mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-15 16:49:55 +00:00
Adil Zouitine
453e0a995f
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility. - Updated `__init__.py` to include `RenameProcessor` in module exports. - Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling. - Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
478 lines
16 KiB
Python
478 lines
16 KiB
Python
from unittest.mock import Mock
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import numpy as np
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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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_convert_stats_to_tensors,
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)
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from lerobot.processor.pipeline import RobotProcessor, TransitionIndex
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def test_numpy_conversion():
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stats = {
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"observation.image": {
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"mean": np.array([0.5, 0.5, 0.5]),
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"std": np.array([0.2, 0.2, 0.2]),
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}
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}
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tensor_stats = _convert_stats_to_tensors(stats)
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assert isinstance(tensor_stats["observation.image"]["mean"], torch.Tensor)
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assert isinstance(tensor_stats["observation.image"]["std"], torch.Tensor)
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assert torch.allclose(tensor_stats["observation.image"]["mean"], torch.tensor([0.5, 0.5, 0.5]))
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assert torch.allclose(tensor_stats["observation.image"]["std"], torch.tensor([0.2, 0.2, 0.2]))
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def test_tensor_conversion():
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stats = {
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"action": {
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"mean": torch.tensor([0.0, 0.0]),
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"std": torch.tensor([1.0, 1.0]),
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}
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}
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tensor_stats = _convert_stats_to_tensors(stats)
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assert tensor_stats["action"]["mean"].dtype == torch.float32
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assert tensor_stats["action"]["std"].dtype == torch.float32
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def test_scalar_conversion():
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stats = {
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"reward": {
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"mean": 0.5,
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"std": 0.1,
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}
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}
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tensor_stats = _convert_stats_to_tensors(stats)
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assert torch.allclose(tensor_stats["reward"]["mean"], torch.tensor(0.5))
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assert torch.allclose(tensor_stats["reward"]["std"], torch.tensor(0.1))
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def test_list_conversion():
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stats = {
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"observation.state": {
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"min": [0.0, -1.0, -2.0],
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"max": [1.0, 1.0, 2.0],
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}
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}
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tensor_stats = _convert_stats_to_tensors(stats)
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assert torch.allclose(tensor_stats["observation.state"]["min"], torch.tensor([0.0, -1.0, -2.0]))
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assert torch.allclose(tensor_stats["observation.state"]["max"], torch.tensor([1.0, 1.0, 2.0]))
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def test_unsupported_type():
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stats = {
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"bad_key": {
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"mean": "string_value",
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}
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}
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with pytest.raises(TypeError, match="Unsupported type"):
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_convert_stats_to_tensors(stats)
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# Fixtures for ObservationNormalizer tests
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@pytest.fixture
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def observation_stats():
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return {
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"observation.image": {
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"mean": np.array([0.5, 0.5, 0.5]),
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"std": np.array([0.2, 0.2, 0.2]),
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},
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"observation.state": {
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"min": np.array([0.0, -1.0]),
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"max": np.array([1.0, 1.0]),
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},
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}
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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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def test_mean_std_normalization(observation_normalizer):
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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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}
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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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# Check mean/std normalization
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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(normalized_obs["observation.image"], expected_image)
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def test_min_max_normalization(observation_normalizer):
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observation = {
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"observation.state": torch.tensor([0.5, 0.0]),
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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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# Check min/max normalization to [-1, 1]
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# For state[0]: 2 * (0.5 - 0.0) / (1.0 - 0.0) - 1 = 0.0
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# For state[1]: 2 * (0.0 - (-1.0)) / (1.0 - (-1.0)) - 1 = 0.0
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expected_state = torch.tensor([0.0, 0.0])
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assert torch.allclose(normalized_obs["observation.state"], expected_state, atol=1e-6)
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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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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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}
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transition = (observation, None, None, None, None, None, None)
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normalized_transition = normalizer(transition)
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normalized_obs = normalized_transition[TransitionIndex.OBSERVATION]
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# Only image should be normalized
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assert torch.allclose(normalized_obs["observation.image"], (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2)
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# State should remain unchanged
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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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observation = {
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"observation.image": torch.tensor([0.7, 0.5, 0.3]).cuda(),
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}
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transition = (observation, None, None, None, None, None, None)
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normalized_transition = normalizer(transition)
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normalized_obs = normalized_transition[TransitionIndex.OBSERVATION]
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assert normalized_obs["observation.image"].device.type == "cuda"
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def test_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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"observation.image": {"mean": [0.5], "std": [0.2]},
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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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# Check that action stats are filtered out
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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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def test_state_dict_save_load(observation_normalizer):
