src.model.deeplearn.dlrun.hierarchical_sg_post_processorpp
Classes
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- class src.model.deeplearn.dlrun.hierarchical_sg_post_processorpp.HierarchicalSGPostProcessorPP(hsg_preproc, **kwargs)
- Author:
Alberto M. Esmoris Pena
Postprocess the data from the first level of the SG hierarchy back to the original space.
See
HierarchicalSGPreProcessor.- Variables:
hsg_preproc – The preprocessor that generated the hierarchical sparse grid that must be reverted by the post-processor.
- __init__(hsg_preproc, **kwargs)
Initialization/instantiation of a hierarchical SG post-processor.
- Parameters:
hsg_preproc – The corresponding hierarchical SG pre-processor.
kwargs – The key-word arguments for the HierarchicalSGPostProcessor.
- __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 a list where each element contains the predictions computed on a sparse grid receptive field.
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
HierarchicalSGPostProcessor.__call__()providing the post-process logic itself.
- static find_cpp_postproc_fun(Xtype, ztype)
Determine the C++ function that must be used to post-process the output of the neural network back to the original point cloud.