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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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216
exps/NATS-Bench/main-sss.py
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@@ -51,23 +51,35 @@ def evaluate_all_datasets(
train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
# load the configuration
if dataset == "cifar10" or dataset == "cifar100":
split_info = load_config("configs/nas-benchmark/cifar-split.txt", None, None)
split_info = load_config(
"configs/nas-benchmark/cifar-split.txt", None, None
)
elif dataset.startswith("ImageNet16"):
split_info = load_config("configs/nas-benchmark/{:}-split.txt".format(dataset), None, None)
split_info = load_config(
"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
)
else:
raise ValueError("invalid dataset : {:}".format(dataset))
config = load_config(config_path, dict(class_num=class_num, xshape=xshape), logger)
config = load_config(
config_path, dict(class_num=class_num, xshape=xshape), logger
)
# check whether use the splitted validation set
if bool(split):
assert dataset == "cifar10"
ValLoaders = {
"ori-test": torch.utils.data.DataLoader(
valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
}
assert len(train_data) == len(split_info.train) + len(
split_info.valid
), "invalid length : {:} vs {:} + {:}".format(len(train_data), len(split_info.train), len(split_info.valid))
), "invalid length : {:} vs {:} + {:}".format(
len(train_data), len(split_info.train), len(split_info.valid)
)
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
@@ -90,47 +102,67 @@ def evaluate_all_datasets(
else:
# data loader
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
train_data,
batch_size=config.batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
if dataset == "cifar10":
ValLoaders = {"ori-test": valid_loader}
elif dataset == "cifar100":
cifar100_splits = load_config("configs/nas-benchmark/cifar100-test-split.txt", None, None)
cifar100_splits = load_config(
"configs/nas-benchmark/cifar100-test-split.txt", None, None
)
ValLoaders = {
"ori-test": valid_loader,
"x-valid": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid),
sampler=torch.utils.data.sampler.SubsetRandomSampler(
cifar100_splits.xvalid
),
num_workers=workers,
pin_memory=True,
),
"x-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest),
sampler=torch.utils.data.sampler.SubsetRandomSampler(
cifar100_splits.xtest
),
num_workers=workers,
pin_memory=True,
),
}
elif dataset == "ImageNet16-120":
imagenet16_splits = load_config("configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None)
imagenet16_splits = load_config(
"configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None
)
ValLoaders = {
"ori-test": valid_loader,
"x-valid": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xvalid),
sampler=torch.utils.data.sampler.SubsetRandomSampler(
imagenet16_splits.xvalid
),
num_workers=workers,
pin_memory=True,
),
"x-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xtest),
sampler=torch.utils.data.sampler.SubsetRandomSampler(
imagenet16_splits.xtest
),
num_workers=workers,
pin_memory=True,
),
@@ -143,19 +175,36 @@ def evaluate_all_datasets(
dataset_key = dataset_key + "-valid"
logger.log(
"Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size
dataset_key,
len(train_data),
len(valid_data),
len(train_loader),
len(valid_loader),
config.batch_size,
)
)
logger.log("Evaluate ||||||| {:10s} ||||||| Config={:}".format(dataset_key, config))
logger.log(
"Evaluate ||||||| {:10s} ||||||| Config={:}".format(dataset_key, config)
)
for key, value in ValLoaders.items():
logger.log("Evaluate ---->>>> {:10s} with {:} batchs".format(key, len(value)))
logger.log(
"Evaluate ---->>>> {:10s} with {:} batchs".format(key, len(value))
)
# arch-index= 9930, arch=|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|
# this genotype is the architecture with the highest accuracy on CIFAR-100 validation set
genotype = "|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|"
arch_config = dict2config(
dict(name="infer.shape.tiny", channels=channels, genotype=genotype, num_classes=class_num), None
dict(
name="infer.shape.tiny",
channels=channels,
genotype=genotype,
num_classes=class_num,
),
None,
)
results = bench_evaluate_for_seed(
arch_config, config, train_loader, ValLoaders, seed, logger
