import math import torch import numpy as np import torch.nn as nn import torch.nn.functional as F from torch.nn.parameter import Parameter from torch.nn.modules.module import Module class GraphConvolution(Module): """ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 """ def __init__(self, in_features, out_features, bias=True, init='xavier'): super(GraphConvolution, self).__init__() self.in_features = in_features self.out_features = out_features self.weight = Parameter(torch.FloatTensor(in_features, out_features)) if bias: self.bias = Parameter(torch.FloatTensor(out_features)) else: self.register_parameter('bias', None) if init == 'uniform': print("| Uniform Initialization") self.reset_parameters_uniform() elif init == 'xavier': print("| Xavier Initialization") self.reset_parameters_xavier() elif init == 'kaiming': print("| Kaiming Initialization") self.reset_parameters_kaiming() else: raise NotImplementedError def reset_parameters_uniform(self): stdv = 1. / math.sqrt(self.weight.size(1)) self.weight.data.uniform_(-stdv, stdv) if self.bias is not None: self.bias.data.uniform_(-stdv, stdv) def reset_parameters_xavier(self): nn.init.xavier_normal_(self.weight.data, gain=0.02) # Implement Xavier Uniform if self.bias is not None: nn.init.constant_(self.bias.data, 0.0) def reset_parameters_kaiming(self): nn.init.kaiming_normal_(self.weight.data, a=0, mode='fan_in') if self.bias is not None: nn.init.constant_(self.bias.data, 0.0) def forward(self, input, adj): support = torch.mm(input, self.weight) output = torch.spmm(adj, support) if self.bias is not None: return output + self.bias else: return output def __repr__(self): return self.__class__.__name__ + ' (' \ + str(self.in_features) + ' -> ' \ + str(self.out_features) + ')' class GraphAttention(nn.Module): """ Simple GAT layer, similar to https://arxiv.org/abs/1710.10903 """ def __init__(self, in_features, out_features, dropout, alpha, concat=True): super(GraphAttention, self).__init__() self.dropout = dropout self.in_features = in_features self.out_features = out_features self.alpha = alpha self.concat = concat self.W = nn.Parameter(nn.init.xavier_normal_(torch.Tensor(in_features, out_features).type(torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor), gain=np.sqrt(2.0)), requires_grad=True) self.a1 = nn.Parameter(nn.init.xavier_normal_(torch.Tensor(out_features, 1).type(torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor), gain=np.sqrt(2.0)), requires_grad=True) self.a2 = nn.Parameter(nn.init.xavier_normal_(torch.Tensor(out_features, 1).type(torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor), gain=np.sqrt(2.0)), requires_grad=True) self.leakyrelu = nn.LeakyReLU(self.alpha) def forward(self, input, adj): h = torch.mm(input, self.W) N = h.size()[0] f_1 = torch.matmul(h, self.a1) f_2 = torch.matmul(h, self.a2) e = self.leakyrelu(f_1 + f_2.transpose(0,1)) zero_vec = -9e15*torch.ones_like(e) attention = torch.where(adj > 0, e, zero_vec) attention = F.softmax(attention, dim=1) attention = F.dropout(attention, self.dropout, training=self.training) h_prime = torch.matmul(attention, h) if self.concat: return F.elu(h_prime) else: return h_prime def __repr__(self): return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'