src.model.deeplearn.dlrun.hierarchical_post_processor
Classes
- class src.model.deeplearn.dlrun.hierarchical_post_processor.HierarchicalPostProcessor(hierarchical_preproc, **kwargs)
- Author:
Alberto M. Esmoris Pena
Postprocess the output of a hierarchical neural network (e.g., hierarchical autoencoders) to transform it to the expected output format.
- Variables:
hierarchical_preproc (
HierarchicalPreProcessor) – The preprocessor that generated the input for the model which output must be handled by the post-processor.
- __init__(hierarchical_preproc, **kwargs)
Initialization/instantiation of a hierarchical post-processor.
- Parameters:
hierarchical_preproc (
HierarchicalPostProcessor) – The pre-processor associated to the model which output must be handled by the post-processor.kwargs – The key-word arguments for the HierarchicalPostProcessor.
- __call__(inputs, reducer=None)
Executes the post-processing logic.
- Parameters:
inputs (dict) – A key-word input where the key “X” give 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 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.