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Revert feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)

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Revert "feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)"

This reverts commit f173265354.
This commit is contained in:
Adil Zouitine
2025-09-29 16:55:52 +02:00
committed by GitHub
parent f173265354
commit 2d3a605b3c
3 changed files with 10 additions and 23 deletions
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20
src/lerobot/processor/normalize_processor.py
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@@ -108,18 +108,16 @@ class _NormalizationMixin:
""" """
# Track if stats were explicitly provided (not None and not empty) # Track if stats were explicitly provided (not None and not empty)
self._stats_explicitly_provided = self.stats is not None and bool(self.stats) self._stats_explicitly_provided = self.stats is not None and bool(self.stats)
# Check if self.features is not empty
if not self.features:
raise ValueError("Normalization features cannot be empty")
# Robust JSON deserialization handling (guard empty maps). # Robust JSON deserialization handling (guard empty maps).
first_val = next(iter(self.features.values())) if self.features:
if isinstance(first_val, dict): first_val = next(iter(self.features.values()))
reconstructed = {} if isinstance(first_val, dict):
for key, ft_dict in self.features.items(): reconstructed = {}
reconstructed[key] = PolicyFeature( for key, ft_dict in self.features.items():
type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"]) reconstructed[key] = PolicyFeature(
) type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"])
self.features = reconstructed )
self.features = reconstructed
# if keys are strings (JSON), rebuild enum map # if keys are strings (JSON), rebuild enum map
if self.norm_map and all(isinstance(k, str) for k in self.norm_map): if self.norm_map and all(isinstance(k, str) for k in self.norm_map):

1
tests/policies/test_policies.py
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@@ -234,6 +234,7 @@ def test_act_backbone_lr():
assert cfg.policy.optimizer_lr_backbone == 0.001 assert cfg.policy.optimizer_lr_backbone == 0.001
dataset = make_dataset(cfg) dataset = make_dataset(cfg)
preprocessor, _ = make_pre_post_processors(cfg.policy, None)
policy = make_policy(cfg.policy, ds_meta=dataset.meta) policy = make_policy(cfg.policy, ds_meta=dataset.meta)
optimizer, _ = make_optimizer_and_scheduler(cfg, policy) optimizer, _ = make_optimizer_and_scheduler(cfg, policy)
assert len(optimizer.param_groups) == 2 assert len(optimizer.param_groups) == 2

12
tests/processor/test_normalize_processor.py
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@@ -534,18 +534,6 @@ def test_empty_observation():
assert result == transition assert result == transition
def test_empty_features_raises_error():
"""Test that empty features dict raises ValueError."""
norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
stats = {OBS_IMAGE: {"mean": [0.5], "std": [0.2]}}
with pytest.raises(ValueError, match="Normalization features cannot be empty"):
NormalizerProcessorStep(features={}, norm_map=norm_map, stats=stats)
with pytest.raises(ValueError, match="Normalization features cannot be empty"):
UnnormalizerProcessorStep(features={}, norm_map=norm_map, stats=stats)
def test_empty_stats(): def test_empty_stats():
features = {OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} features = {OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96))}
norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}