topobench.nn.backbones.hypergraph package#

Submodules#

topobench.nn.backbones.hypergraph.edgnn module#

Class implementing the EDGNN model.

class topobench.nn.backbones.hypergraph.edgnn.EDGNN(num_features, input_dropout=0.2, dropout=0.2, activation='relu', MLP_num_layers=2, MLP2_num_layers=-1, MLP3_num_layers=-1, All_num_layers=2, edconv_type='EquivSet', restart_alpha=0.5, aggregate='add', normalization='None', AllSet_input_norm=False)[source]#

Bases: Module

EDGNN model.

Parameters:

num_featuresint: Number of input features.
input_dropoutfloat, optional: Dropout rate for input features. Defaults to 0.2.
dropoutfloat, optional: Dropout rate for hidden layers. Defaults to 0.2.
activationstr, optional: Activation function. Defaults to ‘relu’.
MLP_num_layersint, optional: Number of layers in MLP. Defaults to 2.
MLP2_num_layersint, optional: Number of layers in the second MLP. Defaults to -1.
MLP3_num_layersint, optional: Number of layers in the third MLP. Defaults to -1.
All_num_layersint, optional: Number of layers in the EDConv. Defaults to 2.
edconv_typestr, optional: Type of EDConv. Defaults to ‘EquivSet’.
restart_alphafloat, optional: Restart alpha. Defaults to 0.5.
aggregatestr, optional: Aggregation method. Defaults to ‘add’.
normalizationstr, optional: Normalization method. Defaults to ‘None’.
AllSet_input_normbool, optional: Whether to normalize input features. Defaults to False.

forward(x, edge_index)[source]#

Forward pass.

Parameters:

xTensor: Input features.
edge_indexLongTensor: Edge index.

Returns:

Tensor: Output features.
None: None object needed for compatibility.

reset_parameters()[source]#: Reset parameters.

class topobench.nn.backbones.hypergraph.edgnn.EquivSetConv(in_features, out_features, mlp1_layers=1, mlp2_layers=1, mlp3_layers=1, aggr='add', alpha=0.5, dropout=0.0, normalization='None', input_norm=False)[source]#

Bases: Module

Class implementing the Equivariant Set Convolution.

Parameters:

in_featuresint: Number of input features.
out_featuresint: Number of output features.
mlp1_layersint, optional: Number of layers in the first MLP. Defaults to 1.
mlp2_layersint, optional: Number of layers in the second MLP. Defaults to 1.
mlp3_layersint, optional: Number of layers in the third MLP. Defaults to 1.
aggrstr, optional: Aggregation method. Defaults to ‘add’.
alphafloat, optional: Alpha value. Defaults to 0.5.
dropoutfloat, optional: Dropout rate. Defaults to 0.0.
normalizationstr, optional: Normalization method. Defaults to ‘None’.
input_normbool, optional: Whether to normalize input features. Defaults to False.

forward(X, vertex, edges, X0)[source]#

Forward pass.

Parameters:

XTensor: Input features.
vertexLongTensor: Vertex index.
edgesLongTensor: Edge index.
X0Tensor: Initial features.

Returns:

Tensor: Output features.

reset_parameters()[source]#: Reset parameters.

class topobench.nn.backbones.hypergraph.edgnn.JumpLinkConv(in_features, out_features, mlp_layers=2, aggr='add', alpha=0.5)[source]#

Bases: Module

Class implementing the JumpLink Convolution.

Parameters:

in_featuresint: Number of input features.
out_featuresint: Number of output features.
mlp_layersint, optional: Number of layers in the MLP. Defaults to 2.
aggrstr, optional: Aggregation method. Defaults to ‘add’.
alphafloat, optional: Alpha value. Defaults to 0.5.

forward(X, vertex, edges, X0, beta=1.0)[source]#

Forward pass.

Parameters:

XTensor: Input features.
vertexLongTensor: Vertex index.
edgesLongTensor: Edge index.
X0Tensor: Initial features.
betafloat, optional: Beta value. Defaults to 1.0.

Returns:

Tensor: Output features.

reset_parameters()[source]#: Reset parameters.

class topobench.nn.backbones.hypergraph.edgnn.MLP(in_channels, hidden_channels, out_channels, num_layers, dropout=0.5, Normalization='bn', InputNorm=False)[source]#

Bases: Module

Class implementing a multi-layer perceptron.

