# 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. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import os.path as osp import datasets from .refer import REFER # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This RefCOCO dataset is designed to load refcoco, refcoco+, and refcocog. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # 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 = {} VALID_SPLIT_NAMES = ("train", "val", "testA", "testB") class ReferitBuilderConfig(datasets.BuilderConfig): def __init__(self, name: str, split_by: str, **kwargs): super().__init__(name, **kwargs) self.split_by = split_by # TODO: Name of the dataset usually matches the script name with CamelCase # instead of snake_case class ReferitDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("0.0.1") # 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 = ReferitBuilderConfig # 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 = [ # refcoco ReferitBuilderConfig( name="refcoco", split_by="unc", version=VERSION, description="refcoco."), # refcoco+ ReferitBuilderConfig( name="refcoco+", split_by="unc", version=VERSION, description="refcoco+"), # refcocog ReferitBuilderConfig( name="refcocog", split_by="umd", version=VERSION, description="refcocog"), ] # It's not mandatory to have a default configuration. # Just use one if it make sense. DEFAULT_CONFIG_NAME = "refcoco" def _info(self): self.config: ReferitBuilderConfig features = datasets.Features( { "ref_id": datasets.Value("int32"), "img_id": datasets.Value("int32"), "ann_id": datasets.Value("int32"), "file_name": datasets.Value("string"), "image_path": datasets.Value("string"), "height": datasets.Value("int32"), "width": datasets.Value("int32"), "coco_url": datasets.Value("string"), "sentences": [datasets.Value("string")], "segmentation": [[[datasets.Value("float")]]], "bbox": [[datasets.Value("float")]], "area": datasets.Value("float"), "iscrowd": datasets.Value("int32"), "category_id": datasets.Value("int32"), } ) 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, # 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) splits = [] split_names = ("train", "val", "test") if self.config.name in ("refcoco", "refcoco+"): split_names += ("testA", "testB") for split in split_names: splits.append(datasets.SplitGenerator( name=datasets.NamedSplit(split), gen_kwargs={ "split": split, }, )) return splits # method parameters are unpacked from `gen_kwargs` as given in # `_split_generators` def _generate_examples(self, split: str): # TODO: This method handles input defined in _split_generators to # yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important # in itself, but must be unique for each example. refer = REFER(data_root=self.config.data_dir, dataset=self.config.name, splitBy=self.config.split_by) ref_ids = refer.getRefIds(split=split) for ref_id in ref_ids: ref = refer.loadRefs(ref_id)[0] ann_id = ref['ann_id'] ann = refer.loadAnns(ann_id)[0] img_id = ann['image_id'] img = refer.loadImgs(img_id)[0] file_name = img['file_name'] image_path = osp.join( self.config.data_dir, "images", "train2014", file_name) descriptions = [r['raw'] for r in ref['sentences']] yield ref_id, { "ref_id": ref_id, "img_id": img_id, "ann_id": ann_id, "file_name": file_name, "image_path": image_path, "height": img['height'], "width": img['width'], "coco_url": img['coco_url'], "sentences": descriptions, "segmentation": [ann['segmentation']], "bbox": [ann['bbox']], "area": ann['area'], "iscrowd": ann['iscrowd'], "category_id": ann['category_id'], }