Datasets:

shunk031
/

MSCOCO

	import abc
	import json
	import logging
	import os
	from collections import defaultdict
	from dataclasses import asdict, dataclass
	from typing import (
	Any,
	Dict,
	Final,
	Iterator,
	List,
	Literal,
	Optional,
	Sequence,
	Tuple,
	TypedDict,
	Union,
	get_args,
	)

	import datasets as ds
	import numpy as np
	from datasets.data_files import DataFilesDict
	from PIL import Image
	from PIL.Image import Image as PilImage
	from pycocotools import mask as cocomask
	from tqdm.auto import tqdm

	logger = logging.getLogger(__name__)

	JsonDict = Dict[str, Any]
	ImageId = int
	AnnotationId = int
	LicenseId = int
	CategoryId = int
	Bbox = Tuple[float, float, float, float]

	MscocoSplits = Literal["train", "val", "test"]

	KEYPOINT_STATE: Final[List[str]] = ["unknown", "invisible", "visible"]


	_CITATION = """
	"""

	_DESCRIPTION = """
	"""

	_HOMEPAGE = """
	"""

	_LICENSE = """
	"""

	_URLS = {
	"2014": {
	"images": {
	"train": "http://images.cocodataset.org/zips/train2014.zip",
	"validation": "http://images.cocodataset.org/zips/val2014.zip",
	"test": "http://images.cocodataset.org/zips/test2014.zip",
	},
	"annotations": {
	"train_validation": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip",
	"test_image_info": "http://images.cocodataset.org/annotations/image_info_test2014.zip",
	},
	},
	"2015": {
	"images": {
	"test": "http://images.cocodataset.org/zips/test2015.zip",
	},
	"annotations": {
	"test_image_info": "http://images.cocodataset.org/annotations/image_info_test2015.zip",
	},
	},
	"2017": {
	"images": {
	"train": "http://images.cocodataset.org/zips/train2017.zip",
	"validation": "http://images.cocodataset.org/zips/val2017.zip",
	"test": "http://images.cocodataset.org/zips/test2017.zip",
	"unlabeled": "http://images.cocodataset.org/zips/unlabeled2017.zip",
	},
	"annotations": {
	"train_validation": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip",
	"stuff_train_validation": "http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip",
	"panoptic_train_validation": "http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip",
	"test_image_info": "http://images.cocodataset.org/annotations/image_info_test2017.zip",
	"unlabeled": "http://images.cocodataset.org/annotations/image_info_unlabeled2017.zip",
	},
	},
	}

	CATEGORIES: Final[List[str]] = [
	"person",
	"bicycle",
	"car",
	"motorcycle",
	"airplane",
	"bus",
	"train",
	"truck",
	"boat",
	"traffic light",
	"fire hydrant",
	"stop sign",
	"parking meter",
	"bench",
	"bird",
	"cat",
	"dog",
	"horse",
	"sheep",
	"cow",
	"elephant",
	"bear",
	"zebra",
	"giraffe",
	"backpack",
	"umbrella",
	"handbag",
	"tie",
	"suitcase",
	"frisbee",
	"skis",
	"snowboard",
	"sports ball",
	"kite",
	"baseball bat",
	"baseball glove",
	"skateboard",
	"surfboard",
	"tennis racket",
	"bottle",
	"wine glass",
	"cup",
	"fork",
	"knife",
	"spoon",
	"bowl",
	"banana",
	"apple",
	"sandwich",
	"orange",
	"broccoli",
	"carrot",
	"hot dog",
	"pizza",
	"donut",
	"cake",
	"chair",
	"couch",
	"potted plant",
	"bed",
	"dining table",
	"toilet",
	"tv",
	"laptop",
	"mouse",
	"remote",
	"keyboard",
	"cell phone",
	"microwave",
	"oven",
	"toaster",
	"sink",
	"refrigerator",
	"book",
	"clock",
	"vase",
	"scissors",
	"teddy bear",
	"hair drier",
	"toothbrush",
	]

	SUPER_CATEGORIES: Final[List[str]] = [
	"person",
	"vehicle",
	"outdoor",
	"animal",
	"accessory",
	"sports",
	"kitchen",
	"food",
	"furniture",
	"electronic",
	"appliance",
	"indoor",
	]


	@dataclass
	class AnnotationInfo(object):
	description: str
	url: str
	version: str
	year: str
	contributor: str
	date_created: str

	@classmethod
	def from_dict(cls, json_dict: JsonDict) -> "AnnotationInfo":
	return cls(**json_dict)


	@dataclass
	class LicenseData(object):
	url: str
	license_id: LicenseId
	name: str

	@classmethod
	def from_dict(cls, json_dict: JsonDict) -> "LicenseData":
	return cls(
	license_id=json_dict["id"],
	url=json_dict["url"],
	name=json_dict["name"],
	)


	@dataclass
	class ImageData(object):
	image_id: ImageId
	license_id: LicenseId
	file_name: str
	coco_url: str
	height: int
	width: int
	date_captured: str
	flickr_url: str

	@classmethod
	def from_dict(cls, json_dict: JsonDict) -> "ImageData":
	return cls(
	image_id=json_dict["id"],
	license_id=json_dict["license"],
	file_name=json_dict["file_name"],
	coco_url=json_dict["coco_url"],
	height=json_dict["height"],
	width=json_dict["width"],
	date_captured=json_dict["date_captured"],
	flickr_url=json_dict["flickr_url"],
	)

	@property
	def shape(self) -> Tuple[int, int]:
	return (self.height, self.width)


	@dataclass
	class CategoryData(object):
	category_id: int
	name: str
	supercategory: str

	@classmethod
	def from_dict(cls, json_dict: JsonDict) -> "CategoryData":
	return cls(
	category_id=json_dict["id"],
	name=json_dict["name"],
	supercategory=json_dict["supercategory"],
	)


	@dataclass
	class AnnotationData(object):
	annotation_id: AnnotationId
	image_id: ImageId


	@dataclass
	class CaptionsAnnotationData(AnnotationData):
	caption: str

	@classmethod
	def from_dict(cls, json_dict: JsonDict) -> "CaptionsAnnotationData":
	return cls(
	annotation_id=json_dict["id"],
	image_id=json_dict["image_id"],
	caption=json_dict["caption"],
	)


	class UncompressedRLE(TypedDict):
	counts: List[int]
	size: Tuple[int, int]


	class CompressedRLE(TypedDict):
	counts: bytes
	size: Tuple[int, int]


	@dataclass
	class InstancesAnnotationData(AnnotationData):
	segmentation: Union[np.ndarray, CompressedRLE]
	area: float
	iscrowd: bool
	bbox: Tuple[float, float, float, float]
	category_id: int

	@classmethod
	def compress_rle(
	cls,
	segmentation: Union[List[List[float]], UncompressedRLE],
	iscrowd: bool,
	height: int,
	width: int,
	) -> CompressedRLE:
	if iscrowd:
	rle = cocomask.frPyObjects(segmentation, h=height, w=width)
	else:
	rles = cocomask.frPyObjects(segmentation, h=height, w=width)
	rle = cocomask.merge(rles)

	return rle # type: ignore

	@classmethod
	def rle_segmentation_to_binary_mask(
	cls, segmentation, iscrowd: bool, height: int, width: int
	) -> np.ndarray:
	rle = cls.compress_rle(
	segmentation=segmentation, iscrowd=iscrowd, height=height, width=width
	)
	return cocomask.decode(rle) # type: ignore

	@classmethod
	def rle_segmentation_to_mask(
	cls,
	segmentation: Union[List[List[float]], UncompressedRLE],
	iscrowd: bool,
	height: int,
	width: int,
	) -> np.ndarray:
	binary_mask = cls.rle_segmentation_to_binary_mask(
	segmentation=segmentation, iscrowd=iscrowd, height=height, width=width
	)
	return binary_mask * 255

