morphoclass.layers.cheb_conv_separable module

Implementation of the ChebConvSeparable layer.

class morphoclass.layers.cheb_conv_separable.ChebConvSeparable(in_channels, out_channels, orders, bias=True, flow='target_to_source', **kwargs)

Bases: torch_geometric.nn.conv.message_passing.MessagePassing

Separable version of the ChebConv layer.

Two changes:

  1. Depth-separable convolutions

  2. Sparse specification of order parameter for polynomials (see the order parameter in __init__).

Parameters

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

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

  • orders (int or iterable of int) – The Chebyshev filter sizes. If an integer is provided then all sizes from 0 to (K-1) will be included, otherwise only those in the iterable.

  • 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.