fix win bug

游雁

2024-01-16 bb97d3ed19ee3a219e67b9568d662df489aa2823

fix win bug

 examples/industrial_data_pretraining/emotion2vec/demo.py

@@ -7,6 +7,6 @@

model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.1")

wav_file = f"{model.model_path}/example/example/test.wav"
wav_file = f"{model.model_path}/example/test.wav"
res = model.generate(wav_file, output_dir="./outputs", granularity="utterance")
print(res)

 funasr/auto/auto_model.py

@@ -183,9 +183,11 @@
            logging.info(f"Loading pretrained params from {init_param}")
            load_pretrained_model(
                model=model,
                init_param=init_param,
                path=init_param,
                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                oss_bucket=kwargs.get("oss_bucket", None),
                scope_map=kwargs.get("scope_map", None),
                excludes=kwargs.get("excludes", None),
            )
        
        return model, kwargs

 funasr/bin/train.py

@@ -96,9 +96,11 @@
            logging.info(f"Loading pretrained params from {p}")
            load_pretrained_model(
                model=model,
                init_param=p,
                path=p,
                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
                oss_bucket=kwargs.get("oss_bucket", None),
                scope_map=kwargs.get("scope_map", None),
                excludes=kwargs.get("excludes", None),
            )
    else:
        initialize(model, kwargs.get("init", "kaiming_normal"))

 funasr/datasets/audio_datasets/datasets.py

@@ -1,7 +1,7 @@
import torch

from funasr.register import tables
from funasr.utils.load_utils import extract_fbank
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video


@tables.register("dataset_classes", "AudioDataset")
@@ -55,7 +55,7 @@
        # import pdb;
        # pdb.set_trace()
        source = item["source"]
        data_src = load_audio(source, fs=self.fs)
        data_src = load_audio_text_image_video(source, fs=self.fs)
        if self.preprocessor_speech:
            data_src = self.preprocessor_speech(data_src)
        speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend) # speech: [b, T, d]

 funasr/train_utils/load_pretrained_model.py

@@ -38,13 +38,51 @@
                )
    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,
    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.

@@ -52,53 +90,10 @@
        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)
@@ -106,23 +101,18 @@
        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()
    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)
    # dst_state.update(src_state)
    obj.load_state_dict(dst_state)