fix(train): ensure consistent color augmentation RNG across base and wrist cameras#914
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…wrist cameras Fixes Physical-Intelligence#859. augmax.Chain splits RNG by transform count, so base cameras (4 transforms) and wrist cameras (1 transform) received different sub-RNGs for ColorJitter despite the same input seed. This caused inconsistent color semantics between camera views. Split augmentation into spatial (camera-specific) and color (shared) stages with deterministic RNG derivation via jax.random.fold_in, ensuring all cameras see the same ColorJitter parameters within each sample.
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Problem
Fixes #859.
augmax.Chainsplits the input RNG into sub-keys by transform count. Base cameras use 4 transforms (RandomCrop, Resize, Rotate, ColorJitter) while wrist cameras use only 1 (ColorJitter). This means ColorJitter receives different sub-RNGs for base vs. wrist cameras, even when given the same input seed.The result: base and wrist cameras see inconsistent color augmentation within the same training sample, which may confuse the VLM about object colors across camera views.
Fix
Split augmentation into two deterministic stages:
Both stages derive their RNG from the original key using
jax.random.fold_inwith different constants, ensuring:Changes
src/openpi/models/model.py: Refactored image augmentation to use split RNG stages