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# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import torch
import argparse
from proard.classification.data_providers.imagenet import ImagenetDataProvider
from proard.classification.data_providers.cifar10 import Cifar10DataProvider
from proard.classification.data_providers.cifar100 import Cifar100DataProvider
from proard.classification.run_manager import ClassificationRunConfig, RunManager
from proard.model_zoo import DYN_net
parser = argparse.ArgumentParser()
parser.add_argument(
"-p", "--path", help="The path of imagenet", type=str, default="/dataset/imagenet"
)
parser.add_argument("-g", "--gpu", help="The gpu(s) to use", type=str, default="all")
parser.add_argument(
"-b",
"--batch-size",
help="The batch on every device for validation",
type=int,
default=16,
)
parser.add_argument("-j", "--workers", help="Number of workers", type=int, default=20)
parser.add_argument(
"-n",
"--net",
metavar="DYNET",
default="ResNet50",
choices=[
"ResNet50",
"MBV3",
"ProxylessNASNet",
"MBV2",
"WideResNet"
],
help="dynamic networks",
)
parser.add_argument(
"--dataset", type=str, default="cifar10" ,choices=["cifar10", "cifar100", "imagenet"]
)
parser.add_argument(
"--attack", type=str, default="autoattack" ,choices=['fgsm', 'linf-pgd', 'fgm', 'l2-pgd', 'linf-df', 'l2-df', 'linf-apgd', 'l2-apgd','squar_attack','autoattack','apgd_ce']
)
parser.add_argument("--train_criterion", type=str, default="trades",choices=["trades","sat","mart","hat"])
parser.add_argument(
"--robust_mode", type=bool, default=True
)
parser.add_argument(
"--WPS", type=bool, default=False
)
parser.add_argument(
"--base", type=bool, default=False
)
args = parser.parse_args()
if args.gpu == "all":
device_list = range(torch.cuda.device_count())
args.gpu = ",".join(str(_) for _ in device_list)
else:
device_list = [int(_) for _ in args.gpu.split(",")]
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
args.batch_size = args.batch_size * max(len(device_list), 1)
ImagenetDataProvider.DEFAULT_PATH = args.path
run_config = ClassificationRunConfig(attack_type=args.attack,dataset= args.dataset, test_batch_size=args.batch_size, n_worker=args.workers,robust_mode=args.robust_mode)
dyn_network = DYN_net(args.net,args.robust_mode,args.dataset, args.train_criterion ,pretrained=True,run_config=run_config,WPS=args.WPS,base=args.base)
""" Randomly sample a sub-network,
you can also manually set the sub-network using:
dyn_network.set_active_subnet(ks=7, e=6, d=4)
"""
if not args.base:
# dyn_network.set_active_subnet(ks=3, e=4, d=2)
dyn_network.set_active_subnet(d=2,e=0.35,w=1.0)
# dyn_network.sample_active_subnet()
# dyn_network.set_max_net()
subnet = dyn_network.get_active_subnet(preserve_weight=True)
# print(subnet)
else:
subnet = dyn_network
""" Test sampled subnet
"""
run_manager = RunManager(".tmp/eval_subnet", subnet, run_config, init=False)
run_config.data_provider.assign_active_img_size(32)
run_manager.reset_running_statistics(net=subnet)
print("Test random subnet:")
# print(subnet.module_str)
loss, (top1, top5,robust1,robust5) = run_manager.validate(net=subnet,is_test=True)
print("Results: loss=%.5f,\t top1=%.1f,\t top5=%.1f,\t robust1=%.1f,\t robust5=%.1f" % (loss, top1, top5,robust1,robust5))
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