hangX/lerobot
1
0
Fork 0
forked from tangger/lerobot
Code Pull Requests Activity

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

Browse Source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot]
2025-03-28 17:20:38 +00:00
committed by Michel Aractingi
parent 8eb3c1510c
commit eb44a06a9b
16 changed files with 93 additions and 91 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
lerobot/common/policies/normalize.py
View File

@@ -82,8 +82,10 @@ def create_stats_buffers(
if stats and key in stats:
if norm_mode is NormalizationMode.MEAN_STD:
if "mean" not in stats[key] or "std" not in stats[key]:
raise ValueError(f"Missing 'mean' or 'std' in stats for key {key} with MEAN_STD normalization")
raise ValueError(
f"Missing 'mean' or 'std' in stats for key {key} with MEAN_STD normalization"
)
if isinstance(stats[key]["mean"], np.ndarray):
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)
@@ -96,12 +98,16 @@ def create_stats_buffers(
buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
else:
type_ = type(stats[key]["mean"])
raise ValueError(f"np.ndarray or torch.Tensor expected for 'mean', but type is '{type_}' instead.")
raise ValueError(
f"np.ndarray or torch.Tensor expected for 'mean', but type is '{type_}' instead."
)
elif norm_mode is NormalizationMode.MIN_MAX:
if "min" not in stats[key] or "max" not in stats[key]:
raise ValueError(f"Missing 'min' or 'max' in stats for key {key} with MIN_MAX normalization")
raise ValueError(
f"Missing 'min' or 'max' in stats for key {key} with MIN_MAX normalization"
)
if isinstance(stats[key]["min"], np.ndarray):
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)
@@ -110,7 +116,9 @@ def create_stats_buffers(
buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32)
else:
type_ = type(stats[key]["min"])
raise ValueError(f"np.ndarray or torch.Tensor expected for 'min', but type is '{type_}' instead.")
raise ValueError(
f"np.ndarray or torch.Tensor expected for 'min', but type is '{type_}' instead."
)
stats_buffers[key] = buffer
return stats_buffers