checkpoint callback 加入了 on_after_trainer_initialized 的逻辑

fastNLP/core/callbacks/checkpoint_callback.py

@@ -48,8 +48,9 @@ class CheckpointCallback(Callback):
             model_save_fn: Optional[Callable] = None,
             **kwargs,
     ):
-        if monitor is None and save_topk is not None:
-            raise ValueError("Parameter `monitor` must be set when you want to use 'save_topk'.")
+        # 我们新加了逻辑，如果 checkpoint callback 自己没有设置 monitor 和 larger_better，那么我们会将其在 trainer 中的设置赋值给它们；
+        # if monitor is None and save_topk is not None:
+        #     raise ValueError("Parameter `monitor` must be set when you want to use 'save_topk'.")
 
         if monitor is not None and not isinstance(monitor, str):
             raise ValueError("Parameter `monitor` should be of 'str' type.")
@@ -119,6 +120,19 @@ class CheckpointCallback(Callback):
         # 我们只需要保证这个创建文件夹的操作只在进程 0 上进行即可；因为后续的实际的保存操作，其它进程实际并不会去执行；
         synchronize_mkdir(self.timestamp_path)
 
+    def on_after_trainer_initialized(self, trainer, driver):
+        if self.monitor is None:
+            if trainer.monitor is not None:
+                self.monitor = trainer.monitor
+                self.larger_better = trainer.larger_better
+            elif self.save_topk is not None:
+                raise RuntimeError("You are using `topk` mode, but you have not set the `monitor` value either in this"
+                                   "callback or in trainer.")
+            else:
+                self.monitor = None
+        if self.save_topk is not None and trainer.evaluator is None:
+            raise RuntimeError("You are using `topk` mode, but there is no `evaluator` in trainer.")
+
     def on_validate_end(self, trainer, validate_res):
         self._save_topk(trainer, validate_res)