def __init__(self,
norm_bound=1.5,
initial_noise_multiplier=5.0,
alpha=6e-4,
decay_policy='Step'):
super(AdaGaussianRandom, self).__init__()
initial_noise_multiplier = check_value_positive(
'initial_noise_multiplier', initial_noise_multiplier)
initial_noise_multiplier = Tensor(
np.array(initial_noise_multiplier, np.float32))
self._initial_noise_multiplier = Parameter(
initial_noise_multiplier, name='initial_noise_multiplier')
self._noise_multiplier = Parameter(initial_noise_multiplier,
name='noise_multiplier')
norm_bound = check_value_positive('norm_bound', norm_bound)
self._norm_bound = Tensor(np.array(norm_bound, np.float32))
alpha = check_param_type('alpha', alpha, float)
self._alpha = Tensor(np.array(alpha, np.float32))
self._decay_policy = check_param_type('decay_policy', decay_policy,
str)
self._mean = 0.0
self._sub = P.Sub()
self._mul = P.Mul()
self._add = P.TensorAdd()
self._div = P.Div()
self._stddev = self._update_stddev()
self._dtype = mstype.float32
def __init__(self,
model,
pop_size=6,
mutation_rate=0.005,
per_bounds=0.15,
max_steps=1000,
step_size=0.20,
temp=0.3,
bounds=(0, 1.0),
adaptive=False,
sparse=True):
super(GeneticAttack, self).__init__()
self._model = check_model('model', model, BlackModel)
self._per_bounds = check_value_positive('per_bounds', per_bounds)
self._pop_size = check_int_positive('pop_size', pop_size)
self._step_size = check_value_positive('step_size', step_size)
self._temp = check_value_positive('temp', temp)
self._max_steps = check_int_positive('max_steps', max_steps)
self._mutation_rate = check_value_positive('mutation_rate',
mutation_rate)
self._adaptive = check_param_type('adaptive', adaptive, bool)
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
# initial global optimum fitness value
self._best_fit = -1
# count times of no progress
self._plateau_times = 0
# count times of changing attack step
self._adap_times = 0
self._sparse = check_param_type('sparse', sparse, bool)
def __init__(self, num_samples, batch_size, initial_noise_multiplier=1.5,
max_eps=10.0, target_delta=1e-3, noise_decay_mode='Time',
noise_decay_rate=6e-4, per_print_times=50, dataset_sink_mode=False):
super(ZCDPMonitor, self).__init__()
check_int_positive('num_samples', num_samples)
check_int_positive('batch_size', batch_size)
if batch_size >= num_samples:
msg = 'Batch_size must be less than num_samples.'
LOGGER.error(TAG, msg)
raise ValueError(msg)
check_value_positive('initial_noise_multiplier',
initial_noise_multiplier)
if noise_decay_mode is not None:
if noise_decay_mode not in ('Step', 'Time', 'Exp'):
msg = "Noise decay mode must be in ('Step', 'Time', 'Exp'), but got {}.".\
format(noise_decay_mode)
LOGGER.error(TAG, msg)
raise ValueError(msg)
noise_decay_rate = check_param_type('noise_decay_rate', noise_decay_rate, float)
check_param_in_range('noise_decay_rate', noise_decay_rate, 0.0, 1.0)
check_int_positive('per_print_times', per_print_times)
check_param_type('dataset_sink_mode', dataset_sink_mode, bool)
self._num_samples = num_samples
self._batch_size = batch_size
self._initial_noise_multiplier = initial_noise_multiplier
self._max_eps = check_value_positive('max_eps', max_eps)
self._target_delta = check_param_in_range('target_delta', target_delta, 0.0, 1.0)
self._noise_decay_mode = noise_decay_mode
self._noise_decay_rate = noise_decay_rate
# initialize zcdp
self._zcdp = 0
self._per_print_times = per_print_times
if dataset_sink_mode:
self._per_print_times = int(self._num_samples / self._batch_size)
def __init__(self, model, model_type='classification', targeted=False, reserve_ratio=0.3, sparse=True,
step_size=0.5, per_bounds=0.6, c1=2.0, c2=2.0, c=2.0, pop_size=6, t_max=1000, pm=0.5, bounds=None):
super(PSOAttack, self).__init__()
self._model = check_model('model', model, BlackModel)
self._step_size = check_value_positive('step_size', step_size)
self._per_bounds = check_value_positive('per_bounds', per_bounds)
self._c1 = check_value_positive('c1', c1)
self._c2 = check_value_positive('c2', c2)
self._c = check_value_positive('c', c)
self._pop_size = check_int_positive('pop_size', pop_size)
self._pm = check_value_non_negative('pm', pm)
if self._pm > 1:
msg = "pm should not be greater than 1.0, but got {}.".format(self._pm)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._bounds = bounds
if self._bounds is not None:
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
self._targeted = check_param_type('targeted', targeted, bool)
self._t_max = check_int_positive('t_max', t_max)
self._sparse = check_param_type('sparse', sparse, bool)
self._model_type = check_param_type('model_type', model_type, str)
if self._model_type not in ('classification', 'detection'):
msg = "Only 'classification' or 'detection' is supported now, but got {}.".format(self._model_type)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._reserve_ratio = check_value_non_negative('reserve_ratio', reserve_ratio)
if self._reserve_ratio > 1:
msg = "reserve_ratio should not be greater than 1.0, but got {}.".format(self._reserve_ratio)
LOGGER.error(TAG, msg)
raise ValueError(msg)
def __init__(self,
network,
eps=0.07,
alpha=None,
bounds=None,
loss_fn=None):
super(GradientMethod, self).__init__()
self._network = check_model('network', network, Cell)
self._eps = check_value_positive('eps', eps)
self._dtype = None
if bounds is not None:
self._bounds = check_param_multi_types('bounds', bounds,
[list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
else:
self._bounds = bounds
if alpha is not None:
self._alpha = check_value_positive('alpha', alpha)
else:
self._alpha = alpha
if loss_fn is None:
loss_fn = SoftmaxCrossEntropyWithLogits(is_grad=False,
sparse=False)
with_loss_cell = WithLossCell(self._network, loss_fn)
self._grad_all = GradWrapWithLoss(with_loss_cell)
self._grad_all.set_train()
def __init__(self, norm_bound=0.5, initial_noise_multiplier=1.5, seed=0):
super(GaussianRandom, self).__init__()
self._norm_bound = check_value_positive('norm_bound', norm_bound)
self._norm_bound = Tensor(norm_bound, mstype.float32)
self._initial_noise_multiplier = check_value_positive(
'initial_noise_multiplier', initial_noise_multiplier)
self._initial_noise_multiplier = Tensor(initial_noise_multiplier,
mstype.float32)
self._mean = Tensor(0, mstype.float32)
self._normal = P.Normal(seed=seed)
def __init__(self, norm_bound=1.5, initial_noise_multiplier=5.0):
super(GaussianRandom, self).__init__()
self._norm_bound = check_value_positive('norm_bound', norm_bound)
self._initial_noise_multiplier = check_value_positive(
'initial_noise_multiplier',
initial_noise_multiplier,
)
stddev = self._norm_bound * self._initial_noise_multiplier
self._stddev = stddev
self._mean = 0
def __init__(self, norm_bound=1.0, initial_noise_multiplier=1.0, seed=0, decay_policy=None):
super(NoiseGaussianRandom, self).__init__()
norm_bound = check_param_type('norm_bound', norm_bound, float)
self._norm_bound = check_value_positive('norm_bound', norm_bound)
self._norm_bound = Tensor(norm_bound, mstype.float32)
initial_noise_multiplier = check_param_type('initial_noise_multiplier', initial_noise_multiplier, float)
self._initial_noise_multiplier = check_value_positive('initial_noise_multiplier',
initial_noise_multiplier)
self._initial_noise_multiplier = Tensor(initial_noise_multiplier, mstype.float32)
