pointconv_sa

PointConv set abstraction modules for hierarchical and global feature extraction.

Classes:

PointConvSetAbstraction –

PointConv set-abstraction block: optional downsampling, \(k\)-NN grouping, and a
PointConvDensitySetAbstraction –

PointConv set-abstraction block with inverse-density re-weighting.
PointConvGlobalSetAbstraction –

Global PointConv set-abstraction block: one weight-net convolution over each whole sample.
PointConvDensityGlobalSetAbstraction –

Global PointConv set-abstraction block with inverse-density re-weighting.

PointConvSetAbstraction ¶

PointConvSetAbstraction(
    in_channels: int,
    num_neighbors: int,
    channels: Sequence[int],
    weight_channels: Sequence[int] = (8, 8),
    expansion: int = 16,
    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,
    bias: bool = True,
    spatial_dim: int = 3,
    downsample: Optional[Module] = None,
)

Bases: Module

PointConv set-abstraction block: optional downsampling, \(k\)-NN grouping, and a weight-net continuous convolution.

Parameters:

in_channels (int) –

Number of input feature channels.
num_neighbors (int) –

Number of neighbors gathered per output point.
channels (Sequence[int]) –

Per-layer channel sizes of the feature MLP.
weight_channels (Sequence[int], default: (8, 8) ) –

Hidden channel sizes of the weight net applied to relative positions.
expansion (int, default: 16 ) –

Channel expansion factor of the final matrix multiplication.
spatial_dim (int, default: 3 ) –

Dimension of point coordinates.
downsample (Optional[Module], default: None ) –

Optional module returning the sampled indices (e.g. FPS). If None, the resolution is unchanged.

PointConvDensitySetAbstraction ¶

PointConvDensitySetAbstraction(
    in_channels: int,
    num_neighbors: int,
    channels: Sequence[int],
    bandwidth: float = 1.0,
    weight_channels: Sequence[int] = (8, 8),
    density_channels: Sequence[int] = (16, 8),
    expansion: int = 16,
    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,
    bias: bool = True,
    spatial_dim: int = 3,
    downsample: Optional[Module] = None,
)

Bases: Module

PointConv set-abstraction block with inverse-density re-weighting.

Same layout as PointConvSetAbstraction, with a Gaussian kernel density estimated per point; the inverse density is transformed by a density net and re-weights the grouped features.

Parameters:

in_channels (int) –

Number of input feature channels.
num_neighbors (int) –

Number of neighbors gathered per output point.
channels (Sequence[int]) –

Per-layer channel sizes of the feature MLP.
bandwidth (float, default: 1.0 ) –

Bandwidth of the Gaussian kernel density estimate.
weight_channels (Sequence[int], default: (8, 8) ) –

Hidden channel sizes of the weight net applied to relative positions.
density_channels (Sequence[int], default: (16, 8) ) –

Hidden channel sizes of the density net.
expansion (int, default: 16 ) –

Channel expansion factor of the final matrix multiplication.
spatial_dim (int, default: 3 ) –

Dimension of point coordinates.
downsample (Optional[Module], default: None ) –

Optional module returning the sampled indices (e.g. FPS). If None, the resolution is unchanged.

PointConvGlobalSetAbstraction ¶

PointConvGlobalSetAbstraction(
    in_channels: int,
    channels: Sequence[int],
    weight_channels: Sequence[int] = (8, 8),
    expansion: int = 16,
    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,
    bias: bool = True,
    aggr: PoolLike = "mean",
    spatial_dim: int = 3,
)

Bases: Module

Global PointConv set-abstraction block: one weight-net convolution over each whole sample.

Parameters:

in_channels (int) –

Number of input feature channels.
channels (Sequence[int]) –

Per-layer channel sizes of the feature MLP.
weight_channels (Sequence[int], default: (8, 8) ) –

Hidden channel sizes of the weight net applied to relative positions.
expansion (int, default: 16 ) –

Channel expansion factor of the final matrix multiplication.
aggr (PoolLike, default: 'mean' ) –

Pooling used to place the single output position of each sample.
spatial_dim (int, default: 3 ) –

Dimension of point coordinates.

PointConvDensityGlobalSetAbstraction ¶

PointConvDensityGlobalSetAbstraction(
    in_channels: int,
    channels: Sequence[int],
    bandwidth: float,
    weight_channels: Sequence[int] = (8, 8),
    density_channels: Sequence[int] = (16, 8),
    expansion: int = 16,
    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,
    bias: bool = True,
    pool: PoolLike = "mean",
    spatial_dim: int = 3,
)

Bases: Module

Global PointConv set-abstraction block with inverse-density re-weighting.

Parameters:

in_channels (int) –

Number of input feature channels.
channels (Sequence[int]) –

Per-layer channel sizes of the feature MLP.
bandwidth (float) –

Bandwidth of the Gaussian kernel density estimate.
weight_channels (Sequence[int], default: (8, 8) ) –

Hidden channel sizes of the weight net applied to relative positions.
density_channels (Sequence[int], default: (16, 8) ) –

Hidden channel sizes of the density net.
expansion (int, default: 16 ) –

Channel expansion factor of the final matrix multiplication.
pool (PoolLike, default: 'mean' ) –

Pooling used to place the single output position of each sample.
spatial_dim (int, default: 3 ) –

Dimension of point coordinates.