# CONFIG SURFACE KNOWLEDGE BASE

## OVERVIEW
`configs/` is the operational API for experiments. Runtime behavior is primarily configured here, not hardcoded.

## STRUCTURE
```text
configs/
├── default.yaml          # base config merged into every run
├── <model-family>/*.yaml # experiment overlays
└── */README.md           # family-specific instructions (when present)
```

## WHERE TO LOOK
| Task | Location | Notes |
|------|----------|-------|
| Global defaults | `default.yaml` | base for all runs |
| Model selection | `model_cfg.model` | must match class name in `modeling/models` |
| Data split binding | `data_cfg.dataset_partition` | points to `datasets/*/*.json` |
| Sampler behavior | `trainer_cfg.sampler`, `evaluator_cfg.sampler` | directly controls collate/sampler path |

## CONVENTIONS
- Config files are overlays merged into `default.yaml` via `MergeCfgsDict`.
- Keys accepted by classes/functions are validated at runtime; unknown keys are logged as unexpected.
- Paths and names here directly determine output directory keying (`output/<dataset>/<model>/<save_name>`).

## ANTI-PATTERNS
- Don’t use model names not registered in `opengait/modeling/models`.
- Don’t treat `batch_size` as scalar in triplet training regimes when config expects `[P, K]`.
- Don’t bypass dataset partition files; loader expects explicit train/test pid sets.