def test_samples_large_batch(fmodel_and_data: ModelAndData, batchsize: int,
dataset: str) -> None:
fmodel, _, _ = fmodel_and_data
data_format = getattr(fmodel, "data_format", "channels_first")
with pytest.warns(UserWarning, match="only 20 samples"):
x, y = fbn.samples(fmodel,
dataset=dataset,
batchsize=batchsize,
data_format=data_format)
assert len(x) == len(y) == batchsize
assert not ep.istensor(x)
assert not ep.istensor(y)
def test_forward_wrapped(fmodel_and_data: ModelAndData) -> None:
fmodel, x, y = fmodel_and_data
assert ep.istensor(x)
logits = fmodel(x)
assert ep.istensor(logits)
assert logits.ndim == 2
assert len(logits) == len(x) == len(y)
_, num_classes = logits.shape
assert (y >= 0).all()
assert (y < num_classes).all()
if hasattr(logits.raw, "requires_grad"):
assert not logits.raw.requires_grad
def test_call_one_epsilon(
fmodel_and_data_ext_for_attacks: ModelDescriptionAndData, attack: fbn.Attack,
) -> None:
(fmodel, x, y), _ = fmodel_and_data_ext_for_attacks
assert ep.istensor(x)
assert ep.istensor(y)
raw, clipped, success = attack(fmodel, x, y, epsilons=1.0)
assert ep.istensor(raw)
assert ep.istensor(clipped)
assert ep.istensor(success)
assert raw.shape == x.shape
assert clipped.shape == x.shape
assert success.shape == (len(x),)
def wrap(*args):
"""Wraps all inputs as EagerPy tensors if they are not already wrapped
and returns a function restoring the original format"""
if len(args) == 0:
return args
restore = wrap_ if ep.istensor(args[0]) else unwrap_
result = wrap_(*args)
return (result, restore) if len(args) == 1 else (*result, restore)
def test_call_one_epsilon(
fmodel_and_data_ext_for_attacks: Tuple[Tuple[fbn.Model, ep.Tensor,
ep.Tensor], bool],
attack: fbn.Attack,
) -> None:
(fmodel, x, y), _ = fmodel_and_data_ext_for_attacks
assert ep.istensor(x)
assert ep.istensor(y)
raw, clipped, success = attack(fmodel, x, y, epsilons=1.0)
assert ep.istensor(raw)
assert ep.istensor(clipped)
assert ep.istensor(success)
assert raw.shape == x.shape
assert clipped.shape == x.shape
assert success.shape == (len(x), )
def test_samples(fmodel_and_data: ModelAndData, batchsize: int,
dataset: str) -> None:
fmodel, _, _ = fmodel_and_data
if hasattr(fmodel, "data_format"):
data_format = fmodel.data_format # type: ignore
x, y = fbn.samples(fmodel, dataset=dataset, batchsize=batchsize)
assert len(x) == len(y) == batchsize
assert not ep.istensor(x)
assert not ep.istensor(y)
x, y = fbn.samples(fmodel,
batchsize=batchsize,
data_format=data_format)
assert len(x) == len(y) == batchsize
assert not ep.istensor(x)
assert not ep.istensor(y)
with pytest.raises(ValueError):
data_format = {
"channels_first": "channels_last",
"channels_last": "channels_first",
}[data_format]
fbn.samples(fmodel, batchsize=batchsize, data_format=data_format)
else:
x, y = fbn.samples(fmodel,
batchsize=batchsize,
data_format="channels_first")
assert len(x) == len(y) == batchsize
assert not ep.istensor(x)
assert not ep.istensor(y)
with pytest.raises(ValueError):
fbn.samples(fmodel, batchsize=batchsize)
def get_numpy_kwargs(kwargs: Any) -> Dict:
return {
k: ep.astensor(t.numpy()) if ep.istensor(t) else t
for k, t in kwargs.items()
}
def test_module() -> None:
assert ep.istensor(ep.numpy.tanh([3, 5]))
assert not ep.istensor(ep.numpy.tanh(3))
def unwrap_(*args):
"""Unwraps all EagerPy tensors if they are not already unwrapped"""
result = tuple(t.tensor if ep.istensor(t) else t for t in args)
return result[0] if len(args) == 1 else result