src.model.deeplearn.dlrun.furthest_point_subsampling_pre_processorpp

Classes

FurthestPointSubsamplingPreProcessorPP(**kwargs)

class src.model.deeplearn.dlrun.furthest_point_subsampling_pre_processorpp.FurthestPointSubsamplingPreProcessorPP(**kwargs)
Author:

Alberto M. Esmoris Pena

C++ implementation of the FurthestPointSubsamplingPreProcessor.

__init__(**kwargs)

C++ version of FurthestPointSubsamplingPreProcessor.__init__().

__call__(inputs)

C++ version of FurthestPointSubsamplingPreProcessor.__call__().

reduce_labels(X_rf, y, I=None)

C++ version of FurthestPointSubsamplingPreProcessor.reduce_labels().

reduce_labels_python(X_rf, y, I=None)

Method that mimics a call to FurthestPointSubsamplingPreProcessor.reduce_labels() to provide a Python alternative to label reduction.

NOTE that this method should only be used for testing and debugging purposes.

static find_cpp_reduce_label_function(y, Ii, N)

Determine the C++ function that must be used to reduce the point-wise labels considering the data types of the input.

Parameters:

  • y (np.ndarray) – The input point-wise labels.

  • Ii (np.ndarray) – The indices of the neighbors in the original point cloud for a given i-th receptive field.

  • N (np.ndarray) – The first downsampling neighborhood of a given i-th receptive field.

Returns:

The C++ function for label reduction.

static prepare_training_class_distribution(training_class_distribution)

Prepare the training class distribution to be used for a C++ deep learning pre-processing call.

Parameters:

training_class_distribution – The training class distribution that must be prepared (typically, it comes from self.training_class_distribution)

Returns:

Prepared training classs distribution.

static prepare_radii(neighborhood_spec)

Prepare the radii argument to be used for a C++ deep learning pre-processing call.

Parameters:

neighborhood_spec (dict) – The neighborhood specification that contains the information that is needed to prepare the radii argument.

Returns:

Prepared radii argument.

Return type:

np.ndarray

static prepare_oversampling(oversampling_spec, num_points)

Prepare the oversampling argument to be used for a C++ deep learning pre-processing call.

Parameters:

  • oversampling_spec (dict) – The oversampling specification that contains the data that is needed to prepare the oversampling arguments.

  • num_points (int) – How many points are requested for the FPS subsampling strategy.

Returns:

Prepared oversampling arguments.

Return type:

list