init param

funasr/train_utils/load_pretrained_model.py

@@ -145,45 +145,3 @@ def load_pretrained_model(
 			
 	flag = obj.load_state_dict(dst_state, strict=False)
 	# print(flag)
-
-# def load_pretrained_model(
-# 	path: str,
-# 	model: torch.nn.Module,
-# 	ignore_init_mismatch: bool,
-# 	map_location: str = "cpu",
-# 	oss_bucket=None,
-# 	scope_map=None,
-# 	excludes=None,
-# ):
-# 	"""Load a model state and set it to the model.
-#
-# 	Args:
-# 		init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
-#
-# 	Examples:
-#
-# 	"""
-#
-# 	obj = model
-#
-# 	if oss_bucket is None:
-# 		src_state = torch.load(path, map_location=map_location)
-# 	else:
-# 		buffer = BytesIO(oss_bucket.get_object(path).read())
-# 		src_state = torch.load(buffer, map_location=map_location)
-# 	src_state = src_state["model"] if "model" in src_state else src_state
-#
-# 	if excludes is not None:
-# 		for e in excludes.split(","):
-# 			src_state = {k: v for k, v in src_state.items() if not k.startswith(e)}
-#
-# 	dst_state = obj.state_dict()
-# 	src_state = assigment_scope_map(dst_state, src_state, scope_map)
-#
-# 	if ignore_init_mismatch:
-# 		src_state = filter_state_dict(dst_state, src_state)
-#
-# 	logging.debug("Loaded src_state keys: {}".format(src_state.keys()))
-# 	logging.debug("Loaded dst_state keys: {}".format(dst_state.keys()))
-# 	dst_state.update(src_state)
-# 	obj.load_state_dict(dst_state, strict=True)