Upload aftdb.py

aftdb.py

@@ -0,0 +1,217 @@
+import os
+import json
+
+import datasets
+from PIL import Image
+
+
+_DESCRIPTION = """
+The Arxiv Figure Table Database (AFTdb) facilitates the linking of documentary
+objects, such as figures and tables, with their captions. This enables a
+comprehensive description of document-oriented images (excluding images from
+cameras). For the table component, the character structure is preserved in
+addition to the image of the table and its caption. This database is ideal
+for multimodal processing of documentary images.
+"""
+_LICENSE = "apache-2.0"
+_CITATION = """
+@online{AFTdb,
+  AUTHOR = {Cyrile Delestre},
+  URL = {},
+  YEAR = {2024},
+  KEYWORDS = {NLP ; Multimodal}
+}
+"""
+_NB_TAR_FIGURE = [158, 4] # train, test
+_NB_TAR_TABLE = [17, 1] # train, test
+
+
+def extract_files_tar(all_path, data_dir, nb_files):
+    paths = [
+        os.path.join(data_dir, f"train-{ii:03d}.tar")
+        for ii in range(nb_files[0])
+    ]
+    all_path['train'] += paths
+    paths = [
+        os.path.join(data_dir, f"test-{ii:03d}.tar")
+        for ii in range(nb_files[1])
+    ]
+    all_path['test'] += paths
+
+
+class AFTConfig(datasets.BuilderConfig):
+    """Builder Config for AFT"""
+ 
+    def __init__(self, nb_files_figure, nb_files_table, **kwargs):
+        """BuilderConfig for Food-101.
+        Args:
+          data_url: `string`, url to download the zip file from.
+        """
+        super().__init__(version=datasets.__version__, **kwargs)
+        self.nb_files_figure = nb_files_figure
+        self.nb_files_table = nb_files_table
+
+
+class AFT_Dataset(datasets.GeneratorBasedBuilder):
+    """Arxiv Figure Table (AFT) dataset"""
+
+    BUILDER_CONFIGS = [
+        AFTConfig(
+            name="figure",
+            description=(
+                "Dataset containing scientific article figures associated "
+                "with their caption, summary, and article title."
+            ),
+            data_dir="./data/arxiv_dataset/{type}",
+            nb_files_figure=_NB_TAR_FIGURE,
+            nb_files_table=None
+        ),
+        AFTConfig(
+            name="table",
+            description=(
+                "Dataset containing tables in JPG image format from "
+                "scientific articles, along with the corresponding textual "
+                "representation of the table, including its caption, summary, "
+                "and article title."
+            ),
+            data_dir="./data/arxiv_dataset/{type}",
+            nb_files_figure=None,
+            nb_files_table=_NB_TAR_TABLE
+        ),
+        AFTConfig(
+            name="figure+table",
+            description=(
+                "Dataset containing figure and tables in JPG image format "
+                "from scientific articles, along with the corresponding "
+                "textual representation of the table, including its caption, "
+                "summary, and article title."
+            ),
+            data_dir="./data/arxiv_dataset/{type}",
+            nb_files_figure=_NB_TAR_FIGURE,
+            nb_files_table=_NB_TAR_TABLE
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "figure+table"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'id': datasets.Value('string'),
+                    'paper_id': datasets.Value('string'),
+                    'type': datasets.Value('string'),
+                    'authors': datasets.Value('string'),
+                    'categories': datasets.Value('string'),
+                    'title': {
+                        'english': datasets.Value('string'),
+                        'french': datasets.Value('string')
+                    },
+                    'summary': {
+                        'english': datasets.Value('string'),
+                        'french': datasets.Value('string')
+                    },
+                    'caption': {
+                        'english': datasets.Value('string'),
+                        'french': datasets.Value('string')
+                    },
+                    'image': datasets.Image(),
+                    'data': datasets.Value('string'),
+                    'newcommands': datasets.Sequence(datasets.Value('string'))
+                }
+            ),
+            citation=_CITATION,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        all_path = dict(train=[], test=[])
+        if self.config.nb_files_figure:
+            extract_files_tar(
+                all_path=all_path,
+                data_dir=self.config.data_dir.format(type='figure'),
+                nb_files=self.config.nb_files_figure
+            )
+        if self.config.nb_files_table:
+            extract_files_tar(
+                all_path=all_path,
+                data_dir=self.config.data_dir.format(type='table'),
+                nb_files=self.config.nb_files_table
+            )
+        if dl_manager.is_streaming:
+            downloaded_files = dl_manager.download(all_path)
+        else:
+            downloaded_files = dl_manager.download_and_extract(all_path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'filepaths': downloaded_files['train'],
+                    'is_streaming': dl_manager.is_streaming
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": downloaded_files['test'],
+                    'is_streaming': dl_manager.is_streaming
+                }
+            )
+        ]
+
+    def _generate_examples(self, filepaths, is_streaming):
+        if is_streaming:
+            _json, _jpg = False, False
+            dl_manager = datasets.DownloadManager()
+            for path_tar in filepaths:
+                iter_tar = dl_manager.iter_archive(path_tar)
+                for path, file_obj in iter_tar:
+                    if path.endswith('.json'):
+                        metadata = json.load(file_obj)
+                        _json = True
+                    if path.endswith('.jpg'):
+                        img = Image.open(file_obj)
+                        _jpg = True
+                    if _json and _jpg:
+                        _json, _jpg = False, False
+                        yield metadata['id'], {
+                            'id': metadata['id'],
+                            'paper_id': metadata['paper_id'],
+                            'type': metadata['type'],
+                            'authors': metadata['authors'],
+                            'categories': metadata['categories'],
+                            'title': metadata['title'],
+                            'summary': metadata['summary'],
+                            'caption': metadata['caption'],
+                            'image': img,
+                            'data': metadata['data'],
+                            'newcommands': metadata['newcommands']
+                        }
+        else:
+            for path in filepaths:
+                all_file = os.listdir(path)
+                all_id_obs = sorted(
+                    set(map(lambda x: x.split('.')[0], all_file))
+                )
+                for id_obs in all_id_obs:
+                    path_metadata = os.path.join(
+                        path,
+                        f"{id_obs}.metadata.json"
+                    )
+                    path_image = os.path.join(path, f"{id_obs}.image.jpg")
+                    metadata = json.load(open(path_metadata, 'r'))
+                    img = Image.open(path_image)
+                    yield id_obs, {
+                        'id': metadata['id'],
+                        'paper_id': metadata['paper_id'],
+                        'type': metadata['type'],
+                        'authors': metadata['authors'],
+                        'categories': metadata['categories'],
+                        'title': metadata['title'],
+                        'summary': metadata['summary'],
+                        'caption': metadata['caption'],
+                        'image': img,
+                        'data': metadata['data'],
+                        'newcommands': metadata['newcommands']
+                    }