mypy fixes

Signed-off-by: GiulioZizzo <giulio.zizzo@yahoo.co.uk>

art/estimators/certification/randomized_smoothing/pytorch.py

@@ -222,17 +222,20 @@ def fit(  # pylint: disable=W0221
             if scheduler is not None:
                 scheduler.step()
 
-    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:  # type: ignore
+    def predict(self, x: np.ndarray, batch_size: int = 128, verbose: bool = False, **kwargs) -> np.ndarray:  # type: ignore
         """
         Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
 
         :param x: Input samples.
         :param batch_size: Batch size.
+        :param verbose: Display training progress bar.
         :param is_abstain: True if function will abstain from prediction and return 0s. Default: True
         :type is_abstain: `boolean`
         :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
         """
-        return RandomizedSmoothingMixin.predict(self, x, batch_size=batch_size, training_mode=False, **kwargs)
+        return RandomizedSmoothingMixin.predict(
+            self, x, batch_size=batch_size, verbose=verbose, training_mode=False, **kwargs
+        )
 
     def loss_gradient(  # type: ignore
         self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs

art/estimators/certification/randomized_smoothing/smooth_adv/tensorflow.py

@@ -125,7 +125,6 @@ def __init__(
             sample_size=sample_size,
             scale=scale,
             alpha=alpha,
-            verbose=verbose,
         )
         self.epsilon = epsilon
         self.num_noise_vec = num_noise_vec

art/estimators/certification/randomized_smoothing/tensorflow.py

@@ -70,7 +70,6 @@ def __init__(
         sample_size: int = 32,
         scale: float = 0.1,
         alpha: float = 0.001,
-        verbose: bool = False,
     ):
         """
         Create a randomized smoothing classifier.
@@ -192,17 +191,20 @@ def train_step(model, images, labels):
             if scheduler is not None:
                 scheduler(epoch)
 
-    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:  # type: ignore
+    def predict(self, x: np.ndarray, batch_size: int = 128, verbose: bool = False, **kwargs) -> np.ndarray:  # type: ignore
         """
         Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
 
         :param x: Input samples.
         :param batch_size: Batch size.
+        :param verbose: Display training progress bar.
         :param is_abstain: True if function will abstain from prediction and return 0s. Default: True
         :type is_abstain: `boolean`
         :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
         """
-        return RandomizedSmoothingMixin.predict(self, x, batch_size=batch_size, training_mode=False, **kwargs)
+        return RandomizedSmoothingMixin.predict(
+            self, x, batch_size=batch_size, verbose=verbose, training_mode=False, **kwargs
+        )
 
     def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
         """