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name: part-segmentation-workflows description: "ShapeNetPart part-segmentation training, evaluation, inference, data layout, and one-hot command workflow guidance for the pointnet2 repository." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Part Segmentation Workflows

Use this sub-skill when the user asks about ShapeNetPart object part segmentation in this PointNet++ repository: training on the official ShapeNetPart normal dataset, evaluating a checkpoint, choosing the one-hot category-conditioned variant, or adapting the legacy single-category visualization path.

Route first by workflow

• All-category plain segmentation: use part_seg/train.py with model pointnet2_part_seg; evaluate an existing checkpoint with part_seg/evaluate.py. Start with references/workflows.md.
• All-category one-hot segmentation: use part_seg/train_one_hot.py with model pointnet2_part_seg_msg_one_hot; the dataset must return a per-shape category label in addition to per-point part labels. Start with references/workflows.md.
• Single-category visualization / test-time inference: treat part_seg/test.py as a legacy reference, not a reliable drop-in script, because it has a ROOT_DIR bug and stale dataset/model assumptions. Start with references/workflows.md.
• Dataset diagnosis before training: run the bundled validator in scripts/validate_shapenetpart_layout.py and read references/data-formats.md.
• Command construction: prefer scripts/build_part_seg_command.py over copying the original background shell snippets.

Mandatory checks before suggesting a run

1. Confirm the user has the ShapeNetPart root with synsetoffset2category.txt, train_test_split/*.json, and category folders matching the intended loader. See references/data-formats.md.
2. Confirm whether the user wants plain or one-hot training. Do not pass the one-hot model to evaluate.py; the source evaluator only builds the plain model interface.
3. Confirm the runtime can import TensorFlow 1.x and the PointNet++ custom ops needed by pointnet_sa_module, pointnet_sa_module_msg, and pointnet_fp_module. CPU-only validation is enough for data/command checks, but native model execution is a legacy CUDA/custom-op path.
4. For visualization requests, explain that test.py needs patching before use; do not present it as a verified runnable script. See references/troubleshooting.md.

Bundled helpers

• scripts/build_part_seg_command.py builds shell commands for plain training, one-hot training, plain evaluation, and legacy test visualization with safer defaults and workflow guards.
• scripts/validate_shapenetpart_layout.py validates ShapeNetPart normal and legacy points/points_label layouts, split JSON membership, empty selected splits, and basic label/column schema.
• scripts/smoke_shapenetpart_loader.py creates a tiny temporary ShapeNetPart-style fixture and runs the validator; use it to check the bundled validator itself without requiring the real dataset.

Evidence and limits

This sub-skill distills repository evidence from the README, part_seg/command*.sh, part_seg/train*.py, part_seg/evaluate.py, part_seg/test.py, part_seg/part_dataset*.py, and the two pointnet2_part_seg* model definitions. It intentionally does not copy the raw trainers because they are Python-2-era, checkpoint/data/GPU heavy, and contain repo-relative assumptions. Workflow-specific failure modes are in references/troubleshooting.md; data contracts are in references/data-formats.md.