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funasr_local/torch_utils/load_pretrained_model.py

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+from typing import Any
+from typing import Dict
+from typing import Union
+from io import BytesIO
+
+import logging
+import torch
+import torch.nn
+import torch.optim
+
+
+def filter_state_dict(
+    dst_state: Dict[str, Union[float, torch.Tensor]],
+    src_state: Dict[str, Union[float, torch.Tensor]],
+):
+    """Filter name, size mismatch instances between dicts.
+
+    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
+
+
+def load_pretrained_model(
+    init_param: str,
+    model: torch.nn.Module,
+    ignore_init_mismatch: bool,
+    map_location: str = "cpu",
+    oss_bucket=None,
+):
+    """Load a model state and set it to the model.
+
+    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
+
+    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)
+    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.info("Loaded src_state keys: {}".format(src_state.keys()))
+    dst_state.update(src_state)
+    obj.load_state_dict(dst_state)