__init__.py 3.0 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. AnomolyDetection,
  31. )
  32. from .ocr import OCRPipeline
  33. from .table_recognition import TableRecPipeline
  34. from .ppchatocrv3 import PPChatOCRPipeline
  35. def create_pipeline(
  36. pipeline: str,
  37. device=None,
  38. pp_option=None,
  39. use_hpip: bool = False,
  40. hpi_params: Optional[Dict[str, Any]] = None,
  41. *args,
  42. **kwargs,
  43. ) -> BasePipeline:
  44. """build model evaluater
  45. Args:
  46. pipeline (str): the pipeline name, that is name of pipeline class
  47. Returns:
  48. BasePipeline: the pipeline, which is subclass of BasePipeline.
  49. """
  50. if not Path(pipeline).exists():
  51. pipeline_path = get_pipeline_path(pipeline)
  52. if pipeline_path is None:
  53. raise Exception(
  54. f"The pipeline({pipeline}) don't exist! Please use the pipeline name or config yaml file!"
  55. )
  56. pipeline_path = pipeline
  57. config = parse_config(pipeline_path)
  58. pipeline_name = config["Global"]["pipeline_name"]
  59. pipeline_setting = config["Pipeline"]
  60. predictor_kwargs = {"use_hpip": use_hpip}
  61. if "use_hpip" in pipeline_setting:
  62. predictor_kwargs["use_hpip"] = use_hpip
  63. if hpi_params is not None:
  64. predictor_kwargs["hpi_params"] = hpi_params
  65. pipeline_setting.pop("hpi_params")
  66. elif "hpi_params" in pipeline_setting:
  67. predictor_kwargs["hpi_params"] = pipeline_setting.pop("hpi_params")
  68. if device is not None:
  69. predictor_kwargs["device"] = device
  70. pipeline_setting.pop("device")
  71. elif "device" in pipeline_setting:
  72. predictor_kwargs["device"] = pipeline_setting.pop("device")
  73. if pp_option is not None:
  74. predictor_kwargs["pp_option"] = pp_option
  75. pipeline_setting.pop("pp_option")
  76. elif "pp_option" in pipeline_setting:
  77. predictor_kwargs["pp_option"] = pipeline_setting.pop("pp_option")
  78. pipeline_setting.update(kwargs)
  79. pipeline = BasePipeline.get(pipeline_name)(
  80. predictor_kwargs=predictor_kwargs, *args, **pipeline_setting
  81. )
  82. return pipeline