docs/CODEBASE_RULES.md

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.