# coding=utf-8 # 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. """RedCaps dataset.""" import collections import json import os import re import datasets _CITATION = """\ @misc{desai2021redcaps, title={RedCaps: web-curated image-text data created by the people, for the people}, author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson}, year={2021}, eprint={2111.11431}, archivePrefix={arXiv}, primaryClass={cs.CV} } """ _DESCRIPTION = """\ RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit. Images and captions from Reddit depict and describe a wide variety of objects and scenes. The data is collected from a manually curated set of subreddits (350 total), which give coarse image labels and allow steering of the dataset composition without labeling individual instances. """ _HOMEPAGE = "https://redcaps.xyz/" _LICENSE = "CC BY 4.0" _URL = "https://huggingface.co/datasets/red_caps/resolve/main/data/redcaps_v1.0_annotations.zip" _SUBREDDITS_WITH_YEAR = """\ abandonedporn_2017 abandonedporn_2018 abandonedporn_2019 abandonedporn_2020 abandoned_2017 abandoned_2018 abandoned_2019 abandoned_2020 absoluteunits_2018 absoluteunits_2019 absoluteunits_2020 airplants_2017 airplants_2018 airplants_2019 airplants_2020 alltheanimals_2019 alltheanimals_2020 amateurphotography_2017 amateurphotography_2018 amateurphotography_2019 amateurphotography_2020 amateurroomporn_2017 amateurroomporn_2018 amateurroomporn_2019 amateurroomporn_2020 animalporn_2017 animalporn_2018 animalporn_2019 animalporn_2020 antiques_2017 antiques_2018 antiques_2019 antiques_2020 antkeeping_2017 antkeeping_2018 antkeeping_2019 antkeeping_2020 ants_2017 ants_2018 ants_2019 ants_2020 aquariums_2017 aquariums_2018 aquariums_2019 aquariums_2020 architectureporn_2017 architectureporn_2018 architectureporn_2019 architectureporn_2020 artefactporn_2017 artefactporn_2018 artefactporn_2019 artefactporn_2020 astronomy_2017 astronomy_2018 astronomy_2019 astronomy_2020 astrophotography_2017 astrophotography_2018 astrophotography_2019 astrophotography_2020 australiancattledog_2017 australiancattledog_2018 australiancattledog_2019 australiancattledog_2020 australianshepherd_2017 australianshepherd_2018 australianshepherd_2019 australianshepherd_2020 autumnporn_2017 autumnporn_2018 autumnporn_2019 autumnporn_2020 averagebattlestations_2017 averagebattlestations_2018 averagebattlestations_2019 averagebattlestations_2020 awwducational_2017 awwducational_2018 awwducational_2019 awwducational_2020 awwnverts_2017 awwnverts_2018 awwnverts_2019 awwnverts_2020 axolotls_2017 axolotls_2018 axolotls_2019 axolotls_2020 backpacking_2017 backpacking_2018 backpacking_2019 backpacking_2020 backyardchickens_2017 backyardchickens_2018 backyardchickens_2019 backyardchickens_2020 baking_2017 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natureporn_2020 nebelung_2017 nebelung_2018 nebelung_2019 nebelung_2020 orchids_2017 orchids_2018 orchids_2019 orchids_2020 otters_2017 otters_2018 otters_2019 otters_2020 outdoors_2017 outdoors_2018 outdoors_2019 outdoors_2020 owls_2017 owls_2018 owls_2019 owls_2020 parrots_2017 parrots_2018 parrots_2019 parrots_2020 pelletgrills_2017 pelletgrills_2018 pelletgrills_2019 pelletgrills_2020 pens_2017 pens_2018 pens_2019 pens_2020 perfectfit_2017 perfectfit_2018 perfectfit_2019 perfectfit_2020 permaculture_2017 permaculture_2018 permaculture_2019 permaculture_2020 photocritique_2017 photocritique_2018 photocritique_2019 photocritique_2020 photographs_2017 photographs_2018 photographs_2019 photographs_2020 pics_2017 pics_2018 pics_2019 pics_2020 pitbulls_2017 pitbulls_2018 pitbulls_2019 pitbulls_2020 pizza_2017 pizza_2018 pizza_2019 pizza_2020 plantbaseddiet_2017 plantbaseddiet_2018 plantbaseddiet_2019 plantbaseddiet_2020 plantedtank_2017 plantedtank_2018 plantedtank_2019 plantedtank_2020 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vegetarian_2020 villageporn_2017 villageporn_2018 villageporn_2019 villageporn_2020 vintageaudio_2017 vintageaudio_2018 vintageaudio_2019 vintageaudio_2020 vintage_2017 vintage_2018 vintage_2019 vintage_2020 vinyl_2017 vinyl_2018 vinyl_2019 vinyl_2020 volumeeating_2017 volumeeating_2018 volumeeating_2019 