def is_adversarial(perturbed: ep.Tensor,
logits: ep.Tensor) -> ep.Tensor:
if change_classes_logits != 0:
logits += ep.onehot_like(logits,
classes,
value=change_classes_logits)
return criterion_(perturbed, logits)
def test_onehot_like_raises(dummy: Tensor) -> None:
t = ep.arange(dummy, 18).float32().reshape((6, 3))
indices = ep.arange(t, 6) // 2
ep.onehot_like(t, indices)
t = ep.arange(dummy, 90).float32().reshape((6, 3, 5))
indices = ep.arange(t, 6) // 2
with pytest.raises(ValueError):
ep.onehot_like(t, indices)
t = ep.arange(dummy, 18).float32().reshape((6, 3))
indices = ep.arange(t, 6).reshape((6, 1)) // 2
with pytest.raises(ValueError):
ep.onehot_like(t, indices)
t = ep.arange(dummy, 18).float32().reshape((6, 3))
indices = ep.arange(t, 5) // 2
with pytest.raises(ValueError):
ep.onehot_like(t, indices)
def best_other_classes(logits, exclude):
other_logits = logits - ep.onehot_like(logits, exclude, value=np.inf)
return other_logits.argmax(axis=-1)
def targeted_is_adv(logits: ep.Tensor, target_classes: ep.Tensor,
confidence) -> ep.Tensor:
logits = logits - ep.onehot_like(logits, target_classes, value=confidence)
classes = logits.argmax(axis=-1)
return classes == target_classes
def untargeted_is_adv(logits: ep.Tensor, labels: ep.Tensor,
confidence) -> ep.Tensor:
logits = logits + ep.onehot_like(logits, labels, value=confidence)
classes = logits.argmax(axis=-1)
return classes != labels
def test_onehot_like(dummy: Tensor, value: float) -> Tensor:
t = ep.arange(dummy, 18).float32().reshape((6, 3))
indices = ep.arange(t, 6) // 2
return ep.onehot_like(t, indices, value=value)
def best_other_classes(logits: ep.Tensor, exclude: ep.Tensor) -> ep.Tensor:
other_logits = logits - ep.onehot_like(logits, exclude, value=ep.inf)
return other_logits.argmax(axis=-1)