	@classmethod
	def from_dict(
	cls,
	json_dict: JsonDict,
	images: Dict[ImageId, ImageData],
	decode_rle: bool,
	) -> "InstancesAnnotationData":
	segmentation = json_dict["segmentation"]
	image_id = json_dict["image_id"]
	image_data = images[image_id]
	iscrowd = bool(json_dict["iscrowd"])

	segmentation_mask = (
	cls.rle_segmentation_to_mask(
	segmentation=segmentation,
	iscrowd=iscrowd,
	height=image_data.height,
	width=image_data.width,
	)
	if decode_rle
	else cls.compress_rle(
	segmentation=segmentation,
	iscrowd=iscrowd,
	height=image_data.height,
	width=image_data.width,
	)
	)
	return cls(
	#
	# for AnnotationData
	#
	annotation_id=json_dict["id"],
	image_id=image_id,
	#
	# for InstancesAnnotationData
	#
	segmentation=segmentation_mask,
	area=json_dict["area"],
	iscrowd=iscrowd,
	bbox=json_dict["bbox"],
	category_id=json_dict["category_id"],
	)


	@dataclass
	class PersonKeypoint(object):
	x: int
	y: int
	v: int
	state: str


	@dataclass
	class PersonKeypointsAnnotationData(InstancesAnnotationData):
	num_keypoints: int
	keypoints: List[PersonKeypoint]

	@classmethod
	def v_keypoint_to_state(cls, keypoint_v: int) -> str:
	return KEYPOINT_STATE[keypoint_v]

	@classmethod
	def get_person_keypoints(
	cls, flatten_keypoints: List[int], num_keypoints: int
	) -> List[PersonKeypoint]:
	keypoints_x = flatten_keypoints[0::3]
	keypoints_y = flatten_keypoints[1::3]
	keypoints_v = flatten_keypoints[2::3]
	assert len(keypoints_x) == len(keypoints_y) == len(keypoints_v)

	keypoints = [
	PersonKeypoint(x=x, y=y, v=v, state=cls.v_keypoint_to_state(v))
	for x, y, v in zip(keypoints_x, keypoints_y, keypoints_v)
	]
	assert len([kp for kp in keypoints if kp.state != "unknown"]) == num_keypoints
	return keypoints

	@classmethod
	def from_dict(
	cls,
	json_dict: JsonDict,
	images: Dict[ImageId, ImageData],
	decode_rle: bool,
	) -> "PersonKeypointsAnnotationData":
	segmentation = json_dict["segmentation"]
	image_id = json_dict["image_id"]
	image_data = images[image_id]
	iscrowd = bool(json_dict["iscrowd"])

	segmentation_mask = (
	cls.rle_segmentation_to_mask(
	segmentation=segmentation,
	iscrowd=iscrowd,
	height=image_data.height,
	width=image_data.width,
	)
	if decode_rle
	else cls.compress_rle(
	segmentation=segmentation,
	iscrowd=iscrowd,
	height=image_data.height,
	width=image_data.width,
	)
	)
	flatten_keypoints = json_dict["keypoints"]
	num_keypoints = json_dict["num_keypoints"]
	keypoints = cls.get_person_keypoints(flatten_keypoints, num_keypoints)

	return cls(
	#
	# for AnnotationData
	#
	annotation_id=json_dict["id"],
	image_id=image_id,
	#
	# for InstancesAnnotationData
	#
	segmentation=segmentation_mask,
	area=json_dict["area"],
	iscrowd=iscrowd,
	bbox=json_dict["bbox"],
	category_id=json_dict["category_id"],
	#
	# PersonKeypointsAnnotationData
	#
	num_keypoints=num_keypoints,
	keypoints=keypoints,
	)


	class LicenseDict(TypedDict):
	license_id: LicenseId
	name: str
	url: str


	class BaseExample(TypedDict):
	image_id: ImageId
	image: PilImage
	file_name: str
	coco_url: str
	height: int
	width: int
	date_captured: str
	flickr_url: str
	license_id: LicenseId
	license: LicenseDict


	class CaptionAnnotationDict(TypedDict):
	annotation_id: AnnotationId
	caption: str


	class CaptionExample(BaseExample):
	annotations: List[CaptionAnnotationDict]


	class CategoryDict(TypedDict):
	category_id: CategoryId
	name: str
	supercategory: str


	class InstanceAnnotationDict(TypedDict):
	annotation_id: AnnotationId
	area: float
	bbox: Bbox
	image_id: ImageId
	category_id: CategoryId
	category: CategoryDict
	iscrowd: bool
	segmentation: np.ndarray


	class InstanceExample(BaseExample):
	annotations: List[InstanceAnnotationDict]


