fix: pyarrow float to int64 conversion error in dataset generator

DocLayNet-small.py

@@ -25,6 +25,7 @@ DocLayNet dataset:
 
 import json
 import os
+
 # import base64
 from PIL import Image
 import datasets
@@ -56,12 +57,19 @@ _LICENSE = "https://github.com/DS4SD/DocLayNet/blob/main/LICENSE"
 #     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
 # }
 
+
 # functions
 def load_image(image_path):
     image = Image.open(image_path).convert("RGB")
     w, h = image.size
     return image, (w, h)
 
+
+def convert_bbox_to_int(bbox):
+    """Convert bounding box coordinates to integers, handling float values."""
+    return [int(round(coord)) for coord in bbox]
+
+
 logger = datasets.logging.get_logger(__name__)
 
 
@@ -75,7 +83,7 @@ class DocLayNetBuilderConfig(datasets.BuilderConfig):
         """
         super().__init__(name, **kwargs)
 
-        
+
 class DocLayNet(datasets.GeneratorBasedBuilder):
     """
     DocLayNet small is a about 1% of the dataset DocLayNet (more information at https://huggingface.co/datasets/pierreguillou/DocLayNet-small)
@@ -100,40 +108,72 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'second_domain')
 
     DEFAULT_CONFIG_NAME = "DocLayNet_2022.08_processed_on_2023.01"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    
+
     BUILDER_CONFIGS = [
-        DocLayNetBuilderConfig(name=DEFAULT_CONFIG_NAME, version=VERSION, description="DocLayNeT small dataset"),
+        DocLayNetBuilderConfig(
+            name=DEFAULT_CONFIG_NAME,
+            version=VERSION,
+            description="DocLayNeT small dataset",
+        ),
     ]
 
     BUILDER_CONFIG_CLASS = DocLayNetBuilderConfig
- 
+
     def _info(self):
-        
+
         features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "texts": datasets.Sequence(datasets.Value("string")),
-                    "bboxes_block": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
-                    "bboxes_line": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
-                    "categories": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=["Caption", "Footnote", "Formula", "List-item", "Page-footer", "Page-header", "Picture", "Section-header", "Table", "Text", "Title"]
-                        )
-                    ),
-                    "image": datasets.features.Image(),
-                    # "pdf": datasets.Value("string"),
-                    "page_hash": datasets.Value("string"), # unique identifier, equal to filename
-				    "original_filename": datasets.Value("string"), # original document filename
-				    "page_no": datasets.Value("int32"), # page number in original document
-				    "num_pages": datasets.Value("int32"), # total pages in original document
-				    "original_width": datasets.Value("int32"), # width in pixels @72 ppi
-				    "original_height": datasets.Value("int32"), # height in pixels @72 ppi
-				    "coco_width": datasets.Value("int32"), # with in pixels in PNG and COCO format
-				    "coco_height": datasets.Value("int32"), # with in pixels in PNG and COCO format
-				    "collection": datasets.Value("string"), # sub-collection name
-				    "doc_category": datasets.Value("string"), # category type of the document
-                }
-            )
+            {
+                "id": datasets.Value("string"),
+                "texts": datasets.Sequence(datasets.Value("string")),
+                "bboxes_block": datasets.Sequence(
+                    datasets.Sequence(datasets.Value("int64"))
+                ),
+                "bboxes_line": datasets.Sequence(
+                    datasets.Sequence(datasets.Value("int64"))
+                ),
+                "categories": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=[
+                            "Caption",
+                            "Footnote",
+                            "Formula",
+                            "List-item",
+                            "Page-footer",
+                            "Page-header",
+                            "Picture",
+                            "Section-header",
+                            "Table",
+                            "Text",
+                            "Title",
+                        ]
+                    )
+                ),
+                "image": datasets.features.Image(),
+                # "pdf": datasets.Value("string"),
+                "page_hash": datasets.Value(
+                    "string"
+                ),  # unique identifier, equal to filename
+                "original_filename": datasets.Value(
+                    "string"
+                ),  # original document filename
+                "page_no": datasets.Value("int32"),  # page number in original document
+                "num_pages": datasets.Value(
+                    "int32"
+                ),  # total pages in original document
+                "original_width": datasets.Value("int32"),  # width in pixels @72 ppi
+                "original_height": datasets.Value("int32"),  # height in pixels @72 ppi
+                "coco_width": datasets.Value(
+                    "int32"
+                ),  # with in pixels in PNG and COCO format
+                "coco_height": datasets.Value(
+                    "int32"
+                ),  # with in pixels in PNG and COCO format
+                "collection": datasets.Value("string"),  # sub-collection name
+                "doc_category": datasets.Value(
+                    "string"
+                ),  # category type of the document
+            }
+        )
 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
@@ -158,8 +198,10 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        
-        downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-small/resolve/main/data/dataset_small.zip")
+
+        downloaded_file = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/pierreguillou/DocLayNet-small/resolve/main/data/dataset_small.zip"
+        )
 
         return [
             datasets.SplitGenerator(
@@ -183,58 +225,80 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(downloaded_file, "small_dataset/test/"),
-                    "split": "test"
+                    "split": "test",
                 },
             ),
         ]
 
