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import json |
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from pathlib import Path |
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from typing import Any, Dict, Iterator, List, Optional, Tuple, Union |
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import datasets |
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from datasets.data_files import DataFilesDict |
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from datasets.download.download_manager import ArchiveIterable, DownloadManager |
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from datasets.features import Features |
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from datasets.info import DatasetInfo |
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_TYPING_BOX = Tuple[float, float, float, float] |
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_CITATION = """\ |
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@article{DBLP:journals/corr/LinMBHPRDZ14, |
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author = {Tsung{-}Yi Lin and |
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Michael Maire and |
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Serge J. Belongie and |
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Lubomir D. Bourdev and |
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Ross B. Girshick and |
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James Hays and |
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Pietro Perona and |
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Deva Ramanan and |
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Piotr Doll{\'{a}}r and |
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C. Lawrence Zitnick}, |
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title = {Microsoft {COCO:} Common Objects in Context}, |
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journal = {CoRR}, |
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volume = {abs/1405.0312}, |
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year = {2014}, |
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url = {http://arxiv.org/abs/1405.0312}, |
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archivePrefix = {arXiv}, |
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eprint = {1405.0312}, |
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timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, |
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biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This dataset contains all COCO 2017 images and annotations split in training (118287 images) \ |
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and validation (5000 images). |
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""" |
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_HOMEPAGE = "https://cocodataset.org" |
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_URLS = { |
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"annotations": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip", |
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"train": "http://images.cocodataset.org/zips/train2017.zip", |
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"val": "http://images.cocodataset.org/zips/val2017.zip", |
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} |
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_SPLITS = ["train", "val"] |
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_PATHS = { |
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"annotations": { |
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"train": Path("annotations/instances_train2017.json"), |
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"val": Path("annotations/instances_val2017.json"), |
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}, |
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"images": { |
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"train": Path("train2017"), |
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"val": Path("val2017"), |
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}, |
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} |
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_CLASSES = [ |
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"None", |
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"person", |
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"bicycle", |
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"car", |
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"motorcycle", |
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"airplane", |
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"bus", |
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"train", |
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"truck", |
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"boat", |
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"traffic light", |
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"fire hydrant", |
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"street sign", |
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"stop sign", |
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"parking meter", |
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"bench", |
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"bird", |
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"cat", |
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"dog", |
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"horse", |
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"sheep", |
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"cow", |
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"elephant", |
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"bear", |
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"zebra", |
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"giraffe", |
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"hat", |
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"backpack", |
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"umbrella", |
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"shoe", |
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"eye glasses", |
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"handbag", |
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"tie", |
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"suitcase", |
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"frisbee", |
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"skis", |
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"snowboard", |
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"sports ball", |
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"kite", |
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"baseball bat", |
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"baseball glove", |
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"skateboard", |
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"surfboard", |
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"tennis racket", |
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"bottle", |
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"plate", |
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"wine glass", |
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"cup", |
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"fork", |
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"knife", |
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"spoon", |
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"bowl", |
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"banana", |
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"apple", |
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"sandwich", |
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"orange", |
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"broccoli", |
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"carrot", |
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"hot dog", |
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"pizza", |
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"donut", |
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"cake", |
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"chair", |
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"couch", |
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"potted plant", |
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"bed", |
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"mirror", |
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"dining table", |
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"window", |
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"desk", |
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"toilet", |
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"door", |
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"tv", |
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"laptop", |
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"mouse", |
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"remote", |
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"keyboard", |
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"cell phone", |
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"microwave", |
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"oven", |
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"toaster", |
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"sink", |
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"refrigerator", |
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"blender", |
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"book", |
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"clock", |
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"vase", |
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"scissors", |
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"teddy bear", |
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"hair drier", |
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"toothbrush", |
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"hair brush", |
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] |
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def round_box_values(box, decimals=2): |
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return [round(val, decimals) for val in box] |
