def forward(self, inputs, rel_rec, rel_send):
if torch.sum(self.adj_A != self.adj_A):
print('nan error \n')
adj_A1 = torch.sinh(3. * self.adj_A)
adj_Aforz = preprocess_adj_new(adj_A1)
adj_A = torch.eye(adj_A1.size()[0]).double()
bninput = self.embed(inputs.long().view(-1, inputs.size(2)))
bninput = bninput.view(*inputs.size(), -1).squeeze()
H1 = F.relu((self.fc1(bninput)))
x = (self.fc2(H1))
logits = torch.matmul(adj_Aforz, x + self.Wa) - self.Wa
prob = my_softmax(logits, -1)
alpha = my_softmax(self.alpha, -1)
return x, prob, adj_A1, adj_A, self.z, self.z_positive, self.adj_A, self.Wa, alpha
def forward(self, inputs):
# Input shape: [num_sims * num_edges, num_dims, num_timesteps]
x = F.relu(self.conv1(inputs))
x = self.bn1(x)
x = F.dropout(x, self.dropout_prob, training=self.training)
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.bn2(x)
pred = self.conv_predict(x)
attention = my_softmax(self.conv_attention(x), axis=2)
edge_prob = (pred * attention).mean(dim=2)
return edge_prob
