class MinkowskiEngine.MinkowskiBroadcastAddition

Broadcast the reduced features to all input coordinates.

$\mathbf{y}_\mathbf{u} = \mathbf{x}_{1, \mathbf{u}} + \mathbf{x}_2 \; \text{for} \; \mathbf{u} \in \mathcal{C}^\text{in}$

For all input $$\mathbf{x}_\mathbf{u}$$, add $$\mathbf{x}_2$$. The output coordinates will be the same as the input coordinates $$\mathcal{C}^\text{in} = \mathcal{C}^\text{out}$$.

Note

The first argument takes a sparse tensor; the second argument takes features that are reduced to the origin. This can be typically done with the global reduction such as the MinkowskiGlobalPooling.

__init__()

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input: MinkowskiSparseTensor.SparseTensor, input_glob: MinkowskiSparseTensor.SparseTensor)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class MinkowskiEngine.MinkowskiBroadcastMultiplication

Broadcast reduced features to all input coordinates.

$\mathbf{y}_\mathbf{u} = \mathbf{x}_{1, \mathbf{u}} \times \mathbf{x}_2 \; \text{for} \; \mathbf{u} \in \mathcal{C}^\text{in}$

For all input $$\mathbf{x}_\mathbf{u}$$, multiply $$\mathbf{x}_2$$ element-wise. The output coordinates will be the same as the input coordinates $$\mathcal{C}^\text{in} = \mathcal{C}^\text{out}$$.

Note

The first argument takes a sparse tensor; the second argument takes features that are reduced to the origin. This can be typically done with the global reduction such as the MinkowskiGlobalPooling.

__init__()

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input: MinkowskiSparseTensor.SparseTensor, input_glob: MinkowskiSparseTensor.SparseTensor)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class MinkowskiEngine.MinkowskiBroadcastConcatenation

Broadcast reduced features to all input coordinates and concatenate to the input.

$\mathbf{y}_\mathbf{u} = [\mathbf{x}_{1,\mathbf{u}}, \mathbf{x}_2] \; \text{for} \; \mathbf{u} \in \mathcal{C}^\text{in}$

For all input $$\mathbf{x}_\mathbf{u}$$, concatenate vector $$\mathbf{x}_2$$. $$[\cdot, \cdot]$$ is a concatenation operator. The output coordinates will be the same as the input coordinates $$\mathcal{C}^\text{in} = \mathcal{C}^\text{out}$$.

Note

The first argument takes a sparse tensor; the second argument takes features that are reduced to the origin. This can be typically done with the global reduction such as the MinkowskiGlobalPooling.

__init__()

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input: MinkowskiSparseTensor.SparseTensor, input_glob: MinkowskiSparseTensor.SparseTensor)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class MinkowskiEngine.MinkowskiBroadcast

Broadcast reduced features to all input coordinates.

$\mathbf{y}_\mathbf{u} = \mathbf{x}_2 \; \text{for} \; \mathbf{u} \in \mathcal{C}^\text{in}$

For all input $$\mathbf{x}_\mathbf{u}$$, copy value $$\mathbf{x}_2$$ element-wise. The output coordinates will be the same as the input coordinates $$\mathcal{C}^\text{in} = \mathcal{C}^\text{out}$$. The first input $$\mathbf{x}_1$$ is only used for defining the output coordinates.

Note

The first argument takes a sparse tensor; the second argument takes features that are reduced to the origin. This can be typically done with the global reduction such as the MinkowskiGlobalPooling.

__init__()

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input: MinkowskiSparseTensor.SparseTensor, input_glob: MinkowskiSparseTensor.SparseTensor)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.