Add comprehensive knowledge base documentation across multiple domains

2026-02-12 14:36:37 +08:00
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+# 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 ...`).
+- 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
+# 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.