OpenGait/opengait/data/AGENTS.md

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.