@@ -159,7 +159,10 @@ class AdaGaussianRandom(Mechanisms): | |||||
alpha = check_param_type('alpha', alpha, float) | alpha = check_param_type('alpha', alpha, float) | ||||
self._alpha = Tensor(np.array(alpha, np.float32)) | self._alpha = Tensor(np.array(alpha, np.float32)) | ||||
self._decay_policy = check_param_type('decay_policy', decay_policy, str) | |||||
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._mean = 0.0 | self._mean = 0.0 | ||||
self._sub = P.Sub() | self._sub = P.Sub() | ||||
self._mul = P.Mul() | self._mul = P.Mul() | ||||
@@ -43,7 +43,7 @@ def check_param_type(arg_name, arg_value, valid_type): | |||||
valid_type, | valid_type, | ||||
type(arg_value).__name__) | type(arg_value).__name__) | ||||
LOGGER.error(TAG, msg) | LOGGER.error(TAG, msg) | ||||
raise ValueError(msg) | |||||
raise TypeError(msg) | |||||
return arg_value | return arg_value | ||||
@@ -54,7 +54,7 @@ def check_param_multi_types(arg_name, arg_value, valid_types): | |||||
msg = 'type of {} must be in {}, but got {}' \ | msg = 'type of {} must be in {}, but got {}' \ | ||||
.format(arg_name, valid_types, type(arg_value).__name__) | .format(arg_name, valid_types, type(arg_value).__name__) | ||||
LOGGER.error(TAG, msg) | LOGGER.error(TAG, msg) | ||||
raise ValueError(msg) | |||||
raise TypeError(msg) | |||||
return arg_value | return arg_value | ||||
@@ -157,7 +157,7 @@ def check_numpy_param(arg_name, arg_value): | |||||
msg = 'type of {} must be in (list, tuple, numpy.ndarray)'.format( | msg = 'type of {} must be in (list, tuple, numpy.ndarray)'.format( | ||||
arg_name) | arg_name) | ||||
LOGGER.error(TAG, msg) | LOGGER.error(TAG, msg) | ||||
raise ValueError(msg) | |||||
raise TypeError(msg) | |||||
return arg_value | return arg_value | ||||
@@ -167,7 +167,7 @@ def test_momentum_diverse_input_iterative_method(): | |||||
@pytest.mark.env_card | @pytest.mark.env_card | ||||
@pytest.mark.component_mindarmour | @pytest.mark.component_mindarmour | ||||
def test_error(): | def test_error(): | ||||
with pytest.raises(ValueError): | |||||
with pytest.raises(TypeError): | |||||
# check_param_multi_types | # check_param_multi_types | ||||
assert IterativeGradientMethod(Net(), bounds=None) | assert IterativeGradientMethod(Net(), bounds=None) | ||||
attack = IterativeGradientMethod(Net(), bounds=(0.0, 1.0)) | attack = IterativeGradientMethod(Net(), bounds=(0.0, 1.0)) | ||||
@@ -100,16 +100,16 @@ def test_value_error(): | |||||
with pytest.raises(ValueError): | with pytest.raises(ValueError): | ||||
assert RegionBasedDetector(model, search_step=0) | assert RegionBasedDetector(model, search_step=0) | ||||
with pytest.raises(ValueError): | |||||
with pytest.raises(TypeError): | |||||
assert RegionBasedDetector(model, sparse='False') | assert RegionBasedDetector(model, sparse='False') | ||||
detector = RegionBasedDetector(model) | detector = RegionBasedDetector(model) | ||||
with pytest.raises(ValueError): | |||||
with pytest.raises(TypeError): | |||||
# radius must not empty | # radius must not empty | ||||
assert detector.detect(adv) | assert detector.detect(adv) | ||||
radius = detector.fit(ori, labels) | radius = detector.fit(ori, labels) | ||||
detector.set_radius(radius) | detector.set_radius(radius) | ||||
with pytest.raises(ValueError): | |||||
with pytest.raises(TypeError): | |||||
# adv type should be in (list, tuple, numpy.ndarray) | # adv type should be in (list, tuple, numpy.ndarray) | ||||
assert detector.detect(adv.tostring()) | assert detector.detect(adv.tostring()) |