dev

GraySpectrogram.py

@@ -10,13 +10,13 @@ from pathlib import Path
 
 
 # ここに設定を記入
-_NAME = "mb23/GraySpectrogram"
+_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/mb23/GraySpectrogram"
+_HOMEPAGE = "https://huggingface.co/datasets/mickylan2367/spectrogram_musicCaps"
 
 _DESCRIPTION = f"""\
 {_NAME} Datasets including spectrogram.png file from Google MusicCaps Datasets!
@@ -34,369 +34,20 @@ Using for Project Learning...
 # DatasetInfo : https://huggingface.co/docs/datasets/package_reference/main_classes
 
 
-def get_information():
-    # データを整理？
-    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"]
-    return (metadata_urls, data_urls)
-
-
-
-class GraySpectrogramConfig(datasets.BuilderConfig):
-    """BuilderConfig for Imagette."""
-
-    def __init__(self, data_url, metadata_url, **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_url = metadata_url
-
-class GraySpectrogram(datasets.GeneratorBasedBuilder):
+class LoadingScriptPractice(datasets.GeneratorBasedBuilder):
 
     # データのサブセットはここで用意
-    metadata_urls, data_urls = get_information()
-    subset_name_list = [
-        "data 0-200",
-        "data 200-600",
-        "data 600-1000",
-        "data 1000-1300",
-        "data 1300-1600",
-        "data 1600-2000",
-    ]
-    for i in range(2000, 2800, 200):
-        subset_name_list.append(f"data {i}-{i+200}")
-    for i in range(3000, 5200, 200):
-        subset_name_list.append(f"data {i}-{i+200}")
-    subset_name_list.append("data 5200-5520")
-
-    config_list = list()
-    for i in range(22):
-        config_list.append(
-            GraySpectrogramConfig(
-                name = subset_name_list[i],
-                description = _DESCRIPTION,
-                data_url = {
-                    "train" : data_urls["train"][i],
-                    "test" : data_urls["test"][i]
-                },
-                metadata_url = {
-                    "train" : metadata_urls["train"][i],
-                    "test" : metadata_urls["test"][i]
-                }
-            )
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="train",
+            description=_DESCRIPTION,
+            # data_url = train_data_url["train"][0],
+            # metadata_urls = {
+            #     "train" : train_metadata_paths["train"][0]
+            # }
         )
-
-    BUILDER_CONFIGS = config_list
-
-    # BUILDER_CONFIGS = [
-    #     GraySpectrogramConfig(
-    #         name="data 0-200",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][0],
-    #             "test" : data_urls["test"][0]
-    #         },
-    #         metadata_url = {
-    #             "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_url = {
-    #             "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_url = {
-    #             "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_url = {
-    #             "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_url = {
-    #             "train" : metadata_urls["train"][4],
-    #             "test" : metadata_urls["test"][4]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 1600-2000",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][5],
-    #             "test" : data_urls["test"][5]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][5],
-    #             "test" : metadata_urls["test"][5]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 2000-2200",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][6],
-    #             "test" : data_urls["test"][6]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][6],
-    #             "test" : metadata_urls["test"][6]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 2200-2600",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][7],
-    #             "test" : data_urls["test"][7]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][7],
-    #             "test" : metadata_urls["test"][7]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 2600-2800",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][8],
-    #             "test" : data_urls["test"][8]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][8],
-    #             "test" : metadata_urls["test"][8]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 3000-3200",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][9],
-    #             "test" : data_urls["test"][9]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][9],
-    #             "test" : metadata_urls["test"][9]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 3200-3400",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][10],
-    #             "test" : data_urls["test"][10]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][11],
-    #             "test" : metadata_urls["test"][11]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 3400-3600",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][12],
-    #             "test" : data_urls["test"][12]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][12],
-    #             "test" : metadata_urls["test"][12]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 3600-3800",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][13],
-    #             "test" : data_urls["test"][1]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][14],
-    #             "test" : metadata_urls["test"][14]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 3800-4000",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][15],
-    #             "test" : data_urls["test"][15]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][15],
-    #             "test" : metadata_urls["test"][15]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 4000-4200",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][16],
-    #             "test" : data_urls["test"][16]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][16],
-    #             "test" : metadata_urls["test"][16]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 4200-4400",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][17],
-    #             "test" : data_urls["test"][17]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][17],
-    #             "test" : metadata_urls["test"][17]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 4400-4600",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][18],
-    #             "test" : data_urls["test"][18]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][18],
-    #             "test" : metadata_urls["test"][18]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 4600-4800",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][19],
-    #             "test" : data_urls["test"][19]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][19],
-    #             "test" : metadata_urls["test"][19]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 4800-5000",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][20],
-    #             "test" : data_urls["test"][20]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][20],
-    #             "test" : metadata_urls["test"][20]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 5000-5200",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][21],
-    #             "test" : data_urls["test"][21]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][4],
-    #             "test" : metadata_urls["test"][4]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 5200-5520",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][4],
-    #             "test" : data_urls["test"][4]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][4],
-    #             "test" : metadata_urls["test"][4]
-    #         }   
-    #     ),
-    #     GraySpectrogramConfig(
-    #         name="data 2800-3000",
-    #         description=_DESCRIPTION,
-    #         data_url = {
-    #             "train" : data_urls["train"][4],
-    #             "test" : data_urls["test"][4]
-    #         },
-    #         metadata_url = {
-    #             "train" : metadata_urls["train"][4],
-    #             "test" : metadata_urls["test"][4]
-    #         }   
-    #     )
-    # ]
+    ]
 
