PVCNN

Bases: Module

Stack of depth point-voxel convolutions, all at the same voxel resolution.

A resolution of \(0\) or None disables voxelization and falls back to per-point MLP layers.

Bases: ClassificationModel

PVCNN classification model from Point-Voxel CNN for Efficient 3D Deep Learning by Zhijian Liu, Haotian Tang, Yujun Lin, Song Han.

A flat stack of point-voxel conv blocks at decreasing voxel resolutions, followed by global pooling, an optional global MLP and a linear head.

Parameters:

• in_channels (int) –

  Number of input feature channels.

• num_classes (int) –

  Number of output classes. \(0\) replaces the head with nn.Identity.

• channels (Sequence[int]) –

  Output feature width of each block.

• global_channels (Optional[Sequence[int]], default: None ) –

  MLP channels applied to the pooled feature. None skips the global MLP.

• depths (Sequence[int]) –

  Number of point-voxel conv layers per block.

• kernel_sizes (Sequence[int]) –

  Voxel conv kernel size per block.

• resolutions (Sequence[int]) –

  Voxel grid resolution per block; \(0\) or None uses MLP layers instead.

• use_se (bool, default: False ) –

  Add a squeeze-and-excitation gate to every voxel branch.

• normalize (bool, default: True ) –

  Normalize coordinates into the unit voxel grid before voxelization.

• dropout (float, default: 0.0 ) –

  Dropout applied to the pooled features before the head.

• global_pool (PoolLike, default: 'max' ) –

  Global pooling reducing the point features to one vector per cloud.

• act (Union[str, Callable, None], default: 'relu' ) –

  Activation function.

• act_kwargs (Optional[Dict[str, Any]], default: None ) –

  Keyword arguments for the activation function.

• act_first (bool, default: False ) –

  Apply the activation before the normalization.

• norm (Union[str, Callable, None], default: 'batch_norm' ) –

  Normalization function.

• norm_kwargs (Optional[Dict[str, Any]], default: None ) –

  Keyword arguments for the normalization function.

Methods:

• configure_blocks –

  Builds the point-voxel conv blocks, one per entry of depths.

• configure_global_mlp –

  Builds the MLP applied to the pooled global feature, or None when global_channels is empty.

Attributes:

• num_features (int) –

  Channel count \(C\) of the pooled features entering the head.

num_features: int

configure_blocks() -> ModuleList

configure_global_mlp() -> Optional[MLP]

Bases: SegmentationModel

PVCNN segmentation model from Point-Voxel CNN for Efficient 3D Deep Learning by Zhijian Liu, Haotian Tang, Yujun Lin, Song Han.

The same flat stack of point-voxel conv blocks as PVCNNClassification, but every layer output is kept at full point resolution and concatenated with the broadcast global feature, so no decoder is needed to recover per-point logits.

Parameters:

• in_channels (int) –

  Number of input feature channels. Raised to spatial_dim when smaller.

• num_classes (int) –

  Number of output classes. \(0\) replaces the head with nn.Identity.

• channels (Sequence[int]) –

  Output feature width of each block.

• global_channels (Optional[Sequence[int]], default: None ) –

  MLP channels applied to the pooled feature. None skips the global MLP.

• depths (Sequence[int]) –

  Number of point-voxel conv layers per block.

• kernel_sizes (Sequence[int]) –

  Voxel conv kernel size per block.

• resolutions (Sequence[int]) –

  Voxel grid resolution per block; \(0\) or None uses MLP layers instead.

• spatial_dim (int, default: 3 ) –

  Spatial dimensionality of the point clouds.

• use_se (bool, default: False ) –

  Add a squeeze-and-excitation gate to every voxel branch.

• normalize (bool, default: True ) –

  Normalize coordinates into the unit voxel grid before voxelization.

• dropout (float, default: 0.0 ) –

  Dropout applied to the concatenated point features before the head.

• global_pool (PoolLike, default: 'max' ) –

  Global pooling producing the cloud-level feature that is broadcast back to the points.

• act (Union[str, Callable, None], default: 'relu' ) –

  Activation function.

• act_kwargs (Optional[Dict[str, Any]], default: None ) –

  Keyword arguments for the activation function.

• act_first (bool, default: False ) –

  Apply the activation before the normalization.

• norm (Union[str, Callable, None], default: 'batch_norm' ) –

  Normalization function.

• norm_kwargs (Optional[Dict[str, Any]], default: None ) –

  Keyword arguments for the normalization function.

• head_channels (Optional[Sequence[int]], default: None ) –

  Hidden MLP channels of the head. None uses a single linear layer.

• head_dropout (float, default: 0.0 ) –

  Dropout between the hidden head layers.

Methods:

• configure_blocks –

  Builds the point-voxel conv blocks, one per entry of depths.

• configure_global_mlp –

  Builds the MLP applied to the pooled global feature, or None when global_channels is empty.

Attributes:

• num_features (int) –

  Channel count \(C\) entering the head: every layer output concatenated with the global feature.

num_features: int

configure_blocks() -> ModuleList

configure_global_mlp() -> Optional[MLP]