---

name: mmaction2 description: "Use MMAction2 for video understanding inference, datasets/configs, training/evaluation, models, registries, and deployment planning." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMAction2 repo skill

Use this skill when a task involves MMAction2, the OpenMMLab video-understanding toolbox for action recognition, skeleton/audio/video-text workflows, temporal localization, spatio-temporal action detection, model zoo configs, training/testing, evaluation, or registry-backed customization.

Start with the right sub-skill

• sub-skills/inference-and-demos/SKILL.md — recognizer inference, init_recognizer, inference_recognizer, ActionRecogInferencer, MMAction2Inferencer, label maps, prediction dumps, visualization, and optional detector/pose-assisted demos.
• sub-skills/data-and-configs/SKILL.md — dataset annotation formats, VideoDataset/RawframeDataset/PoseDataset/AVADataset/ActivityNetDataset, transform pipelines, config inheritance, naming, and --cfg-options overrides.
• sub-skills/training-and-evaluation/SKILL.md — training, testing, work directories, checkpoints, resume, AMP, CPU/GPU/distributed/Slurm command planning, metrics, dumps, and offline result analysis.
• sub-skills/models-and-extension/SKILL.md — model families, model-index categories, MMAction2 registries, custom modules/datasets/pipelines, project-style extensions, export, publishing, and deployment caveats.

Install and import baseline

Read references/environment-and-installation.md before diagnosing setup problems. The public baseline is:

pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0rc4,<2.2.0"
pip install mmaction2

For source-based development or access to the full tool/config tree, install the user's chosen MMAction2 checkout in editable mode after installing compatible PyTorch, MMEngine, and MMCV. CPU mode is supported for many smoke checks and small workflows; CUDA is optional unless the user asks for GPU-scale training/inference or a backend-specific native case.

Minimal import check:

import mmaction, mmcv, mmengine, torch
print(mmaction.__version__, mmcv.__version__, mmengine.__version__, torch.__version__)

Run scripts/check_mmaction2_environment.py when the task starts with installation, optional dependencies, CUDA availability, or config parse uncertainty.

Operating rules

1. Prefer config/API inspection and command previews before launching expensive video workloads, training, distributed jobs, checkpoint conversion, or dataset-scale preprocessing.
2. Pass device="cpu" explicitly on CPU-only hosts; several MMAction2 APIs default to CUDA-like devices.
3. Treat model aliases, remote checkpoints, dataset downloads, and pretrained-weight URLs as network actions. Ask before relying on them when the user did not already authorize network/download work.
4. Keep data/config issues separate from train/test failures: first validate annotation schema, data_prefix, pipeline decode type, class count, and config inheritance, then plan a training/test run.
5. For optional workflows, name the missing extra precisely: mmdet/mmpose for detector/pose-assisted demos, openai-clip/multimodal packages for some multimodal surfaces, ONNX/TorchServe packages for export/deployment helpers.
6. Do not claim that GPU, Slurm, deployment, or optional dependency flows are verified unless they were actually run in the user's current environment.

Common task routing

User task	Read
"Run recognition on a video" / "use MMAction2Inferencer"	inference-and-demos
"What does pred_score contain?" / "map top-k labels"	inference-and-demos API reference
"Prepare custom videos/rawframes/skeleton/AVA data"	data-and-configs
"Fix --cfg-options or config inheritance"	data-and-configs
"Preview a CPU/GPU train/test command"	training-and-evaluation command builder
"Resume training or dump test predictions"	training-and-evaluation
"Which model family/config should I use?"	models-and-extension, then data-and-configs if config editing is needed
"Register a custom backbone/head/dataset/transform"	models-and-extension
"Export/publish/convert a checkpoint"	models-and-extension export/deployment reference
"Is this generated skill current for my checkout?"	references/repo-provenance.md

Shared references and helpers

• references/environment-and-installation.md — public installation modes, dependency variants, optional extras, and safe environment checks.
• references/troubleshooting.md — cross-cutting import, dependency, backend, data/config, checkpoint, and optional-extra failures.
• references/repo-provenance.md — source snapshot used to generate this skill; read it before deciding whether to refresh the skill.
• references/repo-routing-metadata.json — structured scenario metadata for managed repo-skill routing.
• scripts/check_mmaction2_environment.py — safe import/version/backend/config probe.