Source code for torch_geometric.nn.dense.dense_sage_conv

import torch
import torch.nn.functional as F
from torch.nn import Parameter

from ..inits import uniform

[docs]class DenseSAGEConv(torch.nn.Module): r"""See :class:`torch_geometric.nn.conv.SAGEConv`. :rtype: :class:`Tensor` """ def __init__(self, in_channels, out_channels, normalize=False, bias=True): super(DenseSAGEConv, self).__init__() 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, adj, mask=None, add_loop=True): r""" Args: x (Tensor): Node feature tensor :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with batch-size :math:`B`, (maximum) number of nodes :math:`N` for each graph, and feature dimension :math:`F`. adj (Tensor): Adjacency tensor :math:`\mathbf{A} \in \mathbb{R}^{B \times N \times N}`. The adjacency tensor is broadcastable in the batch dimension, resulting in a shared adjacency matrix for the complete batch. mask (BoolTensor, optional): Mask matrix :math:`\mathbf{M} \in {\{ 0, 1 \}}^{B \times N}` indicating the valid nodes for each graph. (default: :obj:`None`) add_loop (bool, optional): If set to :obj:`False`, the layer will not automatically add self-loops to the adjacency matrices. (default: :obj:`True`) """ x = x.unsqueeze(0) if x.dim() == 2 else x adj = adj.unsqueeze(0) if adj.dim() == 2 else adj B, N, _ = adj.size() if add_loop: adj = adj.clone() idx = torch.arange(N, dtype=torch.long, device=adj.device) adj[:, idx, idx] = 1 out = torch.matmul(adj, x) out = out / adj.sum(dim=-1, keepdim=True).clamp(min=1) out = torch.matmul(out, self.weight) if self.bias is not None: out = out + self.bias if self.normalize: out = F.normalize(out, p=2, dim=-1) if mask is not None: out = out * mask.view(B, N, 1).to(x.dtype) return out
def __repr__(self): return '{}({}, {})'.format(self.__class__.__name__, self.in_channels, self.out_channels)