OpenGait/opengait/modeling/AGENTS.md

MODELING DOMAIN KNOWLEDGE BASE

OVERVIEW

opengait/modeling/ defines model contracts and algorithm implementations: BaseModel, loss aggregation, backbones, concrete model classes.

STRUCTURE

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.