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name: classification-workflows description: "Build, validate, and troubleshoot PointNet2 ModelNet40 classification training and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

classification-workflows

Use this sub-skill when the user asks for ModelNet40 classification with this PointNet2 repository: single-GPU training, multi-GPU training, checkpoint evaluation, voting, ModelNet data layout checks, or command construction.

What this sub-skill owns

• ModelNet40 classification workflows driven by train.py, train_multi_gpu.py, and evaluate.py.
• Dataset routing between default HDF5 ModelNet40 data and normal-resampled ModelNet40 text files.
• Model routing between pointnet2_cls_ssg, pointnet2_cls_msg, and the CPU-friendly baseline pointnet_cls_basic.
• Checkpoint path, --num_votes, --num_point, --normal, --batch_size, and multi-GPU command details.

Do not use this sub-skill for ShapeNetPart, ScanNet, visualization, or custom-op compilation. If the user is debugging TensorFlow custom operators, shared PointNet APIs, or a CPU-only baseline graph smoke, route to the sibling model-apis-and-custom-ops sub-skill; this sub-skill only explains how that backend choice affects classification commands.

Fast route

1. Choose the dataset mode.

   • Default HDF5 mode uses data/modelnet40_ply_hdf5_2048/, XYZ only, and --num_point <= 2048.
   • Normal-resampled mode uses data/modelnet40_normal_resampled/, XYZ+normal text files, and --num_point <= 10000. The stock source loaders return 6 channels when --normal is set; the stock model files observed here declare BxNx3 placeholders, so normal-channel experiments require a model adaptation before a real run.

2. Choose the model.

   • pointnet2_cls_ssg: default PointNet++ single-scale grouping classifier; requires the shared custom-op backend.
   • pointnet2_cls_msg: PointNet++ multi-scale grouping classifier; also requires the shared custom-op backend.
   • pointnet_cls_basic: PointNet v1 baseline; best for CPU smoke/baseline work when PointNet++ custom ops are unavailable.

3. Validate the data layout before importing repo loaders. The HDF5 loader has a top-level auto-download side effect when its folder is missing. Prefer the bundled validator:

   python sub-skills/classification-workflows/scripts/validate_modelnet_layout.py --mode h5 --repo-root . --num-point 1024
   python sub-skills/classification-workflows/scripts/validate_modelnet_layout.py --mode normal --repo-root . --num-point 5000
   

4. Build a command instead of editing legacy scripts by hand.

   python sub-skills/classification-workflows/scripts/build_classification_command.py --action train --dataset-mode h5 --model pointnet2_cls_ssg --num-point 1024 --log-dir log_cls_ssg
   python sub-skills/classification-workflows/scripts/build_classification_command.py --action train-multi-gpu --dataset-mode h5 --model pointnet2_cls_msg --num-gpus 2 --cuda-visible-devices 0,1 --batch-size 32 --log-dir log_cls_msg_2gpu
   python sub-skills/classification-workflows/scripts/build_classification_command.py --action evaluate --dataset-mode h5 --model pointnet2_cls_ssg --model-path log_cls_ssg/model.ckpt --dump-dir dump_cls_ssg --num-votes 12
   

5. Run the generated command from a compatible PointNet2 checkout. For PointNet++ models, verify the shared TensorFlow custom-op backend first. For a CPU-only sanity path, generate the command with --model pointnet_cls_basic and keep --num_votes 1 for evaluation.

Bundled references and scripts

• references/workflows.md: command workflows, model selection, checkpoints, voting, and multi-GPU behavior.
• references/data-formats.md: ModelNet40 HDF5 and normal-resampled layouts, loader behavior, and custom-data rules.
• references/troubleshooting.md: common classification failures and recovery steps.
• scripts/build_classification_command.py: safe command builder for training, multi-GPU training, and evaluation.
• scripts/validate_modelnet_layout.py: offline layout validator that avoids the HDF5 loader's auto-download side effect.
• scripts/smoke_modelnet_loader.py: tiny CPU data-loader smoke independent of the repo's TensorFlow code.

Minimum handoff for a classification task

When handing a command to the user, include:

• Dataset mode and validated layout path.
• Model name and whether PointNet++ custom ops are required.
• Exact command, generated by the bundled builder when possible.
• For evaluation: checkpoint prefix, dump directory, and --num_votes rationale.
• Any unresolved backend or normal-channel shape risk.