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class COCOHelper: |
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"""Helper class to load COCO annotations""" |
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def __init__(self, annotation_path: Path, images_dir: Path) -> None: |
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with open(annotation_path, "r") as file: |
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data = json.load(file) |
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self.data = data |
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dict_id2annot: Dict[int, Any] = {} |
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for annot in self.annotations: |
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dict_id2annot.setdefault(annot["image_id"], []).append(annot) |
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dict_id2annot = { |
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k: list(sorted(v, key=lambda a: a["id"])) for k, v in dict_id2annot.items() |
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} |
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self.dict_path2annot: Dict[str, Any] = {} |
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self.dict_path2id: Dict[str, Any] = {} |
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for img in self.images: |
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path_img = images_dir / str(img["file_name"]) |
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path_img_str = str(path_img) |
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idx = int(img["id"]) |
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annot = dict_id2annot.get(idx, []) |
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self.dict_path2annot[path_img_str] = annot |
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self.dict_path2id[path_img_str] = img["id"] |
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def __len__(self) -> int: |
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return len(self.data["images"]) |
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@property |
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def info(self) -> Dict[str, Union[str, int]]: |
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return self.data["info"] |
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@property |
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def licenses(self) -> List[Dict[str, Union[str, int]]]: |
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return self.data["licenses"] |
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@property |
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def images(self) -> List[Dict[str, Union[str, int]]]: |
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return self.data["images"] |
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@property |
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def annotations(self) -> List[Any]: |
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return self.data["annotations"] |
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@property |
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def categories(self) -> List[Dict[str, Union[str, int]]]: |
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return self.data["categories"] |
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def get_annotations(self, image_path: str) -> List[Any]: |
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return self.dict_path2annot.get(image_path, []) |
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def get_image_id(self, image_path: str) -> int: |
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return self.dict_path2id.get(image_path, -1) |
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class COCO2017(datasets.GeneratorBasedBuilder): |
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"""COCO 2017 dataset.""" |
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VERSION = datasets.Version("1.0.1") |
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def _info(self) -> datasets.DatasetInfo: |
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""" |
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Returns the dataset metadata and features. |
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Returns: |
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DatasetInfo: Metadata and features of the dataset. |
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""" |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"image_id": datasets.Value("int64"), |
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"objects": datasets.Sequence( |
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{ |
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"id": datasets.Value("int64"), |
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"area": datasets.Value("float64"), |
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"bbox": datasets.Sequence( |
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datasets.Value("float32"), length=4 |
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), |
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"label": datasets.ClassLabel(names=_CLASSES), |
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"iscrowd": datasets.Value("bool"), |
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} |
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), |
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} |
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), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators( |
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self, dl_manager: DownloadManager |
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) -> List[datasets.SplitGenerator]: |
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""" |
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Provides the split information and downloads the data. |
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Args: |
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dl_manager (DownloadManager): The DownloadManager to use for downloading and |
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extracting data. |
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Returns: |
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List[SplitGenerator]: List of SplitGenerator objects representing the data splits. |
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""" |
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archive_annots = dl_manager.download_and_extract(_URLS["annotations"]) |
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splits = [] |
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for split in _SPLITS: |
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archive_split = dl_manager.download(_URLS[split]) |
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annotation_path = Path(archive_annots) / _PATHS["annotations"][split] |
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images = dl_manager.iter_archive(archive_split) |
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splits.append( |
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datasets.SplitGenerator( |
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name=datasets.Split(split), |
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gen_kwargs={ |
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"annotation_path": annotation_path, |
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"images_dir": _PATHS["images"][split], |
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"images": images, |
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}, |
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) |
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) |
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return splits |
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def _generate_examples( |
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self, annotation_path: Path, images_dir: Path, images: ArchiveIterable |
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) -> Iterator: |
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""" |
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Generates examples for the dataset. |
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Args: |
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annotation_path (Path): The path to the annotation file. |
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images_dir (Path): The path to the directory containing the images. |
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images: (ArchiveIterable): An iterable containing the images. |
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Yields: |
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Dict[str, Union[str, Image]]: A dictionary containing the generated examples. |
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""" |
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coco_annotation = COCOHelper(annotation_path, images_dir) |
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for image_path, f in images: |
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annotations = coco_annotation.get_annotations(image_path) |
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ret = { |
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"image": {"path": image_path, "bytes": f.read()}, |
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"image_id": coco_annotation.get_image_id(image_path), |
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"objects": [ |
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{ |
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"id": annot["id"], |
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"area": annot["area"], |
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"bbox": round_box_values(annot["bbox"], 2), |
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"label": annot["category_id"], |
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"iscrowd": bool(annot["iscrowd"]), |
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} |
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for annot in annotations |
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], |
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} |
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yield image_path, ret |
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