changed repository

GraySpectrogram2.py

@@ -0,0 +1,167 @@
+
+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 = "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...
+"""
+
+# 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 LoadingScriptPractice(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,
+            # data_url = train_data_url["train"][0],
+            # metadata_urls = {
+            #     "train" : train_metadata_paths["train"][0]
+            # }
+        )
+    ]
+
+    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)
+
+        metadata_urls = 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_urls = DataFilesDict.from_hf_repo(
+            {datasets.Split.TRAIN: ["**"]},
+            dataset_info=hfh_dataset_info,
+            allowed_extensions=["zip", ".zip"],
+        )
+
+        data_paths = dict()
+        for path in data_urls["train"]:
+            dname = dirname(path)
+            folder = basename(Path(dname))
+            data_paths[folder] = path
+
+        metadata_paths = dict()
+        for path in metadata_urls["train"]:
+            dname = dirname(path)
+            folder = basename(Path(dname))
+            metadata_paths[folder] = path
+
+        
+        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:
+        #     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]
+            }
+
+

train.zip → data/test/test_0000.zip

@@ -1,3 +1,3 @@
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data/test/test_0001.zip

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data/train/data_0000.zip

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data/train/data_0001.zip

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