	class KeypointDict(TypedDict):
	x: int
	y: int
	v: int
	state: str


	class PersonKeypointAnnotationDict(InstanceAnnotationDict):
	num_keypoints: int
	keypoints: List[KeypointDict]


	class PersonKeypointExample(BaseExample):
	annotations: List[PersonKeypointAnnotationDict]


	class MsCocoProcessor(object, metaclass=abc.ABCMeta):
	def load_image(self, image_path: str) -> PilImage:
	return Image.open(image_path)

	def load_annotation_json(self, ann_file_path: str) -> JsonDict:
	logger.info(f"Load annotation json from {ann_file_path}")
	with open(ann_file_path, "r") as rf:
	ann_json = json.load(rf)
	return ann_json

	def load_licenses_data(
	self, license_dicts: List[JsonDict]
	) -> Dict[LicenseId, LicenseData]:
	licenses = {}
	for license_dict in license_dicts:
	license_data = LicenseData.from_dict(license_dict)
	licenses[license_data.license_id] = license_data
	return licenses

	def load_images_data(
	self,
	image_dicts: List[JsonDict],
	tqdm_desc: str = "Load images",
	) -> Dict[ImageId, ImageData]:
	images = {}
	for image_dict in tqdm(image_dicts, desc=tqdm_desc):
	image_data = ImageData.from_dict(image_dict)
	images[image_data.image_id] = image_data
	return images

	def load_categories_data(
	self,
	category_dicts: List[JsonDict],
	tqdm_desc: str = "Load categories",
	) -> Dict[CategoryId, CategoryData]:
	categories = {}
	for category_dict in tqdm(category_dicts, desc=tqdm_desc):
	category_data = CategoryData.from_dict(category_dict)
	categories[category_data.category_id] = category_data
	return categories

	def get_features_base_dict(self):
	return {
	"image_id": ds.Value("int64"),
	"image": ds.Image(),
	"file_name": ds.Value("string"),
	"coco_url": ds.Value("string"),
	"height": ds.Value("int32"),
	"width": ds.Value("int32"),
	"date_captured": ds.Value("string"),
	"flickr_url": ds.Value("string"),
	"license_id": ds.Value("int32"),
	"license": {
	"url": ds.Value("string"),
	"license_id": ds.Value("int8"),
	"name": ds.Value("string"),
	},
	}

	@abc.abstractmethod
	def get_features(self, args, *kwargs) -> ds.Features:
	raise NotImplementedError

	@abc.abstractmethod
	def load_data(
	self, ann_dicts: List[JsonDict], tqdm_desc: Optional[str] = None, **kwargs
	):
	raise NotImplementedError

	@abc.abstractmethod
	def generate_examples(
	self,
	image_dir: str,
	images: Dict[ImageId, ImageData],
	annotations: Dict[ImageId, List[CaptionsAnnotationData]],
	licenses: Dict[LicenseId, LicenseData],
	**kwargs,
	):
	raise NotImplementedError


	class CaptionsProcessor(MsCocoProcessor):
	def get_features(self, args, *kwargs) -> ds.Features:
	features_dict = self.get_features_base_dict()
	annotations = ds.Sequence(
	{
	"annotation_id": ds.Value("int64"),
	"image_id": ds.Value("int64"),
	"caption": ds.Value("string"),
	}
	)
	features_dict.update({"annotations": annotations})
	return ds.Features(features_dict)

	def load_data(
	self,
	ann_dicts: List[JsonDict],
	tqdm_desc: str = "Load captions data",
	**kwargs,
	) -> Dict[ImageId, List[CaptionsAnnotationData]]:
	annotations = defaultdict(list)
	for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
	ann_data = CaptionsAnnotationData.from_dict(ann_dict)
	annotations[ann_data.image_id].append(ann_data)
	return annotations

	def generate_examples(
	self,
	image_dir: str,
	images: Dict[ImageId, ImageData],
	annotations: Dict[ImageId, List[CaptionsAnnotationData]],
	licenses: Dict[LicenseId, LicenseData],
	**kwargs,
	) -> Iterator[Tuple[int, CaptionExample]]:
	for idx, image_id in enumerate(images.keys()):
	image_data = images[image_id]
	image_anns = annotations[image_id]

