# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Omnilabel dataset.""" import csv import json import os import datasets _CITATION = """\ @misc{schulter2023omnilabel, title={OmniLabel: A Challenging Benchmark for Language-Based Object Detection}, author={Samuel Schulter and Vijay Kumar B G and Yumin Suh and Konstantinos M. Dafnis and Zhixing Zhang and Shiyu Zhao and Dimitris Metaxas}, year={2023}, eprint={2304.11463}, archivePrefix={arXiv}, primaryClass={cs.CV} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """OmniLabel Benchmark.""" # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://www.omnilabel.org" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "https://www.omnilabel.org/dataset/download#h.frhys3no2ecq" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) # _URLS = { # # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", # # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", # } _URLS = { "val": "https://huggingface.co/datasets/xingyaoww/omnilabel/resolve/main/dataset_all_val_v0.1.3.json", } # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class OmniLabel(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # 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') # BUILDER_CONFIGS = [ # datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), # ] # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): features = datasets.Features( { "id": datasets.Value("int64"), # int (Unique instance ID) "image_id": datasets.Value( "int64" ), # the image id this annotation belongs to "image_filename": datasets.Value( "string" ), # the image filename this annotation belongs to "bbox": datasets.features.Sequence( datasets.Value("float64") ), # [x, y, width, height] (Bounding box co-ordinates for the instance) "descriptions": datasets.features.Sequence( datasets.Value("string") ), # list of the object description text } ) 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # 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): # TODO: 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 # 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 # urls = _URLS[self.config.name] # data_dir = dl_manager.download_and_extract(urls) downloaded_files = dl_manager.download_and_extract(_URLS) print("downloaded_files: ", downloaded_files) return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": downloaded_files["val"], "split": "val", }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): if split == "val": with open(filepath) as f: data = json.load(f) IMAGE_ID_TO_FILENAME = {} for image_info in data["images"]: IMAGE_ID_TO_FILENAME[image_info["id"]] = image_info["file_name"] DESCRIPTION_ID_TO_DESC = {} for desc in data["descriptions"]: DESCRIPTION_ID_TO_DESC[desc["id"]] = desc for key, anno in enumerate(data["annotations"]): image_id = anno["image_id"] image_filename = IMAGE_ID_TO_FILENAME[image_id] description_ids = anno["description_ids"] descriptions = [ DESCRIPTION_ID_TO_DESC[description_id]["text"] for description_id in description_ids ] yield key, { "id": anno["id"], "image_id": image_id, # TODO: make this a PIL.Image "image_filename": image_filename, "bbox": anno["bbox"], "descriptions": descriptions, }