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name: models-and-extension description: "Understand MMAction2 model families, registries, customization, projects, and export/deployment utilities." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMAction2 models and extension router

Use this sub-skill when a task is about MMAction2 model families, registry-backed components, custom components, project-style extensions, multimodal/retrieval/audio/skeleton surfaces, or export/deployment/publishing utilities.

Load these bundled references first

• Model and extension reference — model family map, registries, default scope behavior, custom module patterns, project extension pattern, and class-count/shape cautions.
• Export and deployment reference — publish, checkpoint conversion, ONNX, TorchServe, feature extraction, and optional-extra caveats.
• Troubleshooting — registry failures, missing custom imports, tensor shape and num_classes mismatches, multimodal optional dependencies, and export errors.
• Registry probe helper — safe package/registry/version probe; run with --help first.

Route out of this sub-skill

• Routine config editing, annotation schemas, dataset file lists, and pipeline/data-prep details: ../data-and-configs/SKILL.md.
• Training/testing/evaluation command construction, distributed launch, result dumps, and metrics execution: ../training-and-evaluation/SKILL.md.
• Inference APIs, inferencer/demo workflows, visualization outputs, and label maps: ../inference-and-demos/SKILL.md.

Operating rules

1. Prefer registry-aware explanations: MMAction2 builds most user-facing models, datasets, transforms, losses, metrics, hooks, loops, inferencers, and visualizers through mmaction.registry and MMEngine registries.
2. Initialize or preserve the mmaction default scope before building registry objects. Use register_all_modules(init_default_scope=True) for standalone probes; use init_default_scope=False only when another OpenMMLab scope is intentionally active.
3. For custom modules, require both an import path (custom_imports or normal Python import) and a registered class (@MODELS.register_module(), @DATASETS.register_module(), @TRANSFORMS.register_module(), or @METRICS.register_module()).
4. Validate classifier head changes with both num_classes and in_channels; tensor shape errors usually mean the dataset/pipeline format and recognizer/head family are mismatched.
5. Treat multimodal, retrieval, AVA/spatio-temporal detection, ONNX, and TorchServe workflows as optional-extra workflows. Explain missing dependency messages instead of silently falling back to unrelated models.