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, # type: ignore 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, # type: ignore 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: str = "", **kwargs): assert tqdm_desc != "", "tqdm_desc must be provided." 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 # type: ignore features = processor.get_features(decode_rle=self.config.decode_rle) # type: ignore 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 # type: ignore 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 # type: ignore 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}", )