mypy fixes

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

Author: GiulioZizzo

art/estimators/certification/derandomized_smoothing/pytorch.py

@@ -438,10 +438,10 @@ def fit(  # pylint: disable=W0221
         training_mode: bool = True,
         drop_last: bool = False,
         scheduler: Optional[Any] = None,
+        verbose: Optional[Union[bool, int]] = None,
         update_batchnorm: bool = True,
         batchnorm_update_epochs: int = 1,
         transform: Optional["torchvision.transforms.transforms.Compose"] = None,
-        verbose: Optional[Union[bool, int]] = None,
         **kwargs,
     ) -> None:
         """
@@ -457,13 +457,13 @@ def fit(  # pylint: disable=W0221
                           the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then
                           the last batch will be smaller. (default: ``False``)
         :param scheduler: Learning rate scheduler to run at the start of every epoch.
+        :param verbose: if to display training progress bars
         :param update_batchnorm: ViT specific argument.
                                  If to run the training data through the model to update any batch norm statistics prior
                                  to training. Useful on small datasets when using pre-trained ViTs.
         :param batchnorm_update_epochs: ViT specific argument. How many times to forward pass over the training data
                                         to pre-adjust the batchnorm statistics.
         :param transform: ViT specific argument. Torchvision compose of relevant augmentation transformations to apply.
-        :param verbose: if to display training progress bars
         :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
                and providing it takes no effect.
         """

art/estimators/classification/tensorflow.py

@@ -376,7 +376,8 @@ def fit_generator(
             )
         ):
             for _ in tqdm(range(nb_epochs), disable=not display_pb, desc="Epochs"):
-                for _ in tqdm(range(int(generator.size / generator.batch_size)), disable=not display_pb, desc="Batches"):  # type: ignore
+                num_bathces = int(generator.size / generator.batch_size)
+                for _ in tqdm(range(num_bathces), disable=not display_pb, desc="Batches"):  # type: ignore
                     i_batch, o_batch = generator.get_batch()
 
                     if self._reduce_labels: