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name: training-evaluation description: "Guides safe MapTR training and chamfer-evaluation command construction, preflight validation, checkpoint handling, reproducibility, and resource-aware recovery." disable-model-invocation: true metadata: disco-role: operating license: MIT

MapTR Training And Evaluation

Use this sub-skill to construct and review commands for MapTR training or vector-map evaluation. It is deliberately conservative: prove inputs and backend readiness before starting GPU work, prefer dry runs, and never infer that a documented command succeeded on the current system.

Scope And Evidence Status

This guidance covers the MapTR main behavior represented by commit a6872d8d9670bde17b4b01560f1221f88b443d55:

• single-process and one-node distributed training;
• one-node distributed test/evaluation with the MapTR chamfer metric;
• work directories, seeds, deterministic mode, resume, FP16 configuration, checkpoints, result directories, and failure recovery.

The project documents Python 3.8, PyTorch 1.9.1 with CUDA 11.1, mmcv-full==1.4.0, mmdet==2.14.0, mmsegmentation==0.14.1, the bundled mmdetection3d 0.17.2 code, and a compiled GeometryKernelAttention extension. Those are documented compatibility targets, not proof that a current host can import the plugin or run its CUDA kernels. Full data, checkpoints, training, evaluation, and the custom extension were not executed while this skill was produced.

Route First

• For nuScenes/AV2 download, conversion, annotation files, and directory layouts, route to data-preparation.
• For architecture selection, BEV/query settings, losses, or config authoring, route to model-configuration.
• For image/video rendering, qualitative output, or log plotting, route to visualization-benchmarking.
• Stay here for command semantics, launch sizing, metric selection, checkpoint/output behavior, and run-time troubleshooting.

Required Run Contract

Before constructing a launch, obtain all of these values. Keep an unknown as a stop condition rather than filling it with a guess.

1. Intent: train, resume training, or evaluate.
2. Config: exact MapTR config and any --cfg-options overrides.
3. Data: dataset family and proof that every configured annotation/map file and camera asset exists.
4. Weights: required backbone initialization for a new run, or the exact resume/evaluation checkpoint.
5. Backend: compatible legacy OpenMMLab stack, CUDA, mmcv-full ops, and GeometryKernelAttention import/build evidence.
6. Resources: visible GPU count, memory per GPU, requested process count, storage, wall-time, and scheduler rules.
7. Outputs: unique --work-dir, checkpoint retention plan, and evaluation result location.
8. Reproducibility: seed, deterministic policy, final merged config, source revision, and environment inventory.

Read workflows.md for complete gates, commands, and expected observations. Read cli-reference.md before adding flags or overrides.

Safe Command Procedure

1. Stop Before Compute

From the MapTR project root, run only parser/config checks first:

python tools/train.py --help
python tools/test.py --help
python tools/misc/print_config.py projects/configs/maptr/maptr_tiny_r50_24e.py

The config print should show plugin=True, MapTR dataset/model types, evaluation.metric='chamfer', and resolved data/checkpoint paths. Printing a config does not import the plugin, read all data, build CUDA extensions, or prove training readiness.

Do not proceed when a path is missing, an override is unclear, requested GPUs exceed visible GPUs, the config/checkpoint pairing is unverified, or a required CUDA/custom-op probe has not passed.

2. Select The Smallest Launch

For a supported single-process training attempt:

python tools/train.py CONFIG.py \
  --work-dir work_dirs/RUN_ID \
  --gpus 1 --seed 42 --deterministic --launcher none

For one-node distributed training, use the bundled validator from this sub-skill directory. It prints a command by default and starts nothing:

python scripts/launch_distributed.py train \
  --project-root MAPTR_ROOT --config projects/configs/maptr/CONFIG.py \
  --gpus N -- --work-dir work_dirs/RUN_ID --seed 42

For evaluation, use the same validator even with one GPU. The checked-in test entry point intentionally rejects --launcher none:

python scripts/launch_distributed.py test \
  --project-root MAPTR_ROOT --config projects/configs/maptr/CONFIG.py \
  --checkpoint work_dirs/RUN_ID/epoch_N.pth --gpus N

The test command appends --eval chamfer. Never substitute the generic 3D-detection bbox launcher for MapTR. The vector-map datasets accept chamfer and iou; project train/eval instructions and MapTR configs use chamfer.

Review launch_distributed.py, then add --execute only after its dry-run output and warnings pass human review. Execution can allocate GPUs and run for hours.

3. Preserve Run Evidence

A training work directory should receive the merged config, timestamped text log, logger outputs, and checkpoints such as epoch_1.pth according to checkpoint_config.interval. MapTR 24-epoch configs normally evaluate every two epochs and save every epoch. Confirm actual config values rather than assuming those intervals for another config.

Chamfer evaluation should print per-class AP at thresholds 0.5, 1.0, and 1.5, plus NuscMap_chamfer/mAP-style detail keys, and write formatted predictions below a timestamped test/<config-name>/ directory. Treat those as expected shapes only, not benchmark claims.

Record the exact command, CUDA_VISIBLE_DEVICES, seed, deterministic flag, merged config, checkpoint identity, environment versions, and complete log. Do not compare scores from different configs, class maps, point sampling, data versions, or checkpoints as if they were equivalent.

High-Risk Semantics

• --resume-from restores training state; a nonexistent path is silently not applied by the training entry point. Validate it before launch.
• A backbone pretrained path initializes a new model; it is not a training resume checkpoint. load_from loads weights without promising optimizer and epoch continuity.
• Most MapTR configs enable static FP16 with loss_scale=512. Confirm the merged config and backend support. Do not add a second AMP mechanism.
• The distributed training wrapper forces deterministic mode. Determinism may reduce speed and does not guarantee bitwise identity across different GPU, driver, CUDA, library, or process-count combinations.
• tools/test.py --out is parsed but reaches an intentional assertion instead of dumping predictions. --show/--show-dir are not forwarded through its distributed test path. Route artifact visualization elsewhere.

Recovery

Use troubleshooting.md for symptoms, causes, recovery, and explicit stop conditions. In particular:

• metric bbox is not supported → replace --eval bbox with --eval chamfer; do not reinterpret a detector metric as a map metric.
• eight requested processes with one visible GPU → stop and use --gpus 1, or move to a proven eight-GPU host; never oversubscribe silently.
• resume/config mismatch → stop, locate the config dumped beside the checkpoint, reconcile its model/data/class/optimizer/runner settings, and resume with that config or start a clearly new run.

Do not run merely to discover whether a prerequisite was satisfied. If configuration, data, checkpoint provenance, backend compatibility, GPU count, or budget remains unresolved, return a blocked preflight with the missing evidence and the next safe probe.