class PointNetConv(local_nn: Optional[Callable] = None, global_nn: Optional[Callable] = None, add_self_loops: bool = True, **kwargs)[source]

Bases: MessagePassing

The PointNet set layer from the “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation” and “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space” papers.

\[\mathbf{x}^{\prime}_i = \gamma_{\mathbf{\Theta}} \left( \max_{j \in \mathcal{N}(i) \cup \{ i \}} h_{\mathbf{\Theta}} ( \mathbf{x}_j, \mathbf{p}_j - \mathbf{p}_i) \right),\]

where \(\gamma_{\mathbf{\Theta}}\) and \(h_{\mathbf{\Theta}}\) denote neural networks, i.e. MLPs, and \(\mathbf{P} \in \mathbb{R}^{N \times D}\) defines the position of each point.

  • local_nn (torch.nn.Module, optional) – A neural network \(h_{\mathbf{\Theta}}\) that maps node features x and relative spatial coordinates pos_j - pos_i of shape [-1, in_channels + num_dimensions] to shape [-1, out_channels], e.g., defined by torch.nn.Sequential. (default: None)

  • global_nn (torch.nn.Module, optional) – A neural network \(\gamma_{\mathbf{\Theta}}\) that maps aggregated node features of shape [-1, out_channels] to shape [-1, final_out_channels], e.g., defined by torch.nn.Sequential. (default: None)

  • add_self_loops (bool, optional) – If set to False, will not add self-loops to the input graph. (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, positions \((|\mathcal{V}|, 3)\) or \(((|\mathcal{V_s}|, 3), (|\mathcal{V_t}|, 3))\) 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, None, Tuple[Optional[Tensor], Optional[Tensor]]], pos: Union[Tensor, Tuple[Tensor, Tensor]], edge_index: Union[Tensor, SparseTensor]) Tensor[source]

Runs the forward pass of the module.


Resets all learnable parameters of the module.