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* Upgraded Layout Postprocessing, sending old code back to ERZ Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Implement hierachical cluster layout processing Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Pass nested cluster processing through full pipeline Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Pass nested clusters through GLM as payload Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Move to_docling_document from ds-glm to this repo Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Clean up imports again Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * feat(Accelerator): Introduce options to control the num_threads and device from API, envvars, CLI. - Introduce the AcceleratorOptions, AcceleratorDevice and use them to set the device where the models run. - Introduce the accelerator_utils with function to decide the device and resolve the AUTO setting. - Refactor the way how the docling-ibm-models are called to match the new init signature of models. - Translate the accelerator options to the specific inputs for third-party models. - Extend the docling CLI with parameters to set the num_threads and device. - Add new unit tests. - Write new example how to use the accelerator options. * fix: Improve the pydantic objects in the pipeline_options and imports. Signed-off-by: Nikos Livathinos <nli@zurich.ibm.com> * fix: TableStructureModel: Refactor the artifacts path to use the new structure for fast/accurate model Signed-off-by: Nikos Livathinos <nli@zurich.ibm.com> * Updated test ground-truth Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Updated test ground-truth (again), bugfix for empty layout Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * fix: Do proper check to set the device in EasyOCR, RapidOCR. Signed-off-by: Nikos Livathinos <nli@zurich.ibm.com> * Rollback changes from main Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Update test gt Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Remove unused debug settings Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Review fixes Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Nail the accelerator defaults for MPS Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com> Signed-off-by: Nikos Livathinos <nli@zurich.ibm.com> Co-authored-by: Christoph Auer <cau@zurich.ibm.com> Co-authored-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>
176 lines
6.0 KiB
Python
176 lines
6.0 KiB
Python
import json
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import logging
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import time
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from pathlib import Path
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from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import PdfPipelineOptions
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from docling.models.ocr_mac_model import OcrMacOptions
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from docling.models.tesseract_ocr_cli_model import TesseractCliOcrOptions
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from docling.models.tesseract_ocr_model import TesseractOcrOptions
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_log = logging.getLogger(__name__)
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def main():
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logging.basicConfig(level=logging.INFO)
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input_doc_path = Path("./tests/data/2206.01062.pdf")
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###########################################################################
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# The following sections contain a combination of PipelineOptions
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# and PDF Backends for various configurations.
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# Uncomment one section at the time to see the differences in the output.
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# PyPdfium without EasyOCR
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# --------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = False
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = False
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(
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# pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
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# )
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# }
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# )
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# PyPdfium with EasyOCR
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# -----------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(
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# pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
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# )
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# }
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# )
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# Docling Parse without EasyOCR
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# -------------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = False
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with EasyOCR
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# ----------------------
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pipeline_options = PdfPipelineOptions()
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pipeline_options.do_ocr = True
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pipeline_options.do_table_structure = True
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pipeline_options.table_structure_options.do_cell_matching = True
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pipeline_options.ocr_options.lang = ["es"]
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pipeline_options.accelerator_options = AcceleratorOptions(
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num_threads=4, device=Device.AUTO
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)
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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}
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)
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# Docling Parse with EasyOCR (CPU only)
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.ocr_options.use_gpu = False # <-- set this.
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with Tesseract
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# pipeline_options.ocr_options = TesseractOcrOptions()
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with Tesseract CLI
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# pipeline_options.ocr_options = TesseractCliOcrOptions()
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with ocrmac(Mac only)
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# pipeline_options.ocr_options = OcrMacOptions()
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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###########################################################################
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start_time = time.time()
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conv_result = doc_converter.convert(input_doc_path)
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end_time = time.time() - start_time
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_log.info(f"Document converted in {end_time:.2f} seconds.")
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## Export results
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output_dir = Path("scratch")
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output_dir.mkdir(parents=True, exist_ok=True)
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doc_filename = conv_result.input.file.stem
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# Export Deep Search document JSON format:
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with (output_dir / f"{doc_filename}.json").open("w", encoding="utf-8") as fp:
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fp.write(json.dumps(conv_result.document.export_to_dict()))
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# Export Text format:
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with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp:
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fp.write(conv_result.document.export_to_text())
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# Export Markdown format:
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with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp:
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fp.write(conv_result.document.export_to_markdown())
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# Export Document Tags format:
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with (output_dir / f"{doc_filename}.doctags").open("w", encoding="utf-8") as fp:
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fp.write(conv_result.document.export_to_document_tokens())
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if __name__ == "__main__":
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main()
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