From fcfd8c267ed7f82eb4e1ee8826e8c7fe7567abbb Mon Sep 17 00:00:00 2001
From: x54-729 <17307130121@fudan.edu.cn>
Date: Mon, 2 May 2022 11:03:19 +0000
Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9callback=5Fmanager=E7=9A=84?=
 =?UTF-8?q?=E6=8A=A5=E9=94=99=E4=BF=A1=E6=81=AF?=
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---
 fastNLP/core/callbacks/callback_manager.py  | 9 ++++++---
 tests/helpers/callbacks/helper_callbacks.py | 4 +---
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/fastNLP/core/callbacks/callback_manager.py b/fastNLP/core/callbacks/callback_manager.py
index f63c6088..2b8fff60 100644
--- a/fastNLP/core/callbacks/callback_manager.py
+++ b/fastNLP/core/callbacks/callback_manager.py
@@ -11,6 +11,7 @@ from .callback import Callback
 from fastNLP.core.log import logger
 from .progress_callback import ProgressCallback, choose_progress_callback
 from fastNLP.envs import rank_zero_call
+from fastNLP.core.utils.utils import _get_fun_msg
 
 
 def _transfer(func):
@@ -21,10 +22,12 @@ def _transfer(func):
 
     def wrapper(manager, *arg, **kwargs):
         manager.callback_counter[func.__name__] += 1  # 给实际被调用的 callback_fn 的计数加 1；
-        returns = []
         for callback_fn in manager.callback_fns[func.__name__]:
-            returns.append(callback_fn(*arg, **kwargs))
-        return returns
+            try:
+                callback_fn(*arg, **kwargs)
+            except BaseException as e:
+                logger.error(f"The following callback_fn raise exception:{_get_fun_msg(callback_fn)}.")
+                raise e
     return wrapper
 
 
diff --git a/tests/helpers/callbacks/helper_callbacks.py b/tests/helpers/callbacks/helper_callbacks.py
index 4fd5b654..1e0d0e11 100644
--- a/tests/helpers/callbacks/helper_callbacks.py
+++ b/tests/helpers/callbacks/helper_callbacks.py
@@ -36,12 +36,10 @@ class RecordMetricCallback(Callback):
         self.larger_better = larger_better
         self.metric = None
         self.metric_threshold = metric_threshold
-        self.metric_begin_value = None
+        self.metric_begin_value = float('-inf') if larger_better else float('inf')
 
     def on_evaluate_end(self, trainer, results):
         self.metric = results[self.monitor]
-        if self.metric_begin_value is None:
-            self.metric_begin_value = self.metric
 
     def on_train_end(self, trainer):
         if self.larger_better: