| | |
| | 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 |
| |
|
| |
|
| |
|
