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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 argparse
import numpy as np
import os
import random
# using for distributed training
import horovod.torch as hvd
import torch
from proard.classification.elastic_nn.modules.dynamic_op import (
DynamicSeparableConv2d,
)
from proard.classification.elastic_nn.networks import DYNResNets,DYNMobileNetV3,DYNProxylessNASNets,DYNMobileNetV3_Cifar,DYNResNets_Cifar,DYNProxylessNASNets_Cifar
from proard.classification.run_manager import DistributedClassificationRunConfig
from proard.classification.networks import WideResNet
from proard.classification.run_manager import DistributedRunManager
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="MBV2", choices=["ResNet50", "MBV3", "ProxylessNASNet","WideResNet","MBV2"])
parser.add_argument("--teacher_model_name", type=str, default="WideResNet", choices=["WideResNet"])
parser.add_argument("--dataset", type=str, default="cifar100", choices=["cifar10", "cifar100", "imagenet"])
parser.add_argument("--robust_mode", type=bool, default=True)
parser.add_argument("--epsilon", type=float, default=0.031)
parser.add_argument("--num_steps", type=int, default=10)
parser.add_argument("--step_size", type=float, default=0.0078)
parser.add_argument("--clip_min", type=int, default=0)
parser.add_argument("--clip_max", type=int, default=1)
parser.add_argument("--const_init", type=bool, default=False)
parser.add_argument("--beta", type=float, default=6.0)
parser.add_argument("--distance", type=str, default="l_inf",choices=["l_inf","l2"])
parser.add_argument("--train_criterion", type=str, default="trades",choices=["trades","sat","mart","hat"])
parser.add_argument("--test_criterion", type=str, default="ce",choices=["ce"])
parser.add_argument("--kd_criterion", type=str, default="rslad",choices=["ard","rslad","adaad"])
parser.add_argument("--attack_type", type=str, default="linf-pgd",choices=['fgsm', 'linf-pgd', 'fgm', 'l2-pgd', 'linf-df', 'l2-df', 'linf-apgd', 'l2-apgd','squar_attack','autoattack','apgd_ce'])
args = parser.parse_args()
if args.robust_mode:
args.path = 'exp/robust/teacher/' + args.dataset + "/" + args.model_name + '/' + args.train_criterion
else:
args.path = 'exp/teacher/' + args.dataset + "/" + args.model_name
args.n_epochs = 120
args.base_lr = 0.1
args.warmup_epochs = 5
args.warmup_lr = -1
args.manual_seed = 0
args.lr_schedule_type = "cosine"
args.base_batch_size = 128
args.valid_size = None
args.opt_type = "sgd"
args.momentum = 0.9
args.no_nesterov = False
args.weight_decay = 2e-4
args.label_smoothing = 0.0
args.no_decay_keys = "bn#bias"
args.fp16_allreduce = False
args.model_init = "he_fout"
args.validation_frequency = 1
args.print_frequency = 10
args.n_worker = 32
if args.dataset =="imagenet":
args.image_size = "224"
else:
args.image_size = "32"
args.continuous_size = True
args.not_sync_distributed_image_size = False
args.bn_momentum = 0.1
args.bn_eps = 1e-5
args.dropout = 0.0
args.base_stage_width = "google"
###### Parameters for MBV3, ProxylessNet, and MBV2
if args.model_name != "ResNet50":
args.ks_list = '7'
args.expand_list = '6'
args.depth_list = '4'
args.width_mult_list = "1.0"
else:
###### Parameters for ResNet50
args.ks_list = "3"
args.expand_list = "0.35"
args.depth_list = "2"
args.width_mult_list = "1.0"
########################################
args.dy_conv_scaling_mode = 1
args.independent_distributed_sampling = False
args.kd_ratio = 0.0
args.kd_type = "ce"
args.dynamic_batch_size = 1
args.num_gpus = 4
if __name__ == "__main__":
os.makedirs(args.path, exist_ok=True)
# Initialize Horovod
hvd.init()
# Pin GPU to be used to process local rank (one GPU per process)
torch.cuda.set_device(hvd.local_rank())
num_gpus = hvd.size()
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed_all(args.manual_seed)
np.random.seed(args.manual_seed)
random.seed(args.manual_seed)
# image size
args.image_size = [int(img_size) for img_size in args.image_size.split(",")]
if len(args.image_size) == 1:
args.image_size = args.image_size[0]
# build run config from args
args.lr_schedule_param = None
args.opt_param = {
"momentum": args.momentum,
"nesterov": not args.no_nesterov,
}
