Evaluator在结果为空时不会进行输出

fastNLP/core/controllers/evaluator.py

@@ -122,7 +122,7 @@ class Evaluator:
     _evaluate_batch_loop: Loop
 
     def __init__(self, model, dataloaders, metrics: Optional[Dict] = None,
-                 driver: Union[str, Driver] = 'torch', device: Optional[Union[int, List[int], str]] = None,
+                 driver: Union[str, Driver] = 'auto', device: Optional[Union[int, List[int], str]] = None,
                  evaluate_batch_step_fn: Optional[callable] = None, evaluate_fn: Optional[str] = None,
                  input_mapping: Optional[Union[Callable, Dict]] = None,
                  output_mapping: Optional[Union[Callable, Dict]] = None, model_wo_auto_param_call: bool = False,
@@ -279,8 +279,9 @@ class Evaluator:
                 raise e
             finally:
                 self.finally_progress_bar()
+        metric_results = flat_nest_dict(metric_results, separator=self.separator, compress_none_key=True, top_down=False)
         if len(metric_results) > 0:  # 如果 metric 不为 None 需要 print 。
-            metric_results = flat_nest_dict(metric_results, separator=self.separator, compress_none_key=True, top_down=False)
+            # metric_results = flat_nest_dict(metric_results, separator=self.separator, compress_none_key=True, top_down=False)
             if self.verbose:
                 if self.progress_bar == 'rich':
                     f_rich_progress.print(metric_results)