__init__.py 3.7 KB

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  1. # copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
  2. #
  3. # Licensed under the Apache License, Version 2.0 (the "License");
  4. # you may not use this file except in compliance with the License.
  5. # You may obtain a copy of the License at
  6. #
  7. # http://www.apache.org/licenses/LICENSE-2.0
  8. #
  9. # Unless required by applicable law or agreed to in writing, software
  10. # distributed under the License is distributed on an "AS IS" BASIS,
  11. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12. # See the License for the specific language governing permissions and
  13. # limitations under the License.
  14. from pathlib import Path
  15. from typing import Any, Dict, Optional
  16. from ...utils.config import parse_config
  17. from ..utils.get_pipeline_path import get_pipeline_path
  18. from .base import BasePipeline
  19. from .single_model_pipeline import (
  20. _SingleModelPipeline,
  21. ImageClassification,
  22. ObjectDetection,
  23. InstanceSegmentation,
  24. SemanticSegmentation,
  25. TSFc,
  26. TSAd,
  27. TSCls,
  28. MultiLableImageClas,
  29. SmallObjDet,
  30. AnomalyDetection,
  31. )
  32. from .ocr import OCRPipeline
  33. from .formula_recognition import FormulaRecognitionPipeline
  34. from .table_recognition import TableRecPipeline
  35. from .seal_recognition import SealOCRPipeline
  36. from .ppchatocrv3 import PPChatOCRPipeline
  37. from .layout_parsing import LayoutParsingPipeline
  38. from .pp_shitu_v2 import ShiTuV2Pipeline
  39. def load_pipeline_config(pipeline: str) -> Dict[str, Any]:
  40. if not Path(pipeline).exists():
  41. pipeline_path = get_pipeline_path(pipeline)
  42. if pipeline_path is None:
  43. raise Exception(
  44. f"The pipeline ({pipeline}) does not exist! Please use a pipeline name or a config file path!"
  45. )
  46. else:
  47. pipeline_path = pipeline
  48. config = parse_config(pipeline_path)
  49. return config
  50. def create_pipeline_from_config(
  51. config: Dict[str, Any],
  52. device=None,
  53. pp_option=None,
  54. use_hpip: bool = False,
  55. hpi_params: Optional[Dict[str, Any]] = None,
  56. *args,
  57. **kwargs,
  58. ) -> BasePipeline:
  59. pipeline_name = config["Global"]["pipeline_name"]
  60. pipeline_setting = config["Pipeline"]
  61. predictor_kwargs = {"use_hpip": use_hpip}
  62. if "use_hpip" in pipeline_setting:
  63. predictor_kwargs["use_hpip"] = use_hpip
  64. if hpi_params is not None:
  65. predictor_kwargs["hpi_params"] = hpi_params
  66. pipeline_setting.pop("hpi_params", None)
  67. elif "hpi_params" in pipeline_setting:
  68. predictor_kwargs["hpi_params"] = pipeline_setting.pop("hpi_params")
  69. if pp_option is not None:
  70. predictor_kwargs["pp_option"] = pp_option
  71. pipeline_setting.pop("pp_option", None)
  72. elif "pp_option" in pipeline_setting:
  73. predictor_kwargs["pp_option"] = pipeline_setting.pop("pp_option")
  74. if device:
  75. pipeline_setting.pop("device", None)
  76. else:
  77. device = pipeline_setting.pop("device", None)
  78. pipeline_setting.update(kwargs)
  79. pipeline = BasePipeline.get(pipeline_name)(
  80. device=device, predictor_kwargs=predictor_kwargs, *args, **pipeline_setting
  81. )
  82. return pipeline
  83. def create_pipeline(
  84. pipeline: str,
  85. device=None,
  86. pp_option=None,
  87. use_hpip: bool = False,
  88. hpi_params: Optional[Dict[str, Any]] = None,
  89. *args,
  90. **kwargs,
  91. ) -> BasePipeline:
  92. """build model evaluater
  93. Args:
  94. pipeline (str): the pipeline name, that is name of pipeline class
  95. Returns:
  96. BasePipeline: the pipeline, which is subclass of BasePipeline.
  97. """
  98. config = load_pipeline_config(pipeline)
  99. return create_pipeline_from_config(
  100. config,
  101. device=device,
  102. pp_option=pp_option,
  103. use_hpip=use_hpip,
  104. hpi_params=hpi_params,
  105. *args,
  106. **kwargs,
  107. )