---

name: training-and-evaluation description: "Plan and safely launch VoxFormer stage-1 QPN and stage-2 train/test workflows, including distributed execution, checkpoints, validation, outputs, and SSC evaluation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training and Evaluation

Use this sub-skill for running or planning the repository's stage-1 query proposal network (QPN) and stage-2 VoxFormer training and evaluation. Start with workflow commands, then run the non-launching preflight helper before any expensive command. Use evaluation and metrics for result semantics and troubleshooting for failures.

Hard gates

• This repository documents a two-stage pipeline: stage 1 produces QPN/query artifacts used by stage 2. Do not start stage 2 until its configured query files and labels are present; a stage-1 checkpoint alone is not a substitute for stage-2 query data.
• Full train/test requires a CUDA-capable, compatible legacy OpenMMLab stack, SemanticKITTI data and the required pretrained/checkpoint files. Distributed configs use NCCL. The documented examples use four GPUs; one GPU is a diagnostic/smaller run, not an equivalence claim.
• Never infer successful training or metric values from imports, config loads, or command construction. The prepared environment passed project dependency and SSCMetrics imports, but no full training/evaluation was run and no final metric values are verified without real SemanticKITTI artifacts.
• The repository has no small native train/test test suite. Prefer parser/help, config/static checks, and this preflight helper; do not download data/weights or launch training while creating or verifying this skill.

Operating route

1. Identify stage and variant (qpn.py, voxformer-S.py, or voxformer-T.py; use the model-configuration sub-skill for variant details).
2. Validate environment/backend, dataset layout, labels, depth/pseudo-voxel and query artifacts with the sibling environment and dataset sub-skills.
3. Choose a fresh work directory or explicitly confirm resume/overwrite policy.
4. For a deterministic helper-only smoke check, run python scripts/preflight_train_test.py --self-test. It uses only a private temporary fixture, never launches a subprocess, and does not touch user paths. For an actual request, run scripts/preflight_train_test.py with the config and checkpoint paths. It only reads metadata/existence and prints a plan; it never launches, downloads, creates directories, or overwrites results.
5. Review the generated command, GPU count, port, checkpoint/config pairing, output paths, and expected validation behavior with the operator.
6. Only then run the repository command. Record the exact config, commit, checkpoint, GPU count, work directory, output/result paths, and metrics.

Route boundaries

• Environment, CUDA/MMCV ABI, pretrained placement, and custom deform3D builds: ../environment-and-installation/SKILL.md.
• SemanticKITTI layout, preprocessing, labels, and query suffixes: ../dataset-preparation/SKILL.md.
• Stage/config selection, temporal cameras, and standard versus deform3D: ../model-configuration/SKILL.md.
• Do not use this skill to regenerate data, build optional extensions, fetch weights, or claim a reproduced benchmark.

Bundled resources

• references/workflow-commands.md
• references/evaluation-and-metrics.md
• references/troubleshooting.md
• scripts/preflight_train_test.py