---

name: training-and-inference description: "Route legacy SECOND and PointPillars model construction, training, evaluation, checkpoint restoration, and inference with explicit configuration, device, and backend guards." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and inference

Use this sub-skill when a task asks to train or evaluate SECOND/PointPillars, build a VoxelNet, restore a .tckpt, run inference, select a model config, or reason about multi-GPU, fp16, NMS, or spconv errors.

Safety and compatibility boundary

• This repository is deprecated. Its public README recommends OpenPCDet or MMDetection3D for new work; prefer a maintained implementation unless the historical checkpoint or experiment specifically requires this code.
• The detector route is legacy-backend guarded and unverified. Source code depends on old spconv APIs (VoxelGeneratorV2 and NMS symbols), legacy Numba CUDA behavior, and an old PyTorch integration. A modern spconv 2.x import is not proof of compatibility.
• Run the bundled non-invasive probe before any detector import: python <training-skill-root>/scripts/check_legacy_backend.py. Add --require-detector only when deciding whether to attempt a detector route. Without it, the probe is diagnostic and returns success even when the legacy symbols are absent.
• CUDA availability, a successful CUDA tensor smoke test, or a CPU import does not prove that sparse model construction, NMS, training, or inference works. Do not claim detector execution was verified from those observations.
• This package has no setup metadata. Use an explicit, isolated environment only when a separately supplied compatible checkout is intentionally in scope. Do not assume a package install or silently mutate an existing environment. This skill does not bundle the historical detector entry point.

Route the request

1. Config/data contract: choose a config and validate dataset paths, generated info/database files, class order, voxel range, and point feature count. Route dataset generation and layout preparation to data-preparation.
2. Model/training: map network_class_name, VFE, middle extractor, and RPN names through the registries; use the Fire commands and output semantics in workflows.
3. Evaluation/geometry: keep box encoding, NMS, score thresholds, and evaluator implementation questions with geometry-and-evaluation.
4. Viewer/service: route web API, browser, and server operations to visualization-and-serving; do not start the legacy viewer as a training smoke test.

Reference map

• workflows: Fire commands, guarded train/resume, multi-GPU scaling, evaluation outputs, inference, pretrained loading, and NuScenes tuning notes.
• configuration: config families, protobuf fields, model registries, class order, optimizers, and schedules.
• API reference: public signatures, model methods, builder contracts, freeze filters, and torchplus checkpoints.
• compatibility: exact backend gate, known modern-spconv gaps, and evidence needed to widen executable scope.
• troubleshooting: install/import, optional dependencies, data/config, CLI/API, checkpoint, multi-GPU, fp16, and workflow-specific failures.

Guarded command patterns

First run the bundled non-invasive probe:

python <training-skill-root>/scripts/check_legacy_backend.py
python <training-skill-root>/scripts/check_legacy_backend.py --require-detector

The second command is the detector gate. This skill does not bundle or invoke that historical writer/runner, so no source-checkout command is presented as a runnable recipe. If the probe reports missing legacy symbols, stop before any historical train, evaluate, or TorchInferenceContext attempt; capture the exact missing names and use compatibility for recovery. If a user separately supplies a compatible checkout, the historical argument shapes are documented in workflows.

These are guarded historical routes, not verified recipes. train refuses an existing model_dir unless --resume=True; --create_folder=True creates a new timestamped folder when the requested prefix already exists. Keep model outputs outside the source tree and back up checkpoints before experiments.

Core decisions

• batch_size and num_workers in the training input config are per GPU. The implementation multiplies both by the visible GPU count for --multi_gpu=True; do not multiply the config values yourself.
• The README says to divide train_config.steps and steps_per_eval by the number of GPUs when scaling a single-GPU schedule to multi-GPU. Verify this arithmetic before launching; an undivided schedule changes total optimizer updates and evaluation cadence.
• fp16 requires the historical Apex path and a compatible sparse backend; enable_mixed_precision: true is not equivalent to modern PyTorch AMP. Honor the source assertion that max_number_of_voxels * batch_size < 65535.
• measure_time=True enables CUDA synchronization/timers in model paths and can distort throughput. Use it only for a deliberate timing run.
• evaluate writes result.pkl below <result-path>/step_<global_step> and invokes the dataset evaluator. The historical signature does not accept the README-era --pickle_result keyword; do not pass it to a separately supplied runner unless its help output proves otherwise. Route official label conversion to geometry/evaluation after checking the exact dataset API.
• Checkpoints are named by model/optimizer plus step and indexed in checkpoints.json. Restore the latest compatible state, or pass an explicit .tckpt; never mix a checkpoint with an incompatible config.

For exact signatures, config fields, model registry names, checkpoint utilities, and failure recovery, read the linked references before giving a detailed command or API answer.