65c9a4857e41dcde4f5dae8a77e6cdf03953646a,model/model.py,RNNFeaturizer,forward,#RNNFeaturizer#Any#Any#,70

Before Change

        if self.all_layers:
            cell = torch.cat(cell, -1)
        else:
            cell = cell[-1]

        // get last valid cell state
        if seq_len is not None:
            cell = cell[(seq_len-1).view(-1).contiguous(), torch.arange(cell.size(1)).long().cuda()]
        else:
            cell = cell[-1]
        return cell

    def state_dict(self, destination=None, prefix="", keep_vars=False):
        sd = {}

After Change

        self.rnn.detach_hidden()
        if seq_len is None:
            for i in range(input.size(0)):
                emb = self.drop(self.encoder(input[i]))
                _, hidden = self.rnn(emb.unsqueeze(0), collectHidden=True)
            cell = self.get_cell_features(hidden)
        else:
            last_cell = 0
            for i in range(input.size(0)):
                emb = self.drop(self.encoder(input[i]))
                _, hidden = self.rnn(emb.unsqueeze(0), collectHidden=True)
                cell = self.get_cell_features(hidden)
                if i > 0:
                    cell = get_valid_outs(i, seq_len, cell, last_cell)
                last_cell = cell   
        return cell

    def get_cell_features(self, hidden):

Instances