mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-15 16:49:55 +00:00
Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity. - Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`. - Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes. - Enhanced handling of missing statistics in normalization processes.
This commit is contained in:
Adil Zouitine
@@ -5,9 +5,8 @@ import pytest
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import torch
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from lerobot.processor.normalize_processor import (
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ActionUnnormalizer,
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NormalizationProcessor,
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ObservationNormalizer,
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NormalizerProcessor,
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UnnormalizerProcessor,
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_convert_stats_to_tensors,
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)
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from lerobot.processor.pipeline import RobotProcessor, TransitionIndex
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@@ -77,7 +76,7 @@ def test_unsupported_type():
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_convert_stats_to_tensors(stats)
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# Fixtures for ObservationNormalizer tests
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# Fixtures for observation normalisation tests using NormalizerProcessor
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@pytest.fixture
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def observation_stats():
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return {
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@@ -94,7 +93,8 @@ def observation_stats():
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@pytest.fixture
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def observation_normalizer(observation_stats):
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return ObservationNormalizer(stats=observation_stats)
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"""Return a NormalizerProcessor that only has observation stats (no action)."""
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return NormalizerProcessor(stats=observation_stats)
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def test_mean_std_normalization(observation_normalizer):
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@@ -129,7 +129,7 @@ def test_min_max_normalization(observation_normalizer):
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def test_selective_normalization(observation_stats):
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normalizer = ObservationNormalizer(stats=observation_stats, normalize_keys={"observation.image"})
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normalizer = NormalizerProcessor(stats=observation_stats, normalize_keys={"observation.image"})
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observation = {
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"observation.image": torch.tensor([0.7, 0.5, 0.3]),
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@@ -146,46 +146,9 @@ def test_selective_normalization(observation_stats):
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assert torch.allclose(normalized_obs["observation.state"], observation["observation.state"])
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def test_missing_stats_error(observation_stats):
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normalizer = ObservationNormalizer(
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stats={"observation.image": observation_stats["observation.image"]},
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normalize_keys={"observation.image", "observation.missing"},
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)
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observation = {
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"observation.image": torch.tensor([0.5, 0.5, 0.5]),
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"observation.missing": torch.tensor([1.0, 2.0]),
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}
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transition = (observation, None, None, None, None, None, None)
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with pytest.raises(KeyError, match="Stats not found for requested key 'observation.missing'"):
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normalizer(transition)
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@pytest.mark.parametrize(
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"input_type,input_value,expected_type",
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[
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("numpy", np.array([0.7, 0.5, 0.3], dtype=np.float32), torch.Tensor),
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("torch", torch.tensor([0.7, 0.5, 0.3]), torch.Tensor),
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],
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)
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def test_input_types(observation_normalizer, input_type, input_value, expected_type):
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observation = {
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"observation.image": input_value,
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}
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transition = (observation, None, None, None, None, None, None)
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normalized_transition = observation_normalizer(transition)
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normalized_obs = normalized_transition[TransitionIndex.OBSERVATION]
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expected = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2
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assert isinstance(normalized_obs["observation.image"], expected_type)
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assert torch.allclose(normalized_obs["observation.image"], expected)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_device_compatibility(observation_stats):
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normalizer = ObservationNormalizer(stats=observation_stats)
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normalizer = NormalizerProcessor(stats=observation_stats)
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observation = {
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"observation.image": torch.tensor([0.7, 0.5, 0.3]).cuda(),
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}
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@@ -205,11 +168,11 @@ def test_from_lerobot_dataset():
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"action": {"mean": [0.0], "std": [1.0]}, # Should be filtered out
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}
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normalizer = ObservationNormalizer.from_lerobot_dataset(mock_dataset)
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normalizer = NormalizerProcessor.from_lerobot_dataset(mock_dataset)
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# Check that action stats are filtered out
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# Both observation and action statistics should be present in tensor stats
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assert "observation.image" in normalizer._tensor_stats
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assert "action" not in normalizer._tensor_stats
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assert "action" in normalizer._tensor_stats
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def test_state_dict_save_load(observation_normalizer):
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@@ -217,7 +180,7 @@ def test_state_dict_save_load(observation_normalizer):
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state_dict = observation_normalizer.state_dict()
