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import datasets
from huggingface_hub import HfApi
from datasets import DownloadManager, DatasetInfo
from datasets.data_files import DataFilesDict
import os
import json
# ここに設定を記入
_NAME = "mickylan2367/LoadingScriptPractice"
_EXTENSION = [".png"]
_REVISION = "main"
# _HOMEPAGE = "https://github.com/fastai/imagenette"
# プログラムを置く場所が決まったら、ここにホームページURLつける
_HOMEPAGE = "https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps"
_DESCRIPTION = f"""\
{_NAME} Datasets including spectrogram.png file from Google MusicCaps Datasets!
Using for Project Learning...
"""
# え...なにこれ(;´･ω･)
# _IMAGES_DIR = "mickylan2367/images/data/"
# _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata"
# 参考になりそうなURL集
# https://huggingface.co/docs/datasets/v1.1.1/_modules/datasets/utils/download_manager.html
# https://huggingface.co/datasets/animelover/danbooru2022/blob/main/danbooru2022.py
# https://huggingface.co/datasets/food101/blob/main/food101.py
# https://huggingface.co/docs/datasets/about_dataset_load
class LoadingScriptPracticeConfig(datasets.BuilderConfig):
"""Builder Config for spectrogram_MusicCaps"""
def __init__(self, **kwargs):
"""BuilderConfig
Args:
data_url: `string`, url to download the zip file from.
metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
**kwargs: keyword arguments forwarded to super.
"""
super(LoadingScriptPracticeConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
# self.data_url = data_url
# self.metadata_urls = metadata_urls
class LoadingScriptPractice(datasets.GeneratorBasedBuilder):
# データのサブセットはここで用意
BUILDER_CONFIGS = [
LoadingScriptPracticeConfig(
name="MusicCaps data 0_3",
description="this Datasets is personal practice for using loadingScript. Data is from Google/MusicCaps",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"caption": datasets.Value("string"),
"data_idx": datasets.Value("int32"),
"number" : datasets.Value("int32"),
"label" : datasets.Value("string")
}
),
supervised_keys=("image", "caption", "data_idx", "number", "label"),
homepage=_HOMEPAGE,
# citation=_CITATION,
# license=_LICENSE,
# task_templates=[ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager: DownloadManager):
# huggingfaceのディレクトリからデータを取ってくる
hfh_dataset_info = HfApi().dataset_info(_NAME, revision=_REVISION, timeout=100.0)
split_metadata_paths = DataFilesDict.from_hf_repo(
{datasets.Split.TRAIN: ["**"]},
dataset_info=hfh_dataset_info,
allowed_extensions=["jsonl", ".jsonl"],
)
# **.zipのURLをDict型として取得？
data_path = DataFilesDict.from_hf_repo(
{datasets.Split.TRAIN: ["**"]},
dataset_info=hfh_dataset_info,
allowed_extensions=["zip", ".zip"],
)
gs = []
for split, files in data_path.items():
'''
split : "train" or "test" or "val"
files : zip files
'''
# リポジトリからダウンロードしてとりあえずキャッシュしたURLリストを取得
split_metadata_path = split_metadata_paths[split][0]
downloaded_files_path = dl_manager.download_and_extract(files)
# 元のコードではzipファイルの中身を"filepath"としてそのまま_generate_exampleに引き渡している？
gs.append(
datasets.SplitGenerator(
name = split,
gen_kwargs={
"images" : dl_manager.iter_archive(downloaded_files_path),
"metadata_path": split_metadata_path
}
)
)
return gs
def _generate_examples(self, images, metadata_path, dl_manager:DownloadManager):
"""Generate images and captions for splits."""
# with open(metadata_path, encoding="utf-8") as f:
# files_to_keep = set(f.read().split("\n"))
file_list = list()
caption_list = list()
dataIDX_list = list()
num_list = list()
label_list = list()
with open(dl_manager.download_and_extract(metadata_path)) as fin:
for line in fin:
data = json.loads(line)
file_list.append(data["file_name"])
caption_list.append(data["caption"])
dataIDX_list.append(data["data_idx"])
num_list.append(data["number"])
label_list.append(data["label"])
for idx, (file_path, file_obj) in enumerate(images):
yield file_path, {
"image": {
"path": file_path,
"bytes": file_obj.read()
},
"caption" : caption_list[idx],
"data_idx" : dataIDX_list[idx],
"number" : num_list[idx],
"label": label_list[idx]
}
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