-
     def _generate_examples(self, filepath, split):
-            logger.info("⏳ Generating examples from = %s", filepath)
-            ann_dir = os.path.join(filepath, "annotations")
-            img_dir = os.path.join(filepath, "images")
-            # pdf_dir = os.path.join(filepath, "pdfs")
-        
-            for guid, file in enumerate(sorted(os.listdir(ann_dir))):
-                texts = []
-                bboxes_block = []
-                bboxes_line = []
-                categories = []
-        
-                # get json
-                file_path = os.path.join(ann_dir, file)
-                with open(file_path, "r", encoding="utf8") as f:
-                    data = json.load(f)
-                    
-                # get image
-                image_path = os.path.join(img_dir, file)
-                image_path = image_path.replace("json", "png")
-                image, size = load_image(image_path)
-                
-                # # get pdf
-                # pdf_path = os.path.join(pdf_dir, file)
-                # pdf_path = pdf_path.replace("json", "pdf")
-                # with open(pdf_path, "rb") as pdf_file:
-                #     pdf_bytes = pdf_file.read()
-                #     pdf_encoded_string = base64.b64encode(pdf_bytes)
-
-                for item in data["form"]:
-                    text_example, category_example, bbox_block_example, bbox_line_example = item["text"], item["category"], item["box"], item["box_line"]
-                    texts.append(text_example)
-                    categories.append(category_example)
-                    bboxes_block.append(bbox_block_example)
-                    bboxes_line.append(bbox_line_example)
-
-                # get all metadadata
-                page_hash = data["metadata"]["page_hash"]
-                original_filename = data["metadata"]["original_filename"]
-                page_no = data["metadata"]["page_no"]
-                num_pages = data["metadata"]["num_pages"]
-                original_width = data["metadata"]["original_width"]
-                original_height = data["metadata"]["original_height"]
-                coco_width = data["metadata"]["coco_width"]
-                coco_height = data["metadata"]["coco_height"]
-                collection = data["metadata"]["collection"]
-                doc_category = data["metadata"]["doc_category"]
-
-                yield guid, {"id": str(guid), "texts": texts, "bboxes_block": bboxes_block, "bboxes_line": bboxes_line, "categories": categories, "image": image, "page_hash": page_hash, "original_filename": original_filename, "page_no": page_no, "num_pages": num_pages, "original_width": original_width, "original_height": original_height, "coco_width": coco_width, "coco_height": coco_height, "collection": collection, "doc_category": doc_category}
+        logger.info("⏳ Generating examples from = %s", filepath)
+        ann_dir = os.path.join(filepath, "annotations")
+        img_dir = os.path.join(filepath, "images")
+        # pdf_dir = os.path.join(filepath, "pdfs")
+
+        for guid, file in enumerate(sorted(os.listdir(ann_dir))):
+            texts = []
+            bboxes_block = []
+            bboxes_line = []
+            categories = []
+
+            # get json
+            file_path = os.path.join(ann_dir, file)
+            with open(file_path, "r", encoding="utf8") as f:
+                data = json.load(f)
+
+            # get image
+            image_path = os.path.join(img_dir, file)
+            image_path = image_path.replace("json", "png")
+            image, size = load_image(image_path)
+
+            # # get pdf
+            # pdf_path = os.path.join(pdf_dir, file)
+            # pdf_path = pdf_path.replace("json", "pdf")
+            # with open(pdf_path, "rb") as pdf_file:
+            #     pdf_bytes = pdf_file.read()
+            #     pdf_encoded_string = base64.b64encode(pdf_bytes)
+
+            for item in data["form"]:
+                (
+                    text_example,
+                    category_example,
+                    bbox_block_example,
+                    bbox_line_example,
+                ) = (item["text"], item["category"], item["box"], item["box_line"])
+                texts.append(text_example)
+                categories.append(category_example)
+                # Convert bounding boxes to integers to avoid float->int64 conversion errors
+                bboxes_block.append(convert_bbox_to_int(bbox_block_example))
+                bboxes_line.append(convert_bbox_to_int(bbox_line_example))
+
+            # get all metadadata
+            page_hash = data["metadata"]["page_hash"]
+            original_filename = data["metadata"]["original_filename"]
+            page_no = data["metadata"]["page_no"]
+            num_pages = data["metadata"]["num_pages"]
+            original_width = data["metadata"]["original_width"]
+            original_height = data["metadata"]["original_height"]
+            coco_width = data["metadata"]["coco_width"]
+            coco_height = data["metadata"]["coco_height"]
+            collection = data["metadata"]["collection"]
+            doc_category = data["metadata"]["doc_category"]
+
+            yield guid, {
+                "id": str(guid),
+                "texts": texts,
+                "bboxes_block": bboxes_block,
+                "bboxes_line": bboxes_line,
+                "categories": categories,
+                "image": image,
+                "page_hash": page_hash,
+                "original_filename": original_filename,
+                "page_no": page_no,
+                "num_pages": num_pages,
+                "original_width": original_width,
+                "original_height": original_height,
+                "coco_width": coco_width,
+                "coco_height": coco_height,
+                "collection": collection,
+                "doc_category": doc_category,
+            }