)
results = bench_evaluate_for_seed(arch_config, config, train_loader, ValLoaders, seed, logger)
all_infos[dataset_key] = results
all_dataset_keys.append(dataset_key)
all_infos["all_dataset_keys"] = all_dataset_keys
@@ -183,8 +232,12 @@ def main(
logger.log("xargs : cover_mode = {:}".format(cover_mode))
logger.log("-" * 100)
logger.log(
"Start evaluating range =: {:06d} - {:06d}".format(min(to_evaluate_indexes), max(to_evaluate_indexes))
+ "({:} in total) / {:06d} with cover-mode={:}".format(len(to_evaluate_indexes), len(nets), cover_mode)
"Start evaluating range =: {:06d} - {:06d}".format(
min(to_evaluate_indexes), max(to_evaluate_indexes)
)
+ "({:} in total) / {:06d} with cover-mode={:}".format(
len(to_evaluate_indexes), len(nets), cover_mode
)
)
for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
logger.log(
@@ -199,7 +252,13 @@ def main(
channelstr = nets[index]
logger.log(
"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] {:}".format(
time_string(), i, len(to_evaluate_indexes), index, len(nets), seeds, "-" * 15
time_string(),
i,
len(to_evaluate_indexes),
index,
len(nets),
seeds,
"-" * 15,
)
)
logger.log("{:} {:} {:}".format("-" * 15, channelstr, "-" * 15))
@@ -210,17 +269,33 @@ def main(
to_save_name = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
if to_save_name.exists():
if cover_mode:
logger.log("Find existing file : {:}, remove it before evaluation".format(to_save_name))
logger.log(
"Find existing file : {:}, remove it before evaluation".format(
to_save_name
)
)
os.remove(str(to_save_name))
else:
logger.log("Find existing file : {:}, skip this evaluation".format(to_save_name))
logger.log(
"Find existing file : {:}, skip this evaluation".format(
to_save_name
)
)
has_continue = True
continue
results = evaluate_all_datasets(channelstr, datasets, xpaths, splits, opt_config, seed, workers, logger)
results = evaluate_all_datasets(
channelstr, datasets, xpaths, splits, opt_config, seed, workers, logger
)
torch.save(results, to_save_name)
logger.log(
"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}".format(
time_string(), i, len(to_evaluate_indexes), index, len(nets), seeds, to_save_name
time_string(),
i,
len(to_evaluate_indexes),
index,
len(nets),
seeds,
to_save_name,
)
)
# measure elapsed time
@@ -230,7 +305,9 @@ def main(
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True)
)
logger.log("This arch costs : {:}".format(convert_secs2time(epoch_time.val, True)))
logger.log(
"This arch costs : {:}".format(convert_secs2time(epoch_time.val, True))
)
logger.log("{:}".format("*" * 100))
logger.log(
"{:} {:74s} {:}".format(
@@ -277,16 +354,24 @@ def filter_indexes(xlist, mode, save_dir, seeds):
SLURM_PROCID, SLURM_NTASKS = "SLURM_PROCID", "SLURM_NTASKS"
if SLURM_PROCID in os.environ and SLURM_NTASKS in os.environ: # run on the slurm
proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS])
assert 0 <= proc_id < ntasks, "invalid proc_id {:} vs ntasks {:}".format(proc_id, ntasks)
scales = [int(float(i) / ntasks * len(all_indexes)) for i in range(ntasks)] + [len(all_indexes)]
assert 0 <= proc_id < ntasks, "invalid proc_id {:} vs ntasks {:}".format(
proc_id, ntasks
)
scales = [int(float(i) / ntasks * len(all_indexes)) for i in range(ntasks)] + [
len(all_indexes)
]
per_job = []
for i in range(ntasks):
xs, xe = min(max(scales[i], 0), len(all_indexes) - 1), min(max(scales[i + 1] - 1, 0), len(all_indexes) - 1)
xs, xe = min(max(scales[i], 0), len(all_indexes) - 1), min(
max(scales[i + 1] - 1, 0), len(all_indexes) - 1
)
per_job.append((xs, xe))
for i, srange in enumerate(per_job):
print(" -->> {:2d}/{:02d} : {:}".format(i, ntasks, srange))
current_range = per_job[proc_id]
all_indexes = [all_indexes[i] for i in range(current_range[0], current_range[1] + 1)]
all_indexes = [
all_indexes[i] for i in range(current_range[0], current_range[1] + 1)
]
# set the device id
device = proc_id % torch.cuda.device_count()
torch.cuda.set_device(device)
@@ -301,30 +386,67 @@ def filter_indexes(xlist, mode, save_dir, seeds):
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="NATS-Bench (size search space)", formatter_class=argparse.ArgumentDefaultsHelpFormatter
description="NATS-Bench (size search space)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--mode", type=str, required=True, choices=["new", "cover"], help="The script mode.")
parser.add_argument(
"--save_dir", type=str, default="output/NATS-Bench-size", help="Folder to save checkpoints and log."
"--mode",
type=str,
required=True,
choices=["new", "cover"],
help="The script mode.",
)
parser.add_argument(
"--save_dir",
type=str,
default="output/NATS-Bench-size",
help="Folder to save checkpoints and log.",
)
parser.add_argument(
"--candidateC",
type=int,
nargs="+",
default=[8, 16, 24, 32, 40, 48, 56, 64],
help=".",
)
parser.add_argument(
"--num_layers", type=int, default=5, help="The number of layers in a network."
)
parser.add_argument("--candidateC", type=int, nargs="+", default=[8, 16, 24, 32, 40, 48, 56, 64], help=".")
parser.add_argument("--num_layers", type=int, default=5, help="The number of layers in a network.")
parser.add_argument("--check_N", type=int, default=32768, help="For safety.")
# use for train the model
parser.add_argument("--workers", type=int, default=8, help="The number of data loading workers (default: 2)")
parser.add_argument("--srange", type=str, required=True, help="The range of models to be evaluated")
parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
parser.add_argument("--xpaths", type=str, nargs="+", help="The root path for this dataset.")
parser.add_argument("--splits", type=int, nargs="+", help="The root path for this dataset.")
parser.add_argument(
"--hyper", type=str, default="12", choices=["01", "12", "90"], help="The tag for hyper-parameters."
"--workers",
type=int,
default=8,
help="The number of data loading workers (default: 2)",
)
parser.add_argument(
"--srange", type=str, required=True, help="The range of models to be evaluated"
)
parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
parser.add_argument(
"--xpaths", type=str, nargs="+", help="The root path for this dataset."
)
parser.add_argument(
"--splits", type=int, nargs="+", help="The root path for this dataset."
)
parser.add_argument(
"--hyper",
type=str,
default="12",
choices=["01", "12", "90"],
help="The tag for hyper-parameters.",
)
parser.add_argument(
"--seeds", type=int, nargs="+", help="The range of models to be evaluated"
)
parser.add_argument("--seeds", type=int, nargs="+", help="The range of models to be evaluated")
args = parser.parse_args()
nets = traverse_net(args.candidateC, args.num_layers)
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)
)
opt_config = "./configs/nas-benchmark/hyper-opts/{:}E.config".format(args.hyper)
if not os.path.isfile(opt_config):
@@ -337,12 +459,16 @@ if __name__ == "__main__":
raise ValueError("invalid length of seeds args: {:}".format(args.seeds))
if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
raise ValueError(
"invalid infos : {:} vs {:} vs {:}".format(len(args.datasets), len(args.xpaths), len(args.splits))
"invalid infos : {:} vs {:} vs {:}".format(
len(args.datasets), len(args.xpaths), len(args.splits)
)
)
if args.workers <= 0:
raise ValueError("invalid number of workers : {:}".format(args.workers))
target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
target_indexes = filter_indexes(
to_evaluate_indexes, args.mode, save_dir, args.seeds
)
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True