     def _info(self) -> DatasetInfo:
       return datasets.DatasetInfo(
@@ -431,95 +82,57 @@ class GraySpectrogram(datasets.GeneratorBasedBuilder):
       )
 
     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)
 
-        train_metadata_path = dl_manager.download_and_extract(self.config.metadata_url["train"])
-        test_metadata_path = dl_manager.download_and_extract(self.config.metadata_url["test"])
-        train_data_path = dl_manager.download(self.config.data_url["train"])
-        test_data_path = dl_manager.download(self.config.data_url["test"])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "images": dl_manager.iter_archive(train_data_path),
-                    "metadata_path": train_metadata_path,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "images": dl_manager.iter_archive(test_data_path),
-                    "metadata_path": test_metadata_path,
-                }
-            ),
-        ]
-
-        # # 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)
+        metadata_urls = DataFilesDict.from_hf_repo(
+            {datasets.Split.TRAIN: ["**"]},
+            dataset_info=hfh_dataset_info,
+            allowed_extensions=["jsonl", ".jsonl"],
+        )
 
-        # # 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"],
-        # )
+        # **.zipのURLをDict型として取得？
+        data_urls = DataFilesDict.from_hf_repo(
+            {datasets.Split.TRAIN: ["**"]},
+            dataset_info=hfh_dataset_info,
+            allowed_extensions=["zip", ".zip"],
+        )
 
-        # 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"]
+        data_paths = dict()
+        for path in data_urls["train"]:
+            dname = dirname(path)
+            folder = basename(Path(dname))
+            data_paths[folder] = path
 
-        # # 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"],
-        # )
+        metadata_paths = dict()
+        for path in metadata_urls["train"]:
+            dname = dirname(path)
+            folder = basename(Path(dname))
+            metadata_paths[folder] = path
 
-        # 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
+        gs = []
+        for split, files in data_paths.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
+            metadata_path = dl_manager.download_and_extract(metadata_paths[split])
+            downloaded_files_path = dl_manager.download(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": metadata_path
+                     }
+                  )
+            )
+        return gs
 
-    
     def _generate_examples(self, images, metadata_path):
         """Generate images and captions for splits."""
         # with open(metadata_path, encoding="utf-8") as f:
@@ -530,7 +143,7 @@ class GraySpectrogram(datasets.GeneratorBasedBuilder):
         num_list = list()
         label_list = list()
 
-        with open(metadata_path, encoding="utf-8") as fin:
+        with open(metadata_path) as fin:
             for line in fin:
                 data =  json.loads(line)
                 file_list.append(data["file_name"])
@@ -550,3 +163,5 @@ class GraySpectrogram(datasets.GeneratorBasedBuilder):
                 "number" : num_list[idx],
                 "label": label_list[idx]
             }
+
+

README.md

@@ -6,14 +6,12 @@ tags:
 - music
 - spectrogram
 size_categories:
-- 10K<n<100K
+- n<1K
 ---
 
-# Google/MusicCapsをスペクトログラムにしたデータ。
+## Google/MusicCapsをスペクトログラムにしたデータ。
 
-* <font color="red">The dataset viwer of this repository is truncated, so maybe you should see <a href="https://huggingface.co/datasets/mb23/GraySpectrotram_example">this one</a> instaed.</font>
-
-## Dataset information
+### データの基本情報
 <table>
   <thead>
     <td>画像</td>
@@ -31,7 +29,7 @@ size_categories:
   </tbody>
 </table>
 
