src.model.deeplearn.dlrun.hierarchical_fps_post_processor

Classes

HierarchicalFPSPostProcessor(hfps_preproc, ...)

class src.model.deeplearn.dlrun.hierarchical_fps_post_processor.HierarchicalFPSPostProcessor(hfps_preproc, **kwargs)
Author:

Alberto M. Esmoris Pena

Postprocess the data from the first level of the FPS hierarchy back to the original space.

See HierarchicalFPSPreProcessor and FurthestPointSubsamplingPostProcessor.

Variables:

hfps_preproc (HierarchicalFPSPreProcessor.) – The preprocessor that generated the hierarchical furthest point subsampling that must be reverted by the post-processor.

__init__(hfps_preproc, **kwargs)

Initialization/instantiation of a hierarchical FPS post-processor.

Parameters:

  • hfps_preproc – The corresponding hierarchical FPS pre-processor.

  • kwargs – The key-word arguments for the HierarchicalFPSPostProcessor.

__call__(inputs, reducer=None)

Executes the post-processing logic.

Parameters:

  • inputs (dict) – A key-word input where the key “X” gives the coordinates of the points in the original point cloud. Also, the key “z” gives the predictions computed on a receptive field of \(R_1\) points (i.e., at depth \(d=1\)) that must be propagated back to the \(m\) points of the original point cloud.

  • reducer (PredictionReducer) – The prediction reducer for the post-processor, if any.

Returns:

The \(m\) point-wise predictions derived from the \(R\) input predictions on the receptive field.

Return type:

np.ndarray

post_process(inputs, reducer)

Assists the HierarchicalFPSPostProcessor.__call__() providing the post-process logic itself.