	assert len(image_anns) > 0

	image = self.load_image(
	image_path=os.path.join(image_dir, image_data.file_name),
	)
	example = asdict(image_data)
	example["image"] = image
	example["license"] = asdict(licenses[image_data.license_id])

	example["annotations"] = []
	for ann in image_anns:
	example["annotations"].append(asdict(ann))

	yield idx, example # type: ignore


	class InstancesProcessor(MsCocoProcessor):
	def get_features_instance_dict(self, decode_rle: bool):
	segmentation_feature = (
	ds.Image()
	if decode_rle
	else {
	"counts": ds.Sequence(ds.Value("int64")),
	"size": ds.Sequence(ds.Value("int32")),
	}
	)
	return {
	"annotation_id": ds.Value("int64"),
	"image_id": ds.Value("int64"),
	"segmentation": segmentation_feature,
	"area": ds.Value("float32"),
	"iscrowd": ds.Value("bool"),
	"bbox": ds.Sequence(ds.Value("float32"), length=4),
	"category_id": ds.Value("int32"),
	"category": {
	"category_id": ds.Value("int32"),
	"name": ds.ClassLabel(
	num_classes=len(CATEGORIES),
	names=CATEGORIES,
	),
	"supercategory": ds.ClassLabel(
	num_classes=len(SUPER_CATEGORIES),
	names=SUPER_CATEGORIES,
	),
	},
	}

	def get_features(self, decode_rle: bool) -> ds.Features:
	features_dict = self.get_features_base_dict()
	annotations = ds.Sequence(
	self.get_features_instance_dict(decode_rle=decode_rle)
	)
	features_dict.update({"annotations": annotations})
	return ds.Features(features_dict)

	def load_data( # type: ignore[override]
	self,
	ann_dicts: List[JsonDict],
	images: Dict[ImageId, ImageData],
	decode_rle: bool,
	tqdm_desc: str = "Load instances data",
	) -> Dict[ImageId, List[InstancesAnnotationData]]:
	annotations = defaultdict(list)
	ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])

	for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
	ann_data = InstancesAnnotationData.from_dict(
	ann_dict, images=images, decode_rle=decode_rle
	)
	annotations[ann_data.image_id].append(ann_data)

	return annotations

	def generate_examples( # type: ignore[override]
	self,
	image_dir: str,
	images: Dict[ImageId, ImageData],
	annotations: Dict[ImageId, List[InstancesAnnotationData]],
	licenses: Dict[LicenseId, LicenseData],
	categories: Dict[CategoryId, CategoryData],
	) -> Iterator[Tuple[int, InstanceExample]]:
	for idx, image_id in enumerate(images.keys()):
	image_data = images[image_id]
	image_anns = annotations[image_id]

	if len(image_anns) < 1:
	logger.warning(f"No annotation found for image id: {image_id}.")
	continue

	image = self.load_image(
	image_path=os.path.join(image_dir, image_data.file_name),
	)
	example = asdict(image_data)
	example["image"] = image
	example["license"] = asdict(licenses[image_data.license_id])

	example["annotations"] = []
	for ann in image_anns:
	ann_dict = asdict(ann)
	category = categories[ann.category_id]
	ann_dict["category"] = asdict(category)
	example["annotations"].append(ann_dict)

	yield idx, example # type: ignore


	class PersonKeypointsProcessor(InstancesProcessor):
	def get_features(self, decode_rle: bool) -> ds.Features:
	features_dict = self.get_features_base_dict()
	features_instance_dict = self.get_features_instance_dict(decode_rle=decode_rle)
	features_instance_dict.update(
	{
	"keypoints": ds.Sequence(
	{
	"state": ds.Value("string"),
	"x": ds.Value("int32"),
	"y": ds.Value("int32"),
	"v": ds.Value("int32"),
	}
	),
	"num_keypoints": ds.Value("int32"),
	}
	)
	annotations = ds.Sequence(features_instance_dict)
	features_dict.update({"annotations": annotations})
	return ds.Features(features_dict)

	def load_data( # type: ignore[override]
	self,
	ann_dicts: List[JsonDict],
	images: Dict[ImageId, ImageData],
	decode_rle: bool,
	tqdm_desc: str = "Load person keypoints data",
	) -> Dict[ImageId, List[PersonKeypointsAnnotationData]]:
	annotations = defaultdict(list)
	ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])

