Better handling of int values for verbosity. Adding fit_gen to tests for kwargs checks

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

Author: GiulioZizzo

art/estimators/classification/pytorch.py

@@ -371,20 +371,20 @@ def process_verbose(self, verbose: Optional[Union[bool, int]] = None) -> bool:
         Function to unify the various ways implemented in ART of displaying progress bars
         into a single True/False output.
 
-        :param verbose: If to display the progress bar information.
+        :param verbose: If to display the progress bar information in one of a few possible formats.
         :return: True/False if to display the progress bars.
         """
 
         if verbose is not None:
             if isinstance(verbose, int):
-                if verbose == 0:
+                if verbose <= 0:
                     display_pb = False
                 else:
                     display_pb = True
             elif isinstance(verbose, bool):
                 display_pb = verbose
             else:
-                raise ValueError("Verbose should be True/False or a 0/1 int")
+                raise ValueError("Verbose should be True/False or an int")
         else:
             # Check if the verbose attribute is present in the current classifier
             if hasattr(self, "verbose"):

art/estimators/classification/tensorflow.py

@@ -270,7 +270,7 @@ def process_verbose(self, verbose: Optional[Union[bool, int]] = None) -> bool:
         """
         Function to unify the various ways implemented in ART of displaying progress bars
         into a single True/False output.
-        :param verbose: If to display the progress bar information.
+        :param verbose: If to display the progress bar information in one of a few possible formats.
         :return: True/False if to display the progress bars.
         """
 
@@ -1007,13 +1007,14 @@ def process_verbose(self, verbose: Optional[Union[bool, int]] = None) -> bool:
         """
         Function to unify the various ways implemented in ART of displaying progress bars
         into a single True/False output.
-        :param verbose: If to display the progress bar information.
+
+        :param verbose: If to display the progress bar information in one of a few possible formats.
         :return: True/False if to display the progress bars.
         """
 
         if verbose is not None:
             if isinstance(verbose, int):
-                if verbose == 0:
+                if verbose <= 0:
                     display_pb = False
                 else:
                     display_pb = True

tests/estimators/classification/test_deeplearning_common.py

@@ -192,19 +192,25 @@ def test_functional_model(art_warning, image_dl_estimator):
 
 
 @pytest.mark.skip_framework("mxnet", "non_dl_frameworks")
-def test_fit_kwargs(art_warning, image_dl_estimator, get_default_mnist_subset, default_batch_size, framework):
+def test_fit_kwargs(
+    art_warning, image_dl_estimator, get_default_mnist_subset, image_data_generator, default_batch_size, framework
+):
     try:
         (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
 
         def get_lr(_):
             return 0.01
 
         # Test a valid callback
-        classifier, _ = image_dl_estimator(from_logits=True)
+        classifier, sess = image_dl_estimator(from_logits=True)
 
         kwargs = {"callbacks": [LearningRateScheduler(get_lr)], "verbose": True}
         classifier.fit(x_train_mnist, y_train_mnist, batch_size=default_batch_size, nb_epochs=1, **kwargs)
 
+        # Check for fit_generator kwargs as well
+        data_gen = image_data_generator(sess=sess)
+        classifier.fit_generator(generator=data_gen, nb_epochs=1, **kwargs)
+
         # Test failure for invalid parameters: does not apply to many frameworks which allow arbitrary kwargs
         if framework not in [
             "tensorflow1",