Datasets:

mb23
/

GraySpectrogram

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



class GraySpectrogram2(datasets.GeneratorBasedBuilder):

    # データのサブセットはここで用意
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="train",
            description=_DESCRIPTION,
        )
    ]

    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):
        # 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" : 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_list[idx],
                "image": {
                    "path": file_list[idx], 
                    "bytes": file_obj.read()
                },
                "caption" : caption_list[idx],
                "data_idx" : dataIDX_list[idx],
                "number" : num_list[idx],
                "label": label_list[idx]
            }


	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



	class GraySpectrogram2(datasets.GeneratorBasedBuilder):

	# データのサブセットはここで用意
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="train",
	description=_DESCRIPTION,
	)
	]

	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):
	# 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" : 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_list[idx],
	"image": {
	"path": file_list[idx],
	"bytes": file_obj.read()
	},
	"caption" : caption_list[idx],
	"data_idx" : dataIDX_list[idx],
	"number" : num_list[idx],
	"label": label_list[idx]
	}