self._mean = Tensor(0, mstype.float32)
if decay_policy is not None:
raise ValueError('decay_policy must be None in GaussianRandom class, but got {}.'.format(decay_policy))
self._decay_policy = decay_policy
seed = check_param_type('seed', seed, int)
self._seed = check_value_non_negative('seed', seed)
def __init__(self,
network,
eps=1e-5,
bounds=(0.0, 1.0),
is_targeted=True,
nb_iter=150,
search_iters=30,
loss_fn=None,
sparse=False):
super(LBFGS, self).__init__()
self._network = check_model('network', network, Cell)
self._eps = check_value_positive('eps', eps)
self._is_targeted = check_param_type('is_targeted', is_targeted, bool)
self._nb_iter = check_int_positive('nb_iter', nb_iter)
self._search_iters = check_int_positive('search_iters', search_iters)
if loss_fn is None:
loss_fn = SoftmaxCrossEntropyWithLogits(is_grad=False,
sparse=False)
with_loss_cell = WithLossCell(self._network, loss_fn)
self._grad_all = GradWrapWithLoss(with_loss_cell)
self._dtype = None
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
self._sparse = check_param_type('sparse', sparse, bool)
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
box_max, box_min = bounds
if box_max < box_min:
self._box_min = box_max
self._box_max = box_min
else:
self._box_min = box_min
self._box_max = box_max
def __init__(self, network, optimizer, sens=1.0, micro_batches=None, norm_clip=1.0, mech=None):
super(_TrainOneStepCell, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.network.add_flags(defer_inline=True)
self.weights = optimizer.parameters
self.optimizer = optimizer
self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
self.sens = sens
self.reducer_flag = False
self.grad_reducer = None
parallel_mode = _get_parallel_mode()
if parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL):
self.reducer_flag = True
if self.reducer_flag:
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
# dp params
self._micro_batches = micro_batches
norm_clip = check_param_type('norm_clip', norm_clip, float)
self._l2_norm = check_value_positive('norm_clip', norm_clip)
self._split = P.Split(0, self._micro_batches)
self._clip_by_global_norm = _ClipGradients()
self._mech = mech
self._tuple_add = _TupleAdd()
self._hyper_map = C.HyperMap()
self._micro_float = Tensor(micro_batches, mstype.float32)
def __init__(self, decay_policy='Linear', learning_rate=0.001,
target_unclipped_quantile=0.9, fraction_stddev=0.01, seed=0):
super(AdaClippingWithGaussianRandom, self).__init__()
if decay_policy not in ['Linear', 'Geometric']:
msg = "decay policy of adaptive clip must be in ['Linear', 'Geometric'], \
but got: {}".format(decay_policy)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._decay_policy = decay_policy
learning_rate = check_param_type('learning_rate', learning_rate, float)
learning_rate = check_value_positive('learning_rate', learning_rate)
self._learning_rate = Tensor(learning_rate, mstype.float32)
fraction_stddev = check_param_type('fraction_stddev', fraction_stddev, float)
self._fraction_stddev = Tensor(fraction_stddev, mstype.float32)
target_unclipped_quantile = check_param_type('target_unclipped_quantile',
target_unclipped_quantile,
float)
self._target_unclipped_quantile = Tensor(target_unclipped_quantile,
mstype.float32)
self._zero = Tensor(0, mstype.float32)
self._add = P.TensorAdd()
self._sub = P.Sub()
self._mul = P.Mul()
self._exp = P.Exp()
seed = check_param_type('seed', seed, int)
self._seed = check_value_non_negative('seed', seed)
def set_threshold(self, threshold):
"""
Set the parameters threshold.
Args:
threshold (float): Detection threshold. Default: None.
