Merge pull request #925 from AntonioCarta/dpmixture

fix EWC with dynamic models

avalanche/evaluation/metric_utils.py

@@ -248,12 +248,14 @@ def phase_and_task(strategy: "SupervisedTemplate") -> Tuple[str, int]:
     :return: The current phase name as either "Train" or "Task" and the
         associated task label.
     """
-
-    task = strategy.experience.task_labels
-    if len(task) > 1:
-        task = None  # task labels per patterns
+    if hasattr(strategy.experience, 'task_labels'):
+        task = strategy.experience.task_labels
+        if len(task) > 1:
+            task = None  # task labels per patterns
+        else:
+            task = task[0]
     else:
-        task = task[0]
+        task = None
 
     if strategy.is_eval:
         return EVAL, task

avalanche/evaluation/metrics/accuracy.py

@@ -166,7 +166,11 @@ def result(self, strategy=None) -> float:
 
     def update(self, strategy):
         # task labels defined for each experience
-        task_labels = strategy.experience.task_labels
+        if hasattr(strategy.experience, 'task_labels'):
+            task_labels = strategy.experience.task_labels
+        else:
+            task_labels = [0]  # add fixed task label if not available.
+
         if len(task_labels) > 1:
             # task labels defined for each pattern
             task_labels = strategy.mb_task_id

avalanche/evaluation/metrics/loss.py

@@ -116,7 +116,11 @@ def result(self, strategy=None) -> float:
 
     def update(self, strategy):
         # task labels defined for each experience
-        task_labels = strategy.experience.task_labels
+        if hasattr(strategy.experience, 'task_labels'):
+            task_labels = strategy.experience.task_labels
+        else:
+            task_labels = [0]  # add fixed task label if not available.
+
         if len(task_labels) > 1:
             # task labels defined for each pattern
             # fall back to single task case

avalanche/training/plugins/ewc.py

@@ -85,6 +85,10 @@ def before_backward(self, strategy, **kwargs):
                     self.saved_params[experience],
                     self.importances[experience],
                 ):
+                    # dynamic models may add new units
+                    # new units are ignored by the regularization
+                    n_units = saved_param.shape[0]
+                    cur_param = saved_param[:n_units]
                     penalty += (imp * (cur_param - saved_param).pow(2)).sum()
         elif self.mode == "online":
             prev_exp = exp_counter - 1
@@ -93,6 +97,10 @@ def before_backward(self, strategy, **kwargs):
                 self.saved_params[prev_exp],
                 self.importances[prev_exp],
             ):
+                # dynamic models may add new units
+                # new units are ignored by the regularization
+                n_units = saved_param.shape[0]
+                cur_param = saved_param[:n_units]
                 penalty += (imp * (cur_param - saved_param).pow(2)).sum()
         else:
             raise ValueError("Wrong EWC mode.")

avalanche/training/templates/base.py

@@ -61,6 +61,11 @@ def __init__(
         self.current_eval_stream = None
         """ Current evaluation stream. """
 
+    @property
+    def is_eval(self):
+        """True if the strategy is in evaluation mode."""
+        return not self.is_training
+
     def train(
         self,
         experiences: Union[Experience, Sequence[Experience]],

avalanche/training/templates/base_sgd.py

@@ -175,10 +175,13 @@ def _train_exp(self, experience: Experience, eval_streams=None, **kwargs):
             self.training_epoch(**kwargs)
             self._after_training_epoch(**kwargs)
 
-    def _eval_exp(self, **kwargs):
+    def _before_eval_exp(self, **kwargs):
         self.make_eval_dataloader(**kwargs)
         # Model Adaptation (e.g. freeze/add new units)
         self.model = self.model_adaptation()
+        super()._before_eval_exp(**kwargs)
+
+    def _eval_exp(self, **kwargs):
         self.eval_epoch(**kwargs)
 
     def make_train_dataloader(self, **kwargs):

avalanche/training/templates/supervised.py

@@ -125,11 +125,6 @@ def __init__(
             use :attr:`.BaseTemplate.experience`.
         """
 
-    @property
-    def is_eval(self):
-        """True if the strategy is in evaluation mode."""
-        return not self.is_training
-
     @property
     def mb_x(self):
         """Current mini-batch input."""