Source code for torch_geometric.nn.conv.gat_conv

import torch
from torch.nn import Parameter
import torch.nn.functional as F
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops, softmax

from ..inits import glorot, zeros

[docs]class GATConv(MessagePassing): r"""The graph attentional operator from the `"Graph Attention Networks" <>`_ paper .. math:: \mathbf{x}^{\prime}_i = \alpha_{i,i}\mathbf{\Theta}\mathbf{x}_{i} + \sum_{j \in \mathcal{N}(i)} \alpha_{i,j}\mathbf{\Theta}\mathbf{x}_{j}, where the attention coefficients :math:`\alpha_{i,j}` are computed as .. math:: \alpha_{i,j} = \frac{ \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top} [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_j] \right)\right)} {\sum_{k \in \mathcal{N}(i) \cup \{ i \}} \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top} [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_k] \right)\right)}. Args: in_channels (int): Size of each input sample. out_channels (int): Size of each output sample. heads (int, optional): Number of multi-head-attentions. (default: :obj:`1`) concat (bool, optional): If set to :obj:`False`, the multi-head attentions are averaged instead of concatenated. (default: :obj:`True`) negative_slope (float, optional): LeakyReLU angle of the negative slope. (default: :obj:`0.2`) dropout (float, optional): Dropout probability of the normalized attention coefficients which exposes each node to a stochastically sampled neighborhood during training. (default: :obj:`0`) 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, heads=1, concat=True, negative_slope=0.2, dropout=0, bias=True, **kwargs): super(GATConv, self).__init__(aggr='add', **kwargs) self.in_channels = in_channels self.out_channels = out_channels self.heads = heads self.concat = concat self.negative_slope = negative_slope self.dropout = dropout self.weight = Parameter( torch.Tensor(in_channels, heads * out_channels)) self.att = Parameter(torch.Tensor(1, heads, 2 * out_channels)) if bias and concat: self.bias = Parameter(torch.Tensor(heads * out_channels)) elif bias and not concat: self.bias = Parameter(torch.Tensor(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): glorot(self.weight) glorot(self.att) zeros(self.bias)
[docs] def forward(self, x, edge_index, size=None): """""" if size is None and torch.is_tensor(x): edge_index, _ = remove_self_loops(edge_index) edge_index, _ = add_self_loops(edge_index, num_nodes=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)
def message(self, edge_index_i, x_i, x_j, size_i): # Compute attention coefficients. x_j = x_j.view(-1, self.heads, self.out_channels) if x_i is None: alpha = (x_j * self.att[:, :, self.out_channels:]).sum(dim=-1) else: x_i = x_i.view(-1, self.heads, self.out_channels) alpha = ([x_i, x_j], dim=-1) * self.att).sum(dim=-1) alpha = F.leaky_relu(alpha, self.negative_slope) alpha = softmax(alpha, edge_index_i, size_i) # Sample attention coefficients stochastically. alpha = F.dropout(alpha, p=self.dropout, return x_j * alpha.view(-1, self.heads, 1) def update(self, aggr_out): if self.concat is True: aggr_out = aggr_out.view(-1, self.heads * self.out_channels) else: aggr_out = aggr_out.mean(dim=1) if self.bias is not None: aggr_out = aggr_out + self.bias return aggr_out def __repr__(self): return '{}({}, {}, heads={})'.format(self.__class__.__name__, self.in_channels, self.out_channels, self.heads)