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chore(processor): rename RobotProcessor -> DataProcessorPipeline (#1850)

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This commit is contained in:
Steven Palma
2025-09-03 17:13:16 +02:00
committed by GitHub
parent 4ebe482a7e
commit 8c796b39f5
38 changed files with 326 additions and 298 deletions
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26
tests/processor/test_normalize_processor.py
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@@ -21,9 +21,9 @@ import torch
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
from lerobot.processor import (
DataProcessorPipeline,
IdentityProcessor,
NormalizerProcessor,
RobotProcessor,
TransitionKey,
UnnormalizerProcessor,
hotswap_stats,
@@ -508,7 +508,9 @@ def test_get_config(full_stats):
def test_integration_with_robot_processor(normalizer_processor):
"""Test integration with RobotProcessor pipeline"""
robot_processor = RobotProcessor([normalizer_processor], to_transition=lambda x: x, to_output=lambda x: x)
robot_processor = DataProcessorPipeline(
[normalizer_processor], to_transition=lambda x: x, to_output=lambda x: x
)
observation = {
"observation.image": torch.tensor([0.7, 0.5, 0.3]),
@@ -1009,7 +1011,7 @@ def test_hotswap_stats_basic_functionality():
identity = IdentityProcessor()
# Create robot processor
robot_processor = RobotProcessor(steps=[normalizer, unnormalizer, identity])
robot_processor = DataProcessorPipeline(steps=[normalizer, unnormalizer, identity])
# Hotswap stats
new_processor = hotswap_stats(robot_processor, new_stats)
@@ -1046,7 +1048,7 @@ def test_hotswap_stats_deep_copy():
norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
original_processor = RobotProcessor(steps=[normalizer])
original_processor = DataProcessorPipeline(steps=[normalizer])
# Store reference to original stats
original_stats_reference = original_processor.steps[0].stats
@@ -1089,7 +1091,7 @@ def test_hotswap_stats_only_affects_normalizer_steps():
unnormalizer = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=stats)
identity = IdentityProcessor()
robot_processor = RobotProcessor(steps=[normalizer, identity, unnormalizer])
robot_processor = DataProcessorPipeline(steps=[normalizer, identity, unnormalizer])
# Hotswap stats
new_processor = hotswap_stats(robot_processor, new_stats)
@@ -1116,7 +1118,7 @@ def test_hotswap_stats_empty_stats():
norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
robot_processor = RobotProcessor(steps=[normalizer])
robot_processor = DataProcessorPipeline(steps=[normalizer])
# Hotswap with empty stats
new_processor = hotswap_stats(robot_processor, empty_stats)
@@ -1133,7 +1135,7 @@ def test_hotswap_stats_no_normalizer_steps():
}
# Create processor with only identity steps
robot_processor = RobotProcessor(steps=[IdentityProcessor(), IdentityProcessor()])
robot_processor = DataProcessorPipeline(steps=[IdentityProcessor(), IdentityProcessor()])
# Hotswap stats - should work without error
new_processor = hotswap_stats(robot_processor, stats)
@@ -1172,7 +1174,7 @@ def test_hotswap_stats_preserves_other_attributes():
normalize_observation_keys=normalize_observation_keys,
eps=eps,
)
robot_processor = RobotProcessor(steps=[normalizer])
robot_processor = DataProcessorPipeline(steps=[normalizer])
# Hotswap stats
new_processor = hotswap_stats(robot_processor, new_stats)
@@ -1215,7 +1217,7 @@ def test_hotswap_stats_multiple_normalizer_types():
unnormalizer1 = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
unnormalizer2 = UnnormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
robot_processor = RobotProcessor(steps=[normalizer1, unnormalizer1, normalizer2, unnormalizer2])
robot_processor = DataProcessorPipeline(steps=[normalizer1, unnormalizer1, normalizer2, unnormalizer2])
# Hotswap stats
new_processor = hotswap_stats(robot_processor, new_stats)
@@ -1263,7 +1265,7 @@ def test_hotswap_stats_with_different_data_types():
}
normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
robot_processor = RobotProcessor(steps=[normalizer])
robot_processor = DataProcessorPipeline(steps=[normalizer])
# Hotswap stats
new_processor = hotswap_stats(robot_processor, new_stats)
@@ -1319,7 +1321,9 @@ def test_hotswap_stats_functional_test():
# Create original processor
normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=initial_stats)
original_processor = RobotProcessor(steps=[normalizer], to_transition=lambda x: x, to_output=lambda x: x)
original_processor = DataProcessorPipeline(
steps=[normalizer], to_transition=lambda x: x, to_output=lambda x: x
)
# Process with original stats
original_result = original_processor(transition)