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Add int search space

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This commit is contained in:
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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77
exps/algos/reinforce.py
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@@ -16,7 +16,13 @@ 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
@@ -34,7 +40,9 @@ class Policy(nn.Module):
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
self.edge2index[node_str] = len(self.edge2index)
self.arch_parameters = nn.Parameter(1e-3 * torch.randn(len(self.edge2index), len(search_space)))
self.arch_parameters = nn.Parameter(
1e-3 * torch.randn(len(self.edge2index), len(search_space))
)
def generate_arch(self, actions):
genotypes = []
@@ -74,7 +82,9 @@ class ExponentialMovingAverage(object):
self._momentum = momentum
def update(self, value):
self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
self._numerator = (
self._momentum * self._numerator + (1 - self._momentum) * value
)
self._denominator = self._momentum * self._denominator + (1 - self._momentum)
def value(self):
@@ -104,13 +114,17 @@ 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
@@ -137,7 +151,11 @@ 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)
@@ -160,7 +178,9 @@ def main(xargs, nas_bench):
# REINFORCE
# attempts = 0
x_start_time = time.time()
logger.log("Will start searching with time budget of {:} s.".format(xargs.time_budget))
logger.log(
"Will start searching with time budget of {:} s.".format(xargs.time_budget)
)
total_steps, total_costs, trace = 0, 0, []
# for istep in range(xargs.RL_steps):
while total_costs < xargs.time_budget:
@@ -222,16 +242,35 @@ 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("--learning_rate", type=float, help="The learning rate for REINFORCE.")
# parser.add_argument('--RL_steps', type=int, help='The steps for REINFORCE.')
parser.add_argument("--EMA_momentum", type=float, help="The momentum value for EMA.")
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)."
"--num_cells", type=int, help="The number of cells in one stage."
)
parser.add_argument(
"--learning_rate", type=float, help="The learning rate for REINFORCE."
)
# parser.add_argument('--RL_steps', type=int, help='The steps for REINFORCE.')
parser.add_argument(
"--EMA_momentum", type=float, help="The momentum value for EMA."
)
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")
@@ -240,7 +279,11 @@ 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