"""
self._threshold = check_value_positive('threshold', threshold)
def __init__(self, network, eps=0.3, eps_iter=0.1, bounds=(0.0, 1.0), nb_iter=5,
loss_fn=None):
super(IterativeGradientMethod, self).__init__()
self._network = check_model('network', network, Cell)
self._eps = check_value_positive('eps', eps)
self._eps_iter = check_value_positive('eps_iter', eps_iter)
self._nb_iter = check_int_positive('nb_iter', nb_iter)
self._bounds = None
if bounds is not None:
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
if loss_fn is None:
self._loss_grad = network
else:
self._loss_grad = GradWrapWithLoss(WithLossCell(self._network, loss_fn))
self._loss_grad.set_train()
def __init__(self,
model,
number_points=10,
initial_radius=0.0,
max_radius=1.0,
search_step=0.01,
degrade_limit=0.0,
sparse=False):
super(RegionBasedDetector, self).__init__()
self._model = check_model('targeted model', model, Model)
self._number_points = check_int_positive('number_points',
number_points)
self._initial_radius = check_value_non_negative(
'initial_radius', initial_radius)
self._max_radius = check_value_positive('max_radius', max_radius)
self._search_step = check_value_positive('search_step', search_step)
self._degrade_limit = check_value_non_negative('degrade_limit',
degrade_limit)
self._sparse = check_param_type('sparse', sparse, bool)
self._radius = None
def __init__(self,
network,
input_shape,
input_bound,
loss_weights=(1, 0.2, 5)):
self._network = check_param_type('network', network, Cell)
for sub_loss_weight in loss_weights:
check_value_positive('sub_loss_weight', sub_loss_weight)
self._loss = InversionLoss(self._network, loss_weights)
self._input_shape = check_param_type('input_shape', input_shape, tuple)
for shape_dim in input_shape:
check_int_positive('shape_dim', shape_dim)
self._input_bound = check_param_multi_types('input_bound', input_bound,
[list, tuple])
for value_bound in self._input_bound:
check_param_multi_types('value_bound', value_bound, [float, int])
if self._input_bound[0] > self._input_bound[1]:
msg = 'input_bound[0] should not be larger than input_bound[1], but got them as {} and {}'.format(
self._input_bound[0], self._input_bound[1])
raise ValueError(msg)
def set_threshold(self, num_of_neighbors, threshold):
"""
Set the parameters num_of_neighbors and threshold.
Args:
num_of_neighbors (int): Number of the nearest neighbors.
threshold (float): Detection threshold. Default: None.
"""
self._num_of_neighbors = check_int_positive('num_of_neighbors',
num_of_neighbors)
self._threshold = check_value_positive('threshold', threshold)
def __init__(self, network, eps=0.3, eps_iter=0.1, bounds=(0.0, 1.0),
is_targeted=False, nb_iter=5, decay_factor=1.0,
norm_level='inf', loss_fn=None):
super(MomentumIterativeMethod, self).__init__(network,
eps=eps,
eps_iter=eps_iter,
bounds=bounds,
nb_iter=nb_iter,
loss_fn=loss_fn)
self._is_targeted = check_param_type('is_targeted', is_targeted, bool)
self._decay_factor = check_value_positive('decay_factor', decay_factor)
self._norm_level = check_norm_level(norm_level)
def __init__(self, model, model_type='classification', targeted=True, reserve_ratio=0.3, sparse=True,
pop_size=6, mutation_rate=0.005, per_bounds=0.15, max_steps=1000, step_size=0.20, temp=0.3,
bounds=(0, 1.0), adaptive=False, c=0.1):
super(GeneticAttack, self).__init__()
self._model = check_model('model', model, BlackModel)
self._model_type = check_param_type('model_type', model_type, str)
if self._model_type not in ('classification', 'detection'):
msg = "Only 'classification' or 'detection' is supported now, but got {}.".format(self._model_type)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._targeted = check_param_type('targeted', targeted, bool)
self._reserve_ratio = check_value_non_negative('reserve_ratio', reserve_ratio)
