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chore(normalization): addressing comments from copilot

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This commit is contained in:
Adil Zouitine
2025-07-07 13:37:25 +02:00
parent 3b8a3a32a0
commit 097842c70f
2 changed files with 107 additions and 3 deletions
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50
src/lerobot/processor/normalize_processor.py
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@@ -87,6 +87,23 @@ class NormalizerProcessor:
)
def __post_init__(self):
# Handle deserialization from JSON config
if self.features and isinstance(list(self.features.values())[0], dict):
# Features came from JSON - need to reconstruct PolicyFeature objects
reconstructed_features = {}
for key, ft_dict in self.features.items():
reconstructed_features[key] = PolicyFeature(
type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"])
)
self.features = reconstructed_features
if self.norm_map and isinstance(list(self.norm_map.keys())[0], str):
# norm_map came from JSON - need to reconstruct enum keys and values
reconstructed_norm_map = {}
for ft_type_str, norm_mode_str in self.norm_map.items():
reconstructed_norm_map[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str)
self.norm_map = reconstructed_norm_map
# Convert statistics once so we avoid repeated numpy→Tensor conversions
# during runtime.
self.stats = self.stats or {}
@@ -161,7 +178,13 @@ class NormalizerProcessor:
)
def get_config(self) -> dict[str, Any]:
config = {"eps": self.eps}
config = {
"eps": self.eps,
"features": {
key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items()
},
"norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()},
}
if self.normalize_keys is not None:
# Serialise as a list for YAML / JSON friendliness
config["normalize_keys"] = sorted(self.normalize_keys)
@@ -212,6 +235,23 @@ class UnnormalizerProcessor:
return cls(features=features, norm_map=norm_map, stats=dataset.meta.stats, eps=eps)
def __post_init__(self):
# Handle deserialization from JSON config
if self.features and isinstance(list(self.features.values())[0], dict):
# Features came from JSON - need to reconstruct PolicyFeature objects
reconstructed_features = {}
for key, ft_dict in self.features.items():
reconstructed_features[key] = PolicyFeature(
type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"])
)
self.features = reconstructed_features
if self.norm_map and isinstance(list(self.norm_map.keys())[0], str):
# norm_map came from JSON - need to reconstruct enum keys and values
reconstructed_norm_map = {}
for ft_type_str, norm_mode_str in self.norm_map.items():
reconstructed_norm_map[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str)
self.norm_map = reconstructed_norm_map
self.stats = self.stats or {}
self._tensor_stats = _convert_stats_to_tensors(self.stats)
@@ -269,7 +309,13 @@ class UnnormalizerProcessor:
)
def get_config(self) -> dict[str, Any]:
return {"eps": self.eps}
return {
"eps": self.eps,
"features": {
key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items()
},
"norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()},
}
def state_dict(self) -> dict[str, Tensor]:
flat = {}