Proposal for lazy-loaded annotations. #1484
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* A LazyDict data structure calls a loading function when values are retrieved * This enables us to conceptualize a `lazy_detections` objects, which store masks as polygons until loaded. * Such polygons are placed in `data` along with `resolution_xy` * The dataset can take either lazy-loaded or normal Detections * So far, only `yolo_annotations_to_detections` was changed to produce lazy values. * The dataset can still function as before - the main different users will see is the datatype for annotations input: `Union[Dict[str, Detections], LazyDict[str, Detections, Detections]`.
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This branch contains a proposal for lazy-loading annotations for
DetectionsDataset.It's been tested scarcely - enough to show that memory usage does not grow when loading a large YOLO dataset. The main purpose is to have a code reference for further discussions.
Description
lazy_detectionsobjects, which store masks as polygons until loaded.dataalong withresolution_xyyolo_annotations_to_detectionswas changed to produce lazy values.Union[Dict[str, Detections], LazyDict[str, Detections, Detections].Type of change
Please delete options that are not relevant.
How has this change been tested, please provide a testcase or example of how you tested the change?
pytest+ loaded yolo dataset with it, comparing memory use and outputs with non-lazy method.The memory use does not grow, and the results are the same.
I did not test this thoroughly so far.
Any specific deployment considerations
For example, documentation changes, usability, usage/costs, secrets, etc.
Docs