File size: 11,451 Bytes
d335f0f 79490af d335f0f 6038f66 d335f0f c173f70 d335f0f 741161c 6038f66 741161c 6038f66 741161c 6038f66 741161c 6038f66 d335f0f 6038f66 741161c 6038f66 d335f0f 6038f66 02219b3 6038f66 d335f0f 6038f66 d335f0f 6038f66 d335f0f f9a5794 d335f0f 0431810 d335f0f 0431810 d335f0f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
import datasets
from huggingface_hub import HfApi
from datasets import DownloadManager, DatasetInfo
from datasets.data_files import DataFilesDict
import os
import json
from os.path import dirname, basename
from pathlib import Path
# ここに設定を記入
_NAME = "mb23/GraySpectrogram"
_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...
"""
# 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/docs/datasets/package_reference/builder_classes
# 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
# https://huggingface.co/datasets/frgfm/imagenette/blob/main/imagenette.py
# https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html
# DatasetInfo : https://huggingface.co/docs/datasets/package_reference/main_classes
# データを整理？
dl_manager = DownloadManager()
hfh_dataset_info = HfApi().dataset_info(_NAME, revision=_REVISION, timeout=100.0)
# メタデータであるjsonlファイルのURLを取得
# ここの抽出方法変えられないかな？
train_metadata_url = DataFilesDict.from_hf_repo(
{datasets.Split.TRAIN: ["data/train/**"]},
dataset_info=hfh_dataset_info,
allowed_extensions=["jsonl", ".jsonl"],
)
test_metadata_url = DataFilesDict.from_hf_repo(
{datasets.Split.TEST: ["data/test/**"]},
dataset_info=hfh_dataset_info,
allowed_extensions=["jsonl", ".jsonl"],
)
metadata_urls = dict()
metadata_urls["train"] = train_metadata_url["train"]
metadata_urls["test"] = test_metadata_url["test"]
# 画像データは**.zipのURLをDict型として取得？
# **.zipのURLをDict型として取得？
train_data_url = DataFilesDict.from_hf_repo(
{datasets.Split.TRAIN: ["data/train/**"]},
dataset_info=hfh_dataset_info,
allowed_extensions=["zip", ".zip"],
)
test_data_url = DataFilesDict.from_hf_repo(
{datasets.Split.TEST: ["data/test/**"]},
dataset_info=hfh_dataset_info,
allowed_extensions=["zip", ".zip"]
)
data_urls = dict()
data_urls["train"] = train_data_url["train"]
data_urls["test"] = test_data_url["test"]
class GraySpectrogramConfig(datasets.BuilderConfig):
"""BuilderConfig for Imagette."""
def __init__(self, data_url, metadata_urls, **kwargs):
"""BuilderConfig for Imagette.
Args:
data_url: `string`, url to download the zip file from.
matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
**kwargs: keyword arguments forwarded to super.
"""
super(GraySpectrogramConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_url = data_url
self.metadata_urls = metadata_urls
class GraySpectrogram(datasets.GeneratorBasedBuilder):
# データのサブセットはここで用意
BUILDER_CONFIGS = [
GraySpectrogramConfig(
name="data 0-200",
description=_DESCRIPTION,
data_url = {
"train" : data_urls["train"][0],
"test" : data_urls["test"][0]
},
metadata_urls = {
"train" : metadata_urls["train"][0],
"test" : metadata_urls["test"][0]
}
),
GraySpectrogramConfig(
name="data 200-600",
description=_DESCRIPTION,
data_url ={
"train" : data_urls["train"][1],
"test" : data_urls["test"][1]
},
metadata_urls = {
"train": metadata_urls["train"][1],
"test" : metadata_urls["test"][1]
}
),
GraySpectrogramConfig(
name="data 600-1000",
description=_DESCRIPTION,
data_url = {
"train" : data_urls["train"][2],
"test" : data_urls["test"][2]
},
metadata_urls = {
"train" : metadata_urls["train"][2],
"test" : metadata_urls["test"][2]
}
),
GraySpectrogramConfig(
name="data 1000-1300",
description=_DESCRIPTION,
data_url = {
"train" : data_urls["train"][3],
"test" : data_urls["test"][3]
},
metadata_urls = {
"train" : metadata_urls["train"][3],
"test" : metadata_urls["test"][3]
}
),
GraySpectrogramConfig(
name="data 1300-1600",
description=_DESCRIPTION,
data_url = {
"train" : data_urls["train"][4],
"test" : data_urls["test"][4]
},
metadata_urls = {
"train" : metadata_urls["train"][4],
"test" : metadata_urls["test"][4]
}
)
]
def _info(self) -> DatasetInfo:
return datasets.DatasetInfo(
description = self.config.description,
features=datasets.Features(
{
"image": datasets.Image(),
"caption": datasets.Value("string"),
"data_idx": datasets.Value("int32"),
"number" : datasets.Value("int32"),
"label" : datasets.ClassLabel(
names=[
"blues",
"classical",
"country",
"disco",
"hiphop",
"metal",
"pop",
"reggae",
"rock",
"jazz"
]
)
}
),
supervised_keys=("image", "caption"),
homepage=_HOMEPAGE,
citation= "",
# license=_LICENSE,
# task_templates=[ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager: DownloadManager):
metadata_paths = dl_manager.download(self.config.metadata_urls)
data_paths = dl_manager.download(self.config.data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": dl_manager.iter_archive(data_paths["train"]),
"metadata_path": metadata_paths["train"],
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images": dl_manager.iter_archive(data_paths["test"]),
"metadata_path": metadata_paths["test"],
}
),
]
# # 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)
# # メタデータであるjsonlファイルのURLを取得
# # ここの抽出方法変えられないかな？
# train_metadata_url = DataFilesDict.from_hf_repo(
# {datasets.Split.TRAIN: ["data/train/**"]},
# dataset_info=hfh_dataset_info,
# allowed_extensions=["jsonl", ".jsonl"],
# )
# test_metadata_url = DataFilesDict.from_hf_repo(
# {datasets.Split.TEST: ["data/test/**"]},
# dataset_info=hfh_dataset_info,
# allowed_extensions=["jsonl", ".jsonl"],
# )
# metadata_urls = dict()
# metadata_urls["train"] = train_metadata_url["train"]
# metadata_urls["test"] = test_metadata_url["test"]
# # 画像データは**.zipのURLをDict型として取得？
# # **.zipのURLをDict型として取得？
# train_data_url = DataFilesDict.from_hf_repo(
# {datasets.Split.TRAIN: ["data/train/**"]},
# dataset_info=hfh_dataset_info,
# allowed_extensions=["zip", ".zip"],
# )
# test_data_url = DataFilesDict.from_hf_repo(
# {datasets.Split.TEST: ["data/test/**"]},
# dataset_info=hfh_dataset_info,
# allowed_extensions=["zip", ".zip"]
# )
# data_urls = dict()
# data_urls["train"] = train_data_url["train"]
# data_urls["test"] = test_data_url["test"]
# gs = []
# # for split, file_list in data_urls.items():
# # metadata_list = metadata_urls[split]
# # for i, file_ in enumerate(file_list):
# # '''
# # split : "train" or "test" or "val"
# # files : zip files
# # '''
# # # print(file_)
# # # print(metadata_list[0])
# # # # 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
# # metadata_path = dl_manager.download_and_extract(metadata_list[i])
# # downloaded_files = dl_manager.download(file_)
# # # # 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" : iter(iter_archive[split]),
# # "images" : dl_manager.iter_archive(downloaded_files),
# # "metadata_path": metadata_path # メタデータパスを渡す
# # }
# # )
# # )
# return gs
def _generate_examples(self, images, metadata_path):
"""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(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]
}
|