-
Notifications
You must be signed in to change notification settings - Fork 1.2k
Expand file tree
/
Copy path__init__.py
More file actions
98 lines (89 loc) · 3.61 KB
/
Copy path__init__.py
File metadata and controls
98 lines (89 loc) · 3.61 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from importlib import import_module
from pathlib import Path
from typing import Any, Dict, Optional, Union
from ...utils import errors
from ..utils.hpi import HPIConfig
from ..utils.official_models import official_models
# from .table_recognition import TablePredictor
# from .general_recognition import ShiTuRecPredictor
from .anomaly_detection import UadPredictor
from .base import BasePredictor
from .common.genai import GenAIConfig, need_local_model
from .doc_vlm import DocVLMPredictor
from .face_feature import FaceFeaturePredictor
from .formula_recognition import FormulaRecPredictor
from .image_classification import ClasPredictor
from .image_feature import ImageFeaturePredictor
from .image_multilabel_classification import MLClasPredictor
from .image_unwarping import WarpPredictor
from .instance_segmentation import InstanceSegPredictor
from .keypoint_detection import KptPredictor
from .m_3d_bev_detection import BEVDet3DPredictor
# from .face_recognition import FaceRecPredictor
from .multilingual_speech_recognition import WhisperPredictor
from .object_detection import DetPredictor
from .open_vocabulary_detection import OVDetPredictor
from .open_vocabulary_segmentation import OVSegPredictor
from .semantic_segmentation import SegPredictor
from .table_structure_recognition import TablePredictor
from .text_detection import TextDetPredictor
from .text_recognition import TextRecPredictor
from .text_to_pinyin import TextToPinyinPredictor
from .text_to_speech_acoustic import Fastspeech2Predictor
from .text_to_speech_vocoder import PwganPredictor
from .ts_anomaly_detection import TSAdPredictor
from .ts_classification import TSClsPredictor
from .ts_forecasting import TSFcPredictor
from .video_classification import VideoClasPredictor
from .video_detection import VideoDetPredictor
def create_predictor(
model_name: str,
model_dir: Optional[str] = None,
device: Optional[str] = None,
pp_option=None,
use_hpip: bool = False,
hpi_config: Optional[Union[Dict[str, Any], HPIConfig]] = None,
genai_config: Optional[Union[Dict[str, Any], GenAIConfig]] = None,
*args,
**kwargs,
) -> BasePredictor:
# TODO: Check if the model is a genai model
if genai_config is not None:
genai_config = GenAIConfig.model_validate(genai_config)
if need_local_model(genai_config):
if model_dir is None:
model_dir = official_models[model_name]
else:
assert Path(model_dir).exists(), f"{model_dir} is not exists!"
model_dir = Path(model_dir)
config = BasePredictor.load_config(model_dir)
assert (
model_name == config["Global"]["model_name"]
), f"Model name mismatch,please input the correct model dir."
else:
config = None
return BasePredictor.get(model_name)(
model_dir=model_dir,
config=config,
device=device,
pp_option=pp_option,
use_hpip=use_hpip,
hpi_config=hpi_config,
genai_config=genai_config,
model_name=model_name,
*args,
**kwargs,
)