Method decode

examples/pytorch/dtgrnn/model.py:210–225

(self, g, teacher_states, hidden_states, batch_cnt, device)

        return hidden_states

    def decode(self, g, teacher_states, hidden_states, batch_cnt, device):
        outputs = []
        inputs = torch.zeros(g.num_nodes(), self.in_feats).to(device)
        for i in range(self.seq_len):
            if (
                np.random.random() < self.compute_thresh(batch_cnt)
                and self.training
            ):
                inputs, hidden_states = self.decoder(
                    g, teacher_states[i], hidden_states
                )
            else:
                inputs, hidden_states = self.decoder(g, inputs, hidden_states)
            outputs.append(inputs)
        outputs = torch.stack(outputs)
        return outputs

    def forward(self, g, inputs, teacher_states, batch_cnt, device):
        hidden = self.encode(g, inputs, device)