Add doclaynet load script and README skeleton

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

.gitignore

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+# Gradle
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+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
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+atlassian-ide-plugin.xml
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+com_crashlytics_export_strings.xml
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+
+### PyCharm+all Patch ###
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+
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+
+# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023
+
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+
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+*$py.class
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+share/python-wheels/
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+.installed.cfg
+*.egg
+MANIFEST
+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
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+*.cover
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+.pytest_cache/
+
+# Translations
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+# Sphinx documentation
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+# PyBuilder
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+
+# pyenv
+.python-version
+
+# pipenv
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
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+
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+
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+.ropeproject
+
+# Mr Developer
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+.project
+.pydevproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+### VirtualEnv ###
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+pyvenv.cfg
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pip-selfcheck.json
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+### VisualStudioCode ###
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+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
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+*.stackdump
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+[Dd]esktop.ini
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+$RECYCLE.BIN/
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+### JupyterNotebooks ###
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
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+ipython_config.py
+
+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
+
+# End of https://www.toptal.com/developers/gitignore/api/jupyternotebooks

README.md

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 ---
-license: other
+annotations_creators:
+- crowdsourced
+license: CDLA-Permissive-1.0
+pretty_name: DocLayNet
+size_categories:
+- 10K<n<100K
+tags:
+- layout-segmentation
+- COCO
+- document-understanding
+- PDF
+task_categories:
+- object-detection
+- image-segmentation
+task_ids:
+- instance-segmentation
 ---
+
+# Dataset Card for DocLayNet
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

doclaynet.py

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+"""
+Inspired from
+https://huggingface.co/datasets/ydshieh/coco_dataset_script/blob/main/coco_dataset_script.py
+"""
+
+import json
+import os
+import datasets
+
+
+class COCOBuilderConfig(datasets.BuilderConfig):
+
+    def __init__(self, name, splits, **kwargs):
+        super().__init__(name, **kwargs)
+        self.splits = splits
+
+
+# Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
+  doi = {10.1145/3534678.353904},
+  url = {https://arxiv.org/abs/2206.01062},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022}
+}
+"""
+
+# Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+DocLayNet is a human-annotated document layout segmentation dataset from a broad variety of document sources.
+"""
+
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://developer.ibm.com/exchanges/data/all/doclaynet/"
+
+# Add the licence for the dataset here if you can find it
+_LICENSE = "CDLA-Permissive-1.0"
+
+# Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+
+# This script is supposed to work with local (downloaded) COCO dataset.
+_URLs = {
+    "core": "https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip",
+}
+
+
+# Name of the dataset usually match the script name with CamelCase instead of snake_case
+class COCODataset(datasets.GeneratorBasedBuilder):
+    """An example dataset script to work with the local (downloaded) COCO dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG_CLASS = COCOBuilderConfig
+    BUILDER_CONFIGS = [
+        COCOBuilderConfig(name='2022.08', splits=['train', 'val', 'test']),
+    ]
+    DEFAULT_CONFIG_NAME = "2022.08"
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+        feature_dict = {
+            "id": datasets.Value("int64"),
+            "height": datasets.Value("int64"),
+            "width": datasets.Value("int64"),
+            "file_name": datasets.Value("string"),
+
+            # Custom fields
+            "doc_category": datasets.Value("string"),  # high-level document category
+            "collection": datasets.Value("string"),  # sub-collection name
+            "doc_name": datasets.Value("string"),  # original document filename
+            "page_no": datasets.Value("int64"),  # page number in original document
+            # "precedence": datasets.Value("int64"),  # annotation order, non-zero in case of redundant double- or triple-annotation
+        }
+
+        features = datasets.Features(feature_dict)
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # data_dir = self.config.data_dir
+        # if not data_dir:
+        #     raise ValueError(
+        #         "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
+        #     )
+
+        # _DL_URLS = {
+        #     "train": os.path.join(data_dir, "train2017.zip"),
+        #     "val": os.path.join(data_dir, "val2017.zip"),
+        #     "test": os.path.join(data_dir, "test2017.zip"),
+        #     "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
+        #     "image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
+        # }
+        archive_path = dl_manager.download_and_extract(_URLs)
+        print("archive_path: ", archive_path)
+
+        splits = []
+        for split in self.config.splits:
+            if split == 'train':
+                dataset = datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "json_path": os.path.join(archive_path["core"], "COCO", "train.json"),
+                        "image_dir": os.path.join(archive_path["core"], "PNG"),
+                        "split": "train",
+                    }
+                )
+            elif split in ['val', 'valid', 'validation', 'dev']:
+                dataset = datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "json_path": os.path.join(archive_path["core"], "COCO", "val.json"),
+                        "image_dir": os.path.join(archive_path["core"], "PNG"),
+                        "split": "val",
+                    },
+                )
+            elif split == 'test':
+                dataset = datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "json_path": os.path.join(archive_path["core"], "COCO", "test.json"),
+                        "image_dir": os.path.join(archive_path["core"], "PNG"),
+                        "split": "test",
+                    },
+                )
+            else:
+                continue
+
+            splits.append(dataset)
+
+        return splits
+
+    def _generate_examples(
+        # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+        self, json_path, image_dir, split
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        _features = ["image_id", "image_path", "doc_category", "collection", "height", "width", "file_name", "doc_name", "page_no", "id"]
+        features = list(_features)
+
+        with open(json_path, 'r', encoding='UTF-8') as fp:
+            data = json.load(fp)
+
+        # list of dict
+        images = data["images"]
+        entries = images
+
+        # build a dict of image_id -> image info dict
+        d = {image["id"]: image for image in images}
+
+        # list of dict
+        if split in ["train", "val"]:
+            annotations = data["annotations"]
+
+            # build a dict of image_id ->
+            for annotation in annotations:
+                _id = annotation["id"]
+                image_info = d[annotation["image_id"]]
+                annotation.update(image_info)
+                annotation["id"] = _id
+
+            entries = annotations
+
+        for id_, entry in enumerate(entries):
+
+            entry = {k: v for k, v in entry.items() if k in features}
+
+            if split == "test":
+                entry["image_id"] = entry["id"]
+                entry["id"] = -1
+
+            entry["image_path"] = os.path.join(image_dir, entry["file_name"])
+
+            entry = {k: entry[k] for k in _features if k in entry}
+
+            yield str((entry["image_id"], entry["id"])), entry