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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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217
exps/NATS-algos/search-size.py
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@@ -38,7 +38,13 @@ 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, get_nas_search_loaders
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 count_parameters_in_MB, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net, get_search_spaces
@@ -55,7 +61,9 @@ 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)
@property
@@ -85,7 +93,9 @@ def search_func(
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
end = time.time()
network.train()
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
xloader
):
scheduler.update(None, 1.0 * step / len(xloader))
base_inputs = base_inputs.cuda(non_blocking=True)
arch_inputs = arch_inputs.cuda(non_blocking=True)
@@ -101,7 +111,9 @@ def search_func(
base_loss.backward()
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
base_prec1, base_prec5 = obtain_accuracy(
logits.data, base_targets.data, topk=(1, 5)
)
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
@@ -110,7 +122,9 @@ def search_func(
network.zero_grad()
a_optimizer.zero_grad()
_, logits, log_probs = network(arch_inputs)
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
arch_prec1, arch_prec5 = obtain_accuracy(
logits.data, arch_targets.data, topk=(1, 5)
)
if algo == "mask_rl":
with torch.no_grad():
RL_BASELINE_EMA.update(arch_prec1.item())
@@ -134,7 +148,11 @@ def search_func(
end = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = "*SEARCH* " + time_string() + " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
Sstr = (
"*SEARCH* "
+ time_string()
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
)
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
batch_time=batch_time, data_time=data_time
)
@@ -145,7 +163,14 @@ def search_func(
loss=arch_losses, top1=arch_top1, top5=arch_top5
)
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
return (
base_losses.avg,
base_top1.avg,
base_top5.avg,
arch_losses.avg,
arch_top1.avg,
arch_top5.avg,
)
def valid_func(xloader, network, criterion, logger):
@@ -162,7 +187,9 @@ def valid_func(xloader, network, criterion, logger):
_, logits, _ = network(arch_inputs.cuda(non_blocking=True))
arch_loss = criterion(logits, arch_targets)
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
arch_prec1, arch_prec5 = obtain_accuracy(
logits.data, arch_targets.data, topk=(1, 5)
)
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
@@ -181,11 +208,17 @@ def main(xargs):
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
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
)
if xargs.overwite_epochs is None:
extra_info = {"class_num": class_num, "xshape": xshape}
else:
extra_info = {"class_num": class_num, "xshape": xshape, "epochs": xargs.overwite_epochs}
extra_info = {
"class_num": class_num,
"xshape": xshape,
"epochs": xargs.overwite_epochs,
}
config = load_config(xargs.config_path, extra_info, logger)
search_loader, train_loader, valid_loader = get_nas_search_loaders(
train_data,
@@ -223,7 +256,9 @@ def main(xargs):
search_model.set_algo(xargs.algo)
logger.log("{:}".format(search_model))
w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
search_model.weights, config
)
a_optimizer = torch.optim.Adam(
search_model.alphas,
lr=xargs.arch_learning_rate,
@@ -244,13 +279,23 @@ def main(xargs):
api = None
logger.log("{:} create API = {:} done".format(time_string(), api))
last_info, model_base_path, model_best_path = logger.path("info"), logger.path("model"), logger.path("best")
last_info, model_base_path, model_best_path = (
logger.path("info"),
logger.path("model"),
logger.path("best"),
)
network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
last_info, model_base_path, model_best_path = logger.path("info"), logger.path("model"), logger.path("best")
last_info, model_base_path, model_best_path = (
logger.path("info"),
logger.path("model"),
logger.path("best"),
)
if last_info.exists(): # automatically resume from previous checkpoint
logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
logger.log(
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
)
last_info = torch.load(last_info)
start_epoch = last_info["epoch"]
checkpoint = torch.load(last_info["last_checkpoint"])
@@ -261,7 +306,9 @@ def main(xargs):
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
last_info, start_epoch
)
)
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
@@ -276,26 +323,47 @@ def main(xargs):
)
for epoch in range(start_epoch, total_epoch):
w_scheduler.update(epoch, 0.0)
need_time = "Time Left: {:}".format(convert_secs2time(epoch_time.val * (total_epoch - epoch), True))
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
)
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
if xargs.warmup_ratio is None or xargs.warmup_ratio <= float(epoch) / total_epoch:
if (
xargs.warmup_ratio is None
or xargs.warmup_ratio <= float(epoch) / total_epoch
):
enable_controller = True
network.set_warmup_ratio(None)
else:
enable_controller = False
network.set_warmup_ratio(1.0 - float(epoch) / total_epoch / xargs.warmup_ratio)
network.set_warmup_ratio(
1.0 - float(epoch) / total_epoch / xargs.warmup_ratio
)
logger.log(
"\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}".format(
epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller
epoch_str,
need_time,
min(w_scheduler.get_lr()),
network.warmup_ratio,
enable_controller,
)
)
if xargs.algo == "mask_gumbel" or xargs.algo == "tas":
network.set_tau(xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1))
network.set_tau(
xargs.tau_max
- (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
)
logger.log("[RESET tau as : {:}]".format(network.tau))
search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 = search_func(
(
search_w_loss,
search_w_top1,
search_w_top5,
search_a_loss,
search_a_top1,
search_a_top5,
) = search_func(
search_loader,
network,
criterion,
@@ -322,7 +390,9 @@ def main(xargs):
genotype = network.genotype
logger.log("[{:}] - [get_best_arch] : {:}".format(epoch_str, genotype))
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion, logger
)
logger.log(
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}".format(
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype
@@ -331,7 +401,9 @@ def main(xargs):
valid_accuracies[epoch] = valid_a_top1
genotypes[epoch] = genotype
logger.log("<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch]))
logger.log(
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
)
# save checkpoint
save_path = save_checkpoint(
{
@@ -369,8 +441,14 @@ def main(xargs):
genotype = network.genotype
search_time.update(time.time() - start_time)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
logger.log("Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.".format(genotype, valid_a_top1))
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion, logger
)
logger.log(
"Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.".format(
genotype, valid_a_top1
)
)
logger.log("\n" + "-" * 100)
# check the performance from the architecture dataset
@@ -393,8 +471,19 @@ if __name__ == "__main__":
choices=["cifar10", "cifar100", "ImageNet16-120"],
help="Choose between Cifar10/100 and ImageNet-16.",
)
parser.add_argument("--search_space", type=str, default="sss", choices=["sss"], help="The search space name.")
