---

name: model-apis-and-custom-ops description: "Shared PointNet2 TensorFlow layer APIs, PointNet++ set-abstraction/feature-propagation blocks, point-cloud utilities, custom-op readiness, visualization helper build notes, and the CPU PointNet baseline." disable-model-invocation: true metadata: disco-role: operating repo: pointnet2 responsibility: shared-model-apis-custom-ops license: NOASSERTION

model-apis-and-custom-ops

Use this sub-skill when the task is about PointNet2's shared TensorFlow model-building APIs, custom TensorFlow operators, point-cloud geometry utilities, visualization helpers, or the CPU-safe pointnet_cls_basic baseline.

Use this route for

• Building or modifying TensorFlow 1.x layer stacks that use utils/tf_util.py wrappers.
• Understanding pointnet_util.py set-abstraction (SA), multi-scale grouping (MSG), and feature-propagation (FP) call patterns used by PointNet++ models.
• Checking whether tf_sampling_so.so, tf_grouping_so.so, and tf_interpolate_so.so exist and can be loaded.
• Running a CPU-only graph smoke for models/pointnet_cls_basic.py.
• Using or debugging utils/provider.py, utils/pc_util.py, utils/show3d_balls.py, and render_balls_so.so.
• Explaining why TensorFlow import success is not the same as PointNet++ custom-op readiness.

Do not use this route for

• ModelNet40 training/evaluation command construction. Use ../classification-workflows/.
• ShapeNetPart training, evaluation, or dataset layout checks. Use ../part-segmentation-workflows/.
• ScanNet preprocessing, pickle layout, or semantic scene workflows. Use ../scannet-semantic-scene-workflows/.
• Full legacy GPU training as a native verification claim unless the custom-op backend has been separately prepared and proven.

Read first

• references/api-reference.md for exact layer/model signatures, tensor shapes, and model consumer patterns.
• references/custom-ops.md for custom TensorFlow op names, .so locations, original compile assumptions, and health-check workflow.
• references/utilities.md for point-cloud augmentation, geometry conversion, PLY I/O, and visualization-helper behavior.
• references/troubleshooting.md for TensorFlow 1.x/TF2, missing dependency, ABI, missing .so, and renderer failure diagnoses.
• references/source-map.md for the source evidence and verified environment facts behind this sub-skill.

Skill-owned scripts

• scripts/inspect_custom_ops.py — reports TensorFlow import state, expected custom-op files, optional tf.load_op_library results, nvcc/g++ availability, and portable compile hints.
• scripts/smoke_pointnet_baseline.py — imports the checkout's pointnet_cls_basic, builds a [batch_size, 40] TF1 CPU graph, and optionally runs one session step.
• scripts/smoke_geometry_utils.py — runs deterministic tiny-array checks for provider/geometry helpers and separates missing eulerangles/plyfile dependencies from real data-shape failures.
• scripts/compile_render_balls_so.sh — safe wrapper around the renderer build recipe for utils/render_balls_so.cpp; supports --dry-run and explicit source/output paths.

Typical workflows

Run these examples from the pointnet2 skill root so the skill-owned script paths resolve.

CPU-safe baseline graph

python sub-skills/model-apis-and-custom-ops/scripts/smoke_pointnet_baseline.py \
  --repo-root /path/to/pointnet2 --batch-size 2 --num-point 16

Expected success signal: TensorFlow 1.x with tf.contrib imports, pointnet_cls_basic.get_model() builds, and the output tensor/static shape is [2, 40]. This baseline does not require PointNet++ custom ops.

Custom-op readiness check

python sub-skills/model-apis-and-custom-ops/scripts/inspect_custom_ops.py \
  --repo-root /path/to/pointnet2 --require tensorflow
python sub-skills/model-apis-and-custom-ops/scripts/inspect_custom_ops.py \
  --repo-root /path/to/pointnet2 --try-load --require custom-ops

Use the first command to prove TensorFlow metadata. Use the second only when the .so files are expected to exist; it distinguishes missing libraries, load/ABI errors, and TensorFlow-only success.

Geometry utility smoke

python sub-skills/model-apis-and-custom-ops/scripts/smoke_geometry_utils.py \
  --repo-root /path/to/pointnet2

Expected success signal: provider transformations preserve expected shapes and point-cloud conversions/PLY round-trip work. If eulerangles or plyfile is missing, fix those packages before treating failures as data-shape errors.

Renderer compile helper

bash sub-skills/model-apis-and-custom-ops/scripts/compile_render_balls_so.sh \
  --repo-root /path/to/pointnet2 --dry-run
bash sub-skills/model-apis-and-custom-ops/scripts/compile_render_balls_so.sh \
  --repo-root /path/to/pointnet2 --out-dir /path/to/pointnet2/utils

The helper builds only render_balls_so.so for show3d_balls.py; it does not build the TensorFlow PointNet++ ops.

Verification anchors

• pointnet-basic-model-graph: CPU/TF1 graph build for models/pointnet_cls_basic.py, expected output shape [B, 40].
• point-cloud-utility-smoke: NumPy/geometry helper smoke on tiny arrays.
• custom-op-op-tests: optional native tests under tf_ops/*/*_op_test.py, only after compatible compiled ops exist.
• pointnet2-custom-op-model-graphs: optional PointNet++ graph builds that require custom-op import success.

Cross-links for consumers

• Classification PointNet++ models (pointnet2_cls_ssg, pointnet2_cls_msg) consume pointnet_sa_module, pointnet_sa_module_msg, tf_util.fully_connected, and tf_util.dropout; route command/data questions to ../classification-workflows/.
• Part segmentation models consume pointnet_sa_module, pointnet_fp_module, and normal-aware point clouds; route ShapeNetPart data/workflow questions to ../part-segmentation-workflows/.
• ScanNet semantic segmentation consumes pointnet_sa_module, pointnet_fp_module, and weighted sparse softmax loss; route ScanNet data/preprocessing questions to ../scannet-semantic-scene-workflows/.

Hard limits to keep explicit

• The repository is TensorFlow 1.x-era code. tf.contrib is required for the provided batch norm and Xavier initializer paths.
• The Python wrappers in tf_ops/* call tf.load_op_library(...) at import time; a missing .so blocks PointNet++ models before graph construction.
• Original custom-op compile scripts hard-code CUDA 8.0 and Python 2.7 TensorFlow include paths. Modern CUDA drivers/GPU visibility do not prove ABI compatibility.
• show3d_balls.py opens an OpenCV window and loads render_balls_so at import time; do not import it blindly in headless environments.