Delete loading script auxiliary file

image_dataset.py

@@ -1,95 +0,0 @@
-import os
-import json
-import random
-from datasets import (
-    BuilderConfig,
-    DatasetInfo,
-    DownloadManager,
-    GeneratorBasedBuilder,
-    SplitGenerator,
-    Split,
-    Features,
-    Image,
-    Sequence,
-    Value,
-)
-
-class ImageSubsetConfig(BuilderConfig):
-    """BuilderConfig for small vs. full dataset."""
-    def __init__(self, name, sample_size=None, **kwargs):
-        super().__init__(
-            name=name,
-            version="1.0.0",
-            description=kwargs.get("description", "")
-        )
-        self.sample_size = sample_size
-
-class MyImageDataset(GeneratorBasedBuilder):
-    """A dataset of images with actuated_angle from metadata.json."""
-    BUILDER_CONFIGS = [
-        ImageSubsetConfig(
-            name="full",
-            sample_size=None,
-            description="All images (≈100 GB). Use with streaming=True."
-        ),
-        ImageSubsetConfig(
-            name="small",
-            sample_size=2,
-            description="Random subset of 10 000 images for quick iteration."
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "smgit all"
-
-    def _info(self):
-        return DatasetInfo(
-            description="Images with a 2-D actuated_angle from metadata.json",
-            features=Features({
-                "image": Image(),                      # the image file
-                "actuated_angle": Sequence(Value("int32")),  # [angle0, angle1]
-            }),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager: DownloadManager):
-        data_dir = dl_manager.manual_dir or os.path.abspath(os.path.dirname(__file__))
-        images_dir = os.path.join(data_dir, "images")
-        meta_path = os.path.join(data_dir, "metadata.json")
-
-        # load your filename → {"0":…, "1":…} mapping
-        with open(meta_path, "r", encoding="utf-8") as f:
-            metadata = json.load(f)
-
-        # sample if in "small" config
-        if self.config.sample_size:
-            all_fnames = list(metadata.keys())
-            random.seed(42)
-            selected = set(random.sample(all_fnames, self.config.sample_size))
-        else:
-            selected = set(metadata.keys())
-
-        return [
-            SplitGenerator(
-                name=Split.TRAIN,
-                gen_kwargs={
-                    "images_dir": images_dir,
-                    "metadata": metadata,
-                    "selected": selected,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, images_dir, metadata, selected):
-        """Yields examples of the form { "image": path, "actuated_angle": [int, int] }"""
-        for idx, fname in enumerate(selected):
-            if fname not in metadata:
-                continue
-            image_path = os.path.join(images_dir, fname)
-            if not os.path.exists(image_path):
-                continue
-            meta = metadata[fname]
-            # extract the two angle values (keys "0" and "1")
-            angles = [ int(meta.get("0", 0)), int(meta.get("1", 0)) ]
-            yield idx, {
-                "image": image_path,
-                "actuated_angle": angles,
-            }