src.model.deeplearn.dlrun.point_net_pre_processor

Classes

PointNetPreProcessor(**kwargs)

class src.model.deeplearn.dlrun.point_net_pre_processor.PointNetPreProcessor(**kwargs)

Author:

Alberto M. Esmoris Pena

Preprocess the input dictionary of X (coordinates), F (features), and y (expected values) so it can be feed into the PointNet neural network.

Variables:

• pre_processor_type (str) – String representing the type of pre-processor to generate the Input for the PointNet neural network.

• pre_processor (GridSubsamplingPreProcessor or FurthestPointSubsamplingPreProcessor) – The pre-processor itself (instantiated).

__init__(**kwargs)

Initialization/instantiation of a PointNet pre-processor.

Parameters:

kwargs – The key-word arguments for the PointNetPreProcessor.

__call__(inputs)

Executes the pre-processing logic.

See GridSubsamplingPreProcessor and FurthestPointSubsamplingPreProcessor.

Parameters:

inputs (dict) – A key-word input where the key “X” gives the input dataset and the “y” (OPTIONALLY) gives the reference values that can be used to fit/train a PointNet model.

Returns:

Either (Xout, yout) or Xout. Where Xout are the points representing the receptive field and yout (only given when “y” was given in the inputs dictionary) the corresponding reference values for those points.

get_num_input_points()

PointNet pre-processors must provide the expected number of input points to batch the many input neighborhoods.

Returns:

Number of input points per neighborhood.

Return type:

int

overwrite_pretrained_model(spec)

Assist the model.Model.overwrite_pretrained_model() method through assisting the architecture.Architecture.overwrite_pretrained_model() method.

Parameters:

spec (dict) – The key-word specification containing the model’s arguments.