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name: normalization_configuration description: Configure normalization layers for Tent by exposing only affine modulation parameters and target batch statistics.

Normalization Configuration for Tent

Use this skill before running entropy-minimizing test-time adaptation on a model with normalization layers. It is appropriate for creating a Tent configuration report, checking a proxy model, or comparing against normalization-only adaptation. Do not use it to authorize updating all model weights unless the run is an explicit ablation outside standard Tent.

Inputs

• A model inventory JSON with modules, parameter names, layer types, and affine/statistics fields.
• Optional flags for target batch statistics and whether running statistics should be disabled.

Outputs

• Trainable parameter names limited to normalization affine scale and shift.
• A configured module inventory with target-batch statistics enabled for normalization layers.
• Validation errors for missing normalization layers or accidental full-model training.

Workflow

1. Set the model-level mode to training for adaptation.
2. Disable gradients for all parameters by default.
3. For each batch normalization descriptor, enable gradients on weight and bias only.
4. Set track_running_stats false when using target-batch-only statistics.
5. Return a report with trainable parameters and configuration checks.
6. Reject configurations where no normalization affine parameters are available.

Validation

Run python scripts/configure_norm.py --self-test. The test constructs a tiny inventory and confirms that only batch-normalization affine parameters are trainable while classifier parameters remain frozen.

Limitations

The script works on portable inventory dictionaries. For live frameworks such as PyTorch, use this report as a contract and apply the same logic to actual modules.