| |
| import os |
| import datasets |
| import joblib |
| from pathlib import Path |
| from tqdm import tqdm |
|
|
|
|
| _BASE_HF_URL = Path("./data") |
| _CITATION = "" |
| _HOMEPAGE = "" |
| _DESCRIPTION = "" |
| _DATA_URL = { |
| "train": [_BASE_HF_URL/"images.tar.gz"] |
| } |
|
|
|
|
| class AVA(datasets.GeneratorBasedBuilder): |
| VERSION = datasets.Version("1.0.0") |
|
|
| DEFAULT_WRITER_BATCH_SIZE = 1000 |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "image": datasets.Image(), |
| "filename": datasets.Value("string"), |
| "rating_counts": datasets.features.Sequence(datasets.Value("int32")), |
| "text_tag_0": datasets.Value("string"), |
| "text_tag_1": datasets.Value("string") |
| } |
| ), |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| archives = dl_manager.download(_DATA_URL) |
| self.dict_metadata = joblib.load(Path(dl_manager.download_and_extract(_BASE_HF_URL/ "metadata.pkl"))) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]], |
| "split": "train", |
| }, |
| ) |
| ] |
|
|
| def _generate_examples(self, archives, split): |
| """Yields examples.""" |
| idx = 0 |
| for archive in archives: |
| for path, file in tqdm(archive): |
| if path.endswith(".jpg"): |
| |
| _id = int(os.path.splitext(path)[0].split('/')[-1]) |
| _metadata = self.dict_metadata[_id] |
| ex = {"image": {"path": path, "bytes": file.read()}, |
| "filename": str(path).split('/')[-1], |
| "rating_counts": _metadata[0], |
| "text_tag_0":_metadata[1], |
| "text_tag_1": _metadata[2]} |
| yield idx, ex |
| idx += 1 |
|
|
|
|
|
|
