general inclusion of pylint: disable=W0221. Updates to randomized/der…

…andomized tools

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

art/estimators/certification/derandomized_smoothing/pytorch.py

@@ -469,16 +469,7 @@ def fit(  # pylint: disable=W0221
         """
         import torch
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = False
+        display_pb = self.process_verbose(verbose)
 
         # Set model mode
         self._model.train(mode=training_mode)

art/estimators/certification/derandomized_smoothing/tensorflow.py

@@ -178,16 +178,7 @@ def fit(  # pylint: disable=W0221
         """
         import tensorflow as tf
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = False
+        display_pb = self.process_verbose(verbose)
 
         if self._train_step is None:  # pragma: no cover
             if self._loss_object is None:  # pragma: no cover

art/estimators/certification/randomized_smoothing/macer/pytorch.py

@@ -163,16 +163,7 @@ class was initialised.
         import torch.nn.functional as F
         from torch.utils.data import TensorDataset, DataLoader
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = self.verbose
+        display_pb = self.process_verbose(verbose)
 
         # Set model mode
         self._model.train(mode=training_mode)

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

@@ -157,16 +157,7 @@ class was initialised.
         """
         import tensorflow as tf
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = self.verbose
+        display_pb = self.process_verbose(verbose)
 
         if self._train_step is None:  # pragma: no cover
             if self._optimizer is None:  # pragma: no cover

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

@@ -179,16 +179,7 @@ class was initialised.
         import torch
         from torch.utils.data import TensorDataset, DataLoader
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = self.verbose
+        display_pb = self.process_verbose(verbose)
 
         # Set model mode
         self._model.train(mode=training_mode)

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

@@ -174,16 +174,7 @@ class was initialised.
         """
         import tensorflow as tf
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = self.verbose
+        display_pb = self.process_verbose(verbose)
 
         if self._train_step is None:  # pragma: no cover
             if self._loss_object is None:  # pragma: no cover

art/estimators/certification/randomized_smoothing/smooth_mix/pytorch.py

@@ -197,16 +197,7 @@ class was initialised.
         import torch.nn.functional as F
         from torch.utils.data import TensorDataset, DataLoader
 
-        if verbose is not None:
-            if isinstance(verbose, int):
-                if verbose == 0:
-                    display_pb = False
-                else:
-                    display_pb = True
-            else:
-                display_pb = verbose
-        else:
-            display_pb = self.verbose
+        display_pb = self.process_verbose(verbose)
 
         # Set model mode
         self._model.train(mode=training_mode)

art/estimators/certification/randomized_smoothing/tensorflow.py

@@ -131,7 +131,7 @@ def _predict_classifier(self, x: np.ndarray, batch_size: int, training_mode: boo
     def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
         return TensorFlowV2Classifier.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
 
-    def fit(
+    def fit(  # pylint: disable=W0221
         self,
         x: np.ndarray,
         y: np.ndarray,

art/estimators/classification/pytorch.py

@@ -388,7 +388,7 @@ def process_verbose(self, verbose: Optional[Union[bool, int]] = None) -> bool:
         else:
             # Check if the verbose attribute is present in the current classifier
             if hasattr(self, "verbose"):
-                display_pb = self.verbose
+                display_pb = self.verbose  # type: ignore
             # else default to False
             else:
                 display_pb = False
@@ -487,7 +487,7 @@ def fit(  # pylint: disable=W0221
             if scheduler is not None:
                 scheduler.step()
 
-    def fit_generator(
+    def fit_generator(  # pylint: disable=W0221
         self, generator: "DataGenerator", nb_epochs: int = 20, verbose: Optional[Union[bool, int]] = None, **kwargs
     ) -> None:
         """

art/estimators/classification/tensorflow.py

@@ -287,7 +287,7 @@ def process_verbose(self, verbose: Optional[Union[bool, int]] = None) -> bool:
         else:
             # Check if the verbose attribute is present in the current classifier
             if hasattr(self, "verbose"):
-                display_pb = self.verbose
+                display_pb = self.verbose  # type: ignore
             # else default to False
             else:
                 display_pb = False
@@ -1024,14 +1024,14 @@ def process_verbose(self, verbose: Optional[Union[bool, int]] = None) -> bool:
         else:
             # Check if the verbose attribute is present in the current classifier
             if hasattr(self, "verbose"):
-                display_pb = self.verbose
+                display_pb = self.verbose  # type: ignore
             # else default to False
             else:
                 display_pb = False
 
         return display_pb
 
-    def fit(
+    def fit(  # pylint: disable=W0221
         self,
         x: np.ndarray,
         y: np.ndarray,
@@ -1100,7 +1100,7 @@ def train_step(model, images, labels):
             if scheduler is not None:
                 scheduler(epoch)
 
-    def fit_generator(
+    def fit_generator(  # pylint: disable=W0221
         self, generator: "DataGenerator", nb_epochs: int = 20, verbose: Optional[Union[bool, int]] = None, **kwargs
     ) -> None:
         """