Add comprehensive knowledge base documentation across multiple domains

2026-02-12 14:36:37 +08:00
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+# MODELING DOMAIN KNOWLEDGE BASE
+
+## OVERVIEW
+`opengait/modeling/` defines model contracts and algorithm implementations: `BaseModel`, loss aggregation, backbones, concrete model classes.
+
+## STRUCTURE
+```text
+opengait/modeling/
+├── base_model.py        # canonical train/test lifecycle
+├── loss_aggregator.py   # training_feat -> weighted summed loss
+├── modules.py           # shared NN building blocks
+├── backbones/           # backbone registry + implementations
+├── losses/              # loss registry + implementations
+└── models/              # concrete methods (Baseline, ScoNet, DeepGaitV2, ...)
+```
+
+## WHERE TO LOOK
+| Task | Location | Notes |
+|------|----------|-------|
+| Add new model | `models/*.py` + `docs/4.how_to_create_your_model.md` | must inherit `BaseModel` |
+| Add new loss | `losses/*.py` | expose via dynamic registry |
+| Change training lifecycle | `base_model.py` | affects every model |
+| Debug feature/loss key mismatches | `loss_aggregator.py` | checks `training_feat` keys vs `loss_cfg.log_prefix` |
+
+## CONVENTIONS
+- `forward()` output contract is fixed dict with keys: `training_feat`, `visual_summary`, `inference_feat`.
+- `training_feat` subkeys must align with configured `loss_cfg[*].log_prefix`.
+- Backbones/losses/models are discovered dynamically via package `__init__.py`; filenames matter operationally.
+
+## ANTI-PATTERNS
+- Do not return arbitrary forward outputs; `LossAggregator` and evaluator assume fixed contract.
+- Do not put model classes outside `models/`; config lookup by `getattr(models, name)` depends on registry.
+- Do not ignore DDP loss wrapping (`get_ddp_module`) in loss construction.
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+# MODEL ZOO IMPLEMENTATION KNOWLEDGE BASE
+
+## OVERVIEW
+This directory is the algorithm zoo. Each file usually contributes one `BaseModel` subclass selected by `model_cfg.model`.
+
+## WHERE TO LOOK
+| Task | Location | Notes |
+|------|----------|-------|
+| Baseline pattern | `baseline.py` | minimal template for silhouette models |
+| Scoliosis pipeline | `sconet.py` | label remapping + screening-specific head |
+| Large-model fusion | `BiggerGait_DINOv2.py`, `BigGait.py` | external pretrained dependencies |
+| Diffusion/noise handling | `denoisinggait.py`, `diffgait_utils/` | high-complexity flow/feature fusion |
+| Skeleton variants | `skeletongait++.py`, `gaitgraph1.py`, `gaitgraph2.py` | pose-map/graph assumptions |
+
+## CONVENTIONS
+- Most models follow: preprocess input -> backbone -> temporal pooling -> horizontal pooling -> neck/head -> contract dict.
+- Input modality assumptions differ by model (silhouette / RGB / pose / multimodal); config and preprocess script must match.
+- Many models rely on utilities from `modeling/modules.py`; shared changes there are high blast-radius.
+
+## ANTI-PATTERNS
+- Don’t mix modality assumptions silently (e.g., pose tensor layout vs silhouette layout).
+- Don’t rename classes without updating `model_cfg.model` references in configs.
+- Don’t treat `BigGait_utils`/`diffgait_utils` as generic utilities; they are model-family specific.