	for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
	ann_data = PersonKeypointsAnnotationData.from_dict(
	ann_dict, images=images, decode_rle=decode_rle
	)
	annotations[ann_data.image_id].append(ann_data)
	return annotations

	def generate_examples( # type: ignore[override]
	self,
	image_dir: str,
	images: Dict[ImageId, ImageData],
	annotations: Dict[ImageId, List[PersonKeypointsAnnotationData]],
	licenses: Dict[LicenseId, LicenseData],
	categories: Dict[CategoryId, CategoryData],
	) -> Iterator[Tuple[int, PersonKeypointExample]]:
	for idx, image_id in enumerate(images.keys()):
	image_data = images[image_id]
	image_anns = annotations[image_id]

	if len(image_anns) < 1:
	# If there are no persons in the image,
	# no keypoint annotations will be assigned.
	continue

	image = self.load_image(
	image_path=os.path.join(image_dir, image_data.file_name),
	)
	example = asdict(image_data)
	example["image"] = image
	example["license"] = asdict(licenses[image_data.license_id])

	example["annotations"] = []
	for ann in image_anns:
	ann_dict = asdict(ann)
	category = categories[ann.category_id]
	ann_dict["category"] = asdict(category)
	example["annotations"].append(ann_dict)

	yield idx, example # type: ignore


	class MsCocoConfig(ds.BuilderConfig):
	YEARS: Tuple[int, ...] = (
	2014,
	2017,
	)
	TASKS: Tuple[str, ...] = (
	"captions",
	"instances",
	"person_keypoints",
	)

	def __init__(
	self,
	year: int,
	coco_task: Union[str, Sequence[str]],
	version: Optional[Union[ds.Version, str]],
	decode_rle: bool = False,
	data_dir: Optional[str] = None,
	data_files: Optional[DataFilesDict] = None,
	description: Optional[str] = None,
	) -> None:
	super().__init__(
	name=self.config_name(year=year, task=coco_task),
	version=version,
	data_dir=data_dir,
	data_files=data_files,
	description=description,
	)
	self._check_year(year)
	self._check_task(coco_task)

	self._year = year
	self._task = coco_task
	self.processor = self.get_processor()
	self.decode_rle = decode_rle

	def _check_year(self, year: int) -> None:
	assert year in self.YEARS, year

	def _check_task(self, task: Union[str, Sequence[str]]) -> None:
	if isinstance(task, str):
	assert task in self.TASKS, task
	elif isinstance(task, list) or isinstance(task, tuple):
	for t in task:
	assert t, task
	else:
	raise ValueError(f"Invalid task: {task}")

	@property
	def year(self) -> int:
	return self._year

	@property
	def task(self) -> str:
	if isinstance(self._task, str):
	return self._task
	elif isinstance(self._task, list) or isinstance(self._task, tuple):
	return "-".join(sorted(self._task))
	else:
	raise ValueError(f"Invalid task: {self._task}")

	def get_processor(self) -> MsCocoProcessor:
	if self.task == "captions":
	return CaptionsProcessor()
	elif self.task == "instances":
	return InstancesProcessor()
	elif self.task == "person_keypoints":
	return PersonKeypointsProcessor()
	else:
	raise ValueError(f"Invalid task: {self.task}")

	@classmethod
	def config_name(cls, year: int, task: Union[str, Sequence[str]]) -> str:
	if isinstance(task, str):
	return f"{year}-{task}"
	elif isinstance(task, list) or isinstance(task, tuple):
	task = "-".join(task)
	return f"{year}-{task}"
	else:
	raise ValueError(f"Invalid task: {task}")


	def dataset_configs(year: int, version: ds.Version) -> List[MsCocoConfig]:
	return [
	MsCocoConfig(
	year=year,
	coco_task="captions",
	version=version,
	),
	MsCocoConfig(
	year=year,
	coco_task="instances",
	version=version,
	),
	MsCocoConfig(
	year=year,
	coco_task="person_keypoints",
	version=version,
	),
	# MsCocoConfig(
	# year=year,
	# coco_task=("captions", "instances"),
	# version=version,
	# ),
	# MsCocoConfig(
	# year=year,
	# coco_task=("captions", "person_keypoints"),
	# version=version,
	# ),
	]


	def configs_2014(version: ds.Version) -> List[MsCocoConfig]:
	return dataset_configs(year=2014, version=version)


	def configs_2017(version: ds.Version) -> List[MsCocoConfig]:
	return dataset_configs(year=2017, version=version)


	class MsCocoDataset(ds.GeneratorBasedBuilder):
	VERSION = ds.Version("1.0.0")
	BUILDER_CONFIG_CLASS = MsCocoConfig
	BUILDER_CONFIGS = configs_2014(version=VERSION) + configs_2017(version=VERSION)

