
"valid_loader": valid_loader,
}
else:
config_path = "configs/nas-benchmark/algos/R-EA.config"
config = load_config(config_path, None, logger)
@@ -253,7 +297,10 @@ def main(xargs, nas_bench):
# x =random_arch() ; y = mutate_arch(x)
x_start_time = time.time()
logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
logger.log("-" * 30 + " start searching with the time budget of {:} s".format(xargs.time_budget))
logger.log(
"-" * 30
+ " start searching with the time budget of {:} s".format(xargs.time_budget)
)
history, total_cost = regularized_evolution(
xargs.ea_cycles,
xargs.ea_population,
@@ -297,17 +344,36 @@ if __name__ == "__main__":
parser.add_argument("--search_space_name", type=str, help="The search space name.")
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
parser.add_argument("--channel", type=int, help="The number of channels.")
parser.add_argument("--num_cells", type=int, help="The number of cells in one stage.")
parser.add_argument(
"--num_cells", type=int, help="The number of cells in one stage."
)
parser.add_argument("--ea_cycles", type=int, help="The number of cycles in EA.")
parser.add_argument("--ea_population", type=int, help="The population size in EA.")
parser.add_argument("--ea_sample_size", type=int, help="The sample size in EA.")
parser.add_argument("--ea_fast_by_api", type=int, help="Use our API to speed up the experiments or not.")
parser.add_argument("--time_budget", type=int, help="The total time cost budge for searching (in seconds).")
# log
parser.add_argument("--workers", type=int, default=2, help="number of data loading workers (default: 2)")
parser.add_argument("--save_dir", type=str, help="Folder to save checkpoints and log.")
parser.add_argument(
"--arch_nas_dataset", type=str, help="The path to load the architecture dataset (tiny-nas-benchmark)."
"--ea_fast_by_api",
type=int,
help="Use our API to speed up the experiments or not.",
)
parser.add_argument(
"--time_budget",
type=int,
help="The total time cost budge for searching (in seconds).",
)
# log
parser.add_argument(
"--workers",
type=int,
default=2,
help="number of data loading workers (default: 2)",
)
parser.add_argument(
"--save_dir", type=str, help="Folder to save checkpoints and log."
)
parser.add_argument(
"--arch_nas_dataset",
type=str,
help="The path to load the architecture dataset (tiny-nas-benchmark).",
)
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
@@ -318,7 +384,11 @@ if __name__ == "__main__":
if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
nas_bench = None
else:
print("{:} build NAS-Benchmark-API from {:}".format(time_string(), args.arch_nas_dataset))
print(
"{:} build NAS-Benchmark-API from {:}".format(
time_string(), args.arch_nas_dataset
)
)
nas_bench = API(args.arch_nas_dataset)
if args.rand_seed < 0:
save_dir, all_indexes, num = None, [], 500