revert classes timeline values to sets

avalanche/benchmarks/scenarios/supervised.py

@@ -26,7 +26,6 @@
 from avalanche.benchmarks.utils.classification_dataset import (
     ClassificationDataset,
     _as_taskaware_supervised_classification_dataset,
-    TaskAwareSupervisedClassificationDataset,
 )
 from avalanche.benchmarks.utils.data import AvalancheDataset
 from avalanche.benchmarks.utils.data_attribute import DataAttribute
@@ -399,14 +398,12 @@ def _decorate_stream(obj: CLStream):
             new_exp = copy(exp)
             curr_cls = exp.dataset.targets.uniques
 
-            new_exp.classes_in_this_experience = list(curr_cls)
-            new_exp.previous_classes = list(set(prev_cls))
-            new_exp.classes_seen_so_far = list(curr_cls.union(prev_cls))
+            new_exp.classes_in_this_experience = curr_cls
+            new_exp.previous_classes = set(prev_cls)
+            new_exp.classes_seen_so_far = curr_cls.union(prev_cls)
             # TODO: future_classes ignores repetitions right now...
             #  implement and test scenario with repetitions
-            new_exp.future_classes = list(
-                all_cls.difference(new_exp.classes_seen_so_far)
-            )
+            new_exp.future_classes = all_cls.difference(new_exp.classes_seen_so_far)
             new_stream.append(new_exp)
 
             prev_cls = prev_cls.union(curr_cls)

avalanche/models/dynamic_modules.py

@@ -246,7 +246,7 @@ def adaptation(self, experience: CLExperience):
                 self.active_units[: old_act_units.shape[0]] = old_act_units
             # update with new active classes
             if self.training:
-                self.active_units[curr_classes] = 1
+                self.active_units[list(curr_classes)] = 1
 
         # update classifier weights
         if old_nclasses == new_nclasses: