turn ncm to dynamic module that adapts with 0 vectors at evaluation

avalanche/models/ncm_classifier.py

@@ -3,8 +3,10 @@
 import torch
 from torch import Tensor, nn
 
+from avalanche.models import DynamicModule
 
-class NCMClassifier(nn.Module):
+
+class NCMClassifier(DynamicModule):
     """
     NCM Classifier.
     NCMClassifier performs nearest class mean classification
@@ -122,5 +124,15 @@ def replace_class_means_dict(self, class_means_dict: Dict[int, Tensor]):
 
         self._vectorize_means_dict()
 
+    def eval_adaptation(self, experience):
+        if self.class_means is None:
+            return
+        for k in experience.classes_in_this_experience:
+            if k not in self.class_means_dict:
+                self.class_means_dict[k] = torch.zeros(self.class_means.shape[1]).to(
+                    self.class_means.device
+                )
+        self._vectorize_means_dict()
+
 
 __all__ = ["NCMClassifier"]