spconv_blocks

SubMConv3dBlock(
    in_channels: int,
    out_channels: int,
    kernel_size: int,
    padding: int,
    norm: Union[str, Callable, None] = None,
    act: Union[str, Callable, None] = None,
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm_kwargs: Optional[Dict[str, Any]] = None,
    bias: bool = True,
    stem_indice_key: Optional[str] = None,
)

Bases: Module

Submanifold sparse 3D convolution followed by normalization and activation, on packed point features.

Unlike SparseConvBlock, this block takes and returns packed \((N, C)\) features: it builds the SparseConvTensor from pos and batch itself, so it drops into a point-based backbone as a conditional position embedding.

Parameters:

• in_channels (int) –

  Number of input channels.

• out_channels (int) –

  Number of output channels.

• kernel_size (int) –

  Convolution kernel size.

• padding (int) –

  Convolution padding.

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

  Normalization passed to create_norm.

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

  Activation passed to create_act.

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

  Extra keyword arguments for the activation.

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

  Extra keyword arguments for the normalization.

• bias (bool, default: True ) –

  Whether the convolution has a bias term.

• stem_indice_key (Optional[str], default: None ) –

  spconv index key for the convolution.

SparseConvBlock(
    in_channels: int,
    out_channels: int,
    kernel_size: Union[int, Tuple[int, ...]],
    *,
    stride: Union[int, Tuple[int, ...]] = 1,
    padding: Union[int, Tuple[int, ...]] = 0,
    indice_key: str,
    conv_type: str = "subm",
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: SparseSequential

Sparse 3D convolution followed by normalization and activation.

A subm (submanifold), spconv (regular, optionally strided), or inverseconv convolution over a SparseConvTensor, followed by normalization and activation on the features. As a SparseSequential subclass it drops directly into the sparse voxel backbones' convolution stacks.

Parameters:

• in_channels (int) –

  Number of input channels.

• out_channels (int) –

  Number of output channels.

• kernel_size (Union[int, Tuple[int, ...]]) –

  Convolution kernel size.

• stride (Union[int, Tuple[int, ...]], default: 1 ) –

  Convolution stride (used by spconv).

• padding (Union[int, Tuple[int, ...]], default: 0 ) –

  Convolution padding (used by spconv).

• indice_key (str) –

  spconv index key for the convolution.

• conv_type (str, default: 'subm' ) –

  One of subm, spconv, or inverseconv.

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

  Normalization passed to create_norm.

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

  Extra keyword arguments for the normalization (e.g. eps, momentum).

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

  Activation passed to create_act.

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

  Extra keyword arguments for the activation.

SparseResidualBlock(
    in_channels: int,
    out_channels: int,
    *,
    indice_key: Optional[str] = None,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: SparseModule

Pre-activation sparse residual block shared by the SPFormer and SphereFormer SpConv U-Nets.

The convolutional branch applies \(\text{norm} \to \text{act} \to \text{conv} \to \text{norm} \to \text{act} \to \text{conv}\) over two \(3\times3\times3\) submanifold convolutions; the identity branch is nn.Identity when the channels match, else a \(1\times1\times1\) SubMConv3d projection.

Parameters:

• in_channels (int) –

  Number of input channels.

• out_channels (int) –

  Number of output channels.

• indice_key (Optional[str], default: None ) –

  spconv index key shared by the two submanifold convolutions.

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

  Activation passed to create_act.

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

  Extra keyword arguments for the activation.

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

  Normalization passed to create_norm.

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

  Extra keyword arguments for the normalization (e.g. eps, momentum).

SparseBasicBlock(
    in_channels: int,
    out_channels: int,
    kernel_size: int = 3,
    bias: bool = False,
    indice_key: Optional[str] = None,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: SparseModule

Residual block of two submanifold sparse convolutions: conv, norm, act, conv, norm, add the input, act.

A pointwise submanifold convolution projects the skip connection when the channel count changes.

Parameters:

• in_channels (int) –

  Number of input channels.

• out_channels (int) –

  Number of output channels.

• kernel_size (int, default: 3 ) –

  Kernel size of the two submanifold convolutions.

• bias (bool, default: False ) –

  Whether the two submanifold convolutions carry a bias.

• indice_key (Optional[str], default: None ) –

  spconv index key shared by the two submanifold convolutions.

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

  Activation passed to create_act.

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

  Extra keyword arguments for the activation.

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

  Normalization passed to create_norm.

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

  Extra keyword arguments for the normalization.

SubMConv3dResidualBlock(
    channels: int,
    *,
    indice_key: str,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: SparseModule

Residual block with a single \(3\times3\times3\) submanifold convolution: conv, norm, add the input, act.

Parameters:

• channels (int) –

  Input and output channels.

• indice_key (str) –

  Shared submanifold indice key.

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

  Activation type or callable.

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

  Extra activation arguments.

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

  Normalization type or callable.

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

  Extra normalization arguments.