first

data_process.py

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+import os
+import json
+
+import datasets
+
+_HOMEPAGE = "https://huggingface.co/datasets/zhenzi/datasets"
+
+_LICENSE = "Apache License 2.0"
+
+_CITATION = """\
+@software{2022,
+    title=数据集标题,
+    author=zhenzi,
+    year={2022},
+    month={March},
+    publisher = {GitHub}
+}
+"""
+
+_DESCRIPTION = """\
+数据集描述.
+"""
+
+_REPO = "https://huggingface.co/datasets/zhenzi/datasets/resolve/main/metadata"
+
+
+class ImageConfig(datasets.BuilderConfig):
+    """BuilderConfig for Imagette."""
+
+    def __init__(self, data_url, metadata_urls, **kwargs):
+        super(ImageConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+        self.metadata_urls = metadata_urls
+
+
+class Imagenette(datasets.GeneratorBasedBuilder):
+    """Imagenette dataset."""
+
+    BUILDER_CONFIGS = [
+        ImageConfig(
+            name="tests",
+            description="测试",
+            data_url="https://huggingface.co/datasets/zhenzi/test/resolve/main/tests.zip",
+            metadata_urls={
+                "train": f"{_REPO}/tests/train.txt"
+            },
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + self.config.description,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(self.config.data_url)
+        metadata_paths = dl_manager.download(self.config.metadata_urls)
+        archive_iter = dl_manager.iter_archive(archive_path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": archive_iter,
+                    "metadata_path": metadata_paths["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images": archive_iter,
+                    "metadata_path": metadata_paths["validation"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        with open(metadata_path, encoding="utf-8") as f:
+            files_to_keep = set(f.read().split("\n"))
+        for file_path, file_obj in images:
+            print(file_path)
+            if file_path in files_to_keep:
+                yield file_path, {
+                    "image": {"path": file_path, "bytes": file_obj.read()},
+                    "text": "dee",
+                }

metadata/tests/train.txt

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+tests/train/001.jpeg 酷酷的女机器人
+tests/train/002.jpeg 穿警服的兔子