class NNConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, nn: Callable, aggr: str = 'add', root_weight: bool = True, bias: bool = True, **kwargs)[source]

Bases: MessagePassing

The continuous kernel-based convolutional operator from the “Neural Message Passing for Quantum Chemistry” paper.

This convolution is also known as the edge-conditioned convolution from the “Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs” paper (see torch_geometric.nn.conv.ECConv for an alias):

\[\mathbf{x}^{\prime}_i = \mathbf{\Theta} \mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \cdot h_{\mathbf{\Theta}}(\mathbf{e}_{i,j}),\]

where \(h_{\mathbf{\Theta}}\) denotes a neural network, .i.e. a MLP.

  • in_channels (int or tuple) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.

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

  • nn (torch.nn.Module) – A neural network \(h_{\mathbf{\Theta}}\) that maps edge features edge_attr of shape [-1, num_edge_features] to shape [-1, in_channels * out_channels], e.g., defined by torch.nn.Sequential.

  • aggr (str, optional) – The aggregation scheme to use ("add", "mean", "max"). (default: "add")

  • root_weight (bool, optional) – If set to False, the layer will not add the transformed root node features to the output. (default: True)

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

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

  • input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge features \((|\mathcal{E}|, D)\) (optional)

  • output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite

forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Union[Tensor, SparseTensor], edge_attr: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None) Tensor[source]

Runs the forward pass of the module.


Resets all learnable parameters of the module.