if self._reserve_ratio > 1:
msg = "reserve_ratio should not be greater than 1.0, but got {}.".format(self._reserve_ratio)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._sparse = check_param_type('sparse', sparse, bool)
self._per_bounds = check_value_positive('per_bounds', per_bounds)
self._pop_size = check_int_positive('pop_size', pop_size)
self._step_size = check_value_positive('step_size', step_size)
self._temp = check_value_positive('temp', temp)
self._max_steps = check_int_positive('max_steps', max_steps)
self._mutation_rate = check_value_non_negative('mutation_rate', mutation_rate)
if self._mutation_rate > 1:
msg = "mutation_rate should not be greater than 1.0, but got {}.".format(self._mutation_rate)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._adaptive = check_param_type('adaptive', adaptive, bool)
# initial global optimum fitness value
self._best_fit = -np.inf
# count times of no progress
self._plateau_times = 0
# count times of changing attack step_size
self._adap_times = 0
self._bounds = bounds
if self._bounds is not None:
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
self._c = check_value_positive('c', c)
def __init__(self,
model,
step_size=0.5,
per_bounds=0.6,
c1=2.0,
c2=2.0,
c=2.0,
pop_size=6,
t_max=1000,
pm=0.5,
bounds=None,
targeted=False,
reduction_iters=3,
sparse=True):
super(PSOAttack, self).__init__()
self._model = check_model('model', model, BlackModel)
self._step_size = check_value_positive('step_size', step_size)
self._per_bounds = check_value_positive('per_bounds', per_bounds)
self._c1 = check_value_positive('c1', c1)
self._c2 = check_value_positive('c2', c2)
self._c = check_value_positive('c', c)
self._pop_size = check_int_positive('pop_size', pop_size)
self._pm = check_value_positive('pm', pm)
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
self._targeted = check_param_type('targeted', targeted, bool)
self._t_max = check_int_positive('t_max', t_max)
self._reduce_iters = check_int_positive('reduction_iters',
reduction_iters)
self._sparse = check_param_type('sparse', sparse, bool)
def __init__(self,
network,
num_classes,
box_min=0.0,
box_max=1.0,
bin_search_steps=5,
max_iterations=1000,
confidence=0,
learning_rate=5e-3,
initial_const=1e-2,
abort_early_check_ratio=5e-2,
targeted=False,
fast=True,
abort_early=True,
sparse=True):
LOGGER.info(TAG, "init CW object.")
super(CarliniWagnerL2Attack, self).__init__()
self._network = check_model('network', network, Cell)
self._network.set_grad(True)
self._num_classes = check_int_positive('num_classes', num_classes)
self._min = check_param_type('box_min', box_min, float)
self._max = check_param_type('box_max', box_max, float)
self._bin_search_steps = check_int_positive('search_steps',
bin_search_steps)
self._max_iterations = check_int_positive('max_iterations',
max_iterations)
self._confidence = check_param_multi_types('confidence', confidence,
[int, float])
self._learning_rate = check_value_positive('learning_rate',
learning_rate)
self._initial_const = check_value_positive('initial_const',
initial_const)
self._abort_early = check_param_type('abort_early', abort_early, bool)
self._fast = check_param_type('fast', fast, bool)
self._abort_early_check_ratio = check_value_positive(
'abort_early_check_ratio', abort_early_check_ratio)
self._targeted = check_param_type('targeted', targeted, bool)
self._net_grad = GradWrap(self._network)
self._sparse = check_param_type('sparse', sparse, bool)
self._dtype = None
def __init__(self, network, optimizer, scale_update_cell=None, micro_batches=None, norm_clip=1.0, mech=None):
super(_TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.network.add_flags(defer_inline=True)
self.weights = ParameterTuple(network.trainable_params())
self.optimizer = optimizer
