9iSSKJrJrJrJrJrJrJr SSKr SSK J r SSK J r SSKJr SSKJr SS KJr S S KJr S S KJr \R0"S S\55r\ "S5"SS\55rg))AnyDictListLiteralOptionalTupleUnionN)pipeline_requires_extra) SegResult) benchmark) HPIConfig)PaddlePredictorOption)(AutoParallelImageSimpleInferencePipeline) BasePipelinec^\rSrSrSrSS\S\S\S\S\ \ \\\ 4\ 4S S4 U4S jjjr SS \ \\\\R \\R 4S \ \S S\\\4S \4SjjrSrU=r$)_SemanticSegmentationPipelinezSemantic Segmentation PipelineNconfigdevice pp_optionuse_hpip hpi_configreturncr>[TU]X#XES9 USSnURU5UlUSUlg)ah Initializes the class with given configurations and options. Args: config (Dict): Configuration dictionary containing model and other parameters. device (str): The device to run the prediction on. Default is None. pp_option (PaddlePredictorOption): Options for PaddlePaddle predictor. Default is None. use_hpip (bool, optional): Whether to use the high-performance inference plugin (HPIP) by default. Defaults to False. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): The default high-performance inference configuration dictionary. Defaults to None. )rrrr SubModulesSemanticSegmentation target_sizeN)super__init__ create_modelsemantic_segmentation_modelr )selfrrrrr"semantic_segmentation_model_config __class__s z/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/paddlex/inference/pipelines/semantic_segmentation/pipeline.pyr"&_SemanticSegmentationPipeline.__init__ sX*   .4L-A ". *,0+<+< ., (>mLinputr c+># URXS9ShvN gN7f)aPredicts semantic segmentation results for the given input. Args: input (str | list[str] | np.ndarray | list[np.ndarray]): The input image(s) or path(s) to the images. target_size (Literal[-1] | None | int | tuple[int]): The Image size model used to do prediction. Default is None. If it's set to -1, the original image size will be used. If it's set to None, the previous level's setting will be used. If it's set to an integer value, the image will be rescaled to the size of (value, value). If it's set to a tuple of two integers, the image will be rescaled to the size of (height, width). **kwargs: Additional keyword arguments that can be passed to the function. Returns: SegResult: The predicted segmentation results. )r N)r$)r%r+r kwargss r(predict%_SemanticSegmentationPipeline.predictAs(33E3SSSs )r$r )NNFNN)__name__ __module__ __qualname____firstlineno____doc__rstrrboolrr rrr"rnpndarrayrintrr r/__static_attributes__ __classcell__)r's@r(rrs( +/AE MMM) M  M U4S>9#<=> M MMHBFTS$s)RZZbjj1AABT72;c5:=>T  TTr*rcvc.\rSrSrSr\S5rSrSrg)SemanticSegmentationPipelineXsemantic_segmentationc[$r1)r)r%s r( _pipeline_cls*SemanticSegmentationPipeline._pipeline_cls\s,,r*c2USSRSS5$)Nrr batch_size)get)r%rs r(_get_batch_size,SemanticSegmentationPipeline._get_batch_size`s l#$:;?? aPPr*N) r2r3r4r5entitiespropertyrDrJr<rLr*r(r@r@Xs&H --Qr*r@)typingrrrrrrr numpyr9 utils.depsr #models.semantic_segmentation.resultr utils.benchmarkr utils.hpirutils.pp_optionr _parallelrbaser time_methodsrr@rLr*r(rYsrDCC2<("4@ 8TL8T8TvQ#KQQr*