morphoclass.layers.cheb_conv module

Implementation of the ChebConv layer.

class morphoclass.layers.cheb_conv.ChebConv(in_channels, out_channels, K, bias=True, flow='target_to_source', **kwargs)

Bases: torch_geometric.nn.conv.message_passing.MessagePassing

ChebConv layer based on torch_geometric.nn.conv.ChebConv.

This implementation corrects a few things that were not quite correct in the original implementation:

  1. Initialization set to glorot rather then uniform

  2. The default flow is set to target_to_source. This way the call self.propagate(…) computes M @ x rather than M.T @ x.

  3. The computation of the normalised Laplacian

These points might be implemented in future versions of PyG so that this implementation will become redundant.

Parameters

  • in_channels (int) – Size of each input sample.

  • out_channels (int) – Size of each output sample.

  • K (int) – Chebyshev filter size, i.e. number of hops \(K\).

  • bias (bool, default True) – If set to False, the layer will not learn an additive bias.

  • **kwargs – Additional arguments of torch_geometric.nn.conv.MessagePassing.

forward(x, edge_index, edge_weight=None, batch=None, lambda_max=3.0)

Compute the forward pass.

Parameters

  • x – The batched input node features.

  • edge_index – The batched input adjacency matrices.

  • edge_weight – The edge weights.

  • batch – Not used?

  • lambda_max (float) – The lambda_max value to use for the normalization of the graph Laplacian.

Returns

out – The output feature maps.

Return type

torch.Tensor

message(x_j, norm)

Perform a message passing step.

Parameters

  • x_j – Node features.

  • norm – The edge weights (?). See documentation of pytorch-geometric for more details.

Returns

The result of message passing.

Return type

torch.Tensor

static norm(edge_index, num_nodes, edge_weight, dtype=None, lambda_max=3.0)

Given the adjacency matrix calculate the normalised Laplacian.

Parameters

  • edge_index – The edge indices of the adjacency matrix.

  • num_nodes – The number of nodes in the graph, the adjacency matrix is of dimension (num_nodes, num_nodes).

  • edge_weight – The entries in the adjacency matrix index by edge_index

  • dtype – The dtype of the adjacency matrix.

  • lambda_max – The pre-computed maximal eigenvalue of the normalised Laplacian. Note that in the original PyG implementation this was implicitly assumed to have value 2.0.

Returns

The sparse form of the normalised graph Laplacian.

Return type

edge_index, norm

reset_parameters()

Initialize all weights with Glorot and set all biases to zero.