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tnet

Orthogonality regularizer of the T-Net transforms (PointNet's feature-transform regularization).

Classes:

TNetOrthogonalityRegularizer [source]

Bases: Module

Module form of tnet_orthogonality_regularizer.

Example 
import torch
from torch_pointcloud.layers import TNet
from torch_pointcloud.losses import TNetOrthogonalityRegularizer
ftnet = TNet(local_channels=[32, 64], global_channels=[32], k=16)
out = ftnet(torch.randn(10, 16), torch.arange(2).repeat_interleave(5))
TNetOrthogonalityRegularizer()(ftnet).shape  # torch.Size([])

tnet_orthogonality_regularizer [source] 

tnet_orthogonality_regularizer(module: Module) -> Tensor

Pull the transforms predicted by every T-Net in module towards an orthogonal matrix.

The feature-transform regularization of PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (Qi et al., 2017), \(\lVert I - A A^\top \rVert_F\) averaged over the samples of the batch and summed over the T-Nets found in module. Pass the T-Net (or the sub-module) to regularize, e.g. a PointNet's feature transform model.ftnet to leave its input transform free, as the PointNet paper does.

The matrices are read from the running_transform the layers keep from their last forward pass, so call it after the forward and before the backward of the same batch.

Parameters:

  • module (Module) –

    A TNet / DynamicTNet, or a module containing some.

Returns:

  • Tensor –

    The scalar regularizer (zero when module holds no T-Net).

Raises:

  • RuntimeError –

    If a T-Net has no stored transform (no forward pass yet, or track_running_stats=False).

Example 
>>> from torch_pointcloud.layers.tnet import TNet
>>> tnet = TNet(local_channels=[32, 64], global_channels=[32], k=8)
>>> _ = tnet(torch.randn(6, 8), torch.tensor([0, 0, 0, 1, 1, 1]))
>>> tnet_orthogonality_regularizer(tnet).shape
torch.Size([])