| import datasets | |
| import json | |
| import os | |
| from .classes import IMAGENET2012_CLASSES | |
| _URL_BASE = "https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/" | |
| _URLS = { | |
| "img_data": _URL_BASE + "images.tar.gz", | |
| "mask_data": _URL_BASE + "masks.tar.gz", | |
| "train_json": _URL_BASE + "train.json", | |
| "val_json": _URL_BASE + "val.json", | |
| "test_json": _URL_BASE + "test.json", | |
| } | |
| class SegmentedImagenet1kDataset(datasets.GeneratorBasedBuilder): | |
| datasets.Version("1.1.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description="Machine generated instance segmentation results of subset of ImageNet-1k", | |
| homepage="https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset", | |
| features = datasets.Features({ | |
| "image": datasets.Image(), | |
| "imagenet_label": datasets.Value("string"), | |
| "boxes": datasets.Sequence(datasets.Sequence(datasets.Value('int32'))), | |
| "labels": datasets.Sequence(datasets.Value("string")), | |
| "scores": datasets.Sequence(datasets.Value("float32")) , | |
| "masks": datasets.Sequence(datasets.Image()), | |
| }), | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager): | |
| dirs = dl_manager.download_and_extract(_URLS) | |
| root_folder_kwargs = {"image_root": dirs["img_data"], "mask_root": dirs["mask_data"]} | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={"json_path": dirs["train_json"], "get_imagenet_string": True, **root_folder_kwargs}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, | |
| gen_kwargs={"json_path": dirs["test_json"], "get_imagenet_string": False, **root_folder_kwargs}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, | |
| gen_kwargs={"json_path": dirs["val_json"], "get_imagenet_string": True, **root_folder_kwargs}), | |
| ] | |
| def _generate_examples(self, json_path, image_root, mask_root, get_imagenet_string): | |
| with open(json_path, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for id, item in enumerate(data): | |
| if get_imagenet_string: | |
| imagenet_label = IMAGENET2012_CLASSES[os.path.basename(item['image']).replace(".JPEG", "").rsplit("_", 1)[1]] | |
| pass | |
| else: | |
| imagenet_label = "None" | |
| yield id, { | |
| "image" : os.path.join(image_root,item['image']), | |
| "imagenet_label": imagenet_label, | |
| "boxes": item['boxes'], | |
| "scores": item['scores'], | |
| "labels": item['labels'], | |
| "masks": [os.path.join(mask_root, p) for p in item['masks']] | |
| } | |