self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
self.hyper_map = C.HyperMap()
if context.get_context("device_target") == "GPU":
self.gpu_target = True
self.float_status = P.FloatStatus()
self.addn = P.AddN()
self.reshape = P.Reshape()
else:
self.gpu_target = False
self.alloc_status = NPUAllocFloatStatus()
self.get_status = NPUGetFloatStatus()
self.clear_status = NPUClearFloatStatus()
self.reduce_sum = ReduceSum(keep_dims=False)
self.base = Tensor(1, mstype.float32)
self.less_equal = LessEqual()
self.depend_parameter_use = ControlDepend(depend_mode=1)
self.allreduce = P.AllReduce()
self.parallel_mode = _get_parallel_mode()
self.grad_reducer = F.identity
self.reducer_flag = self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]
if self.reducer_flag:
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
self.is_distributed = self.parallel_mode != ParallelMode.STAND_ALONE
self.loss_scale = None
self.loss_scaling_manager = scale_update_cell
if scale_update_cell:
self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
self.add_flags(has_effect=True)
# dp params
self._micro_batches = micro_batches
norm_clip = check_param_type('norm_clip', norm_clip, float)
self._l2_norm = check_value_positive('norm_clip', norm_clip)
self._split = P.Split(0, self._micro_batches)
self._clip_by_global_norm = _ClipGradients()
self._mech = mech
self._tuple_add = _TupleAdd()
self._hyper_map = C.HyperMap()
self._micro_float = Tensor(micro_batches, mstype.float32)
def __init__(self, network, num_classes, max_iters=50, overshoot=0.02,
norm_level=2, bounds=None, sparse=True):
super(DeepFool, self).__init__()
self._network = check_model('network', network, Cell)
self._network.set_grad(True)
self._max_iters = check_int_positive('max_iters', max_iters)
self._overshoot = check_value_positive('overshoot', overshoot)
self._norm_level = check_norm_level(norm_level)
self._num_classes = check_int_positive('num_classes', num_classes)
self._net_grad = GradWrap(self._network)
self._bounds = check_param_multi_types('bounds', bounds, [list, tuple])
self._sparse = check_param_type('sparse', sparse, bool)
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
def __init__(self,
micro_batches=2,
norm_bound=1.0,
noise_mech=None,
clip_mech=None,
**kwargs):
if micro_batches:
self._micro_batches = check_int_positive('micro_batches',
micro_batches)
else:
self._micro_batches = None
norm_bound = check_param_type('norm_bound', norm_bound, float)
norm_bound = check_value_positive('norm_bound', norm_bound)
norm_bound = Tensor(norm_bound, mstype.float32)
self._norm_bound = Parameter(norm_bound, 'norm_bound')
opt = kwargs['optimizer']
opt_name = opt.__class__.__name__
# Check whether noise_mech and DPOptimizer are both None or not None, if so, raise ValueError.
# And check whether noise_mech or DPOtimizer's mech method is adaptive while clip_mech is not None,
# if so, rasie ValuerError too.
if noise_mech is not None and "DPOptimizer" in opt_name:
msg = 'DPOptimizer is not supported while noise_mech is not None'
LOGGER.error(TAG, msg)
raise ValueError(msg)
if noise_mech is None:
if "DPOptimizer" in opt_name:
if context.get_context('mode') != context.PYNATIVE_MODE:
msg = 'DPOptimizer just support pynative mode currently.'
LOGGER.error(TAG, msg)
raise ValueError(msg)
if 'Ada' in opt._mech.__class__.__name__ and clip_mech is not None:
msg = "When DPOptimizer's mech method is adaptive, clip_mech must be None."
LOGGER.error(TAG, msg)
raise ValueError(msg)
else:
msg = 'DPModel should set noise_mech or DPOptimizer configure, ' \
'please refer to example.'
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._noise_mech = noise_mech
if noise_mech is not None:
if 'Ada' in noise_mech.__class__.__name__ and clip_mech is not None:
msg = 'When noise_mech is Adaptive, clip_mech must be None.'
LOGGER.error(TAG, msg)
raise ValueError(msg)
if clip_mech is None or isinstance(clip_mech, Cell):
self._clip_mech = clip_mech
super(DPModel, self).__init__(**kwargs)
def __init__(self,
norm_bound=1.0,
initial_noise_multiplier=1.5,
noise_decay_rate=6e-4,
decay_policy='Time',
seed=0):
super(AdaGaussianRandom, self).__init__()
norm_bound = check_value_positive('norm_bound', norm_bound)
initial_noise_multiplier = check_value_positive(
'initial_noise_multiplier', initial_noise_multiplier)
self._norm_bound = Tensor(norm_bound, mstype.float32)
initial_noise_multiplier = Tensor(initial_noise_multiplier,
mstype.float32)
self._initial_noise_multiplier = Parameter(
initial_noise_multiplier, name='initial_noise_multiplier')
self._noise_multiplier = Parameter(initial_noise_multiplier,
name='noise_multiplier')
self._mean = Tensor(0, mstype.float32)
noise_decay_rate = check_param_type('noise_decay_rate',
noise_decay_rate, float)
check_param_in_range('noise_decay_rate', noise_decay_rate, 0.0, 1.0)
self._noise_decay_rate = Tensor(noise_decay_rate, mstype.float32)
if decay_policy not in ['Time', 'Step']:
raise NameError(
"The decay_policy must be in ['Time', 'Step'], but "
"get {}".format(decay_policy))
self._decay_policy = decay_policy
self._sub = P.Sub()
self._mul = P.Mul()
self._add = P.TensorAdd()
self._div = P.Div()
self._dtype = mstype.float32
self._normal = P.Normal(seed=seed)
self._assign = P.Assign()
self._one = Tensor(1, self._dtype)
def __init__(self, network, num_classes, box_min=0.0, box_max=1.0,
theta=1.0, max_iteration=1000, max_count=3, increase=True,
sparse=True):
super(JSMAAttack).__init__()
LOGGER.debug(TAG, "init jsma class.")
self._network = check_model('network', network, Cell)
self._min = check_value_non_negative('box_min', box_min)
self._max = check_value_non_negative('box_max', box_max)
self._num_classes = check_int_positive('num_classes', num_classes)
self._theta = check_value_positive('theta', theta)
self._max_iter = check_int_positive('max_iteration', max_iteration)
self._max_count = check_int_positive('max_count', max_count)
self._increase = check_param_type('increase', increase, bool)
self._net_grad = GradWrap(self._network)
self._bit_map = None
self._sparse = check_param_type('sparse', sparse, bool)
def __init__(self,
model,
init_num_evals=100,
max_num_evals=1000,
stepsize_search='geometric_progression',
num_iterations=20,
gamma=1.0,
constraint='l2',
batch_size=32,
clip_min=0.0,
clip_max=1.0,
sparse=True):
super(HopSkipJumpAttack, self).__init__()
self._model = check_model('model', model, BlackModel)
self._init_num_evals = check_int_positive('initial_num_evals',
init_num_evals)
self._max_num_evals = check_int_positive('max_num_evals',
max_num_evals)
self._batch_size = check_int_positive('batch_size', batch_size)
self._clip_min = check_value_non_negative('clip_min', clip_min)
self._clip_max = check_value_non_negative('clip_max', clip_max)
self._sparse = check_param_type('sparse', sparse, bool)
self._np_dtype = np.dtype('float32')
if stepsize_search in ['geometric_progression', 'grid_search']:
self._stepsize_search = stepsize_search
else:
msg = "stepsize_search must be in ['geometric_progression'," \
" 'grid_search'], but got {}".format(stepsize_search)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._num_iterations = check_int_positive('num_iterations',
num_iterations)
self._gamma = check_value_positive('gamma', gamma)
if constraint in ['l2', 'linf']:
self._constraint = constraint
else:
msg = "constraint must be in ['l2', 'linf'], " \
"but got {}".format(constraint)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self.queries = 0
self.is_adv = True
self.y_targets = None
self.image_targets = None
self.y_target = None
self.image_target = None
def __init__(self, micro_batches=2, norm_clip=1.0, mech=None, **kwargs):
if micro_batches:
self._micro_batches = check_int_positive('micro_batches', micro_batches)
else:
self._micro_batches = None
norm_clip = check_param_type('norm_clip', norm_clip, float)
self._norm_clip = check_value_positive('norm_clip', norm_clip)
if mech is not None and "DPOptimizer" in kwargs['optimizer'].__class__.__name__:
raise ValueError('DPOptimizer is not supported while mech is not None')
if mech is None:
if "DPOptimizer" in kwargs['optimizer'].__class__.__name__:
if context.get_context('mode') != context.PYNATIVE_MODE:
raise ValueError('DPOptimizer just support pynative mode currently.')
