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https://github.com/huggingface/lerobot.git
synced 2026-05-16 17:20:05 +00:00
feat(processor): enhance type safety with generic DataProcessorPipeline for policy and robot pipelines (#1915)
* refactor(processor): enhance type annotations for processors in record, replay, teleoperate, and control utils - Updated type annotations for preprocessor and postprocessor parameters in record_loop and predict_action functions to specify the expected dictionary types. - Adjusted robot_action_processor type in ReplayConfig and TeleoperateConfig to improve clarity and maintainability. - Ensured consistency in type definitions across multiple files, enhancing overall code readability. * refactor(processor): enhance type annotations for RobotProcessorPipeline in various files - Updated type annotations for RobotProcessorPipeline instances in evaluate.py, record.py, replay.py, teleoperate.py, and other related files to specify input and output types more clearly. - Introduced new type conversions for PolicyAction and EnvTransition to improve type safety and maintainability across the processing pipelines. - Ensured consistency in type definitions, enhancing overall code readability and reducing potential runtime errors. * refactor(processor): update transition handling in processors to use transition_to_batch - Replaced direct transition handling with transition_to_batch in various processor tests and implementations to ensure consistent batching of input data. - Updated assertions in tests to reflect changes in data structure, enhancing clarity and maintainability. - Improved overall code readability by standardizing the way transitions are processed across different processor types. * refactor(tests): standardize transition key usage in processor tests - Updated assertions in processor test files to utilize the TransitionKey for action references, enhancing consistency across tests. - Replaced direct string references with TransitionKey constants for improved readability and maintainability. - Ensured that all relevant tests reflect these changes, contributing to a more uniform approach in handling transitions.
This commit is contained in:
Adil Zouitine
@@ -31,7 +31,7 @@ from lerobot.processor import (
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NormalizerProcessorStep,
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TransitionKey,
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)
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from lerobot.processor.converters import create_transition, identity_transition
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from lerobot.processor.converters import create_transition, transition_to_batch
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def create_default_config():
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@@ -93,8 +93,6 @@ def test_classifier_processor_normalization():
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Create test data
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@@ -104,14 +102,15 @@ def test_classifier_processor_normalization():
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}
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action = torch.randn(1) # Dummy action/reward
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(transition)
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processed = preprocessor(batch)
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# Check that data is processed
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (10,)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (3, 224, 224)
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assert processed[TransitionKey.ACTION].shape == (1,)
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assert processed[OBS_STATE].shape == (10,)
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assert processed[OBS_IMAGE].shape == (3, 224, 224)
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assert processed[TransitionKey.ACTION.value].shape == (1,)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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@@ -124,8 +123,6 @@ def test_classifier_processor_cuda():
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Create CPU data
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@@ -136,20 +133,22 @@ def test_classifier_processor_cuda():
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action = torch.randn(1)
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(transition)
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processed = preprocessor(batch)
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# Check that data is on CUDA
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda"
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].device.type == "cuda"
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assert processed[TransitionKey.ACTION].device.type == "cuda"
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assert processed[OBS_STATE].device.type == "cuda"
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assert processed[OBS_IMAGE].device.type == "cuda"
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assert processed[TransitionKey.ACTION.value].device.type == "cuda"
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# Process through postprocessor
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reward_transition = create_transition(action=processed[TransitionKey.ACTION])
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postprocessed = postprocessor(reward_transition)
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postprocessed = postprocessor(processed[TransitionKey.ACTION.value])
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# Check that output is back on CPU
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assert postprocessed[TransitionKey.ACTION].device.type == "cpu"
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assert postprocessed.device.type == "cpu"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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@@ -162,8 +161,6 @@ def test_classifier_processor_accelerate_scenario():
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Simulate Accelerate: data already on GPU
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@@ -175,13 +172,16 @@ def test_classifier_processor_accelerate_scenario():
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action = torch.randn(1).to(device)
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(transition)
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processed = preprocessor(batch)
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# Check that data stays on same GPU
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].device == device
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION].device == device
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assert processed[OBS_STATE].device == device
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assert processed[OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION.value].device == device
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs")
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@@ -202,13 +202,16 @@ def test_classifier_processor_multi_gpu():
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action = torch.randn(1).to(device)
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(transition)
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processed = preprocessor(batch)
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# Check that data stays on cuda:1
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].device == device
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION].device == device
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assert processed[OBS_STATE].device == device
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assert processed[OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION.value].device == device
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def test_classifier_processor_without_stats():
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@@ -229,7 +232,9 @@ def test_classifier_processor_without_stats():
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action = torch.randn(1)
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transition = create_transition(observation, action)
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processed = preprocessor(transition)
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batch = transition_to_batch(transition)
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processed = preprocessor(batch)
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assert processed is not None
