[Fix] Avoid creating a new logger in PretrainedInit (#791)

* use current logger

* remove get_current_instance

* remove logger parameter at weight_init

* remove elif branch

mmengine/model/weight_init.py

@@ -8,7 +8,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from mmengine.logging import MMLogger, print_log
+from mmengine.logging import print_log
 from mmengine.registry import WEIGHT_INITIALIZERS, build_from_cfg
 
 
@@ -481,22 +481,21 @@ def __call__(self, module):
         from mmengine.runner.checkpoint import (_load_checkpoint_with_prefix,
                                                 load_checkpoint,
                                                 load_state_dict)
-        logger = MMLogger.get_instance('mmengine')
         if self.prefix is None:
-            print_log(f'load model from: {self.checkpoint}', logger=logger)
+            print_log(f'load model from: {self.checkpoint}', logger='current')
             load_checkpoint(
                 module,
                 self.checkpoint,
                 map_location=self.map_location,
                 strict=False,
-                logger=logger)
+                logger='current')
         else:
             print_log(
                 f'load {self.prefix} in model from: {self.checkpoint}',
-                logger=logger)
+                logger='current')
             state_dict = _load_checkpoint_with_prefix(
                 self.prefix, self.checkpoint, map_location=self.map_location)
-            load_state_dict(module, state_dict, strict=False, logger=logger)
+            load_state_dict(module, state_dict, strict=False, logger='current')
 
         if hasattr(module, '_params_init_info'):
             update_init_info(module, init_info=self._get_init_info())

mmengine/runner/checkpoint.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import io
+import logging
 import os
 import os.path as osp
 import pkgutil
@@ -106,10 +107,8 @@ def load(module, prefix=''):
         err_msg = '\n'.join(err_msg)
         if strict:
             raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
         else:
-            print(err_msg)
+            print_log(err_msg, logger=logger, level=logging.WARNING)
 
 
 def get_torchvision_models():