else:
raise ValueError('DPModel should set mech or DPOptimizer configure, please refer to example.')
self._mech = mech
super(DPModel, self).__init__(**kwargs)
def __init__(self,
layer_name,
grad_idx,
is_add_noise,
is_lower_clip,
min_num,
upper_bound=1.20):
self.layer_name = check_param_type('layer_name', layer_name, str)
check_param_type('grad_idx', grad_idx, int)
self.grad_idx = check_value_non_negative('grad_idx', grad_idx)
self.is_add_noise = check_param_type('is_add_noise', is_add_noise,
bool)
self.is_lower_clip = check_param_type('is_lower_clip', is_lower_clip,
bool)
self.min_num = check_param_type('min_num', min_num, int)
self.upper_bound = check_value_positive('upper_bound', upper_bound)
self.inited = False
def __init__(self,
array,
is_add_noise,
is_lower_clip,
min_num,
upper_bound=1.20):
super(GradMaskInCell, self).__init__()
self.mul_mask_array_shape = array.shape
mul_mask_array = array.copy()
self.mul_mask_array_flat = mul_mask_array.flatten()
self.mul_mask_tensor = Tensor(array, mstype.float32)
self.mask_able = False
self.is_add_noise = is_add_noise
self.is_lower_clip = is_lower_clip
self.min_num = min_num
self.upper_bound = max(
0.10, check_value_positive('upper_bound', upper_bound))
self.para_num = array.size
self.is_approximity = False
self.sparse_pos_list = [0]
self.part_num = 1
self.part_size = self.para_num
self.part_num_max = 16
self.para_many_num = 10000
self.para_huge_num = 10 * 10000 * 10000
if self.para_num > self.para_many_num:
self.is_approximity = True
self.is_add_noise = False
self.is_lower_clip = False
ratio = 2
if self.part_size > self.para_huge_num:
while self.part_size % ratio == 0 and self.part_size > self.para_huge_num \
and self.part_num < self.part_num_max:
self.part_num = self.part_num * ratio
self.part_size = int(self.part_size / ratio)
msg = "this layer has {} para, disable the operation of clipping lower, clipping upper_bound, " \
"adding noise. \n part_num={}, part_size={}" \
.format(self.para_num, self.part_num, self.part_size)
LOGGER.info(TAG, msg)
def __init__(self,
network,
num_classes,
model_type='classification',
reserve_ratio=0.3,
max_iters=50,
overshoot=0.02,
norm_level=2,
bounds=None,
sparse=True):
super(DeepFool, self).__init__()
self._network = check_model('network', network, Cell)
self._max_iters = check_int_positive('max_iters', max_iters)
self._overshoot = check_value_positive('overshoot', overshoot)
self._norm_level = check_norm_level(norm_level)
self._num_classes = check_int_positive('num_classes', num_classes)
self._net_grad = GradWrap(self._network)
self._bounds = bounds
if self._bounds is not None:
self._bounds = check_param_multi_types('bounds', bounds,
[list, tuple])
for b in self._bounds:
_ = check_param_multi_types('bound', b, [int, float])
self._sparse = check_param_type('sparse', sparse, bool)
self._model_type = check_param_type('model_type', model_type, str)
if self._model_type not in ('classification', 'detection'):
msg = "Only 'classification' or 'detection' is supported now, but got {}.".format(
self._model_type)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._reserve_ratio = check_value_non_negative('reserve_ratio',
reserve_ratio)
if self._reserve_ratio > 1:
msg = 'reserve_ratio should be less than 1.0, but got {}.'.format(
self._reserve_ratio)
LOGGER.error(TAG, msg)
raise ValueError(TAG, msg)
