Download code/tt_diffusion_planner/tests/_research.py from changh95/diffusion-planner-p150: direct link, hf CLI and curl.
- Browser
- Download file 1.92 kB
-
https://huggingface.co/changh95/diffusion-planner-p150/resolve/main/code/tt_diffusion_planner/tests/_research.py
- Command line
-
hf download hf://changh95/diffusion-planner-p150/code/tt_diffusion_planner/tests/_research.py
-
curl -L -o _research.py https://huggingface.co/changh95/diffusion-planner-p150/resolve/main/code/tt_diffusion_planner/tests/_research.py
1.92 kB
| # SPDX-License-Identifier: Apache-2.0 | |
| """Access to the verified research scripts of the porting workspace (``research/diffusion-planner/scripts``), the | |
| independent implementations the host code is unit-tested against. Absent in an installed package or the image: | |
| tests that need them skip.""" | |
| from __future__ import annotations | |
| import importlib.util | |
| import sys | |
| from pathlib import Path | |
| from typing import Optional | |
| import numpy as np | |
| PKG = Path(__file__).resolve().parents[1] | |
| RESEARCH = PKG.parents[3] / "research" / "diffusion-planner" | |
| SCRIPTS = RESEARCH / "scripts" | |
| ORT_GOLDENS = RESEARCH / "ort" | |
| FULL_GOLDENS = RESEARCH / "goldens" | |
| SAMPLES = PKG / "samples" | |
| SMALL_GOLDENS = PKG / "tests" / "goldens" | |
| def load_script(name: str): | |
| """Import ``research/diffusion-planner/scripts/<name>.py`` by path (None when absent). Its directory is put on | |
| ``sys.path`` only while it imports (``dp_scene`` imports ``dp_common`` by name).""" | |
| path = SCRIPTS / f"{name}.py" | |
| if not path.is_file(): | |
| return None | |
| key = f"_dp_research_{name}" | |
| if key in sys.modules: | |
| return sys.modules[key] | |
| spec = importlib.util.spec_from_file_location(key, path) | |
| mod = importlib.util.module_from_spec(spec) | |
| sys.path.insert(0, str(SCRIPTS)) | |
| try: | |
| spec.loader.exec_module(mod) | |
| finally: | |
| sys.path.remove(str(SCRIPTS)) | |
| sys.modules[key] = mod | |
| return mod | |
| def sample_raw(stem: str) -> dict: | |
| with np.load(SAMPLES / f"{stem}.npz") as z: | |
| return {k: np.array(z[k]) for k in z.files} | |
| def research_scene_raw(scene: str) -> Optional[dict]: | |
| path = ORT_GOLDENS / f"golden_{scene}.npz" | |
| if not path.is_file(): | |
| return None | |
| with np.load(path) as z: | |
| return {k[len("raw/"):]: np.array(z[k]) for k in z.files if k.startswith("raw/")} | |
| def research_scenes() -> list: | |
| return sorted(p.stem[len("golden_"):] for p in ORT_GOLDENS.glob("golden_*.npz")) | |