Update DocLayNet-base.py

DocLayNet-base.py

@@ -42,7 +42,7 @@ _CITATION = """\
 
 # You can copy an official description
 _DESCRIPTION = """\
-Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.
+Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide smallline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.
 """
 
 _HOMEPAGE = "https://developer.ibm.com/exchanges/data/all/doclaynet/"
@@ -65,17 +65,17 @@ def load_image(image_path):
 logger = datasets.logging.get_logger(__name__)
 
 
-class DocLayNetConfig(datasets.BuilderConfig):
+class DocLayNetBuilderConfig(datasets.BuilderConfig):
     """BuilderConfig for DocLayNet base"""
 
-    def __init__(self, **kwargs):
+    def __init__(self, name, **kwargs):
         """BuilderConfig for DocLayNet base.
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
-        super(DocLayNetConfig, self).__init__(**kwargs)
-
+        super().__init__(name, **kwargs)
 
+        
 class DocLayNet(datasets.GeneratorBasedBuilder):
     """
     DocLayNet base is a about 10% of the dataset DocLayNet (more information at https://huggingface.co/datasets/pierreguillou/DocLayNet-base)
@@ -98,12 +98,15 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # 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 = [
-        DocLayNetConfig(name="DocLayNet", version=datasets.Version("1.0.0"), description="DocLayNeT base dataset"),
+        DocLayNetBuilderConfig(name=DEFAULT_CONFIG_NAME, version=VERSION, description="DocLayNet base dataset"),
     ]
 
-    #DEFAULT_CONFIG_NAME = "DocLayNet"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-         
+    BUILDER_CONFIG_CLASS = DocLayNetBuilderConfig
+ 
     def _info(self):
         
         features = datasets.Features(
@@ -155,54 +158,49 @@ 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-base/resolve/main/data/dataset_base.zip")
-        splits = []
-        for split in self.config.splits:
-            if split == "train":
-                dataset = datasets.SplitGenerator(                
+
+        return [
+            datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(downloaded_file, "base_dataset/train/"),
-                    "split": "train",
+                    # "split_key": "train",
                 },
-            )
-            elif split in ["val", "valid", "validation", "dev"]:
-                dataset = datasets.SplitGenerator(
+            ),
+            datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(downloaded_file, "base_dataset/val/"),
-                    "split": "val",
+                    # "split_key": "validation",
                 },
-            )
-            elif split in ["test"]:
-                dataset = datasets.SplitGenerator(
+            ),
+            datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(downloaded_file, "base_dataset/test/"),
-                    "split": "test",
+                    # "split_key": "test"
                 },
-            )
-            else:
-                continue
+            ),
+        ]
 
-            splits.append(dataset)
-        return splits
 
-    def _generate_examples(self, filepath, split):
+    def _generate_examples(self, filepath):
             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: