open3d.ml.tf.models.PVCNN#

class open3d.ml.tf.models.PVCNN(*args, **kwargs)#

Semantic Segmentation model. Based on Point Voxel Convolutions. https://arxiv.org/abs/1907.03739

Uses PointNet architecture with separate Point and Voxel processing.

name#: Name of model. Default to “PVCNN”.

num_classes#: Number of classes.

num_points#: Number of points to sample per pointcloud.

extra_feature_channels#: Number of extra features. Default to 6 (RGB + Coordinate norms).

batcher#: Batching method for dataloader.

augment#: dictionary for augmentation.

__init__(name='PVCNN', device='cuda', num_classes=13, num_points=40960, extra_feature_channels=6, width_multiplier=1, voxel_resolution_multiplier=1, batcher='DefaultBatcher', augment=None, **kwargs)#

call(inputs, training=False)#

Forward pass for the model.

Parameters:

inputs – A dict object for inputs with following keys
point (tf.float32) – Input pointcloud (B, N,3)
feat (tf.float32) – Input features (B, N, 9)
training (bool) – Whether model is in training phase.

Returns:

probability distribution (B, N, C).

Return type:

tf.float32

get_batch_gen(dataset, steps_per_epoch=None, batch_size=1)#

get_loss(sem_seg_loss, results, inputs)#

Calculate the loss on output of the model.

Loss#: Object of type SemSegLoss.

results#: Output of the model.

inputs#: Input of the model.

device#: device(cpu or cuda).

Returns:: Returns loss, labels and scores.

get_optimizer(cfg_pipeline)#

Returns an optimizer object for the model.

Parameters:: cfg_pipeline – A Config object with the configuration of the pipeline.
Returns:: Returns a new optimizer object.

inference_begin(data)#

Function called right before running inference.

Parameters:: data – A data from the dataset.

inference_end(inputs, results)#

This function is called after the inference.

This function can be implemented to apply post-processing on the network outputs.

Parameters:: results – The model outputs as returned by the call() function. Post-processing is applied on this object.
Returns:: Returns True if the inference is complete and otherwise False. Returning False can be used to implement inference for large point clouds which require multiple passes.

inference_preprocess()#

This function prepares the inputs for the model.

Returns:: The inputs to be consumed by the call() function of the model.

preprocess(data, attr)#

Data preprocessing function.

This function is called before training to preprocess the data from a dataset. It consists of subsampling and normalizing the pointcloud and creating new features.

Parameters:

data – A sample from the dataset.
attr – The corresponding attributes.

Returns:

Returns the preprocessed data

transform(points, feat, labels)#

Transform function for the point cloud and features.

This function is called after preprocess method by dataset generator. It consists of mapping data to dict.

Parameters:

points – Input pointcloud.
feat – Input features.
labels – Input labels.

Returns:

Returns dictionary data with keys (point, feat, label).

blocks = ((64, 1, 32), (64, 2, 16), (128, 1, 16), (1024, 1, None))#