-## How this dataset was made
+### データ作った方法
 
 * コード：https://colab.research.google.com/drive/13m792FEoXszj72viZuBtusYRUL1z6Cu2?usp=sharing
 * 参考にしたKaggle Notebook : https://www.kaggle.com/code/osanseviero/musiccaps-explorer
@@ -50,7 +48,7 @@ image = Image.fromarray(np.uint8(D), mode='L')  # 'L'は1チャンネルのグ
 image.save('spectrogram_{}.png')
 ```
 
-## Recover music(wave form) from sprctrogram
+### ♪復元方法
 ```python
 im = Image.open("pngファイル")
 db_ud = np.uint8(np.array(im))
@@ -63,136 +61,3 @@ print(amp.shape)
 y_inv = librosa.griffinlim(amp*200)
 display(IPython.display.Audio(y_inv, rate=sr))
 ```
-
-## Example : How to use this
-* <font color="red">Subset <b>data 1300-1600</b> and <b>data 3400-3600</b> are not working now, so please get subset_name_list</n>
-  those were removed first</font>.
-### 1 : get information about this dataset:
-* copy this code~~
-
-```python
-'''
-  if you use GoogleColab, remove # to install packages below..
-'''
-#!pip install datasets
-#!pip install huggingface-hub
-#!huggingface-cli login
-import datasets
-from datasets import load_dataset
-
-# make subset_name_list
-subset_name_list = [
-  'data 0-200',
-  'data 200-600',
-  'data 600-1000',
-  'data 1000-1300',
-  'data 1600-2000',
-  'data 2000-2200',
-  'data 2200-2400',
-  'data 2400-2600',
-  'data 2600-2800',
-  'data 3000-3200',
-  'data 3200-3400',
-  'data 3600-3800',
-  'data 3800-4000',
-  'data 4000-4200',
-  'data 4200-4400',
-  'data 4400-4600',
-  'data 4600-4800',
-  'data 4800-5000',
-  'data 5000-5200',
-  'data 5200-5520'
-]
-
-# load_all_datasets
-data = load_dataset("mb23/GraySpectrogram", subset_name_list[0])
-for subset in subset_name_list:
-    # Confirm subset_list doesn't include "remove_list" datasets in the above cell.
-    print(subset)
-    new_ds = load_dataset("mb23/GraySpectrogram", subset)
-    new_dataset_train = datasets.concatenate_datasets([data["train"], new_ds["train"]])
-    new_dataset_test = datasets.concatenate_datasets([data["test"], new_ds["test"]])
-
-    # take place of data[split]
-    data["train"] = new_dataset_train
-    data["test"] = new_dataset_test
-
-data
-```
-
-
-
-### 2 : load dataset and change to dataloader:
-* You can use the code below:
-* <font color="red">...but (;・∀・)I don't know whether this code works efficiently, because I haven't tried this code so far</color>
-```python
-import datasets
-from datasets import load_dataset, DatasetDict
-from torchvision import transforms
-from torch.utils.data import DataLoader
-# BATCH_SIZE = ??? 
-# IMAGE_SIZE = ???
-# TRAIN_SIZE = ??? # the number of training data
-# TEST_SIZE = ??? # the number of test data
-
-def load_datasets():
-
-    # Define data transforms
-    data_transforms = [
-        transforms.Resize((IMG_SIZE, IMG_SIZE)),
-        transforms.ToTensor(), # Scales data into [0,1]
-        transforms.Lambda(lambda t: (t * 2) - 1) # Scale between [-1, 1]
-    ]
-    data_transform = transforms.Compose(data_transforms)
-
-    data = load_dataset("mb23/GraySpectrogram", subset_name_list[0])
-    for subset in subset_name_list:
-        # Confirm subset_list doesn't include "remove_list" datasets in the above cell.
-        print(subset)
-        new_ds = load_dataset("mb23/GraySpectrogram", subset)
-        new_dataset_train = datasets.concatenate_datasets([data["train"], new_ds["train"]])
-        new_dataset_test = datasets.concatenate_datasets([data["test"], new_ds["test"]])
-
-        # take place of data[split]
-        data["train"] = new_dataset_train
-        data["test"] = new_dataset_test
-
-    # memo:
-    # 特徴量上手く抽出する方法が...わからん。これは力づく。
-    # 本当はload_dataset()の時点で抽出したかったけど、無理そう
-    # リポジトリ作り直してpush_to_hub()したほうがいいかもしれない。
-
-    new_dataset = dict()
-    new_dataset["train"] = Dataset.from_dict({
-        "image" : data["train"]["image"],
-        "caption" : data["train"]["caption"]
-    })
-    
-    new_dataset["test"] = Dataset.from_dict({
-        "image" : data["test"]["image"],
-        "caption" : data["test"]["caption"]
-    })
-    data = datasets.DatasetDict(new_dataset)
-    train = data["train"]
-    test = data["test"]
-
-    for idx in range(len(train["image"])):
-        train["image"][idx] = data_transform(train["image"][idx])
-        test["image"][idx] = data_transform(test["image"][idx])
-    
-    train = Dataset.from_dict(train)
-    train = train.with_format("torch") # リスト型回避
-    test = Dataset.from_dict(train)
-    test = test.with_format("torch") # リスト型回避
-
-    # or
-    train_loader = DataLoader(train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
-    test_loader = DataLoader(test, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
-    return train_loader, test_loader
-
-```
-* then try this?
-```
-train_loader, test_loader = load_datasets()
-```
-

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