Adapted from CUAI/CorrectAndSmooth

Parameters:

in_channelsint: Number of input features.
hidden_channelsint: Number of hidden features.
out_channelsint: Number of output features.
num_layersint: Number of layers.
dropoutfloat, optional: Dropout rate. Defaults to 0.5.
Normalizationstr, optiona: Normalization method. Defaults to ‘bn’.
InputNormbool, optional: Whether to normalize input features. Defaults to False.

flops(x)[source]#

Calculate FLOPs.

Parameters:

xTensor: Input features.

Returns:

int: FLOPs.

forward(x)[source]#

Forward pass.

Parameters:

xTensor: Input features.

Returns:

Tensor: Output features.

reset_parameters()[source]#: Reset parameters.

class topobench.nn.backbones.hypergraph.edgnn.MeanDegConv(in_features, out_features, init_features=None, mlp1_layers=1, mlp2_layers=1, mlp3_layers=2)[source]#

Bases: Module

Class implementing the Mean Degree Convolution.

Parameters:

in_featuresint: Number of input features.
out_featuresint: Number of output features.
init_featuresint, optional: Number of initial features. Defaults to None.
mlp1_layersint, optional: Number of layers in the first MLP. Defaults to 1.
mlp2_layersint, optional: Number of layers in the second MLP. Defaults to 1.
mlp3_layersint, optional: Number of layers in the third MLP. Defaults to 2.

forward(X, vertex, edges, X0)[source]#

Forward pass.

Parameters:

XTensor: Input features.
vertexLongTensor: Vertex index.
edgesLongTensor: Edge index.
X0Tensor: Initial features.

Returns:

Tensor: Output features.

reset_parameters()[source]#: Reset parameters.

class topobench.nn.backbones.hypergraph.edgnn.PlainMLP(in_channels, hidden_channels, out_channels, num_layers, dropout=0.5)[source]#

Bases: Module

Class implementing a multi-layer perceptron without normalization.

Adapted from CUAI/CorrectAndSmooth.

Parameters:

in_channelsint: Number of input features.
hidden_channelsint: Number of hidden features.
out_channelsint: Number of output features.
num_layersint: Number of layers.
dropoutfloat, optional: Dropout rate. Defaults to 0.5.

forward(x)[source]#

Forward pass.

Parameters:

xTensor: Input features.

Returns:

Tensor: Output features.

reset_parameters()[source]#: Reset parameters.

Module contents#

Hypergraph backbones with automated exports.

class topobench.nn.backbones.hypergraph.EDGNN(num_features, input_dropout=0.2, dropout=0.2, activation='relu', MLP_num_layers=2, MLP2_num_layers=-1, MLP3_num_layers=-1, All_num_layers=2, edconv_type='EquivSet', restart_alpha=0.5, aggregate='add', normalization='None', AllSet_input_norm=False)#

Bases: Module

EDGNN model.

Parameters:

num_featuresint: Number of input features.
input_dropoutfloat, optional: Dropout rate for input features. Defaults to 0.2.
dropoutfloat, optional: Dropout rate for hidden layers. Defaults to 0.2.
activationstr, optional: Activation function. Defaults to ‘relu’.
MLP_num_layersint, optional: Number of layers in MLP. Defaults to 2.
MLP2_num_layersint, optional: Number of layers in the second MLP. Defaults to -1.
MLP3_num_layersint, optional: Number of layers in the third MLP. Defaults to -1.
All_num_layersint, optional: Number of layers in the EDConv. Defaults to 2.
edconv_typestr, optional: Type of EDConv. Defaults to ‘EquivSet’.
restart_alphafloat, optional: Restart alpha. Defaults to 0.5.
aggregatestr, optional: Aggregation method. Defaults to ‘add’.
normalizationstr, optional: Normalization method. Defaults to ‘None’.
AllSet_input_normbool, optional: Whether to normalize input features. Defaults to False.

forward(x, edge_index)#

Forward pass.

Parameters:

xTensor: Input features.
edge_indexLongTensor: Edge index.

Returns:

Tensor: Output features.
None: None object needed for compatibility.

reset_parameters()#: Reset parameters.

class topobench.nn.backbones.hypergraph.EquivSetConv(in_features, out_features, mlp1_layers=1, mlp2_layers=1, mlp3_layers=1, aggr='add', alpha=0.5, dropout=0.0, normalization='None', input_norm=False)#

Bases: Module

Class implementing the Equivariant Set Convolution.