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# Create new normalizer and load state
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new_normalizer = ObservationNormalizer(stats={})
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new_normalizer = NormalizerProcessor(stats={})
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new_normalizer.load_state_dict(state_dict)
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# Test that it works the same
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@@ -248,7 +211,7 @@ def action_stats_min_max():
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def test_mean_std_unnormalization(action_stats_mean_std):
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unnormalizer = ActionUnnormalizer(action_stats=action_stats_mean_std)
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unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_mean_std})
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normalized_action = torch.tensor([1.0, -0.5, 2.0])
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transition = (None, normalized_action, None, None, None, None, None)
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@@ -262,7 +225,7 @@ def test_mean_std_unnormalization(action_stats_mean_std):
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def test_min_max_unnormalization(action_stats_min_max):
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unnormalizer = ActionUnnormalizer(action_stats=action_stats_min_max)
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unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_min_max})
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# Actions in [-1, 1]
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normalized_action = torch.tensor([0.0, -1.0, 1.0])
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@@ -284,7 +247,7 @@ def test_min_max_unnormalization(action_stats_min_max):
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def test_numpy_action_input(action_stats_mean_std):
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unnormalizer = ActionUnnormalizer(action_stats=action_stats_mean_std)
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unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_mean_std})
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normalized_action = np.array([1.0, -0.5, 2.0], dtype=np.float32)
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transition = (None, normalized_action, None, None, None, None, None)
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@@ -298,7 +261,7 @@ def test_numpy_action_input(action_stats_mean_std):
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def test_none_action(action_stats_mean_std):
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unnormalizer = ActionUnnormalizer(action_stats=action_stats_mean_std)
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unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_mean_std})
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transition = (None, None, None, None, None, None, None)
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result = unnormalizer(transition)
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@@ -308,40 +271,13 @@ def test_none_action(action_stats_mean_std):
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def test_action_from_lerobot_dataset():
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# Mock dataset
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mock_dataset = Mock()
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mock_dataset.meta.stats = {
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"action": {"mean": [0.0], "std": [1.0]},
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"observation.image": {"mean": [0.5], "std": [0.2]},
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}
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unnormalizer = ActionUnnormalizer.from_lerobot_dataset(mock_dataset)
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assert "mean" in unnormalizer._tensor_stats
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assert "std" in unnormalizer._tensor_stats
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mock_dataset.meta.stats = {"action": {"mean": [0.0], "std": [1.0]}}
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unnormalizer = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset)
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assert "mean" in unnormalizer._tensor_stats["action"]
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def test_missing_action_stats_error():
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mock_dataset = Mock()
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mock_dataset.meta.stats = {
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"observation.image": {"mean": [0.5], "std": [0.2]},
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}
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with pytest.raises(ValueError, match="Dataset does not contain action statistics"):
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ActionUnnormalizer.from_lerobot_dataset(mock_dataset)
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def test_invalid_stats_error():
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unnormalizer = ActionUnnormalizer(action_stats={"invalid": [1.0]})
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action = torch.tensor([1.0])
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transition = (None, action, None, None, None, None, None)
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with pytest.raises(ValueError, match="Action stats must contain"):
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unnormalizer(transition)
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# Fixtures for NormalizationProcessor tests
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# Fixtures for NormalizerProcessor tests
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@pytest.fixture
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def full_stats():
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return {
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@@ -361,11 +297,11 @@ def full_stats():
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@pytest.fixture
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def normalization_processor(full_stats):
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return NormalizationProcessor(stats=full_stats)
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def normalizer_processor(full_stats):
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return NormalizerProcessor(stats=full_stats)
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def test_combined_normalization_unnormalization(normalization_processor):
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def test_combined_normalization(normalizer_processor):
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observation = {
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"observation.image": torch.tensor([0.7, 0.5, 0.3]),
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"observation.state": torch.tensor([0.5, 0.0]),
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@@ -373,16 +309,16 @@ def test_combined_normalization_unnormalization(normalization_processor):
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action = torch.tensor([1.0, -0.5])
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transition = (observation, action, 1.0, False, False, {}, {})
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processed_transition = normalization_processor(transition)
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processed_transition = normalizer_processor(transition)
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# Check normalized observations
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processed_obs = processed_transition[TransitionIndex.OBSERVATION]
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expected_image = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2
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assert torch.allclose(processed_obs["observation.image"], expected_image)
