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# coding=utf-8
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(),
"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"):
# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
_id = int(os.path.splitext(path)[0].split('/')[-1])
_metadata = self.dict_metadata[_id]
ex = {"image": {"path": path, "bytes": file.read()},
"rating_counts": _metadata[0],
"text_tag_0":_metadata[1],
"text_tag_1": _metadata[2]}
yield idx, ex
idx += 1
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