brainstorming
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
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Desarrollo · SKILL.md
training-and-evaluation
training-and-evaluationPlan and safely launch VoxFormer stage-1 QPN and stage-2 train/test workflows, including distributed execution, checkpoints, validation, outputs, and SSC evaluation.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentación de la Skill
GitHubname: 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
SSCMetricsimports, 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
- Identify stage and variant (
qpn.py,voxformer-S.py, orvoxformer-T.py; use the model-configuration sub-skill for variant details). - Validate environment/backend, dataset layout, labels, depth/pseudo-voxel and query artifacts with the sibling environment and dataset sub-skills.
- Choose a fresh work directory or explicitly confirm resume/overwrite policy.
- 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, runscripts/preflight_train_test.pywith the config and checkpoint paths. It only reads metadata/existence and prints a plan; it never launches, downloads, creates directories, or overwrites results. - Review the generated command, GPU count, port, checkpoint/config pairing, output paths, and expected validation behavior with the operator.
- 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.