volumeeating_2020 watches_2017 watches_2018 watches_2019 watches_2020 waterporn_2017 waterporn_2018 waterporn_2019 waterporn_2020 weatherporn_2017 weatherporn_2018 weatherporn_2019 weatherporn_2020 wewantplates_2017 wewantplates_2018 wewantplates_2019 wewantplates_2020 wildernessbackpacking_2017 wildernessbackpacking_2018 wildernessbackpacking_2019 wildernessbackpacking_2020 wildlifephotography_2017 wildlifephotography_2018 wildlifephotography_2019 wildlifephotography_2020 wine_2017 wine_2018 wine_2019 wine_2020 winterporn_2017 winterporn_2018 winterporn_2019 winterporn_2020 woodcarving_2017 woodcarving_2018 woodcarving_2019 woodcarving_2020 woodworking_2017 woodworking_2018 woodworking_2019 woodworking_2020 workbenches_2017 workbenches_2018 workbenches_2019 workbenches_2020 workspaces_2017 workspaces_2018 workspaces_2019 workspaces_2020 yarnaddicts_2017 yarnaddicts_2018 yarnaddicts_2019 yarnaddicts_2020 zerowaste_2017 zerowaste_2018 zerowaste_2019 zerowaste_2020 """ _SUBREDDITS_WITH_YEAR = _SUBREDDITS_WITH_YEAR.strip().split() _SUBREDDIT_TO_YEAR = collections.defaultdict(list) for subreddit_with_year in _SUBREDDITS_WITH_YEAR: subreddit, year = subreddit_with_year.split("_") _SUBREDDIT_TO_YEAR[subreddit].append(year) _SUBREDDITS = list(_SUBREDDIT_TO_YEAR.keys()) def _config_name_to_subreddits_with_year(config_name): if config_name == "all": return _SUBREDDITS_WITH_YEAR elif re.match(r".*_\d{4}$", config_name): return [config_name] else: return [f"{config_name}_{year}" for year in _SUBREDDIT_TO_YEAR[config_name]] def _config_name_to_description(config_name): if config_name == "all": return "Contains data from all the subreddits" else: if re.match(r".*_\d{4}$", config_name): subreddit, year = config_name.split("_") year_str = "2008 - 2017" if year == "2017" else year else: subreddit = config_name year_str = ", ".join( ["2008 - 2017" if year == "2017" else year for year in _SUBREDDIT_TO_YEAR[config_name]] ) return f"Contains data from the {subreddit} subreddit posted in {year_str}" class RedCapsConfig(datasets.BuilderConfig): """BuilderConfig for RedCaps.""" def __init__(self, name, **kwargs): """BuilderConfig for RedCaps. Args: **kwargs: keyword arguments forwarded to super. """ assert "description" not in kwargs kwargs["description"] = _config_name_to_description(name) super(RedCapsConfig, self).__init__(version=datasets.Version("1.0.0", ""), name=name, **kwargs) class RedCaps(datasets.GeneratorBasedBuilder): """RedCaps dataset.""" BUILDER_CONFIGS = [ RedCapsConfig("all"), ] BUILDER_CONFIGS += [RedCapsConfig(subreddit) for subreddit in _SUBREDDITS] BUILDER_CONFIGS += [RedCapsConfig(subreddit_with_year) for subreddit_with_year in _SUBREDDITS_WITH_YEAR] DEFAULT_CONFIG_NAME = "all" def _info(self): features = datasets.Features( { "image_id": datasets.Value("string"), "author": datasets.Value("string"), "image_url": datasets.Value("string"), "raw_caption": datasets.Value("string"), "caption": datasets.Value("string"), "subreddit": datasets.ClassLabel(names=_SUBREDDITS), "score": datasets.Value("int32"), "created_utc": datasets.Value("timestamp[s, tz=UTC]"), "permalink": datasets.Value("string"), "crosspost_parents": datasets.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): annotations_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "annotations_dir": annotations_dir, "subreddits": _config_name_to_subreddits_with_year(self.config.name), }, ), ] def _generate_examples(self, annotations_dir, subreddits): annotations_dir = os.path.join(annotations_dir, "annotations") idx = 0 for subreddit in subreddits: subreddit_file = os.path.join(annotations_dir, subreddit + ".json") with open(subreddit_file, encoding="utf-8") as f: data = json.load(f) for annot in data["annotations"]: yield idx, { "image_id": annot["image_id"], "author": annot["author"], "image_url": annot["url"], "raw_caption": annot["raw_caption"], "caption": annot["caption"], "subreddit": annot["subreddit"], "score": annot["score"] if "score" in annot else None, "created_utc": annot["created_utc"], "permalink": annot["permalink"], "crosspost_parents": annot["crosspost_parents"] if "crosspost_parents" in annot and annot["crosspost_parents"] else None, } idx += 1