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refactor(pipeline): feature contract now categorizes between OBS or Action (#1867)

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* refactor(processor): signature of transform_features

* refactor(processor): remove prefixes + processor respect new transform_features signature + update test accordingly

* refactor(processor): rename now is only for visual

* refactor(processor): update normalize processor

* refactor(processor): update vanilla processor features

* refactor(processor): feature contract now uses its own enum

* chore(processor): rename renameprocessor

* chore(processor): minor changes

* refactor(processor): add create & change aggregate

* refactor(processor): update aggregate

* refactor(processor): simplify to functions, fix features contracts and rename function

* test(processor): remove to converter tests as now they are very simple

* chore(docs): recover docs joint observations processor

* fix(processor): update RKP

* fix(tests): recv diff test_pipeline

* chore(tests): add docs to test

* chore(processor): leave obs language constant untouched

* fix(processor): correct new shape of feature in crop image processor
This commit is contained in:
Steven Palma
2025-09-09 18:27:30 +02:00
committed by GitHub
parent acf0ba7fb3
commit e881fb6678
47 changed files with 781 additions and 616 deletions
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46
tests/processor/test_tokenizer_processor.py
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@@ -8,7 +8,7 @@ from unittest.mock import patch
import pytest
import torch
from lerobot.configs.types import FeatureType, PolicyFeature
from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
from lerobot.constants import OBS_LANGUAGE
from lerobot.processor import DataProcessorPipeline, TokenizerProcessorStep, TransitionKey
from tests.utils import require_package
@@ -512,23 +512,27 @@ def test_features_basic():
processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=128)
input_features = {
"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(10,)),
"action": PolicyFeature(type=FeatureType.ACTION, shape=(5,)),
PipelineFeatureType.OBSERVATION: {
"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(10,))
},
PipelineFeatureType.ACTION: {"action": PolicyFeature(type=FeatureType.ACTION, shape=(5,))},
}
output_features = processor.transform_features(input_features)
# Check that original features are preserved
assert "observation.state" in output_features
assert "action" in output_features
assert "observation.state" in output_features[PipelineFeatureType.OBSERVATION]
assert "action" in output_features[PipelineFeatureType.ACTION]
# Check that tokenized features are added
assert f"{OBS_LANGUAGE}.tokens" in output_features
assert f"{OBS_LANGUAGE}.attention_mask" in output_features
assert f"{OBS_LANGUAGE}.tokens" in output_features[PipelineFeatureType.OBSERVATION]
assert f"{OBS_LANGUAGE}.attention_mask" in output_features[PipelineFeatureType.OBSERVATION]
# Check feature properties
tokens_feature = output_features[f"{OBS_LANGUAGE}.tokens"]
attention_mask_feature = output_features[f"{OBS_LANGUAGE}.attention_mask"]
tokens_feature = output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.tokens"]
attention_mask_feature = output_features[PipelineFeatureType.OBSERVATION][
f"{OBS_LANGUAGE}.attention_mask"
]
assert tokens_feature.type == FeatureType.LANGUAGE
assert tokens_feature.shape == (128,)
@@ -542,15 +546,17 @@ def test_features_with_custom_max_length():
mock_tokenizer = MockTokenizer(vocab_size=100)
processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=64)
input_features = {}
input_features = {PipelineFeatureType.OBSERVATION: {}}
output_features = processor.transform_features(input_features)
# Check that features use correct max_length
assert f"{OBS_LANGUAGE}.tokens" in output_features
assert f"{OBS_LANGUAGE}.attention_mask" in output_features
assert f"{OBS_LANGUAGE}.tokens" in output_features[PipelineFeatureType.OBSERVATION]
assert f"{OBS_LANGUAGE}.attention_mask" in output_features[PipelineFeatureType.OBSERVATION]
tokens_feature = output_features[f"{OBS_LANGUAGE}.tokens"]
attention_mask_feature = output_features[f"{OBS_LANGUAGE}.attention_mask"]
tokens_feature = output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.tokens"]
attention_mask_feature = output_features[PipelineFeatureType.OBSERVATION][
f"{OBS_LANGUAGE}.attention_mask"
]
assert tokens_feature.shape == (64,)
assert attention_mask_feature.shape == (64,)
@@ -563,15 +569,19 @@ def test_features_existing_features():
processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=256)
input_features = {
f"{OBS_LANGUAGE}.tokens": PolicyFeature(type=FeatureType.LANGUAGE, shape=(100,)),
f"{OBS_LANGUAGE}.attention_mask": PolicyFeature(type=FeatureType.LANGUAGE, shape=(100,)),
PipelineFeatureType.OBSERVATION: {
f"{OBS_LANGUAGE}.tokens": PolicyFeature(type=FeatureType.LANGUAGE, shape=(100,)),
f"{OBS_LANGUAGE}.attention_mask": PolicyFeature(type=FeatureType.LANGUAGE, shape=(100,)),
}
}
output_features = processor.transform_features(input_features)
# Should not overwrite existing features
assert output_features[f"{OBS_LANGUAGE}.tokens"].shape == (100,) # Original shape preserved
assert output_features[f"{OBS_LANGUAGE}.attention_mask"].shape == (100,)
assert output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.tokens"].shape == (
100,
) # Original shape preserved
assert output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"].shape == (100,)
@require_package("transformers")