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>
This commit is contained in:
Caroline Pascal
2026-06-15 17:55:52 +02:00
committed by GitHub
parent 9555efc02c
commit 38327fdc84
2 changed files with 26 additions and 6 deletions
+5 -3
View File
@@ -70,19 +70,21 @@ def aggregate_pipeline_dataset_features(
initial_features: dict[PipelineFeatureType, dict[str, Any]],
*,
use_videos: bool = True,
exclude_images: bool = False,
patterns: Sequence[str] | None = None,
) -> dict[str, dict]:
"""
Aggregates and filters pipeline features to create a dataset-ready features dictionary.
This function transforms initial features using the pipeline, categorizes them as action or observations
(image or state), filters them based on `use_videos` and `patterns`, and finally
(image or state), filters them based on `exclude_images` and `patterns`, and finally
formats them for use with a Hugging Face LeRobot Dataset.
Args:
pipeline: The DataProcessorPipeline to apply.
initial_features: A dictionary of raw feature specs for actions and observations.
use_videos: If False, image features are excluded.
use_videos: Controls the storage dtype for image features. If True, images are stored as "video"; if False, they are stored as "image".
exclude_images: If True, image features are dropped entirely from the output.
patterns: A sequence of regex patterns to filter action and state features.
Image features are not affected by this filter.
@@ -120,7 +122,7 @@ def aggregate_pipeline_dataset_features(
)
# 2. Apply filtering rules.
if is_image and not use_videos:
if is_image and exclude_images:
continue
if not is_image and not should_keep(key, compiled_patterns):
continue