src.model.deeplearn.arch.point_net

Classes

PointNet(**kwargs)

class src.model.deeplearn.arch.point_net.PointNet(**kwargs)
Author:

Alberto M. Esmoris Pena

The PointNet architecture.

See https://arxiv.org/abs/1612.00593 and https://keras.io/examples/vision/pointnet_segmentation/#pointnet-model

__init__(**kwargs)

See architecture.Architecture.__init__().

build_input()

Build the input layer of the neural network. By default, only the 3D coordinates are considered as input, i.e., input dimensionality is three.

See architecture.Architecture.build_input().

Returns:

Built layer.

Return type:

tf.Tensor

static build_point_net_input(pnet)

See PointNet.build_input().

build_hidden(x, **kwargs)

Build the hidden layers of the PointNet neural network.

Parameters:

x (tf.Tensor or list) – The input layer for the first hidden layer. Alternatively, it can be a list with many input layers.

Returns:

The last hidden layer. Alternatively, it can be a list with many hidden layers.

Return type:

tf.Tensor or list

static build_hidden_pointnet(x, pretransf_feats, postransf_feats, tnet_pre_filters, tnet_post_filters, kernel_initializer, space_dimensionality=3, prefix='X_')

Build the PointNet block of the architecture on the given input.

Parameters:

  • x (tf.Tensor) – The input for the PointNet block/architecture.

  • pretransf_feats (list) – The specification of the filters and the name for each feature extraction layer before the transformation block in the middle.

  • postransf_feats (list) – The specification of the filters and the name for each feature extraction layer after the transformation block in the middle.

  • tnet_pre_filters (list) – The list of number of filters (integer) defining each convolutional block before the global pooling.

  • tnet_post_filters (list) – The list of number of filters (integer) defining each MLP block after the global pooling.

  • kernel_initializer (str) – The name of the kernel initializer

  • space_dimensionality (int) – The dimensionality of the space where the PointNet block operates (typicalle 3D for the structure space, i.e., x,y,z).

Returns:

Last layer of the built PointNet, the list of pre-transformations, the layer of transformed features, and the list of post-transformations. Finally, the aligned input tensor.

Return type:

tf.Tensor and list and keras.Layer and list and tf.Tensor

static build_transformation_block(inputs, num_features, name, tnet_pre_filters, tnet_post_filters, kernel_initializer)

Build a transformation block.

Parameters:

  • inputs (tf.Tensor) – The input tensor.

  • num_features (int) – The number of features to be transformed.

  • name (str) – The name of the block.

  • tnet_pre_filters (list) – The list of number of filters (integer) defining each convolutional block before the global pooling.

  • tnet_post_filters (list) – The list of nubmer of filters (integer) defining each MLP block after the global pooling.

  • kernel_initializer (str) – The name of the kernel initializer

Returns:

The last layer of the transformation block

static build_transformation_net(inputs, num_features, name, tnet_pre_filters, tnet_post_filters, kernel_initializer)

Assists the point_net.PointNet.build_transformation_block() method.

static build_conv_block(x, filters, name, kernel_initializer)

Build a convolutional block.

Parameters:

  • x (tf.Tensor) – The input tensor.

  • filters (int) – The dimensionality of the output.

  • name (str) – The name of the block

  • kernel_initializer (str) – The name of the kernel initializer

static build_mlp_block(x, filters, name, kernel_initializer, batch_normalization=True)

Build an MLP block.

Parameters:

  • x (tf.Tensor) – The input tensor.

  • filters (int) – The dimensionality of the output.

  • kernel_initializer (str) – The name of the kernel initializer

  • name (str) – The name of the block.

  • batch_normalization (bool) – Whether to apply batch normalization (True) or not (False).