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fix(train): ensure consistent color augmentation RNG across base and wrist cameras #914

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fix(train): ensure consistent color augmentation RNG across base and wrist cameras #914

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17 changes: 10 additions & 7 deletions src/openpi/models/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -168,20 +168,23 @@ def preprocess_observation(
if train:
# Convert from [-1, 1] to [0, 1] for augmax.
image = image / 2.0 + 0.5
sub_rngs = jax.random.split(rng, image.shape[0])

transforms = []
# Stage 1: Apply spatial transforms (base cameras only)
if "wrist" not in key:
height, width = image.shape[1:3]
transforms += [
spatial_rngs = jax.vmap(lambda k: jax.random.fold_in(k, 0))(sub_rngs)
image = jax.vmap(augmax.Chain(
augmax.RandomCrop(int(width * 0.95), int(height * 0.95)),
augmax.Resize(width, height),
augmax.Rotate((-5, 5)),
]
transforms += [
))(spatial_rngs, image)

# Stage 2: Apply color transforms (all cameras, consistent RNG)
color_rngs = jax.vmap(lambda k: jax.random.fold_in(k, 1))(sub_rngs)
image = jax.vmap(augmax.Chain(
augmax.ColorJitter(brightness=0.3, contrast=0.4, saturation=0.5),
]
sub_rngs = jax.random.split(rng, image.shape[0])
image = jax.vmap(augmax.Chain(*transforms))(sub_rngs, image)
))(color_rngs, image)

# Back to [-1, 1].
image = image * 2.0 - 1.0
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