multitask model adjustments #3

flair/models/multitask_model.py

@@ -39,7 +39,7 @@ def __init__(self, models: Dict):
         self._label_type = label_types
         self.to(flair.device)
 
-    def forward_loss(self, sentences: Union[List[Sentence], Sentence]) -> torch.Tensor:
+    def forward_loss(self, sentences: Union[List[Sentence], Sentence]):
         """
         Abstract forward loss implementation of flair.nn.Model's interface.
         Calls the respective forward loss of each model.
@@ -48,11 +48,13 @@ def forward_loss(self, sentences: Union[List[Sentence], Sentence]) -> torch.Tens
         """
         batch_split = self.split_batch_to_task_ids(sentences)
         loss = 0
+        count = 0
         for model, split in batch_split.items():
             task_loss, task_count = self.__getattr__(model).forward_loss([sentences[i] for i in split])
-            loss += task_loss / task_count
+            loss += task_loss
+            count += task_count
 
-        return loss
+        return loss, count
 
     @staticmethod
     def split_batch_to_task_ids(sentences: Union[List[Sentence], Sentence]) -> Dict:
@@ -82,7 +84,7 @@ def evaluate(
             num_workers: int = 8,
             main_evaluation_metric: Tuple[str, str] = ("micro avg", "f1-score"),
             exclude_labels: List[str] = [],
-            gold_label_dictionary: Optional[Dictionary] = None,
+            gold_label_dictionary: Optional[Dictionary] = None
     ) -> Result:
         """
         :param sentences: batch of sentences
@@ -96,9 +98,10 @@ def evaluate(
         batch_split = self.split_batch_to_task_ids(data_points)
 
         # Evaluate each split on its respective model
-        results = []
+        loss = 0
+        main_score = 0
         for task, split in batch_split.items():
-            task_result = self.__getattr__(task).evaluate(data_points=[data_points[i] for i in split],
+            result = self.__getattr__(task).evaluate(data_points=[data_points[i] for i in split],
                                                    gold_label_type=gold_label_type[task],
                                                    out_path=out_path,
                                                    embedding_storage_mode=embedding_storage_mode,
@@ -107,13 +110,11 @@ def evaluate(
                                                    main_evaluation_metric=main_evaluation_metric,
                                                    exclude_labels=exclude_labels,
                                                    gold_label_dictionary=gold_label_dictionary)
-            results.append(task_result)
-            results.append(self.__getattr__(task).result)
-            # Since our Task Model's do not keep track when evaluate is over (they just get a batch of sentences)
-            # we need to reset the evaluation metrics after each batch.
-            self.__getattr__(task)._reset_eval_metrics()
+            loss += result.loss
+            main_score += result.main_score
 
-        result = MultitaskResult(results)
+        result.loss = (loss / len(batch_split))
+        result.main_score = (main_score / len(batch_split))
 
         return result
 
@@ -138,7 +139,8 @@ def _init_model_with_state_dict(state):
         models = {}
 
         for task, task_state in state.items():
-            models[task] = task_state["class"]._init_model_with_state_dict(task_state["state_dict"])
+            if task != "model_card":
+                models[task] = task_state["class"]._init_model_with_state_dict(task_state["state_dict"])
 
         model = MultitaskModel(models=models)
         return model