# DATA PIPELINE KNOWLEDGE BASE

## OVERVIEW
`opengait/data/` converts preprocessed dataset trees into training/evaluation batches for all models.

## WHERE TO LOOK
| Task | Location | Notes |
|------|----------|-------|
| Dataset parsing + file loading | `dataset.py` | expects partition json and `.pkl` sequence files |
| Sequence sampling strategy | `collate_fn.py` | fixed/unfixed/all + ordered/unordered behavior |
| Augmentations/transforms | `transform.py` | transform factories resolved from config |
| Batch identity sampling | `sampler.py` | sampler types referenced from config |

## CONVENTIONS
- Dataset root layout is `id/type/view/*.pkl` after preprocessing.
- `dataset_partition` JSON with `TRAIN_SET` / `TEST_SET` is required.
- `sample_type` drives control flow (`fixed_unordered`, `all_ordered`, etc.) and shape semantics downstream.

## ANTI-PATTERNS
- Never pass non-`.pkl` sequence files (`dataset.py` raises hard ValueError).
- Don’t violate expected `batch_size` semantics for triplet samplers (`[P, K]` list).
- Don’t assume all models use identical feature counts; collate is feature-index sensitive.