def test_genattack_numpy(request: Any) -> None:
class Model:
def __call__(self, inputs: Any) -> Any:
return inputs.mean(axis=(2, 3))
model = Model()
with pytest.raises(ValueError):
fbn.NumPyModel(model, bounds=(0, 1), data_format="foo")
fmodel = fbn.NumPyModel(model, bounds=(0, 1))
x, y = ep.astensors(
*fbn.samples(
fmodel, dataset="imagenet", batchsize=16, data_format="channels_first"
)
)
with pytest.raises(ValueError, match="data_format"):
fbn.attacks.GenAttack(reduced_dims=(2, 2)).run(
fmodel, x, fbn.TargetedMisclassification(y), epsilon=0.3
)
with pytest.raises(ValueError, match="channel_axis"):
fbn.attacks.GenAttack(channel_axis=2, reduced_dims=(2, 2)).run(
fmodel, x, fbn.TargetedMisclassification(y), epsilon=0.3
)
def test_blur_numpy(request: Any) -> None:
class Model:
def __call__(self, inputs: Any) -> Any:
return inputs.mean(axis=(2, 3))
model = Model()
with pytest.raises(ValueError):
fbn.NumPyModel(model, bounds=(0, 1), data_format="foo")
fmodel = fbn.NumPyModel(model, bounds=(0, 1))
x, y = ep.astensors(*fbn.samples(
fmodel, dataset="imagenet", batchsize=16,
data_format="channels_first"))
with pytest.raises(ValueError, match="data_format"):
fbn.attacks.GaussianBlurAttack()(fmodel, x, y, epsilons=None)
def numpy_simple_model(request: Any) -> ModelAndData:
class Model:
def __call__(self, inputs: Any) -> Any:
return inputs.mean(axis=(2, 3))
model = Model()
with pytest.raises(ValueError):
fbn.NumPyModel(model, bounds=(0, 1), data_format="foo")
fmodel = fbn.NumPyModel(model, bounds=(0, 1))
with pytest.raises(ValueError, match="data_format"):
x, _ = fbn.samples(fmodel, dataset="imagenet", batchsize=16)
fmodel = fbn.NumPyModel(model, bounds=(0, 1), data_format="channels_first")
with pytest.warns(UserWarning, match="returning NumPy arrays"):
x, _ = fbn.samples(fmodel, dataset="imagenet", batchsize=16)
x = ep.astensor(x)
y = fmodel(x).argmax(axis=-1)
return fmodel, x, y