Cleaned up logs, added pages to vlm_pipeline, basic timing per page measurement in smol_docling models

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

docling/models/smol_docling_model.py

@@ -1,4 +1,5 @@
 import logging
+import time
 from pathlib import Path
 from typing import Iterable, List, Optional
 
@@ -10,14 +11,7 @@ from transformers import (  # type: ignore
     Idefics3ForConditionalGeneration,
 )
 
-from docling.datamodel.base_models import (
-    BoundingBox,
-    Cell,
-    Cluster,
-    DocTagsPrediction,
-    LayoutPrediction,
-    Page,
-)
+from docling.datamodel.base_models import DocTagsPrediction, Page
 from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import AcceleratorDevice, AcceleratorOptions
 from docling.datamodel.settings import settings
@@ -31,7 +25,6 @@ _log = logging.getLogger(__name__)
 class SmolDoclingModel(BasePageModel):
 
     def __init__(self, artifacts_path: Path, accelerator_options: AcceleratorOptions):
-        print("SmolDocling, init...")
         device = decide_device(accelerator_options.device)
         self.device = device
         _log.info("Available device for SmolDocling: {}".format(device))
@@ -59,12 +52,10 @@ class SmolDoclingModel(BasePageModel):
                 torch_dtype="auto",
                 quantization_config=self.param_quantization_config,
             )
-        print("SmolDocling, init... done!")
 
     def __call__(
         self, conv_res: ConversionResult, page_batch: Iterable[Page]
     ) -> Iterable[Page]:
-        print("SmolDocling, processing...")
         for page in page_batch:
             assert page._backend is not None
             if not page._backend.is_valid():
@@ -72,6 +63,7 @@ class SmolDoclingModel(BasePageModel):
             else:
                 with TimeRecorder(conv_res, "smolvlm"):
                     assert page.size is not None
+                    start_time = time.time()
 
                     hi_res_image = page.get_image(scale=2.0)  # 144dpi
                     # populate page_tags with predicted doc tags
@@ -113,6 +105,9 @@ class SmolDoclingModel(BasePageModel):
                     )[0]
                     generated_texts = generated_texts.replace("Assistant: ", "")
                     page_tags = generated_texts
+
+                    inference_time = time.time() - start_time
+                    print(f"Page Inference Time: {inference_time:.2f} seconds")
                     print("Page predictions:")
                     print(page_tags)
 

docling/pipeline/vlm_pipeline.py

@@ -16,10 +16,12 @@ from docling_core.types.doc import (
     ImageRefMode,
     PictureItem,
     ProvenanceItem,
+    Size,
     TableCell,
     TableData,
     TableItem,
 )
+from docling_core.types.doc.tokens import DocumentToken, TableToken
 from PIL.Image import Image
 
 from docling.backend.abstract_backend import AbstractDocumentBackend
@@ -39,7 +41,6 @@ class VlmPipeline(PaginatedPipeline):
 
     def __init__(self, pipeline_options: PdfPipelineOptions):
         super().__init__(pipeline_options)
-        print("------> Init VLM Pipeline!")
         self.pipeline_options: PdfPipelineOptions
 
         if pipeline_options.artifacts_path is None:
@@ -91,7 +92,6 @@ class VlmPipeline(PaginatedPipeline):
         return page
 
     def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult:
-        print("VLM, assembling document...")
         with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT):
 
             # Read and concatenate the page doctags:
@@ -202,7 +202,6 @@ class VlmPipeline(PaginatedPipeline):
                 if not x
             ]
             table_cells = []
-            # print("\nText parts:")
             r_idx = 0
             c_idx = 0
 
@@ -227,7 +226,6 @@ class VlmPipeline(PaginatedPipeline):
                 return span
 
             for i, text in enumerate(texts):
-                # print(f"  {text}")
                 cell_text = ""
                 if text in ["<fcel>", "<ecel>", "<ched>", "<rhed>", "<srow>"]:
                     row_span = 1
@@ -323,8 +321,13 @@ class VlmPipeline(PaginatedPipeline):
         for pg_idx, xml_content in enumerate(full_doc_xml_content):
             pil_image = pil_images[pg_idx]
             page_no = pg_idx + 1
+
+            if pil_image:
+                pg_width, pg_height = pil_image.size
+                size = Size(width=pg_width, height=pg_height)
+                parent_page = doc.add_page(page_no=page_no, size=size)
+
             lines = xml_content.split("\n")
-            # pil_image = input_image #Image.open(BytesIO(image_bytes))
             bounding_boxes = []
 
             for line in lines:

docs/examples/minimal_smol_docling.py

@@ -1,4 +1,7 @@
+import os
+import time
 from pathlib import Path
+from urllib.parse import urlparse
 
 from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
 from docling.datamodel.base_models import InputFormat
@@ -6,12 +9,18 @@ from docling.datamodel.pipeline_options import PdfPipelineOptions
 from docling.document_converter import DocumentConverter, PdfFormatOption
 from docling.pipeline.vlm_pipeline import VlmPipeline
 
-source = "https://arxiv.org/pdf/2408.09869"  # document per local path or URL
+# source = "https://arxiv.org/pdf/2408.09869"  # document per local path or URL
 # source = "tests/data/2305.03393v1-pg9-img.png"
-# source = "tests/data/2305.03393v1-pg9.pdf"
+source = "tests/data/2305.03393v1-pg9.pdf"
 # source = "demo_data/page.png"
 # source = "demo_data/original_tables.pdf"
 
+parsed = urlparse(source)
+if parsed.scheme in ("http", "https"):
+    out_name = os.path.basename(parsed.path)
+else:
+    out_name = os.path.basename(source)
+
 pipeline_options = PdfPipelineOptions()
 pipeline_options.generate_page_images = True
 pipeline_options.artifacts_path = "model_artifacts"
@@ -32,6 +41,7 @@ converter = DocumentConverter(
     }
 )
 
+start_time = time.time()
 print("============")
 print("starting...")
 print("============")
@@ -39,12 +49,6 @@ print("")
 
 result = converter.convert(source)
 
-# print("------------")
-# print("result:")
-# print("------------")
-# print("")
-# print(result)
-
 print("------------")
 print("MD:")
 print("------------")
@@ -53,12 +57,16 @@ print(result.document.export_to_markdown())
 
 Path("scratch").mkdir(parents=True, exist_ok=True)
 result.document.save_as_html(
-    filename=Path("scratch/smol_export.html"),
+    filename=Path("scratch/{}.html".format(out_name)),
     image_mode=ImageRefMode.REFERENCED,
     labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE],
 )
 
+pg_num = result.document.num_pages()
+
 print("")
+inference_time = time.time() - start_time
+print(f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}")
 print("============")
 print("done!")
 print("============")