topobench.nn.backbones.simplicial.sccnn module#

Implementation of the Simplicial Complex Convolutional Neural Network (SCCNN) for complex classification.

class Parameter(data=None, requires_grad=True)#

Bases: Tensor

A kind of Tensor that is to be considered a module parameter.

Parameters are Tensor subclasses, that have a very special property when used with Module s - when they’re assigned as Module attributes they are automatically added to the list of its parameters, and will appear e.g. in parameters() iterator. Assigning a Tensor doesn’t have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. If there was no such class as Parameter, these temporaries would get registered too.

Parameters:

data (Tensor) – parameter tensor.
requires_grad (bool, optional) – if the parameter requires gradient. Note that the torch.no_grad() context does NOT affect the default behavior of Parameter creation–the Parameter will still have requires_grad=True in no_grad mode. See locally-disable-grad-doc for more details. Default: True

class SCCNNCustom(in_channels_all, hidden_channels_all, conv_order, sc_order, aggr_norm=False, update_func=None, n_layers=2)#

Bases: Module

SCCNN implementation for complex classification.

Note: In this task, we can consider the output on any order of simplices for the classification task, which of course can be amended by a readout layer.

Parameters:

in_channels_alltuple of int: Dimension of input features on (nodes, edges, faces).
hidden_channels_alltuple of int: Dimension of features of hidden layers on (nodes, edges, faces).
conv_orderint: Order of convolutions, we consider the same order for all convolutions.
sc_orderint: Order of simplicial complex.
aggr_normbool, optional: Whether to normalize the aggregation (default: False).
update_funcstr, optional: Update function for the simplicial complex convolution (default: None).
n_layersint, optional: Number of layers (default: 2).

__init__(in_channels_all, hidden_channels_all, conv_order, sc_order, aggr_norm=False, update_func=None, n_layers=2)#: Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x_all, laplacian_all, incidence_all)#

Forward computation.

Parameters:

x_alltuple(tensors): Tuple of feature tensors (node, edge, face).
laplacian_alltuple(tensors): Tuple of Laplacian tensors (graph laplacian L0, down edge laplacian L1_d, upper edge laplacian L1_u, face laplacian L2).
incidence_alltuple(tensors): Tuple of order 1 and 2 incidence matrices.

Returns:

tuple(tensors): Tuple of final hidden state tensors (node, edge, face).

class SCCNNLayer(in_channels, out_channels, conv_order, sc_order, aggr_norm=False, update_func=None, initialization='xavier_normal')#

Bases: Module

Layer of a Simplicial Complex Convolutional Neural Network.

Parameters:

in_channelstuple of int: Dimensions of input features on nodes, edges, and faces.
out_channelstuple of int: Dimensions of output features on nodes, edges, and faces.
conv_orderint: Convolution order of the simplicial filters.
sc_orderint: SC order.
aggr_normbool, optional: Whether to normalize the aggregated message by the neighborhood size (default: False).
update_funcstr, optional: Activation function used in aggregation layers (default: None).
initializationstr, optional: Initialization method for the weights (default: “xavier_normal”).

__init__(in_channels, out_channels, conv_order, sc_order, aggr_norm=False, update_func=None, initialization='xavier_normal')#

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

aggr_norm_func(conv_operator, x)#

Perform aggregation normalization.

Parameters:

conv_operatortorch.sparse: Convolution operator.
xtorch.Tensor: Feature tensor.

Returns:

torch.Tensor: Normalized feature tensor.

chebyshev_conv(conv_operator, conv_order, x)#

Perform Chebyshev convolution.

Parameters:

conv_operatortorch.sparse: Convolution operator.
conv_orderint: Order of the convolution.
xtorch.Tensor: Feature tensor.

Returns:

torch.Tensor: Output tensor.

forward(x_all, laplacian_all, incidence_all)#

Forward computation.

Parameters:

x_alltuple of tensors: Tuple of input feature tensors (node, edge, face).
laplacian_alltuple of tensors: Tuple of Laplacian tensors (graph laplacian L0, down edge laplacian L1_d, upper edge laplacian L1_u, face laplacian L2).
incidence_alltuple of tensors: Tuple of order 1 and 2 incidence matrices.

Returns:

torch.Tensor: Output tensor for each 0-cell.
torch.Tensor: Output tensor for each 1-cell.
torch.Tensor: Output tensor for each 2-cell.

reset_parameters(gain=1.414)#

Reset learnable parameters.

Parameters:

gainfloat: Gain for the weight initialization.

update(x)#

Update embeddings on each cell (step 4).

Parameters:

xtorch.Tensor: Input tensor.

Returns:

torch.Tensor: Updated tensor.