parser.add_argument("--algo", type=str, choices=["tas", "mask_gumbel", "mask_rl"], help="The search space name.")
parser.add_argument(
"--search_space",
type=str,
default="sss",
choices=["sss"],
help="The search space name.",
)
parser.add_argument(
"--algo",
type=str,
choices=["tas", "mask_gumbel", "mask_rl"],
help="The search space name.",
)
parser.add_argument(
"--genotype",
type=str,
@@ -402,13 +491,23 @@ if __name__ == "__main__":
help="The genotype.",
)
parser.add_argument(
"--use_api", type=int, default=1, choices=[0, 1], help="Whether use API or not (which will cost much memory)."
"--use_api",
type=int,
default=1,
choices=[0, 1],
help="Whether use API or not (which will cost much memory).",
)
# FOR GDAS
parser.add_argument("--tau_min", type=float, default=0.1, help="The minimum tau for Gumbel Softmax.")
parser.add_argument("--tau_max", type=float, default=10, help="The maximum tau for Gumbel Softmax.")
parser.add_argument(
"--tau_min", type=float, default=0.1, help="The minimum tau for Gumbel Softmax."
)
parser.add_argument(
"--tau_max", type=float, default=10, help="The maximum tau for Gumbel Softmax."
)
# FOR ALL
parser.add_argument("--warmup_ratio", type=float, help="The warmup ratio, if None, not use warmup.")
parser.add_argument(
"--warmup_ratio", type=float, help="The warmup ratio, if None, not use warmup."
)
#
parser.add_argument(
"--track_running_stats",
@@ -418,7 +517,11 @@ if __name__ == "__main__":
help="Whether use track_running_stats or not in the BN layer.",
)
parser.add_argument(
"--affine", type=int, default=0, choices=[0, 1], help="Whether use affine=True or False in the BN layer."
"--affine",
type=int,
default=0,
choices=[0, 1],
help="Whether use affine=True or False in the BN layer.",
)
parser.add_argument(
"--config_path",
@@ -427,25 +530,57 @@ if __name__ == "__main__":
help="The path of configuration.",
)
parser.add_argument(
"--overwite_epochs", type=int, help="The number of epochs to overwrite that value in config files."
"--overwite_epochs",
type=int,
help="The number of epochs to overwrite that value in config files.",
)
# architecture leraning rate
parser.add_argument("--arch_learning_rate", type=float, default=3e-4, help="learning rate for arch encoding")
parser.add_argument("--arch_weight_decay", type=float, default=1e-3, help="weight decay for arch encoding")
parser.add_argument("--arch_eps", type=float, default=1e-8, help="weight decay for arch encoding")
parser.add_argument(
"--arch_learning_rate",
type=float,
default=3e-4,
help="learning rate for arch encoding",
)
parser.add_argument(
"--arch_weight_decay",
type=float,
default=1e-3,
help="weight decay for arch encoding",
)
parser.add_argument(
"--arch_eps", type=float, default=1e-8, help="weight decay for arch encoding"
)
# log
parser.add_argument("--workers", type=int, default=2, help="number of data loading workers (default: 2)")
parser.add_argument("--save_dir", type=str, default="./output/search", help="Folder to save checkpoints and log.")
parser.add_argument("--print_freq", type=int, default=200, help="print frequency (default: 200)")
parser.add_argument(
"--workers",
type=int,
default=2,
help="number of data loading workers (default: 2)",
)
parser.add_argument(
"--save_dir",
type=str,
default="./output/search",
help="Folder to save checkpoints and log.",
)
parser.add_argument(
"--print_freq", type=int, default=200, help="print frequency (default: 200)"
)
parser.add_argument("--rand_seed", type=int, help="manual seed")
args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0:
args.rand_seed = random.randint(1, 100000)
dirname = "{:}-affine{:}_BN{:}-AWD{:}-WARM{:}".format(
args.algo, args.affine, args.track_running_stats, args.arch_weight_decay, args.warmup_ratio
args.algo,
args.affine,
args.track_running_stats,
args.arch_weight_decay,
args.warmup_ratio,
)
if args.overwite_epochs is not None:
dirname = dirname + "-E{:}".format(args.overwite_epochs)
args.save_dir = os.path.join("{:}-{:}".format(args.save_dir, args.search_space), args.dataset, dirname)
args.save_dir = os.path.join(
"{:}-{:}".format(args.save_dir, args.search_space), args.dataset, dirname
)
main(args)