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name: maptr description: "Guides agents through MapTR online vectorized HD-map workflows, including environment setup, nuScenes and Argoverse2 preparation, model configuration, training, evaluation, visualization, benchmarking, and compatibility troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

MapTR

MapTR is an OpenMMLab-based camera-centric framework for online vectorized high-definition map construction. Use this skill when a task names MapTR, MapTRv2-compatible concepts, vectorized HD maps, nuScenes/Argoverse2 map annotations, MapTR configs, MapTRHead, MapTRPerceptionTransformer, Geometric Kernel Attention, or the MapTR chamfer evaluator.

This graph is for the main checkout represented by the provenance snapshot, not for the separate maptrv2 branch. Read repository provenance before treating a checkout as current. The generated guidance is self-contained and distinguishes source-backed procedure from native runtime evidence.

Route by task

• Dataset layout, custom annotation preparation, nuScenes CAN bus, AV2 logs, or map-vector schemas: read data-preparation.
• Config selection or edits, model components, BEV encoder variants, point-cloud geometry, registry imports, or custom-op compatibility: read model-configuration.
• Training, distributed launch, checkpoint evaluation, chamfer metrics, reproducibility, FP16, or memory planning: read training-evaluation.
• Prediction rendering, GT display formats, video assembly, throughput, timing, or log analysis: read visualization-benchmarking.

When a request spans routes, validate data first, then config, then launch; visualization and benchmarking consume the resulting checkpoint/artifacts.

Compatibility boundary

The repository documents an older stack: Python 3.8, PyTorch 1.9.1 with CUDA 11.1, mmcv-full==1.4.0, mmdet==2.14.0, mmsegmentation==0.14.1, timm, bundled mmdetection3d==0.17.2, shapely==1.8.5.post1, av2, and a compiled Geometric Kernel Attention extension. Treat these as a compatibility target, not a generic modern install recipe. Do not mix modern MMCV/MMDetection versions into a legacy checkout without checking the compatibility reference.

The full model path needs a CUDA-capable PyTorch runtime, compiled upstream MMCV/MMDetection3D operators, and the MapTR Geometric Kernel Attention extension. A CPU import or a visible NVIDIA device does not prove that path. During construction, static/config/data checks were verified; full model forward, training, evaluation, dataset conversion, checkpoint inference, and custom-op execution remain explicitly unverified.

Minimal safe checks

Use the bundled helpers before expensive actions:

python <skill-root>/sub-skills/data-preparation/scripts/check_dataset_layout.py --help
python <skill-root>/sub-skills/model-configuration/scripts/check_maptr_config.py --help
python <skill-root>/sub-skills/training-evaluation/scripts/launch_distributed.py --help
python <skill-root>/sub-skills/visualization-benchmarking/scripts/make_video.py --self-check

The helpers are path-safe and do not download data or checkpoints. Their references explain when a target checkout, dataset, framework, GPU, or codec is still required. Do not treat a dry-run command as successful model execution.

Operating order

1. Establish the documented dependency/ABI target and run the safe checks.
2. Preflight dataset roots and generated annotation names with the data route.
3. Select one complete model/config family and run the static config checker.
4. Confirm the checkpoint, data, GPU count, custom operators, and output budget.
5. Generate a dry-run command; execute only after reviewing paths and costs.
6. Evaluate with MapTR's vector metric contract, then render or benchmark outputs.
7. Preserve logs, config, checkpoint identity, dataset version, GPU/runtime details, and unresolved compatibility failures with the experiment.

For cross-cutting symptoms such as MMCV version assertions, missing compiled operators, missing data files, invalid config inheritance, or misleading FPS, read troubleshooting. The route-specific references contain the detailed command and schema contracts.