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fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion (#3783)
* fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion when videos are not used * fix(docstrings): improving docstrings Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com> --------- Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>
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@@ -70,19 +70,21 @@ def aggregate_pipeline_dataset_features(
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initial_features: dict[PipelineFeatureType, dict[str, Any]],
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*,
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use_videos: bool = True,
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exclude_images: bool = False,
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patterns: Sequence[str] | None = None,
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) -> dict[str, dict]:
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"""
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Aggregates and filters pipeline features to create a dataset-ready features dictionary.
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This function transforms initial features using the pipeline, categorizes them as action or observations
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(image or state), filters them based on `use_videos` and `patterns`, and finally
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(image or state), filters them based on `exclude_images` and `patterns`, and finally
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formats them for use with a Hugging Face LeRobot Dataset.
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Args:
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pipeline: The DataProcessorPipeline to apply.
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initial_features: A dictionary of raw feature specs for actions and observations.
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use_videos: If False, image features are excluded.
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use_videos: Controls the storage dtype for image features. If True, images are stored as "video"; if False, they are stored as "image".
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exclude_images: If True, image features are dropped entirely from the output.
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patterns: A sequence of regex patterns to filter action and state features.
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Image features are not affected by this filter.
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@@ -120,7 +122,7 @@ def aggregate_pipeline_dataset_features(
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)
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# 2. Apply filtering rules.
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if is_image and not use_videos:
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if is_image and exclude_images:
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continue
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if not is_image and not should_keep(key, compiled_patterns):
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continue
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