src.tests.vl3dpp_dl_hierarchical_sg_post_proc_test

Classes

VL3DPPDLHierarchicalSGPostProcTest()

class src.tests.vl3dpp_dl_hierarchical_sg_post_proc_test.VL3DPPDLHierarchicalSGPostProcTest

Author:

Alberto M. Esmrois Pena

Deep learning post-processor test that checks the C++ implementation of the hierarchical sparse-grid post-processing logic for deep learning models.

__init__()

Basic configuration for any VL3D test.

Parameters:

name (str) – Test name

run()

Run C++ deep learning hierarchical post-processors test.

Returns:

True if the C++ hierarchical sparse grid post-processor works as expected, False otherwise.

Return type:

bool

static z_from_y(y, ny)

Generate probabilities from labels.

Parameters:

• y – The labels from which the probabilities must be derived.

• ny – The number of different classes.

Returns:

The matrix of probabilities for each receptive field.

Return type:

list of np.ndarray

static testSparseDLPostProc(hsgpre, inputspre, X, y, ny)

Check whether the HierarchicalSGPostProcessorPP yields the expected output or not.

Parameters:

• hsgpre – The HierarchicalSGPreProcessorPP associated to the HierarchicalSGPostProcessorPP.

• inputspre – The input dictionary for the pre-processor.

• X – The structure space representing the original input point cloud.

• y – The point-wise labels for the original input point cloud.

• ny – The number of classes.

Returns:

True if the test is passed, False otherwise.

Return type:

bool