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# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from proard.utils import calc_learning_rate, build_optimizer
from proard.classification.data_providers import ImagenetDataProvider
from proard.classification.data_providers import Cifar10DataProvider
from proard.classification.data_providers import Cifar100DataProvider
from robust_loss.trades import trades_loss
from robust_loss.adaad import adaad_loss
from robust_loss.ard import ard_loss
from robust_loss.hat import hat_loss
from robust_loss.mart import mart_loss
from robust_loss.sat import sat_loss
from robust_loss.rslad import rslad_loss
import torch
__all__ = ["RunConfig", "ClassificationRunConfig", "DistributedClassificationRunConfig"]
class RunConfig:
def __init__(
self,
n_epochs,
init_lr,
lr_schedule_type,
lr_schedule_param,
dataset,
train_batch_size,
test_batch_size,
valid_size,
opt_type,
opt_param,
weight_decay,
label_smoothing,
no_decay_keys,
mixup_alpha,
model_init,
validation_frequency,
print_frequency,
):
self.n_epochs = n_epochs
self.init_lr = init_lr
self.lr_schedule_type = lr_schedule_type
self.lr_schedule_param = lr_schedule_param
self.dataset = dataset
self.train_batch_size = train_batch_size
self.test_batch_size = test_batch_size
self.valid_size = valid_size
self.opt_type = opt_type
self.opt_param = opt_param
self.weight_decay = weight_decay
self.label_smoothing = label_smoothing
self.no_decay_keys = no_decay_keys
self.mixup_alpha = mixup_alpha
self.model_init = model_init
self.validation_frequency = validation_frequency
self.print_frequency = print_frequency
@property
def config(self):
config = {}
for key in self.__dict__:
if not key.startswith("_"):
config[key] = self.__dict__[key]
return config
def copy(self):
return RunConfig(**self.config)
""" learning rate """
def adjust_learning_rate(self, optimizer, epoch, batch=0, nBatch=None):
"""adjust learning of a given optimizer and return the new learning rate"""
new_lr = calc_learning_rate(
epoch, self.init_lr, self.n_epochs, batch, nBatch, self.lr_schedule_type
)
for param_group in optimizer.param_groups:
param_group["lr"] = new_lr
return new_lr
def warmup_adjust_learning_rate(
self, optimizer, T_total, nBatch, epoch, batch=0, warmup_lr=0
):
T_cur = epoch * nBatch + batch + 1
new_lr = T_cur / T_total * (self.init_lr - warmup_lr) + warmup_lr
for param_group in optimizer.param_groups:
param_group["lr"] = new_lr
return new_lr
""" data provider """
@property
def data_provider(self):
raise NotImplementedError
@property
def train_loader(self):
return self.data_provider.train
@property
def valid_loader(self):
return self.data_provider.valid
@property
def test_loader(self):
return self.data_provider.test
def random_sub_train_loader(
self, n_images, batch_size, num_worker=None, num_replicas=None, rank=None
):
return self.data_provider.build_sub_train_loader(
n_images, batch_size, num_worker, num_replicas, rank
)
""" optimizer """
def build_optimizer(self, net_params):
return build_optimizer(
net_params,
self.opt_type,
self.opt_param,
self.init_lr,
self.weight_decay,
self.no_decay_keys,
)
class ClassificationRunConfig(RunConfig):
def __init__(
self,
n_epochs=150,
init_lr=0.05,
lr_schedule_type="cosine",
lr_schedule_param=None,
dataset="imagenet", # 'cifar10' or 'cifar100'
train_batch_size=256,
test_batch_size=500,
valid_size=None,
opt_type="sgd",
opt_param=None,
weight_decay=4e-5,
label_smoothing=0.1,
no_decay_keys=None,
mixup_alpha=None,
model_init="he_fout",
validation_frequency=1,
print_frequency=10,
n_worker=32,
resize_scale=0.08,
distort_color="tf",
image_size=224, # 32
robust_mode = False,
epsilon_train = 0.031,
num_steps_train = 10,
step_size_train = 0.0078,
clip_min_train = 0 ,
clip_max_train = 1,
const_init_train = False,
beta_train = 6.0,
distance_train ="l_inf",
epsilon_test = 0.031,
num_steps_test = 20,
step_size_test = 0.0078,
clip_min_test = 0,
clip_max_test = 1,
const_init_test = False,
beta_test = 6.0,
distance_test = "l_inf",
train_criterion = "trades",
test_criterion = "ce",
kd_criterion = 'rslad',
attack_type = "linf-pgd",
**kwargs
):
super(ClassificationRunConfig, self).__init__(
n_epochs,
init_lr,
lr_schedule_type,
lr_schedule_param,
dataset,
train_batch_size,
test_batch_size,
valid_size,
opt_type,
opt_param,
weight_decay,
label_smoothing,
no_decay_keys,
mixup_alpha,
model_init,
validation_frequency,
print_frequency,
)
self.n_worker = n_worker
self.resize_scale = resize_scale
self.distort_color = distort_color
self.image_size = image_size
self.epsilon_train = epsilon_train
self.num_steps_train = num_steps_train
self.step_size_train = step_size_train
self.clip_min_train = clip_min_train
self.clip_max_train = clip_max_train
self.const_init_train = const_init_train
self.beta_train = beta_train
self.distance_train = distance_train
self.epsilon_test = epsilon_test