	@property
	def year(self) -> int:
	config: MsCocoConfig = self.config # type: ignore
	return config.year

	@property
	def task(self) -> str:
	config: MsCocoConfig = self.config # type: ignore
	return config.task

	def _info(self) -> ds.DatasetInfo:
	processor: MsCocoProcessor = self.config.processor
	features = processor.get_features(decode_rle=self.config.decode_rle)
	return ds.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	features=features,
	)

	def _split_generators(self, dl_manager: ds.DownloadManager):
	file_paths = dl_manager.download_and_extract(_URLS[f"{self.year}"])

	imgs = file_paths["images"] # type: ignore
	anns = file_paths["annotations"] # type: ignore

	return [
	ds.SplitGenerator(
	name=ds.Split.TRAIN, # type: ignore
	gen_kwargs={
	"base_image_dir": imgs["train"],
	"base_annotation_dir": anns["train_validation"],
	"split": "train",
	},
	),
	ds.SplitGenerator(
	name=ds.Split.VALIDATION, # type: ignore
	gen_kwargs={
	"base_image_dir": imgs["validation"],
	"base_annotation_dir": anns["train_validation"],
	"split": "val",
	},
	),
	# ds.SplitGenerator(
	# name=ds.Split.TEST, # type: ignore
	# gen_kwargs={
	# "base_image_dir": imgs["test"],
	# "test_image_info_path": anns["test_image_info"],
	# "split": "test",
	# },
	# ),
	]

	def _generate_train_val_examples(
	self, split: str, base_image_dir: str, base_annotation_dir: str
	):
	image_dir = os.path.join(base_image_dir, f"{split}{self.year}")

	ann_dir = os.path.join(base_annotation_dir, "annotations")
	ann_file_path = os.path.join(ann_dir, f"{self.task}_{split}{self.year}.json")

	processor: MsCocoProcessor = self.config.processor

	ann_json = processor.load_annotation_json(ann_file_path=ann_file_path)

	# info = AnnotationInfo.from_dict(ann_json["info"])
	licenses = processor.load_licenses_data(license_dicts=ann_json["licenses"])
	images = processor.load_images_data(image_dicts=ann_json["images"])

	category_dicts = ann_json.get("categories")
	categories = (
	processor.load_categories_data(category_dicts=category_dicts)
	if category_dicts is not None
	else None
	)

	config: MsCocoConfig = self.config
	yield from processor.generate_examples(
	annotations=processor.load_data(
	ann_dicts=ann_json["annotations"],
	images=images,
	decode_rle=config.decode_rle,
	),
	categories=categories,
	image_dir=image_dir,
	images=images,
	licenses=licenses,
	)

	def _generate_test_examples(self, test_image_info_path: str):
	raise NotImplementedError

	def _generate_examples(
	self,
	split: MscocoSplits,
	base_image_dir: Optional[str] = None,
	base_annotation_dir: Optional[str] = None,
	test_image_info_path: Optional[str] = None,
	):
	if split == "test" and test_image_info_path is not None:
	yield from self._generate_test_examples(
	test_image_info_path=test_image_info_path
	)
	elif (
	split in get_args(MscocoSplits)
	and base_image_dir is not None
	and base_annotation_dir is not None
	):
	yield from self._generate_train_val_examples(
	split=split,
	base_image_dir=base_image_dir,
	base_annotation_dir=base_annotation_dir,
	)
	else:
	raise ValueError(
	f"Invalid arguments: split = {split}, "
	f"base_image_dir = {base_image_dir}, "
	f"base_annotation_dir = {base_annotation_dir}, "
	f"test_image_info_path = {test_image_info_path}",
	)