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@@ -238,22 +243,14 @@ def test_classifier_processor_save_and_load():
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config = create_default_config()
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stats = create_default_stats()
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# Get the steps from the factory function
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factory_preprocessor, factory_postprocessor = make_classifier_processor(config, stats)
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# Create new processors with EnvTransition input/output
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preprocessor = DataProcessorPipeline(
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factory_preprocessor.steps, to_transition=identity_transition, to_output=identity_transition
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)
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Save preprocessor
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preprocessor.save_pretrained(tmpdir)
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# Load preprocessor
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loaded_preprocessor = DataProcessorPipeline.from_pretrained(
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tmpdir, to_transition=identity_transition, to_output=identity_transition
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)
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loaded_preprocessor = DataProcessorPipeline.from_pretrained(tmpdir)
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# Test that loaded processor works
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observation = {
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@@ -262,11 +259,12 @@ def test_classifier_processor_save_and_load():
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}
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action = torch.randn(1)
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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processed = loaded_preprocessor(transition)
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (10,)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (3, 224, 224)
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assert processed[TransitionKey.ACTION].shape == (1,)
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processed = loaded_preprocessor(batch)
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assert processed[OBS_STATE].shape == (10,)
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assert processed[OBS_IMAGE].shape == (3, 224, 224)
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assert processed[TransitionKey.ACTION.value].shape == (1,)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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@@ -276,21 +274,16 @@ def test_classifier_processor_mixed_precision():
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config.device = "cuda"
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stats = create_default_stats()
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# Get the steps from the factory function
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factory_preprocessor, factory_postprocessor = make_classifier_processor(config, stats)
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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# Replace DeviceProcessorStep with one that uses float16
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modified_steps = []
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for step in factory_preprocessor.steps:
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for step in preprocessor.steps:
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if isinstance(step, DeviceProcessorStep):
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modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16"))
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else:
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modified_steps.append(step)
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# Create new processors with EnvTransition input/output
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preprocessor = DataProcessorPipeline(
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modified_steps, to_transition=identity_transition, to_output=identity_transition
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)
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preprocessor.steps = modified_steps
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# Create test data
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observation = {
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@@ -300,13 +293,16 @@ def test_classifier_processor_mixed_precision():
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action = torch.randn(1, dtype=torch.float32)
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(transition)
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processed = preprocessor(batch)
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# Check that data is converted to float16
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.float16
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].dtype == torch.float16
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assert processed[TransitionKey.ACTION].dtype == torch.float16
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assert processed[OBS_STATE].dtype == torch.float16
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assert processed[OBS_IMAGE].dtype == torch.float16
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assert processed[TransitionKey.ACTION.value].dtype == torch.float16
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def test_classifier_processor_batch_data():
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@@ -317,8 +313,6 @@ def test_classifier_processor_batch_data():
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Test with batched data
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@@ -330,13 +324,16 @@ def test_classifier_processor_batch_data():
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action = torch.randn(batch_size, 1)
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transition = create_transition(observation, action)
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(transition)
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processed = preprocessor(batch)
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# Check that batch dimension is preserved
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (batch_size, 10)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (batch_size, 3, 224, 224)
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assert processed[TransitionKey.ACTION].shape == (batch_size, 1)
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assert processed[OBS_STATE].shape == (batch_size, 10)
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assert processed[OBS_IMAGE].shape == (batch_size, 3, 224, 224)
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assert processed[TransitionKey.ACTION.value].shape == (batch_size, 1)
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def test_classifier_processor_postprocessor_identity():
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@@ -347,17 +344,17 @@ def test_classifier_processor_postprocessor_identity():
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Create test data for postprocessor
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reward = torch.tensor([[0.8], [0.3], [0.9]]) # Batch of rewards/predictions
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transition = create_transition(action=reward)
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_ = transition_to_batch(transition)
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# Process through postprocessor
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processed = postprocessor(transition)
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processed = postprocessor(reward)
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# IdentityProcessor should leave values unchanged (except device)
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assert torch.allclose(processed[TransitionKey.ACTION].cpu(), reward.cpu())
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assert processed[TransitionKey.ACTION].device.type == "cpu"
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assert torch.allclose(processed.cpu(), reward.cpu())
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assert processed.device.type == "cpu"
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