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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot]
2025-03-24 13:16:38 +00:00
committed by Michel Aractingi
parent cdcf346061
commit 1c8daf11fd
95 changed files with 1592 additions and 491 deletions
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36
lerobot/common/policies/normalize.py
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@@ -82,25 +82,43 @@ def create_stats_buffers(
if stats:
if isinstance(stats[key]["mean"], np.ndarray):
if norm_mode is NormalizationMode.MEAN_STD:
buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32)
buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32)
buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(
dtype=torch.float32
)
buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(
dtype=torch.float32
)
elif norm_mode is NormalizationMode.MIN_MAX:
buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32)
buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32)
buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(
dtype=torch.float32
)
buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(
dtype=torch.float32
)
elif isinstance(stats[key]["mean"], torch.Tensor):
# Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
# tensors anywhere (for example, when we use the same stats for normalization and
# unnormalization). See the logic here
# https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
if norm_mode is NormalizationMode.MEAN_STD:
buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32)
buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
buffer["mean"].data = (
stats[key]["mean"].clone().to(dtype=torch.float32)
)
buffer["std"].data = (
stats[key]["std"].clone().to(dtype=torch.float32)
)
elif norm_mode is NormalizationMode.MIN_MAX:
buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32)
buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32)
buffer["min"].data = (
stats[key]["min"].clone().to(dtype=torch.float32)
)
buffer["max"].data = (
stats[key]["max"].clone().to(dtype=torch.float32)
)
else:
type_ = type(stats[key]["mean"])
raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.")
raise ValueError(
f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead."
)
stats_buffers[key] = buffer
return stats_buffers