Source code for torch_geometric.nn.conv.point_conv

from typing import Callable, Optional, Union

import torch
from torch import Tensor

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.inits import reset
from torch_geometric.typing import (
from torch_geometric.utils import add_self_loops, remove_self_loops

[docs]class PointNetConv(MessagePassing): r"""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. .. math:: \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 :math:`\gamma_{\mathbf{\Theta}}` and :math:`h_{\mathbf{\Theta}}` denote neural networks, *i.e.* MLPs, and :math:`\mathbf{P} \in \mathbb{R}^{N \times D}` defines the position of each point. Args: local_nn (torch.nn.Module, optional): A neural network :math:`h_{\mathbf{\Theta}}` that maps node features :obj:`x` and relative spatial coordinates :obj:`pos_j - pos_i` of shape :obj:`[-1, in_channels + num_dimensions]` to shape :obj:`[-1, out_channels]`, *e.g.*, defined by :class:`torch.nn.Sequential`. (default: :obj:`None`) global_nn (torch.nn.Module, optional): A neural network :math:`\gamma_{\mathbf{\Theta}}` that maps aggregated node features of shape :obj:`[-1, out_channels]` to shape :obj:`[-1, final_out_channels]`, *e.g.*, defined by :class:`torch.nn.Sequential`. (default: :obj:`None`) add_self_loops (bool, optional): If set to :obj:`False`, will not add self-loops to the input graph. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F_{in})` or :math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))` if bipartite, positions :math:`(|\mathcal{V}|, 3)` or :math:`((|\mathcal{V_s}|, 3), (|\mathcal{V_t}|, 3))` if bipartite, edge indices :math:`(2, |\mathcal{E}|)` - **output:** node features :math:`(|\mathcal{V}|, F_{out})` or :math:`(|\mathcal{V}_t|, F_{out})` if bipartite """ def __init__(self, local_nn: Optional[Callable] = None, global_nn: Optional[Callable] = None, add_self_loops: bool = True, **kwargs): kwargs.setdefault('aggr', 'max') super().__init__(**kwargs) self.local_nn = local_nn self.global_nn = global_nn self.add_self_loops = add_self_loops self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() reset(self.local_nn) reset(self.global_nn)
[docs] def forward( self, x: Union[OptTensor, PairOptTensor], pos: Union[Tensor, PairTensor], edge_index: Adj, ) -> Tensor: if not isinstance(x, tuple): x = (x, None) if isinstance(pos, Tensor): pos = (pos, pos) if self.add_self_loops: if isinstance(edge_index, Tensor): edge_index, _ = remove_self_loops(edge_index) edge_index, _ = add_self_loops( edge_index, num_nodes=min(pos[0].size(0), pos[1].size(0))) elif isinstance(edge_index, SparseTensor): edge_index = torch_sparse.set_diag(edge_index) # propagate_type: (x: PairOptTensor, pos: PairTensor) out = self.propagate(edge_index, x=x, pos=pos) if self.global_nn is not None: out = self.global_nn(out) return out
def message(self, x_j: Optional[Tensor], pos_i: Tensor, pos_j: Tensor) -> Tensor: msg = pos_j - pos_i if x_j is not None: msg =[x_j, msg], dim=1) if self.local_nn is not None: msg = self.local_nn(msg) return msg def __repr__(self) -> str: return (f'{self.__class__.__name__}(local_nn={self.local_nn}, ' f'global_nn={self.global_nn})')