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name: runtime-and-custom-ops description: "Set up and diagnose the legacy TensorFlow runtime and PointNet++ custom operators used by Frustum PointNets." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Runtime and custom operators

Use this route for installation, version compatibility, TensorFlow graph-mode imports, CUDA/toolkit decisions, and the custom operators used by the v2 model. It is deliberately conservative: the repository is a Python-2/early-TensorFlow release, not a modern pip package.

Choose the runtime

1. Prefer an isolated legacy environment. The repository documents Python 2.7 with TensorFlow 1.2/1.4, while the verified inspection baseline is Python 3.7 with TensorFlow 1.15.5, NumPy 1.18.5, SciPy 1.4.1, OpenCV 4.5.5, Pillow 8.4.0, and protobuf 3.20.3.
2. For source-level KITTI geometry and v1 graph inspection, a CPU TensorFlow 1.x environment is sufficient. It does not validate v2 custom ops.
3. For v2, training, or meaningful inference throughput, require a compatible CUDA TensorFlow build, compiler/toolkit, and all three compiled operators. A visible NVIDIA GPU is not proof that the old TensorFlow build can use it.
4. Run the bundled diagnostic before changing packages: python scripts/check_legacy_runtime.py --json.

Read compatibility for version choices and custom operators for build and load checks. Read troubleshooting whenever an import or loader fails.

Custom-op boundary

models/pointnet_util.py imports sampling, grouping, and 3D-interpolation wrappers. Their Python modules call tf.load_op_library on local shared objects, so v2 cannot be treated as a pure-Python CPU workflow. Build and test operators in an isolated copy, discover TensorFlow's include/library paths from the selected environment, and never paste machine-specific paths from an old shell script into a reusable setup.

Safe verification

Verify in this order: Python/TensorFlow import, graph-mode placeholder/session smoke, framework device listing, compiler/toolkit visibility, one operator load smoke, then the native op tests. Stop at the first failed gate. The current generated skill has verified only the CPU graph smoke; CUDA and the old custom-op ABI remain explicitly unverified. Route data preparation to ../kitti-data-preparation/SKILL.md, training to ../training/SKILL.md, and KITTI scoring to ../inference-and-evaluation/SKILL.md.