__init__.py 3.6 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 .table_recognition import TableRecPipeline
  34. from .seal_recognition import SealOCRPipeline
  35. from .ppchatocrv3 import PPChatOCRPipeline
  36. def load_pipeline_config(pipeline: str) -> Dict[str, Any]:
  37. if not Path(pipeline).exists():
  38. pipeline_path = get_pipeline_path(pipeline)
  39. if pipeline_path is None:
  40. raise Exception(
  41. f"The pipeline ({pipeline}) does not exist! Please use a pipeline name or a config file path!"
  42. )
  43. else:
  44. pipeline_path = pipeline
  45. config = parse_config(pipeline_path)
  46. return config
  47. def create_pipeline_from_config(
  48. config: Dict[str, Any],
  49. device=None,
  50. pp_option=None,
  51. use_hpip: bool = False,
  52. hpi_params: Optional[Dict[str, Any]] = None,
  53. *args,
  54. **kwargs,
  55. ) -> BasePipeline:
  56. pipeline_name = config["Global"]["pipeline_name"]
  57. pipeline_setting = config["Pipeline"]
  58. predictor_kwargs = {"use_hpip": use_hpip}
  59. if "use_hpip" in pipeline_setting:
  60. predictor_kwargs["use_hpip"] = use_hpip
  61. if hpi_params is not None:
  62. predictor_kwargs["hpi_params"] = hpi_params
  63. pipeline_setting.pop("hpi_params", None)
  64. elif "hpi_params" in pipeline_setting:
  65. predictor_kwargs["hpi_params"] = pipeline_setting.pop("hpi_params")
  66. if device is not None:
  67. predictor_kwargs["device"] = device
  68. pipeline_setting.pop("device", None)
  69. elif "device" in pipeline_setting:
  70. predictor_kwargs["device"] = pipeline_setting.pop("device")
  71. if pp_option is not None:
  72. predictor_kwargs["pp_option"] = pp_option
  73. pipeline_setting.pop("pp_option", None)
  74. elif "pp_option" in pipeline_setting:
  75. predictor_kwargs["pp_option"] = pipeline_setting.pop("pp_option")
  76. pipeline_setting.update(kwargs)
  77. pipeline = BasePipeline.get(pipeline_name)(
  78. predictor_kwargs=predictor_kwargs, *args, **pipeline_setting
  79. )
  80. return pipeline
  81. def create_pipeline(
  82. pipeline: str,
  83. device=None,
  84. pp_option=None,
  85. use_hpip: bool = False,
  86. hpi_params: Optional[Dict[str, Any]] = None,
  87. *args,
  88. **kwargs,
  89. ) -> BasePipeline:
  90. """build model evaluater
  91. Args:
  92. pipeline (str): the pipeline name, that is name of pipeline class
  93. Returns:
  94. BasePipeline: the pipeline, which is subclass of BasePipeline.
  95. """
  96. config = load_pipeline_config(pipeline)
  97. return create_pipeline_from_config(
  98. config,
  99. device=device,
  100. pp_option=pp_option,
  101. use_hpip=use_hpip,
  102. hpi_params=hpi_params,
  103. *args,
  104. **kwargs,
  105. )