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pvcnn

PVCNN classification and segmentation models.

First page of Point-Voxel CNN for Efficient 3D Deep Learning

1907.03739 · July 2019

Classes:

PVConvBlock [source] 

PVConvBlock(
    in_channels: int,
    out_channels: int,
    depth: int,
    kernel_size: int,
    resolution: int,
    use_se: bool = False,
    normalize: bool = True,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    act_first: bool = False,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

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.

PVCNNClassification [source] 

PVCNNClassification(
    in_channels: int,
    num_classes: int,
    *,
    channels: Sequence[int],
    global_channels: Optional[Sequence[int]] = None,
    depths: Sequence[int],
    kernel_sizes: Sequence[int],
    resolutions: Sequence[int],
    use_se: bool = False,
    normalize: bool = True,
    dropout: float = 0.0,
    global_pool: PoolLike = "max",
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    act_first: bool = False,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

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 property 

num_features: int

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

configure_blocks [source] 

configure_blocks() -> ModuleList

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

configure_global_mlp [source] 

configure_global_mlp() -> Optional[MLP]

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

PVCNNSegmentation [source] 

PVCNNSegmentation(
    in_channels: int,
    num_classes: int,
    *,
    channels: Sequence[int],
    global_channels: Optional[Sequence[int]] = None,
    depths: Sequence[int],
    kernel_sizes: Sequence[int],
    resolutions: Sequence[int],
    spatial_dim: int = 3,
    use_se: bool = False,
    normalize: bool = True,
    dropout: float = 0.0,
    global_pool: PoolLike = "max",
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    act_first: bool = False,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
    head_channels: Optional[Sequence[int]] = None,
    head_dropout: float = 0.0,
)

Bases: SemanticSegmentationModel

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 property 

num_features: int

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

configure_blocks [source] 

configure_blocks() -> ModuleList

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

configure_global_mlp [source] 

configure_global_mlp() -> Optional[MLP]

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