fix win bug

游雁

2024-01-16 bb97d3ed19ee3a219e67b9568d662df489aa2823

 funasr/train_utils/load_pretrained_model.py

@@ -10,119 +10,109 @@


def filter_state_dict(
    dst_state: Dict[str, Union[float, torch.Tensor]],
    src_state: Dict[str, Union[float, torch.Tensor]],
   dst_state: Dict[str, Union[float, torch.Tensor]],
   src_state: Dict[str, Union[float, torch.Tensor]],
):
    """Filter name, size mismatch instances between dicts.
   """Filter name, size mismatch instances between dicts.

    Args:
        dst_state: reference state dict for filtering
        src_state: target state dict for filtering
   Args:
      dst_state: reference state dict for filtering
      src_state: target state dict for filtering

    """
    match_state = {}
    for key, value in src_state.items():
        if key in dst_state and (dst_state[key].size() == src_state[key].size()):
            match_state[key] = value
        else:
            if key not in dst_state:
                logging.warning(
                    f"Filter out {key} from pretrained dict"
                    + " because of name not found in target dict"
                )
            else:
                logging.warning(
                    f"Filter out {key} from pretrained dict"
                    + " because of size mismatch"
                    + f"({dst_state[key].size()}-{src_state[key].size()})"
                )
    return match_state
   """
   match_state = {}
   for key, value in src_state.items():
      if key in dst_state and (dst_state[key].size() == src_state[key].size()):
         match_state[key] = value
      else:
         if key not in dst_state:
            logging.warning(
               f"Filter out {key} from pretrained dict"
               + " because of name not found in target dict"
            )
         else:
            logging.warning(
               f"Filter out {key} from pretrained dict"
               + " because of size mismatch"
               + f"({dst_state[key].size()}-{src_state[key].size()})"
            )
   return match_state

def assigment_scope_map(dst_state: dict, src_state: dict, scope_map: str=None):
   """Compute the union of the current variables and checkpoint variables."""
   import collections
   import re

   # current model variables
   name_to_variable = collections.OrderedDict()
   for name, var in dst_state.items():
      name_to_variable[name] = var
	
   scope_map_num = 0
   if scope_map is not None:
      scope_map = scope_map.split(",")
      scope_map_num = len(scope_map) // 2
      for scope_map_idx in range(scope_map_num):
         scope_map_id = scope_map_idx * 2
         logging.info('assignment_map from scope {} to {}'.format(scope_map[scope_map_id], scope_map[scope_map_id+1]))
	
   assignment_map = {}
   for name, var in src_state.items():

      if scope_map:
         for scope_map_idx in range(scope_map_num):
            scope_map_id = scope_map_idx * 2
            try:
               idx = name.index(scope_map[scope_map_id])
               new_name = scope_map[scope_map_id+1] + name[idx + len(scope_map[scope_map_id]):]
               if new_name in name_to_variable:
                  assignment_map[name] = var
            except:
               continue
      else:
         if name in name_to_variable:
            assignment_map[name] = var
	
   return assignment_map

def load_pretrained_model(
    init_param: str,
    model: torch.nn.Module,
    ignore_init_mismatch: bool,
    map_location: str = "cpu",
    oss_bucket=None,
   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.
   """Load a model state and set it to the model.

    Args:
        init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
   Args:
      init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>

    Examples:
        >>> load_pretrained_model("somewhere/model.pb", model)
        >>> load_pretrained_model("somewhere/model.pb:decoder:decoder", model)
        >>> load_pretrained_model("somewhere/model.pb:decoder:decoder:", model)
        >>> load_pretrained_model(
        ...     "somewhere/model.pb:decoder:decoder:decoder.embed", model
        ... )
        >>> load_pretrained_model("somewhere/decoder.pb::decoder", model)
    """
    sps = init_param.split(":", 4)
    if len(sps) == 4:
        path, src_key, dst_key, excludes = sps
    elif len(sps) == 3:
        path, src_key, dst_key = sps
        excludes = None
    elif len(sps) == 2:
        path, src_key = sps
        dst_key, excludes = None, None
    else:
        (path,) = sps
        src_key, dst_key, excludes = None, None, None
    if src_key == "":
        src_key = None
    if dst_key == "":
        dst_key = None
   Examples:

    if dst_key is None:
        obj = model
    else:

        def get_attr(obj: Any, key: str):
            """Get an nested attribute.

            >>> class A(torch.nn.Module):
            ...     def __init__(self):
            ...         super().__init__()
            ...         self.linear = torch.nn.Linear(10, 10)
            >>> a = A()
            >>> assert A.linear.weight is get_attr(A, 'linear.weight')

            """
            if key.strip() == "":
                return obj
            for k in key.split("."):
                obj = getattr(obj, k)
            return obj

        obj = get_attr(model, dst_key)

    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)}

    if src_key is not None:
        src_state = {
            k[len(src_key) + 1 :]: v
            for k, v in src_state.items()
            if k.startswith(src_key)
        }

    dst_state = obj.state_dict()
    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)
    
   """
	
   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)