Dev gzf (#1402)

zhifu gao

2024-02-28 b9cfd9953a88db445e3fd499d9fc40d713672152

 funasr/train_utils/load_pretrained_model.py

@@ -38,52 +38,17 @@
            )
   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(
   path: str,
   model: torch.nn.Module,
   ignore_init_mismatch: bool,
   ignore_init_mismatch: bool=True,
   map_location: str = "cpu",
   oss_bucket=None,
   scope_map="module.:none",
   scope_map=[],
   excludes=None,
   ignore_mismatch=False,
   **kwargs,
):
   """Load a model state and set it to the model.

@@ -110,12 +75,10 @@
   
   if isinstance(scope_map, str):
      scope_map = scope_map.split(",")
   scope_map += ["module.", "None"]
   
   for k in dst_state.keys():
      # if not k.startswith("module.") and "module." + k in src_state.keys():
      #    k_ddp = "module." + k
      # else:
      #    k_ddp = k
		
      k_src = k

      if scope_map is not None:
@@ -124,66 +87,25 @@
         for i in range(0, len(scope_map), 2):
            src_prefix = scope_map[i] if scope_map[i].lower() != "none" else ""
            dst_prefix = scope_map[i+1] if scope_map[i+1].lower() != "none" else ""

            if k.startswith(dst_prefix) and k.replace(dst_prefix, src_prefix) in src_state.keys():
               k_src = k.replace(dst_prefix, src_prefix)
               print(f"init param, map: {k} from {k_src} in ckpt")
				
            if dst_prefix == "" and (src_prefix + k) in src_state.keys():
               k_src = src_prefix + k
               if not k_src.startswith("module."):
                  print(f"init param, map: {k} from {k_src} in ckpt")
            elif k.startswith(dst_prefix) and k.replace(dst_prefix, src_prefix, 1) in src_state.keys():
               k_src = k.replace(dst_prefix, src_prefix, 1)
               if not k_src.startswith("module."):
                  print(f"init param, map: {k} from {k_src} in ckpt")
               
      if k_src in src_state.keys():
         dst_state[k] = src_state[k_src]
				
      # if k_ddp.startswith("audio_encoder"):
      #    if k_ddp.replace("audio_encoder", "encoder.model") in src_state.keys():
      #       k_ddp = k_ddp.replace("audio_encoder", "encoder.model")
      # if k_ddp.startswith("adaptor"):
      #    if k_ddp.replace("adaptor", "encoder_projector") in src_state.keys():
      #       k_ddp = k_ddp.replace("adaptor", "encoder_projector")
      # if k_ddp in src_state:
      #    dst_state[k] = src_state[k_ddp]
         if ignore_init_mismatch and dst_state[k].shape != src_state[k_src].shape:
            print(f"ignore_mismatch:{ignore_mismatch}, dst: {k, dst_state[k].shape}, src: {k_src, src_state[k_src].shape}")
         else:
            dst_state[k] = src_state[k_src]


      else:
         print(f"Warning, miss key in ckpt: {k}, mapped: {k_src}")
         
   flag = obj.load_state_dict(dst_state, strict=False)
   flag = obj.load_state_dict(dst_state, strict=True)
   # 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)