Source code for torch_geometric.nn.conv.edge_conv

import torch
from torch_geometric.nn.conv import MessagePassing

from ..inits import reset

try:
    from torch_cluster import knn_graph
except ImportError:
    knn_graph = None


[docs]class EdgeConv(MessagePassing): r"""The edge convolutional operator from the `"Dynamic Graph CNN for Learning on Point Clouds" <https://arxiv.org/abs/1801.07829>`_ paper .. math:: \mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i)} h_{\mathbf{\Theta}}(\mathbf{x}_i \, \Vert \, \mathbf{x}_j - \mathbf{x}_i), where :math:`h_{\mathbf{\Theta}}` denotes a neural network, *.i.e.* a MLP. Args: nn (torch.nn.Module): A neural network :math:`h_{\mathbf{\Theta}}` that maps pair-wise concatenated node features :obj:`x` of shape :obj:`[-1, 2 * in_channels]` to shape :obj:`[-1, out_channels]`, *e.g.*, defined by :class:`torch.nn.Sequential`. aggr (string, optional): The aggregation scheme to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`). (default: :obj:`"max"`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. """ def __init__(self, nn, aggr='max', **kwargs): super(EdgeConv, self).__init__(aggr=aggr, **kwargs) self.nn = nn self.reset_parameters()
[docs] def reset_parameters(self): reset(self.nn)
[docs] def forward(self, x, edge_index): """""" x = x.unsqueeze(-1) if x.dim() == 1 else x return self.propagate(edge_index, x=x)
def message(self, x_i, x_j): return self.nn(torch.cat([x_i, x_j - x_i], dim=1)) def __repr__(self): return '{}(nn={})'.format(self.__class__.__name__, self.nn)
[docs]class DynamicEdgeConv(EdgeConv): r"""The dynamic edge convolutional operator from the `"Dynamic Graph CNN for Learning on Point Clouds" <https://arxiv.org/abs/1801.07829>`_ paper (see :class:`torch_geometric.nn.conv.EdgeConv`), where the graph is dynamically constructed using nearest neighbors in the feature space. Args: nn (torch.nn.Module): A neural network :math:`h_{\mathbf{\Theta}}` that maps pair-wise concatenated node features :obj:`x` of shape `:obj:`[-1, 2 * in_channels]` to shape :obj:`[-1, out_channels]`, *e.g.* defined by :class:`torch.nn.Sequential`. k (int): Number of nearest neighbors. aggr (string): The aggregation operator to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`). (default: :obj:`"max"`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. """ def __init__(self, nn, k, aggr='max', **kwargs): super(DynamicEdgeConv, self).__init__(nn=nn, aggr=aggr, **kwargs) if knn_graph is None: raise ImportError('`DynamicEdgeConv` requires `torch-cluster`.') self.k = k
[docs] def forward(self, x, batch=None): """""" edge_index = knn_graph(x, self.k, batch, loop=False, flow=self.flow) return super(DynamicEdgeConv, self).forward(x, edge_index)
def __repr__(self): return '{}(nn={}, k={})'.format(self.__class__.__name__, self.nn, self.k)