args.init_lr = args.base_lr * num_gpus # linearly rescale the learning rate
if args.warmup_lr < 0:
args.warmup_lr = args.base_lr
args.train_batch_size = args.base_batch_size
args.test_batch_size = args.base_batch_size
print(args.__dict__)
run_config = DistributedClassificationRunConfig(
**args.__dict__,num_replicas=num_gpus, rank=hvd.rank()
)
# print run config information
if hvd.rank() == 0:
print("Run config:")
for k, v in run_config.config.items():
print("\t%s: %s" % (k, v))
if args.dy_conv_scaling_mode == -1:
args.dy_conv_scaling_mode = None
DynamicSeparableConv2d.KERNEL_TRANSFORM_MODE = args.dy_conv_scaling_mode
# build net from args
args.width_mult_list = [
float(width_mult) for width_mult in args.width_mult_list.split(",")
]
args.ks_list = [int(ks) for ks in args.ks_list.split(",")]
args.expand_list = [float(e) for e in args.expand_list.split(",")]
args.depth_list = [int(d) for d in args.depth_list.split(",")]
args.width_mult_list = (
args.width_mult_list[0]
if len(args.width_mult_list) == 1
else args.width_mult_list
)
if args.model_name == "ResNet50":
if args.dataset == "cifar10" or args.dataset == "cifar100":
# net = ResNet50_Cifar(n_classes=run_config.data_provider.n_classes)
net = DYNResNets_Cifar( n_classes=run_config.data_provider.n_classes,
bn_param=(args.bn_momentum, args.bn_eps),
dropout_rate=args.dropout,
depth_list=args.depth_list,
expand_ratio_list=args.expand_list,
width_mult_list=args.width_mult_list,)
else:
net = DYNResNets( n_classes=run_config.data_provider.n_classes,
bn_param=(args.bn_momentum, args.bn_eps),
dropout_rate=args.dropout,
depth_list=args.depth_list,
expand_ratio_list=args.expand_list,
width_mult_list=args.width_mult_list,)
elif args.model_name == "MBV3":
if args.dataset == "cifar10" or args.dataset == "cifar100":
net = DYNMobileNetV3_Cifar(n_classes=run_config.data_provider.n_classes,bn_param=(args.bn_momentum,args.bn_eps),
dropout_rate= args.dropout, ks_list=args.ks_list , expand_ratio_list= args.expand_list , depth_list= args.depth_list)
else:
net = DYNMobileNetV3(n_classes=run_config.data_provider.n_classes,bn_param=(args.bn_momentum,args.bn_eps),
dropout_rate= args.dropout, ks_list=args.ks_list , expand_ratio_list= args.expand_list , depth_list= args.depth_list)
elif args.model_name == "ProxylessNASNet":
if args.dataset == "cifar10" or args.dataset == "cifar100":
net = DYNProxylessNASNets_Cifar(n_classes=run_config.data_provider.n_classes,bn_param=(args.bn_momentum,args.bn_eps),
dropout_rate= args.dropout, ks_list=args.ks_list , expand_ratio_list= args.expand_list , depth_list= args.depth_list)
else:
net = DYNProxylessNASNets(n_classes=run_config.data_provider.n_classes,bn_param=(args.bn_momentum,args.bn_eps),
dropout_rate= args.dropout, ks_list=args.ks_list , expand_ratio_list= args.expand_list , depth_list= args.depth_list)
elif args.model_name == "MBV2":
if args.dataset == "cifar10" or args.dataset == "cifar100":
net = DYNProxylessNASNets_Cifar(n_classes=run_config.data_provider.n_classes,bn_param=(args.bn_momentum,args.bn_eps),base_stage_width=args.base_stage_width,
dropout_rate= args.dropout, ks_list=args.ks_list , expand_ratio_list= args.expand_list , depth_list= args.depth_list)
else:
net = DYNProxylessNASNets(n_classes=run_config.data_provider.n_classes,bn_param=(args.bn_momentum,args.bn_eps),base_stage_width=args.base_stage_width,
dropout_rate= args.dropout, ks_list=args.ks_list , expand_ratio_list= args.expand_list , depth_list= args.depth_list)
else:
raise NotImplementedError
if args.teacher_model_name == "WideResNet":
if args.dataset == "cifar10" or args.dataset == "cifar100":
net = WideResNet(num_classes=run_config.data_provider.n_classes)
else:
raise NotImplementedError
else:
raise NotImplementedError
args.teacher_model = None #'exp/teacher/' + args.dataset + "/" + "WideResNet"
""" Distributed RunManager """
#Horovod: (optional) compression algorithm.
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
distributed_run_manager = DistributedRunManager(
args.path,
net,
run_config,
compression,
backward_steps=args.dynamic_batch_size,
is_root=(hvd.rank() == 0),
)
distributed_run_manager.save_config()
distributed_run_manager.broadcast()
distributed_run_manager.train(args)
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