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""" |
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Custom Hugging Face dataset loader for the INR-benchmark repository. |
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* Place this file in the root of the dataset repo alongside README.md. |
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* Use `load_dataset("username/INR-benchmark", "spheres", split="1234", trust_remote_code=True)`. |
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* Each example yields only the **file path** to keep memory/lightweight; users can `np.load` or `cv2.imread` themselves. |
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Supported configs |
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├── `div2k` – 10 RGB PNG images (HR or ×4 LR) |
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├── `ct` – single chest CT slice (PNG) |
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├── `spheres` – generated sparse-sphere .npy grids for 5 seeds |
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├── `bandlimited` – band-limited white-noise .npy grids for 5 seeds |
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├── `sierpinski` – 9 depth levels of Sierpinski triangle .npy |
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└── `star_target` – 1 synthetic star-resolution target .npy |
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The loader intentionally returns **file paths** so that 2-D PNGs and 2-/3-D NPYs |
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coexist without coercing them into a single Arrow schema. |
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""" |
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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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_HOMEPAGE = "https://huggingface.co/datasets/etoilekim/INR-benchmark" |
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_LICENSE = "CC-BY-4.0" |
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_CONFIG_MAP: Dict[str, List[Tuple[str, str]]] = { |
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"div2k": [(f"DIV2K/{name}.png") for i, name in enumerate( |
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["0064", "0007", "0010", "0029", "0063", "0072", "0079", "0088", "0093", "0131"] |
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)], |
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"ct": [("chest.png")], |
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"spheres": [(seed, f"SparseSphereSignal/{seed}/*.npy") for seed in ["1234", "2024", "5678", "7618", "7890"]], |
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"bandlimited": [(seed, f"BandlimitedSignal/{seed}/*.npy") for seed in ["1234", "2024", "5678", "7618", "7890"]], |
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"sierpinski": [(f"sierpinski_triangle/*{i}.npy") for i in range(9)], |
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"star_target": [("star_target", "star_resolution_target.npy")], |
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} |
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class INRBenchmark(datasets.GeneratorBasedBuilder): |
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"""GeneratorBasedBuilder with one config per logical subset.""" |
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BUILDER_CONFIGS = [datasets.BuilderConfig(name=cfg, version=datasets.Version("1.0.0")) |
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for cfg in _CONFIG_MAP.keys()] |
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DEFAULT_CONFIG_NAME = "div2k" |
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def _info(self) -> datasets.DatasetInfo: |
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return datasets.DatasetInfo( |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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description="INR-benchmark: collection of synthetic & real signals for implicit neural representation research.", |
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features=datasets.Features({ |
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"file_path": datasets.Value("string"), |
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}), |
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) |
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def _split_generators(self, dl_manager: datasets.download.DownloadManager): |
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cfg_name = self.config.name |
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if cfg_name not in _CONFIG_MAP: |
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raise ValueError(f"Unknown config: {cfg_name}") |
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base_dir = Path(dl_manager.download_and_extract(".")) |
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splits = [] |
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for split_name, pattern in _CONFIG_MAP[cfg_name]: |
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abs_pattern = base_dir / pattern |
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splits.append( |
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datasets.SplitGenerator(name=split_name, gen_kwargs={"glob_pattern": abs_pattern}) |
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) |
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return splits |
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def _generate_examples(self, glob_pattern: Path): |
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"""Yields index, {file_path} for each matched file.""" |
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files = sorted(Path().glob(str(glob_pattern))) |
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if not files: |
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if glob_pattern.exists(): |
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files = [glob_pattern] |
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else: |
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raise FileNotFoundError(f"No files matched pattern: {glob_pattern}") |
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for idx, path in enumerate(files): |
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yield idx, {"file_path": str(path.relative_to(Path.cwd()))} |
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