# PROJECT KNOWLEDGE BASE

**Generated:** 2026-02-11T10:53:29Z  
**Commit:** f754f6f  
**Branch:** master

## OVERVIEW
OpenGait is a research-grade, config-driven gait analysis framework centered on distributed PyTorch training/testing.  
Core runtime lives in `opengait/`; `configs/` and `datasets/` are first-class operational surfaces, not just support folders.

## STRUCTURE
```text
OpenGait/
├── opengait/          # runtime package (train/test, model/data/eval pipelines)
├── configs/           # model- and dataset-specific experiment specs
├── datasets/          # preprocessing/rearrangement scripts + partitions
├── docs/              # user workflow docs
├── train.sh           # launch patterns (DDP)
└── test.sh            # eval launch patterns (DDP)
```

## WHERE TO LOOK
| Task | Location | Notes |
|------|----------|-------|
| Train/test entry | `opengait/main.py` | DDP init + config load + model dispatch |
| Model registration | `opengait/modeling/models/__init__.py` | dynamic class import/registration |
| Backbone/loss registration | `opengait/modeling/backbones/__init__.py`, `opengait/modeling/losses/__init__.py` | same dynamic pattern |
| Config merge behavior | `opengait/utils/common.py::config_loader` | merges into `configs/default.yaml` |
| Data loading contract | `opengait/data/dataset.py`, `opengait/data/collate_fn.py` | `.pkl` only, sequence sampling modes |
| Evaluation dispatch | `opengait/evaluation/evaluator.py` | dataset-specific eval routines |
| Dataset preprocessing | `datasets/pretreatment.py` + dataset subdirs | many standalone CLI tools |

## CODE MAP
| Symbol / Module | Type | Location | Refs | Role |
|-----------------|------|----------|------|------|
| `config_loader` | function | `opengait/utils/common.py` | high | YAML merge + default overlay |
| `get_ddp_module` | function | `opengait/utils/common.py` | high | wraps modules with DDP passthrough |
| `BaseModel` | class | `opengait/modeling/base_model.py` | high | canonical train/test lifecycle |
| `LossAggregator` | class | `opengait/modeling/loss_aggregator.py` | medium | consumes `training_feat` contract |
| `DataSet` | class | `opengait/data/dataset.py` | high | dataset partition + sequence loading |
| `CollateFn` | class | `opengait/data/collate_fn.py` | high | fixed/unfixed/all sampling policy |
| `evaluate_*` funcs | functions | `opengait/evaluation/evaluator.py` | medium | metric/report orchestration |
| `models` package registry | dynamic module | `opengait/modeling/models/__init__.py` | high | config string → model class |

## CONVENTIONS
- Launch pattern is DDP-first (`python -m torch.distributed.launch ... opengait/main.py --cfgs ... --phase ...`).
- DDP Constraints: `world_size` must equal number of visible GPUs; test `evaluator_cfg.sampler.batch_size` must equal `world_size`.
- Model/loss/backbone discoverability is filesystem-driven via package-level dynamic imports.
- Experiment config semantics: custom YAML overlays `configs/default.yaml` (local key precedence).
- Outputs are keyed by config identity: `output/${dataset_name}/${model}/${save_name}`.

## ANTI-PATTERNS (THIS PROJECT)
- Do not feed non-`.pkl` sequence files into runtime loaders (`opengait/data/dataset.py`).
- Do not violate sampler shape assumptions (`trainer_cfg.sampler.batch_size` is `[P, K]` for triplet regimes).
- Do not ignore DDP cleanup guidance; abnormal exits can leave zombie processes (`misc/clean_process.sh`).
- Do not add unregistered model/loss classes outside expected directories (`opengait/modeling/models`, `opengait/modeling/losses`).

## UNIQUE STYLES
- `datasets/` is intentionally script-heavy (rearrange/extract/pretreat), not a pure library package.
- Research model zoo is broad; many model files co-exist as first-class references.
- Recent repo trajectory includes scoliosis screening models (ScoNet lineage), not only person-ID gait benchmarks.

## COMMANDS
```bash
# install (uv)
uv sync --extra torch

# train (uv)
CUDA_VISIBLE_DEVICES=0,1 uv run python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase train

# test (uv)
CUDA_VISIBLE_DEVICES=0,1 uv run python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase test

# ScoNet 1-GPU eval
CUDA_VISIBLE_DEVICES=0 uv run python -m torch.distributed.launch --nproc_per_node=1 opengait/main.py --cfgs ./configs/sconet/sconet_scoliosis1k_local_eval_1gpu.yaml --phase test

# train
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase train

# test
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/baseline/baseline.yaml --phase test

# preprocess (generic)
python datasets/pretreatment.py --input_path <raw_or_rearranged> --output_path <pkl_root>
```

## NOTES
- LSP symbol map was unavailable in this environment (missing `basedpyright-langserver`), so centrality here is import/search-derived.
- `train.sh` / `test.sh` are canonical launch examples across datasets/models.
- Academic-use-only restriction is stated in repository README.