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COCO / COCO.py

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Add a new configuration in which all the captions associated with an image are listed in a single example (#1)

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	# 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