Using clip values in notebook. CI error fixes

Signed-off-by: GiulioZizzo <[email protected]>

art/attacks/poisoning/backdoor_attack.py

@@ -54,15 +54,14 @@ def __init__(self, perturbation: Union[Callable, List[Callable]]) -> None:
         self._check_params()
 
     def poison(  # pylint: disable=W0221
-        self, x: np.ndarray, y: Optional[np.ndarray] = None, broadcast=False, channels_first: bool = False, **kwargs
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, broadcast=False, **kwargs
     ) -> Tuple[np.ndarray, np.ndarray]:
         """
         Calls perturbation function on input x and returns the perturbed input and poison labels for the data.
 
         :param x: An array with the points that initialize attack points.
         :param y: The target labels for the attack.
         :param broadcast: whether or not to broadcast single target label
-        :param channels_first: it the data is fed in channels_first foramt
         :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
         """
         if y is None:  # pragma: no cover

art/attacks/poisoning/clean_label_backdoor_attack.py

@@ -102,20 +102,14 @@ def __init__(
         self._check_params()
 
     def poison(  # pylint: disable=W0221
-        self,
-        x: np.ndarray,
-        y: Optional[np.ndarray] = None,
-        broadcast: bool = True,
-        channels_first: bool = False,
-        **kwargs
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, broadcast: bool = True, **kwargs
     ) -> Tuple[np.ndarray, np.ndarray]:
         """
         Calls perturbation function on input x and returns the perturbed input and poison labels for the data.
 
         :param x: An array with the points that initialize attack points.
         :param y: The target labels for the attack.
         :param broadcast: whether or not to broadcast single target label
-        :param channels_first: it the data is fed in channels_first foramt
         :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
         """
         data = np.copy(x)

conftest.py

@@ -550,7 +550,7 @@ def _image_dl_gan(**kwargs):
 
 
 @pytest.fixture
-def image_dl_estimator(framework):
+def image_dl_estimator(framework, get_image_classifier_mx_instance):
     def _image_dl_estimator(functional=False, **kwargs):
         sess = None
         wildcard = False

tests/attacks/poison/test_clean_label_backdoor_attack.py

@@ -37,17 +37,20 @@ def test_poison(art_warning, get_default_mnist_subset, image_dl_estimator, frame
         classifier, _ = image_dl_estimator()
         target = to_categorical([9], 10)[0]
         if framework in ["pytorch", "huggingface"]:
+
             def mod(x):
                 original_dtype = x.dtype
                 x = add_pattern_bd(x, channels_first=True)
                 return x.astype(original_dtype)
+
         else:
+
             def mod(x):
                 original_dtype = x.dtype
                 x = add_pattern_bd(x)
                 return x.astype(original_dtype)
 
-        backdoor = PoisoningAttackBackdoor(add_pattern_bd)
+        backdoor = PoisoningAttackBackdoor(mod)
         attack = PoisoningAttackCleanLabelBackdoor(backdoor, classifier, target)
         poison_data, poison_labels = attack.poison(x_train, y_train)