From bb97d3ed19ee3a219e67b9568d662df489aa2823 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 16 一月 2024 15:47:01 +0800
Subject: [PATCH] fix win bug
---
examples/industrial_data_pretraining/emotion2vec/demo.py | 2
funasr/bin/train.py | 4
funasr/auto/auto_model.py | 4
funasr/datasets/audio_datasets/datasets.py | 4
funasr/train_utils/load_pretrained_model.py | 202 ++++++++++++++++++++++++--------------------------
5 files changed, 105 insertions(+), 111 deletions(-)
diff --git a/examples/industrial_data_pretraining/emotion2vec/demo.py b/examples/industrial_data_pretraining/emotion2vec/demo.py
index 91d00aa..a41641e 100644
--- a/examples/industrial_data_pretraining/emotion2vec/demo.py
+++ b/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)
\ No newline at end of file
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 580cca8..ffb56a5 100644
--- a/funasr/auto/auto_model.py
+++ b/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
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index 0881cb2..ef0d205 100644
--- a/funasr/bin/train.py
+++ b/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"))
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index edf127f..5af33fc 100644
--- a/funasr/datasets/audio_datasets/datasets.py
+++ b/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]
diff --git a/funasr/train_utils/load_pretrained_model.py b/funasr/train_utils/load_pretrained_model.py
index ef9d93a..16feabd 100644
--- a/funasr/train_utils/load_pretrained_model.py
+++ b/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)
-
\ No newline at end of file
+ """
+
+ 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)
\ No newline at end of file
--
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