self.num_steps_test = num_steps_test
self.step_size_test = step_size_test
self.clip_min_test = clip_min_test
self.clip_max_test = clip_max_test
self.const_init_test = const_init_test
self.beta_test = beta_test
self.distance_test = distance_test
self.train_criterion = train_criterion
self.test_criterion = test_criterion
self.kd_criterion = kd_criterion
self.attack_type = attack_type
self.robust_mode = robust_mode
@property
def data_provider(self):
if self.__dict__.get("_data_provider", None) is None:
if self.dataset == ImagenetDataProvider.name():
DataProviderClass = ImagenetDataProvider
elif self.dataset == Cifar10DataProvider.name():
DataProviderClass = Cifar10DataProvider
elif self.dataset == Cifar100DataProvider.name():
DataProviderClass = Cifar100DataProvider
else:
raise NotImplementedError
self.__dict__["_data_provider"] = DataProviderClass(
train_batch_size=self.train_batch_size,
test_batch_size=self.test_batch_size,
valid_size=self.valid_size,
n_worker=self.n_worker,
resize_scale=self.resize_scale,
distort_color=self.distort_color,
image_size=self.image_size,
)
return self.__dict__["_data_provider"]
@property
def train_criterion_loss (self):
if self.train_criterion == "trades" :
return trades_loss
elif self.train_criterion == "mart" :
return mart_loss
elif self.train_criterion == "sat" :
return sat_loss
elif self.train_criterion == "hat" :
return hat_loss
@property
def test_criterion_loss (self) :
if self.test_criterion == "ce" :
return torch.nn.CrossEntropyLoss()
@property
def kd_criterion_loss (self) :
if self.kd_criterion =="ard" :
return ard_loss
elif self.kd_criterion == "adaad" :
return adaad_loss
elif self.kd_criterion == "rslad" :
return rslad_loss
class DistributedClassificationRunConfig(ClassificationRunConfig):
def __init__(
self,
n_epochs=150,
init_lr=0.05,
lr_schedule_type="cosine",
lr_schedule_param=None,
dataset="imagenet",
train_batch_size=64,
test_batch_size=64,
valid_size=None,
opt_type="sgd",
opt_param=None,
weight_decay=4e-5,
label_smoothing=0.1,
no_decay_keys=None,
mixup_alpha=None,
model_init="he_fout",
validation_frequency=1,
print_frequency=10,
n_worker=8,
resize_scale=0.08,
distort_color="tf",
image_size=224,
robust_mode = False,
epsilon = 0.031,
num_steps = 10,
step_size = 0.0078,
clip_min = 0,
clip_max = 1,
const_init = False,
beta = 6.0,
distance = "l_inf",
train_criterion = "trades",
test_criterion = "ce",
kd_criterion = 'rslad',
attack_type = "linf-pgd",
**kwargs
):
super(DistributedClassificationRunConfig, self).__init__(
n_epochs,
init_lr,
lr_schedule_type,
lr_schedule_param,
dataset,
train_batch_size,
test_batch_size,
valid_size,
opt_type,
opt_param,
weight_decay,
label_smoothing,
no_decay_keys,
mixup_alpha,
model_init,
validation_frequency,
print_frequency,
n_worker,
resize_scale,
distort_color,
image_size,
robust_mode,
epsilon,
num_steps,
step_size,
clip_min,
clip_max,
const_init,
beta,
distance,
epsilon,
num_steps * 2,
step_size,
clip_min,clip_max,
const_init,
beta,
distance,
train_criterion,
test_criterion,
kd_criterion,
attack_type,
**kwargs
)
self._num_replicas = kwargs["num_replicas"]
self._rank = kwargs["rank"]
@property
def data_provider(self):
if self.__dict__.get("_data_provider", None) is None:
if self.dataset == ImagenetDataProvider.name():
DataProviderClass = ImagenetDataProvider
elif self.dataset == Cifar10DataProvider.name():
DataProviderClass = Cifar10DataProvider
elif self.dataset == Cifar100DataProvider.name():
DataProviderClass = Cifar100DataProvider
else:
raise NotImplementedError
if self.dataset == "imagenet":
self.__dict__["_data_provider"] = DataProviderClass(
train_batch_size=self.train_batch_size,
test_batch_size=self.test_batch_size,
valid_size=self.valid_size,
n_worker=self.n_worker,
resize_scale=self.resize_scale,
distort_color=self.distort_color,
image_size=self.image_size,
num_replicas=self._num_replicas,
rank=self._rank,
)
else:
self.__dict__["_data_provider"] = DataProviderClass(
train_batch_size=self.train_batch_size,
test_batch_size=self.test_batch_size,
valid_size=self.valid_size,
n_worker=self.n_worker,
resize_scale=None,
distort_color=None,
image_size=self.image_size,
num_replicas=self._num_replicas,
rank=self._rank,
)
return self.__dict__["_data_provider"]
@property
def train_criterion_loss (self):
if self.train_criterion == "trades" :
return trades_loss
elif self.train_criterion == "mart" :
return mart_loss
elif self.train_criterion == "sat" :
return sat_loss
elif self.train_criterion == "hat" :
return hat_loss
@property
def test_criterion_loss (self) :
if self.test_criterion == "ce" :
return torch.nn.CrossEntropyLoss()
@property
def kd_criterion_loss (self) :
if self.kd_criterion =="ard" :
return ard_loss
elif self.kd_criterion == "adaad" :
return adaad_loss
elif self.kd_criterion == "rslad" :
return rslad_loss
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