trainer增加对evaluate结果的check

fastNLP/core/trainer.py

@@ -48,7 +48,7 @@ class Trainer(object):
         :param use_cuda:
         :param str save_path: file path to save models
         :param Optimizer optimizer: an optimizer object
-        :param int check_code_level: level of FastNLP code checker. 0: ignore. 1: warning. 2: strict.
+        :param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict.
         :param str metric_key: a single indicator used to decide the best model based on metric results. It must be one
             of the keys returned by the FIRST metric in `metrics`. If the overall result gets better if the indicator gets
             smaller, add a `-` character in front of the string. For example
@@ -91,7 +91,7 @@ class Trainer(object):
 
         if check_code_level > -1:
             _check_code(dataset=train_data, model=model, losser=losser, metrics=metrics, dev_data=dev_data,
-                        check_level=check_code_level)
+                        metric_key=metric_key, check_level=check_code_level)
 
         self.train_data = train_data
         self.dev_data = dev_data  # If None, No validation.
@@ -294,7 +294,7 @@ DEFAULT_CHECK_NUM_BATCH = 2
 
 
 def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_SIZE,
-                dev_data=None,
+                dev_data=None, metric_key=None,
                 check_level=0):
     # check get_loss 方法
     model_devcie = model.parameters().__next__().device
@@ -340,7 +340,7 @@ def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_
         tester = Tester(data=dataset[:batch_size * DEFAULT_CHECK_NUM_BATCH], model=model, metrics=metrics,
                         batch_size=batch_size, verbose=-1)
         evaluate_results = tester.test()
-        # TODO 这里需要检查是否返回来的值是否是合理的
+        _check_eval_results(metrics=evaluate_results, metric_key=metric_key, metric_list=metrics)
 
 
 def _check_eval_results(metrics, metric_key, metric_list):