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use quantiles

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This commit is contained in:
Pepijn
2026-02-21 17:12:43 +01:00
parent 5221647b5e
commit 6705876d47
2 changed files with 17 additions and 11 deletions
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12
src/lerobot/processor/normalize_processor.py
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@@ -331,11 +331,9 @@ class _NormalizationMixin:
)
mean, std = stats["mean"], stats["std"]
# Avoid division by zero by adding a small epsilon.
denom = std + self.eps
if inverse:
return tensor * std + mean
return (tensor - mean) / denom
return tensor * (std + 1e-6) + mean
return (tensor - mean) / (std + 1e-6)
if norm_mode == NormalizationMode.MIN_MAX:
min_val = stats.get("min", None)
@@ -367,11 +365,7 @@ class _NormalizationMixin:
"QUANTILES normalization mode requires q01 and q99 stats, please update the dataset with the correct stats using the `augment_dataset_quantile_stats.py` script"
)
denom = q99 - q01
# Avoid division by zero by adding epsilon when quantiles are identical
denom = torch.where(
denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom
)
denom = q99 - q01 + 1e-6
if inverse:
return (tensor + 1.0) * denom / 2.0 + q01
return 2.0 * (tensor - q01) / denom - 1.0