Merge branch 'dev_1.14.1' into development_issue_2116

art/defences/trainer/ibp_certified_trainer_pytorch.py

@@ -109,8 +109,8 @@ def __init__(
         classifier: "IBP_CERTIFIER_TYPE",
         nb_epochs: Optional[int] = 20,
         bound: float = 0.1,
-        loss_weighting: float = 0.1,
         batch_size: int = 32,
+        loss_weighting: Optional[int] = None,
         use_certification_schedule: bool = True,
         certification_schedule: Optional[Any] = None,
         use_loss_weighting_schedule: bool = True,
@@ -133,9 +133,9 @@ def __init__(
                            * *max_iter*: The maximum number of iterations.
                            * *batch_size*: Size of the batch on which adversarial samples are generated.
                            * *num_random_init*: Number of random initialisations within the epsilon ball.
+        :param loss_weighting: Weighting factor for the certified loss.
         :param bound: The perturbation range for the interval. If the default certification schedule is used
                       will be the upper limit.
-        :param loss_weighting: Weighting factor for the certified loss.
         :param nb_epochs: Number of training epochs.
         :param use_certification_schedule: If to use a training schedule for the certification radius.
         :param certification_schedule: Schedule for gradually increasing the certification radius. Empirical studies
@@ -152,6 +152,14 @@ def __init__(
                 "art.estimators.certification.interval.pytorch.PyTorchIBPClassifier"
             )
 
+        if not use_loss_weighting_schedule and loss_weighting is None:
+            raise ValueError(
+                "If a loss weighting schedule is not used then a value for loss_weighting should be supplied."
+            )
+
+        if use_loss_weighting_schedule and loss_weighting is not None:
+            raise ValueError("Using a loss weighting schedule is incompatible with a fixed loss_weighting.")
+
         super().__init__(classifier=classifier)
         self.classifier: "IBP_CERTIFIER_TYPE"
         self.pgd_params: "PGDParamDict"
@@ -300,8 +308,10 @@ def fit(  # pylint: disable=W0221
                 self.loss_weighting_schedule = self.initialise_default_scheduler(
                     initial_val=0.0, final_val=0.5, epochs=epochs
                 )
+        elif self.loss_weighting is not None:
+            loss_weighting_k = self.loss_weighting
         else:
-            loss_weighting_k = 0.1
+            raise ValueError("Unable to determine loss weighting.")
 
         for _ in tqdm(range(epochs)):
             if self.use_certification_schedule and self.certification_schedule is not None: