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chore: fix or catch deprecation warnings

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Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
This commit is contained in:
Cesar Berrospi Ramis 2025-05-26 05:47:57 +02:00
parent 106951e71e
commit 53ffc565ca
6 changed files with 113 additions and 81 deletions
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22
docling/models/layout_model.py
View File

@ -185,13 +185,23 @@ class LayoutModel(BasePageModel):
).postprocess()
# processed_clusters, processed_cells = clusters, page.cells
conv_res.confidence.pages[page.page_no].layout_score = float(
np.mean([c.confidence for c in processed_clusters])
)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
"Mean of empty slice|invalid value encountered in scalar divide",
RuntimeWarning,
"numpy",
)
conv_res.confidence.pages[page.page_no].ocr_score = float(
np.mean([c.confidence for c in processed_cells if c.from_ocr])
)
conv_res.confidence.pages[page.page_no].layout_score = float(
np.mean([c.confidence for c in processed_clusters])
)
conv_res.confidence.pages[page.page_no].ocr_score = float(
np.mean(
[c.confidence for c in processed_cells if c.from_ocr]
)
)
page.cells = processed_cells
page.predictions.layout = LayoutPrediction(

17
docling/models/page_preprocessing_model.py
View File

@ -1,4 +1,5 @@
import re
import warnings
from collections.abc import Iterable
from pathlib import Path
from typing import Optional
@ -7,7 +8,7 @@ import numpy as np
from PIL import ImageDraw
from pydantic import BaseModel
from docling.datamodel.base_models import Page, ScoreValue
from docling.datamodel.base_models import Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.settings import settings
from docling.models.base_model import BasePageModel
@ -76,11 +77,15 @@ class PagePreprocessingModel(BasePageModel):
score = self.rate_text_quality(c.text)
text_scores.append(score)
conv_res.confidence.pages[page.page_no].parse_score = float(
np.nanquantile(
text_scores, q=0.10
) # To emphasise problems in the parse_score, we take the 10% percentile score of all text cells.
)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "Mean of empty slice", RuntimeWarning, "numpy"
)
conv_res.confidence.pages[page.page_no].parse_score = float(
np.nanquantile(
text_scores, q=0.10
) # To emphasise problems in the parse_score, we take the 10% percentile score of all text cells.
)
# DEBUG code:
def draw_text_boxes(image, cells, show: bool = False):

122
docling/pipeline/standard_pdf_pipeline.py
View File

@ -8,7 +8,7 @@ from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem
from docling.backend.abstract_backend import AbstractDocumentBackend
from docling.backend.pdf_backend import PdfDocumentBackend
from docling.datamodel.base_models import AssembledUnit, Page, PageConfidenceScores
from docling.datamodel.base_models import AssembledUnit, Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.settings import settings
@ -55,11 +55,13 @@ class StandardPdfPipeline(PaginatedPipeline):
"When defined, it must point to a folder containing all models required by the pipeline."
)
self.keep_images = (
self.pipeline_options.generate_page_images
or self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
)
with warnings.catch_warnings(): # deprecated generate_table_images
warnings.filterwarnings("ignore", category=DeprecationWarning)
self.keep_images = (
self.pipeline_options.generate_page_images
or self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
)
self.reading_order_model = ReadingOrderModel(options=ReadingOrderOptions())
@ -210,64 +212,74 @@ class StandardPdfPipeline(PaginatedPipeline):
)
# Generate images of the requested element types
if (
self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
):
scale = self.pipeline_options.images_scale
for element, _level in conv_res.document.iterate_items():
if not isinstance(element, DocItem) or len(element.prov) == 0:
continue
if (
isinstance(element, PictureItem)
and self.pipeline_options.generate_picture_images
) or (
isinstance(element, TableItem)
and self.pipeline_options.generate_table_images
):
page_ix = element.prov[0].page_no - 1
page = next(
(p for p in conv_res.pages if p.page_no == page_ix),
cast("Page", None),
)
assert page is not None
assert page.size is not None
assert page.image is not None
with warnings.catch_warnings(): # deprecated generate_table_images
warnings.filterwarnings("ignore", category=DeprecationWarning)
if (
self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
):
scale = self.pipeline_options.images_scale
for element, _level in conv_res.document.iterate_items():
if not isinstance(element, DocItem) or len(element.prov) == 0:
continue
if (
isinstance(element, PictureItem)
and self.pipeline_options.generate_picture_images
) or (
isinstance(element, TableItem)
and self.pipeline_options.generate_table_images
):
page_ix = element.prov[0].page_no - 1
page = next(
(p for p in conv_res.pages if p.page_no == page_ix),
cast("Page", None),
)
assert page is not None
assert page.size is not None
assert page.image is not None
crop_bbox = (
element.prov[0]
.bbox.scaled(scale=scale)
.to_top_left_origin(page_height=page.size.height * scale)
)
crop_bbox = (
element.prov[0]
.bbox.scaled(scale=scale)
.to_top_left_origin(
page_height=page.size.height * scale
)
)
cropped_im = page.image.crop(crop_bbox.as_tuple())
element.image = ImageRef.from_pil(
cropped_im, dpi=int(72 * scale)
)
cropped_im = page.image.crop(crop_bbox.as_tuple())
element.image = ImageRef.from_pil(
cropped_im, dpi=int(72 * scale)
)
# Aggregate confidence values for document:
if len(conv_res.pages) > 0:
conv_res.confidence.layout_score = float(
np.nanmean(
[c.layout_score for c in conv_res.confidence.pages.values()]
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
category=RuntimeWarning,
message="Mean of empty slice|All-NaN slice encountered",
)
)
conv_res.confidence.parse_score = float(
np.nanquantile(
[c.parse_score for c in conv_res.confidence.pages.values()],
q=0.1, # parse score should relate to worst 10% of pages.
conv_res.confidence.layout_score = float(
np.nanmean(
[c.layout_score for c in conv_res.confidence.pages.values()]
)
)
)
conv_res.confidence.table_score = float(
np.nanmean(
[c.table_score for c in conv_res.confidence.pages.values()]
conv_res.confidence.parse_score = float(
np.nanquantile(
[c.parse_score for c in conv_res.confidence.pages.values()],
q=0.1, # parse score should relate to worst 10% of pages.
)
)
)
conv_res.confidence.ocr_score = float(
np.nanmean(
[c.ocr_score for c in conv_res.confidence.pages.values()]
conv_res.confidence.table_score = float(
np.nanmean(
[c.table_score for c in conv_res.confidence.pages.values()]
)
)
conv_res.confidence.ocr_score = float(
np.nanmean(
[c.ocr_score for c in conv_res.confidence.pages.values()]
)
)
)
return conv_res

