Source code for torch_geometric.nn.conv.sage_conv

import torch
import torch.nn.functional as F
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops

from ..inits import uniform

[docs]class SAGEConv(MessagePassing): r"""The GraphSAGE operator from the `"Inductive Representation Learning on Large Graphs" <>`_ paper .. math:: \mathbf{\hat{x}}_i &= \mathbf{\Theta} \cdot \mathrm{mean}_{j \in \mathcal{N(i) \cup \{ i \}}}(\mathbf{x}_j) \mathbf{x}^{\prime}_i &= \frac{\mathbf{\hat{x}}_i} {\| \mathbf{\hat{x}}_i \|_2}. Args: in_channels (int): Size of each input sample. out_channels (int): Size of each output sample. normalize (bool, optional): If set to :obj:`True`, output features will be :math:`\ell_2`-normalized. (default: :obj:`False`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. """ def __init__(self, in_channels, out_channels, normalize=False, bias=True, **kwargs): super(SAGEConv, self).__init__(aggr='mean', **kwargs) self.in_channels = in_channels self.out_channels = out_channels self.normalize = normalize self.weight = Parameter(torch.Tensor(self.in_channels, out_channels)) if bias: self.bias = Parameter(torch.Tensor(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): uniform(self.in_channels, self.weight) uniform(self.in_channels, self.bias)
[docs] def forward(self, x, edge_index, edge_weight=None, size=None): """""" if size is None and torch.is_tensor(x): edge_index, edge_weight = add_remaining_self_loops( edge_index, edge_weight, 1, x.size(0)) if torch.is_tensor(x): x = torch.matmul(x, self.weight) else: x = (None if x[0] is None else torch.matmul(x[0], self.weight), None if x[1] is None else torch.matmul(x[1], self.weight)) return self.propagate(edge_index, size=size, x=x, edge_weight=edge_weight)
def message(self, x_j, edge_weight): return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j def update(self, aggr_out): if self.bias is not None: aggr_out = aggr_out + self.bias if self.normalize: aggr_out = F.normalize(aggr_out, p=2, dim=-1) return aggr_out def __repr__(self): return '{}({}, {})'.format(self.__class__.__name__, self.in_channels, self.out_channels)