Parameters:

in_featuresint: Number of input features.
out_featuresint: Number of output features.
mlp1_layersint, optional: Number of layers in the first MLP. Defaults to 1.
mlp2_layersint, optional: Number of layers in the second MLP. Defaults to 1.
mlp3_layersint, optional: Number of layers in the third MLP. Defaults to 1.
aggrstr, optional: Aggregation method. Defaults to ‘add’.
alphafloat, optional: Alpha value. Defaults to 0.5.
dropoutfloat, optional: Dropout rate. Defaults to 0.0.
normalizationstr, optional: Normalization method. Defaults to ‘None’.
input_normbool, optional: Whether to normalize input features. Defaults to False.

forward(X, vertex, edges, X0)#

Forward pass.

Parameters:

XTensor: Input features.
vertexLongTensor: Vertex index.
edgesLongTensor: Edge index.
X0Tensor: Initial features.

Returns:

Tensor: Output features.

reset_parameters()#: Reset parameters.

class topobench.nn.backbones.hypergraph.JumpLinkConv(in_features, out_features, mlp_layers=2, aggr='add', alpha=0.5)#

Bases: Module

Class implementing the JumpLink Convolution.

Parameters:

in_featuresint: Number of input features.
out_featuresint: Number of output features.
mlp_layersint, optional: Number of layers in the MLP. Defaults to 2.
aggrstr, optional: Aggregation method. Defaults to ‘add’.
alphafloat, optional: Alpha value. Defaults to 0.5.

forward(X, vertex, edges, X0, beta=1.0)#

Forward pass.

Parameters:

XTensor: Input features.
vertexLongTensor: Vertex index.
edgesLongTensor: Edge index.
X0Tensor: Initial features.
betafloat, optional: Beta value. Defaults to 1.0.

Returns:

Tensor: Output features.

reset_parameters()#: Reset parameters.

class topobench.nn.backbones.hypergraph.MLP(in_channels, hidden_channels, out_channels, num_layers, dropout=0.5, Normalization='bn', InputNorm=False)#

Bases: Module

Class implementing a multi-layer perceptron.

Adapted from CUAI/CorrectAndSmooth

Parameters:

in_channelsint: Number of input features.
hidden_channelsint: Number of hidden features.
out_channelsint: Number of output features.
num_layersint: Number of layers.
dropoutfloat, optional: Dropout rate. Defaults to 0.5.
Normalizationstr, optiona: Normalization method. Defaults to ‘bn’.
InputNormbool, optional: Whether to normalize input features. Defaults to False.

flops(x)#

Calculate FLOPs.

Parameters:

xTensor: Input features.

Returns:

int: FLOPs.

forward(x)#

Forward pass.

Parameters:

xTensor: Input features.

Returns:

Tensor: Output features.

reset_parameters()#: Reset parameters.

class topobench.nn.backbones.hypergraph.MeanDegConv(in_features, out_features, init_features=None, mlp1_layers=1, mlp2_layers=1, mlp3_layers=2)#

Bases: Module

Class implementing the Mean Degree Convolution.

Parameters:

in_featuresint: Number of input features.
out_featuresint: Number of output features.
init_featuresint, optional: Number of initial features. Defaults to None.
mlp1_layersint, optional: Number of layers in the first MLP. Defaults to 1.
mlp2_layersint, optional: Number of layers in the second MLP. Defaults to 1.
mlp3_layersint, optional: Number of layers in the third MLP. Defaults to 2.

forward(X, vertex, edges, X0)#

Forward pass.

Parameters:

XTensor: Input features.
vertexLongTensor: Vertex index.
edgesLongTensor: Edge index.
X0Tensor: Initial features.

Returns:

Tensor: Output features.

reset_parameters()#: Reset parameters.

class topobench.nn.backbones.hypergraph.PlainMLP(in_channels, hidden_channels, out_channels, num_layers, dropout=0.5)#

Bases: Module

Class implementing a multi-layer perceptron without normalization.

Adapted from CUAI/CorrectAndSmooth.

Parameters:

in_channelsint: Number of input features.
hidden_channelsint: Number of hidden features.
out_channelsint: Number of output features.
num_layersint: Number of layers.
dropoutfloat, optional: Dropout rate. Defaults to 0.5.

forward(x)#

Forward pass.

Parameters:

xTensor: Input features.

Returns:

Tensor: Output features.

reset_parameters()#: Reset parameters.

topobench.nn.backbones.hypergraph package#

Submodules#

topobench.nn.backbones.hypergraph.edgnn module#

Module contents#

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