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# Check unnormalized action
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# Check normalized action
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processed_action = processed_transition[TransitionIndex.ACTION]
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expected_action = torch.tensor([1.0 * 1.0 + 0.0, -0.5 * 2.0 + 0.0])
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expected_action = torch.tensor([(1.0 - 0.0) / 1.0, (-0.5 - 0.0) / 2.0])
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assert torch.allclose(processed_action, expected_action)
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# Check other fields remain unchanged
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@@ -390,45 +326,28 @@ def test_combined_normalization_unnormalization(normalization_processor):
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assert not processed_transition[TransitionIndex.DONE]
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def test_disable_action_unnormalization(full_stats):
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processor = NormalizationProcessor(stats=full_stats, unnormalize_action=False)
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action = torch.tensor([1.0, -0.5])
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transition = (None, action, None, None, None, None, None)
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processed_transition = processor(transition)
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# Action should remain unchanged
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assert torch.allclose(processed_transition[TransitionIndex.ACTION], action)
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def test_processor_from_lerobot_dataset(full_stats):
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# Mock dataset
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mock_dataset = Mock()
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mock_dataset.meta.stats = full_stats
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processor = NormalizationProcessor.from_lerobot_dataset(
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mock_dataset, normalize_keys={"observation.image"}, unnormalize_action=True
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)
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processor = NormalizerProcessor.from_lerobot_dataset(mock_dataset, normalize_keys={"observation.image"})
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assert processor.normalize_keys == {"observation.image"}
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assert processor.unnormalize_action
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assert "observation.image" in processor._tensor_stats
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assert "action" in processor._tensor_stats
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def test_get_config(full_stats):
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processor = NormalizationProcessor(
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stats=full_stats, normalize_keys={"observation.image"}, unnormalize_action=False, eps=1e-6
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)
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processor = NormalizerProcessor(stats=full_stats, normalize_keys={"observation.image"}, eps=1e-6)
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config = processor.get_config()
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assert config == {"normalize_keys": ["observation.image"], "unnormalize_action": False, "eps": 1e-6}
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assert config == {"normalize_keys": ["observation.image"], "eps": 1e-6}
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def test_integration_with_robot_processor(normalization_processor):
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def test_integration_with_robot_processor(normalizer_processor):
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"""Test integration with RobotProcessor pipeline"""
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robot_processor = RobotProcessor([normalization_processor])
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robot_processor = RobotProcessor([normalizer_processor])
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observation = {
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"observation.image": torch.tensor([0.7, 0.5, 0.3]),
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@@ -447,7 +366,7 @@ def test_integration_with_robot_processor(normalization_processor):
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# Edge case tests
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def test_empty_observation():
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stats = {"observation.image": {"mean": [0.5], "std": [0.2]}}
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normalizer = ObservationNormalizer(stats=stats)
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normalizer = NormalizerProcessor(stats=stats)
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transition = (None, None, None, None, None, None, None)
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result = normalizer(transition)
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@@ -456,7 +375,7 @@ def test_empty_observation():
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def test_empty_stats():
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normalizer = ObservationNormalizer(stats={})
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normalizer = NormalizerProcessor(stats={})
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observation = {"observation.image": torch.tensor([0.5])}
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transition = (observation, None, None, None, None, None, None)
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@@ -466,12 +385,20 @@ def test_empty_stats():
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def test_partial_stats():
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stats = {
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"observation.image": {"mean": [0.5]}, # Missing std
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}
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normalizer = ObservationNormalizer(stats=stats)
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"""If statistics are incomplete, the value should pass through unchanged."""
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stats = {"observation.image": {"mean": [0.5]}} # Missing std / (min,max)
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normalizer = NormalizerProcessor(stats=stats)
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observation = {"observation.image": torch.tensor([0.7])}
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transition = (observation, None, None, None, None, None, None)
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with pytest.raises(ValueError, match="must contain either"):
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normalizer(transition)
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processed = normalizer(transition)[TransitionIndex.OBSERVATION]
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assert torch.allclose(processed["observation.image"], observation["observation.image"])
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def test_missing_action_stats_no_error():
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mock_dataset = Mock()
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mock_dataset.meta.stats = {"observation.image": {"mean": [0.5], "std": [0.2]}}
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processor = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset)
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# The tensor stats should not contain the 'action' key
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assert "action" not in processor._tensor_stats
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