11
tests/test_backend_csv.py
View File

@ -39,8 +39,15 @@ def test_e2e_valid_csv_conversions():
print(f"converting {csv_path}")
gt_path = csv_path.parent.parent / "groundtruth" / "docling_v2" / csv_path.name
conv_result: ConversionResult = converter.convert(csv_path)
if csv_path.stem in (
"csv-too-few-columns",
"csv-too-many-columns",
"csv-inconsistent-header",
):
with warns(UserWarning, match="Inconsistent column lengths"):
conv_result: ConversionResult = converter.convert(csv_path)
else:
conv_result: ConversionResult = converter.convert(csv_path)
doc: DoclingDocument = conv_result.document

14
tests/test_legacy_format_transform.py
View File

@ -38,17 +38,15 @@ def get_converter():
def test_compare_legacy_output(test_doc_paths):
converter = get_converter()
res = converter.convert_all(test_doc_paths, raises_on_error=True)
for conv_res in res:
print(f"Results for {conv_res.input.file}")
print(
json.dumps(
conv_res.legacy_document.model_dump(
mode="json", by_alias=True, exclude_none=True
with pytest.warns(DeprecationWarning, match="Use document instead"):
print(
json.dumps(
conv_res.legacy_document.model_dump(
mode="json", by_alias=True, exclude_none=True
)
)
)
)
# assert res.legacy_output == res.legacy_output_transformed

8
tests/verify_utils.py
View File

@ -4,6 +4,7 @@ import warnings
from pathlib import Path
from typing import List, Optional
import pytest
from docling_core.types.doc import (
DocItem,
DoclingDocument,
@ -302,9 +303,8 @@ def verify_conversion_result_v1(
)
doc_pred_pages: List[Page] = doc_result.pages
doc_pred: DsDocument = doc_result.legacy_document
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
with pytest.warns(DeprecationWarning, match="Use document instead"):
doc_pred: DsDocument = doc_result.legacy_document
doc_pred_md = doc_result.legacy_document.export_to_markdown()
doc_pred_dt = doc_result.legacy_document.export_to_document_tokens()
@ -391,7 +391,7 @@ def verify_conversion_result_v2(
doc_pred_pages: List[Page] = doc_result.pages
doc_pred: DoclingDocument = doc_result.document
doc_pred_md = doc_result.document.export_to_markdown()
doc_pred_dt = doc_result.document.export_to_document_tokens()
doc_pred_dt = doc_result.document.export_to_doctags()
engine_suffix = "" if ocr_engine is None else f".{ocr_engine}"