torch_geometric.nn.conv.EdgeConv

class EdgeConv(nn: Callable, aggr: str = 'max', **kwargs)

Bases: MessagePassing

The edge convolutional operator from the “Dynamic Graph CNN for Learning on Point Clouds” paper.

\[\mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i)} h_{\mathbf{\Theta}}(\mathbf{x}_i \, \Vert \, \mathbf{x}_j - \mathbf{x}_i),\]

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

Parameters:

• nn (torch.nn.Module) – A neural network \(h_{\mathbf{\Theta}}\) that maps pair-wise concatenated node features x of shape [-1, 2 * in_channels] to shape [-1, out_channels], e.g., defined by torch.nn.Sequential.

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

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

Shapes:

• input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V}|, F_{in}), (|\mathcal{V}|, F_{in}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\)

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

forward(x: Union[Tensor, Tuple[Tensor, Tensor]], edge_index: Union[Tensor, SparseTensor]) → Tensor

Runs the forward pass of the module.

reset_parameters()

Resets all learnable parameters of the module.