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refactor(processor): improve processor pipeline typing with generic type (#1810)
* refactor(processor): introduce generic type for to_output - Always return `TOutput` - Remove `_prepare_transition`, so `__call__` now always returns `TOutput` - Update tests accordingly - This refactor paves the way for adding settings for `to_transition` and `to_output` in `make_processor` and the post-processor * refactor(processor): consolidate ProcessorKwargs usage across policies - Removed the ProcessorTypes module and integrated ProcessorKwargs directly into the processor pipeline. - Updated multiple policy files to utilize the new ProcessorKwargs structure for preprocessor and postprocessor arguments. - Simplified the handling of processor kwargs by initializing them to empty dictionaries when not provided.
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@@ -188,7 +188,7 @@ def test_integration_with_robot_processor():
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}
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rename_processor = RenameProcessor(rename_map=rename_map)
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pipeline = RobotProcessor([rename_processor])
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pipeline = RobotProcessor([rename_processor], to_transition=lambda x: x, to_output=lambda x: x)
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observation = {
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"agent_pos": np.array([1.0, 2.0, 3.0]),
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@@ -236,7 +236,9 @@ def test_save_and_load_pretrained():
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assert len(state_files) == 0
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# Load pipeline
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loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir)
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loaded_pipeline = RobotProcessor.from_pretrained(
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tmp_dir, to_transition=lambda x: x, to_output=lambda x: x
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)
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assert loaded_pipeline.name == "TestRenameProcessor"
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assert len(loaded_pipeline) == 1
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@@ -277,7 +279,7 @@ def test_registry_functionality():
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def test_registry_based_save_load():
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"""Test save/load using registry name instead of module path."""
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processor = RenameProcessor(rename_map={"key1": "renamed_key1"})
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pipeline = RobotProcessor([processor])
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pipeline = RobotProcessor([processor], to_transition=lambda x: x, to_output=lambda x: x)
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Save and load
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@@ -318,7 +320,7 @@ def test_chained_rename_processors():
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}
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)
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pipeline = RobotProcessor([processor1, processor2])
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pipeline = RobotProcessor([processor1, processor2], to_transition=lambda x: x, to_output=lambda x: x)
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observation = {
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"pos": np.array([1.0, 2.0]),
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