# Copyright (c) 2025 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 .._utils.cli import ( add_simple_inference_args, get_subcommand_args, perform_simple_inference, str2bool, ) from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor from .utils import create_config_from_structure _SUPPORTED_VL_BACKENDS = ["native", "vllm-server", "sglang-server", "fastdeploy-server"] class PaddleOCRVL(PaddleXPipelineWrapper): def __init__( self, layout_detection_model_name=None, layout_detection_model_dir=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, vl_rec_model_name=None, vl_rec_model_dir=None, vl_rec_backend=None, vl_rec_server_url=None, vl_rec_max_concurrency=None, doc_orientation_classify_model_name=None, doc_orientation_classify_model_dir=None, doc_unwarping_model_name=None, doc_unwarping_model_dir=None, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, use_chart_recognition=None, format_block_content=None, **kwargs, ): if vl_rec_backend is not None and vl_rec_backend not in _SUPPORTED_VL_BACKENDS: raise ValueError( f"Invalid backend for the VL recognition module: {vl_rec_backend}. Supported values are {_SUPPORTED_VL_BACKENDS}." ) params = locals().copy() params.pop("self") params.pop("kwargs") self._params = params super().__init__(**kwargs) @property def _paddlex_pipeline_name(self): return "PaddleOCR-VL" def predict_iter( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, use_chart_recognition=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, use_queues=None, prompt_label=None, format_block_content=None, repetition_penalty=None, temperature=None, top_p=None, min_pixels=None, max_pixels=None, **kwargs, ): return self.paddlex_pipeline.predict( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_layout_detection=use_layout_detection, use_chart_recognition=use_chart_recognition, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, use_queues=use_queues, prompt_label=prompt_label, format_block_content=format_block_content, repetition_penalty=repetition_penalty, temperature=temperature, top_p=top_p, min_pixels=min_pixels, max_pixels=max_pixels, **kwargs, ) def predict( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_layout_detection=None, use_chart_recognition=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, use_queues=None, prompt_label=None, format_block_content=None, repetition_penalty=None, temperature=None, top_p=None, min_pixels=None, max_pixels=None, **kwargs, ): return list( self.predict_iter( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_layout_detection=use_layout_detection, use_chart_recognition=use_chart_recognition, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, use_queues=use_queues, prompt_label=prompt_label, format_block_content=format_block_content, repetition_penalty=repetition_penalty, temperature=temperature, top_p=top_p, min_pixels=min_pixels, max_pixels=max_pixels, **kwargs, ) ) def concatenate_markdown_pages(self, markdown_list): return self.paddlex_pipeline.concatenate_markdown_pages(markdown_list) @classmethod def get_cli_subcommand_executor(cls): return PaddleOCRVLCLISubcommandExecutor() def _get_paddlex_config_overrides(self): STRUCTURE = { "SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[ "use_doc_orientation_classify" ], "SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[ "use_doc_unwarping" ], "use_doc_preprocessor": self._params["use_doc_orientation_classify"] or self._params["use_doc_unwarping"], "use_layout_detection": self._params["use_layout_detection"], "use_chart_recognition": self._params["use_chart_recognition"], "format_block_content": self._params["format_block_content"], "SubModules.LayoutDetection.model_name": self._params[ "layout_detection_model_name" ], "SubModules.LayoutDetection.model_dir": self._params[ "layout_detection_model_dir" ], "SubModules.LayoutDetection.threshold": self._params["layout_threshold"], "SubModules.LayoutDetection.layout_nms": self._params["layout_nms"], "SubModules.LayoutDetection.layout_unclip_ratio": self._params[ "layout_unclip_ratio" ], "SubModules.LayoutDetection.layout_merge_bboxes_mode": self._params[ "layout_merge_bboxes_mode" ], "SubModules.VLRecognition.model_name": self._params["vl_rec_model_name"], "SubModules.VLRecognition.model_dir": self._params["vl_rec_model_dir"], "SubModules.VLRecognition.genai_config.backend": self._params[ "vl_rec_backend" ], "SubModules.VLRecognition.genai_config.server_url": self._params[ "vl_rec_server_url" ], "SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[ "doc_orientation_classify_model_name" ], "SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[ "doc_orientation_classify_model_dir" ], "SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[ "doc_unwarping_model_name" ], "SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[ "doc_unwarping_model_dir" ], } return create_config_from_structure(STRUCTURE) class PaddleOCRVLCLISubcommandExecutor(PipelineCLISubcommandExecutor): @property def subparser_name(self): return "doc_parser" def _update_subparser(self, subparser): add_simple_inference_args(subparser) subparser.add_argument( "--layout_detection_model_name", type=str, help="Name of the layout detection model.", ) subparser.add_argument( "--layout_detection_model_dir", type=str, help="Path to the layout detection model directory.", ) subparser.add_argument( "--layout_threshold", type=float, help="Score threshold for the layout detection model.", ) subparser.add_argument( "--layout_nms", type=str2bool, help="Whether to use NMS in layout detection.", ) subparser.add_argument( "--layout_unclip_ratio", type=float, help="Expansion coefficient for layout detection.", ) subparser.add_argument( "--layout_merge_bboxes_mode", type=str, help="Overlapping box filtering method.", ) subparser.add_argument( "--vl_rec_model_name", type=str, help="Name of the VL recognition model.", ) subparser.add_argument( "--vl_rec_model_dir", type=str, help="Path to the VL recognition model directory.", ) subparser.add_argument( "--vl_rec_backend", type=str, help="Backend used by the VL recognition module.", choices=_SUPPORTED_VL_BACKENDS, ) subparser.add_argument( "--vl_rec_server_url", type=str, help="Server URL used by the VL recognition module.", ) subparser.add_argument( "--vl_rec_max_concurrency", type=str, help="Maximum concurrency for making VLM requests.", ) subparser.add_argument( "--doc_orientation_classify_model_name", type=str, help="Name of the document image orientation classification model.", ) subparser.add_argument( "--doc_orientation_classify_model_dir", type=str, help="Path to the document image orientation classification model directory.", ) subparser.add_argument( "--doc_unwarping_model_name", type=str, help="Name of the text image unwarping model.", ) subparser.add_argument( "--doc_unwarping_model_dir", type=str, help="Path to the image unwarping model directory.", ) subparser.add_argument( "--use_doc_orientation_classify", type=str2bool, help="Whether to use document image orientation classification.", ) subparser.add_argument( "--use_doc_unwarping", type=str2bool, help="Whether to use text image unwarping.", ) subparser.add_argument( "--use_layout_detection", type=str2bool, help="Whether to use layout detection.", ) subparser.add_argument( "--use_chart_recognition", type=str2bool, help="Whether to use chart recognition.", ) subparser.add_argument( "--format_block_content", type=str2bool, help="Whether to format block content to Markdown.", ) subparser.add_argument( "--use_queues", type=str2bool, help="Whether to use queues for asynchronous processing.", ) subparser.add_argument( "--prompt_label", type=str, help="Prompt label for the VLM.", ) subparser.add_argument( "--repetition_penalty", type=float, help="Repetition penalty used in sampling for the VLM.", ) subparser.add_argument( "--temperature", type=float, help="Temperature parameter used in sampling for the VLM.", ) subparser.add_argument( "--top_p", type=float, help="Top-p parameter used in sampling for the VLM.", ) subparser.add_argument( "--min_pixels", type=int, help="Minimum pixels for image preprocessing for the VLM.", ) subparser.add_argument( "--max_pixels", type=int, help="Maximum pixels for image preprocessing for the VLM.", ) def execute_with_args(self, args): params = get_subcommand_args(args) perform_simple_inference( PaddleOCRVL, params, predict_param_names={ "use_queues", "prompt_label", "repetition_penalty", "temperature", "top_p", "min_pixels", "max_pixels", }, )