# coding=utf-8 # Copyright 2022 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. """COCO""" import json import os from pathlib import Path import datasets _CITATION = """ @article{DBLP:journals/corr/LinMBHPRDZ14, author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick}, title = {Microsoft {COCO:} Common Objects in Context}, journal = {CoRR}, volume = {abs/1405.0312}, year = {2014}, url = {http://arxiv.org/abs/1405.0312}, eprinttype = {arXiv}, eprint = {1405.0312}, timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DESCRIPTION = """ MS COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints. """ _HOMEPAGE = "https://cocodataset.org/#home" _LICENSE = "CC BY 4.0" _IMAGES_URLS = { "train": "http://images.cocodataset.org/zips/train2014.zip", "validation": "http://images.cocodataset.org/zips/val2014.zip", } _KARPATHY_FILES_URL = "https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip" _SPLIT_MAP = {"train": "train2014", "validation": "val2014"} _FEATURES = datasets.Features( { "image": datasets.Image(), "filepath": datasets.Value("string"), "sentids": [datasets.Value("int32")], "filename": datasets.Value("string"), "imgid": datasets.Value("int32"), "split": datasets.Value("string"), "sentences": { "tokens": [datasets.Value("string")], "raw": datasets.Value("string"), "imgid": datasets.Value("int32"), "sentid": datasets.Value("int32"), }, "cocoid": datasets.Value("int32"), } ) _FEATURES_CAPTIONS = datasets.Features( { "image": datasets.Image(), "filepath": datasets.Value("string"), "sentids": [datasets.Value("int32")], "filename": datasets.Value("string"), "imgid": datasets.Value("int32"), "split": datasets.Value("string"), "sentences_tokens": [[datasets.Value("string")]], "sentences_raw": [datasets.Value("string")], "sentences_sentid": [datasets.Value("int32")], "cocoid": datasets.Value("int32"), } ) class COCO(datasets.GeneratorBasedBuilder): """COCO""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="2014", version=VERSION, description="2014 version of COCO with Karpathy annotations and splits" ), datasets.BuilderConfig( name="2014_captions", version=VERSION, description="Same as 2014 but with all captions of one image gathered in a single example", ), ] DEFAULT_CONFIG_NAME = "2014" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=_FEATURES if self.config.name == "2014" else _FEATURES_CAPTIONS, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): annotation_file = os.path.join(dl_manager.download_and_extract(_KARPATHY_FILES_URL), "dataset_coco.json") image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_IMAGES_URLS).items()} return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "annotation_file": annotation_file, "image_folders": image_folders, "split_key": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "annotation_file": annotation_file, "image_folders": image_folders, "split_key": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "annotation_file": annotation_file, "image_folders": image_folders, "split_key": "test", }, ), ] def _generate_examples(self, annotation_file, image_folders, split_key): if self.config.name == "2014_captions": return self._generate_examples_2014_captions(annotation_file, image_folders, split_key) elif self.config.name == "2014": return self._generate_examples_2014(annotation_file, image_folders, split_key) def _generate_examples_2014_captions(self, annotation_file, image_folders, split_key): with open(annotation_file, "r", encoding="utf-8") as fi: annotations = json.load(fi) for image_metadata in annotations["images"]: if split_key == "train": if image_metadata["split"] != "train" and image_metadata["split"] != "restval": continue elif split_key == "validation": if image_metadata["split"] != "val": continue elif split_key == "test": if image_metadata["split"] != "test": continue if "val2014" in image_metadata["filename"]: image_path = image_folders["validation"] / _SPLIT_MAP["validation"] else: image_path = image_folders["train"] / _SPLIT_MAP["train"] image_path = image_path / image_metadata["filename"] record = { "image": str(image_path.absolute()), "filepath": image_metadata["filename"], "sentids": image_metadata["sentids"], "filename": image_metadata["filename"], "imgid": image_metadata["imgid"], "split": image_metadata["split"], "cocoid": image_metadata["cocoid"], "sentences_tokens": [caption["tokens"] for caption in image_metadata["sentences"]], "sentences_raw": [caption["raw"] for caption in image_metadata["sentences"]], "sentences_sentid": [caption["sentid"] for caption in image_metadata["sentences"]], } yield record["imgid"], record def _generate_examples_2014(self, annotation_file, image_folders, split_key): counter = 0 with open(annotation_file, "r", encoding="utf-8") as fi: annotations = json.load(fi) for image_metadata in annotations["images"]: if split_key == "train": if image_metadata["split"] != "train" and image_metadata["split"] != "restval": continue elif split_key == "validation": if image_metadata["split"] != "val": continue elif split_key == "test": if image_metadata["split"] != "test": continue if "val2014" in image_metadata["filename"]: image_path = image_folders["validation"] / _SPLIT_MAP["validation"] else: image_path = image_folders["train"] / _SPLIT_MAP["train"] image_path = image_path / image_metadata["filename"] for caption in image_metadata["sentences"]: yield counter, { "image": str(image_path.absolute()), "filepath": image_metadata["filename"], "sentids": image_metadata["sentids"], "filename": image_metadata["filename"], "imgid": image_metadata["imgid"], "split": image_metadata["split"], "sentences": { "tokens": caption["tokens"], "raw": caption["raw"], "imgid": caption["imgid"], "sentid": caption["sentid"], }, "cocoid": image_metadata["cocoid"], } counter += 1