torch_geometric.nn.conv.PointNetConv
- class PointNetConv(local_nn: Optional[Callable] = None, global_nn: Optional[Callable] = None, add_self_loops: bool = True, **kwargs)[source]
Bases:
MessagePassingThe 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.
- Parameters:
local_nn (torch.nn.Module, optional) – A neural network \(h_{\mathbf{\Theta}}\) that maps node features
xand relative spatial coordinatespos_j - pos_iof shape[-1, in_channels + num_dimensions]to shape[-1, out_channels], e.g., defined bytorch.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 bytorch.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.
- Shapes:
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