Create INRBenchmark.py

INRBenchmark.py

@@ -0,0 +1,88 @@
+"""
+Custom Hugging Face dataset loader for the INR-benchmark repository.
+
+* Place this file in the root of the dataset repo alongside README.md.
+* Use `load_dataset("username/INR-benchmark", "spheres", split="1234", trust_remote_code=True)`.
+* Each example yields only the **file path** to keep memory/lightweight; users can `np.load` or `cv2.imread` themselves.
+
+Supported configs  
+ ├── `div2k`        – 10 RGB PNG images (HR or ×4 LR)  
+ ├── `ct`           – single chest CT slice (PNG)  
+ ├── `spheres`      – generated sparse-sphere .npy grids for 5 seeds  
+ ├── `bandlimited`  – band-limited white-noise .npy grids for 5 seeds  
+ ├── `sierpinski`   – 9 depth levels of Sierpinski triangle .npy  
+ └── `star_target`  – 1 synthetic star-resolution target .npy
+
+The loader intentionally returns **file paths** so that 2-D PNGs and 2-/3-D NPYs
+coexist without coercing them into a single Arrow schema.
+"""
+
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+_HOMEPAGE = "https://huggingface.co/datasets/etoilekim/INR-benchmark"
+_LICENSE = "CC-BY-4.0"
+
+# Mapping config → {split → glob-pattern list}
+_CONFIG_MAP: Dict[str, List[Tuple[str, str]]] = {
+    "div2k": [(str(i), f"DIV2K/{name}.png") for i, name in enumerate(
+        ["0064", "0007", "0010", "0029", "0063", "0072", "0079", "0088", "0093", "0131"]
+    )],
+    "ct": [("slice", "chest.png")],
+    "spheres": [(seed, f"SparseSphereSignal/{seed}/*.npy") for seed in ["1234", "2024", "5678", "7618", "7890"]],
+    "bandlimited": [(seed, f"BandlimitedSignal/{seed}/*.npy") for seed in ["1234", "2024", "5678", "7618", "7890"]],
+    "sierpinski": [(f"0.{i+1}", f"sierpinski_triangle/*{i}.npy") for i in range(9)],
+    "star_target": [("train", "star_resolution_target.npy")],
+}
+
+
+class INRBenchmark(datasets.GeneratorBasedBuilder):
+    """GeneratorBasedBuilder with one config per logical subset."""
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name=cfg, version=datasets.Version("1.0.0"))
+                       for cfg in _CONFIG_MAP.keys()]
+
+    DEFAULT_CONFIG_NAME = "div2k"
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            description="INR-benchmark: collection of synthetic & real signals for implicit neural representation research.",
+            features=datasets.Features({
+                "file_path": datasets.Value("string"),
+            }),
+        )
+
+    def _split_generators(self, dl_manager: datasets.download.DownloadManager):
+        cfg_name = self.config.name
+        if cfg_name not in _CONFIG_MAP:
+            raise ValueError(f"Unknown config: {cfg_name}")
+
+        # Ensure local path (no remote download; dataset files live in repo)
+        base_dir = Path(dl_manager.download_and_extract("."))
+
+        splits = []
+        for split_name, pattern in _CONFIG_MAP[cfg_name]:
+            abs_pattern = base_dir / pattern
+            # keep as glob pattern; actual resolution happens in _generate_examples
+            splits.append(
+                datasets.SplitGenerator(name=split_name, gen_kwargs={"glob_pattern": abs_pattern})
+            )
+        return splits
+
+    def _generate_examples(self, glob_pattern: Path):
+        """Yields index, {file_path} for each matched file."""
+        files = sorted(Path().glob(str(glob_pattern)))
+        if not files:
+            # allow single file pattern without wildcard
+            if glob_pattern.exists():
+                files = [glob_pattern]
+            else:
+                raise FileNotFoundError(f"No files matched pattern: {glob_pattern}")
+
+        for idx, path in enumerate(files):
+            yield idx, {"file_path": str(path.relative_to(Path.cwd()))}