# Codebase Rules

This repo has accumulated several near-duplicate implementations and a few
hidden shape/device assumptions. Use the rules below for any further cleanup or
feature work.

## Ownership and layout

- Put shared GP-NeRF / Semantic-Frustum transformer pieces in
  `gpnerf/transformer_blocks.py`.
- Keep `gpnerf/transformer_network.py` focused on GP-NeRF assembly logic.
- Keep `s_frustum/transformer.py` focused on frustum-specific logic only.
- Prefer extending existing modules over copy-pasting whole files.

## Device and distributed rules

- Never hardcode `.cuda()` in model or dataloader utilities; use `.to(device)`.
- Never hardcode output directories; resolve them from `args.rootdir`.
- Convert to `SyncBatchNorm` only inside the distributed wrapping path, before
  `DistributedDataParallel`.

## Shape and indexing rules

- Do not hardcode image sizes such as `320x240` or feature sizes such as
  `160x120`; infer them from tensors.
- Assume flattened pixel indices are zero-based unless a caller explicitly says
  otherwise.
- When projecting semantic features, derive any downsampled index mapping from
  the current feature map stride instead of dataset-specific constants.

## Transformer rules

- Use the fused PyTorch SDPA path for standard self-attention whenever possible.
- If a block needs explicit attention weights for density sampling, keep that
  path isolated from the main attention implementation.
- Avoid channel-wise hacks like `repeat_interleave(8)`; compute grouping from
  the actual tensor shapes.

## Validation rules

- Any change touching render paths should be covered by at least one synthetic
  shape test.
- Any change touching sampling or semantic distillation should be checked for:
  device placement, index bounds, and `N_importance == 0`.