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# Save state
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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.load_state_dict(state_dict)
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# Test that it works the same
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observation = {"observation.image": torch.tensor([0.7, 0.5, 0.3])}
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transition = (observation, None, None, None, None, None, None)
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result1 = observation_normalizer(transition)[0]
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result2 = new_normalizer(transition)[0]
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assert torch.allclose(result1["observation.image"], result2["observation.image"])
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# Fixtures for ActionUnnormalizer tests
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@pytest.fixture
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def action_stats_mean_std():
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return {
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"mean": np.array([0.0, 0.0, 0.0]),
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"std": np.array([1.0, 2.0, 0.5]),
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}
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@pytest.fixture
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def action_stats_min_max():
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return {
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"min": np.array([-1.0, -2.0, 0.0]),
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"max": np.array([1.0, 2.0, 1.0]),
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}
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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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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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unnormalized_transition = unnormalizer(transition)
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unnormalized_action = unnormalized_transition[TransitionIndex.ACTION]
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# action * std + mean
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expected = torch.tensor([1.0 * 1.0 + 0.0, -0.5 * 2.0 + 0.0, 2.0 * 0.5 + 0.0])
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assert torch.allclose(unnormalized_action, expected)
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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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# Actions in [-1, 1]
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normalized_action = torch.tensor([0.0, -1.0, 1.0])
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transition = (None, normalized_action, None, None, None, None, None)
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unnormalized_transition = unnormalizer(transition)
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unnormalized_action = unnormalized_transition[TransitionIndex.ACTION]
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# Map from [-1, 1] to [min, max]
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# (action + 1) / 2 * (max - min) + min
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expected = torch.tensor(
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[
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(0.0 + 1) / 2 * (1.0 - (-1.0)) + (-1.0), # 0.0
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(-1.0 + 1) / 2 * (2.0 - (-2.0)) + (-2.0), # -2.0
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(1.0 + 1) / 2 * (1.0 - 0.0) + 0.0, # 1.0
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]
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)
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assert torch.allclose(unnormalized_action, expected)
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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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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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unnormalized_transition = unnormalizer(transition)
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unnormalized_action = unnormalized_transition[TransitionIndex.ACTION]
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assert isinstance(unnormalized_action, torch.Tensor)
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expected = torch.tensor([1.0, -1.0, 1.0])
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assert torch.allclose(unnormalized_action, expected)
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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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transition = (None, None, None, None, None, None, None)
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result = unnormalizer(transition)
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# Should return transition unchanged
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assert result == transition
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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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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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@pytest.fixture
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def full_stats():
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return {
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"observation.image": {
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"mean": np.array([0.5, 0.5, 0.5]),
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"std": np.array([0.2, 0.2, 0.2]),
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},
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"observation.state": {
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"min": np.array([0.0, -1.0]),
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"max": np.array([1.0, 1.0]),
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},
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"action": {
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"mean": np.array([0.0, 0.0]),
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"std": np.array([1.0, 2.0]),
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},
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}
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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 test_combined_normalization_unnormalization(normalization_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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}
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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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# 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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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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assert torch.allclose(processed_action, expected_action)
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# Check other fields remain unchanged
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assert processed_transition[TransitionIndex.REWARD] == 1.0
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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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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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config = processor.get_config()
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assert config == {"normalize_keys": ["observation.image"], "unnormalize_action": False, "eps": 1e-6}
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def test_integration_with_robot_processor(normalization_processor):
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"""Test integration with RobotProcessor pipeline"""
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robot_processor = RobotProcessor([normalization_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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}
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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 = robot_processor(transition)
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# Verify the processing worked
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assert isinstance(processed_transition[TransitionIndex.OBSERVATION], dict)
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assert isinstance(processed_transition[TransitionIndex.ACTION], torch.Tensor)
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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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transition = (None, None, None, None, None, None, None)
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result = normalizer(transition)
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assert result == transition
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def test_empty_stats():
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normalizer = ObservationNormalizer(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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result = normalizer(transition)
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# Should return observation unchanged
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assert torch.allclose(result[0]["observation.image"], observation["observation.image"])
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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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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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