72e28f315e6458993407beb4d7041ccebca90735,parlai/core/torch_ranker_agent.py,TorchRankerAgent,eval_step,#TorchRankerAgent#Any#,113

Before Change

                rank = (ranks[b] == label_inds[b]).nonzero().item()
                self.metrics["rank"] += 1 + rank

        cand_preds = [[cands[i] for i in row] for row in ranks]
        preds = [row[0] for row in cand_preds]
        return Output(preds, cand_preds)

After Change

                self.metrics["rank"] += 1 + rank

        cand_preds = []
        for i, ordering in enumerate(ranks):
            if cand_vecs.dim() == 2:
                cand_list = cands
            elif cand_vecs.dim() == 3:
                cand_list = cands[i]
            cand_preds.append([cand_list[rank] for rank in ordering])
        preds = [cand_preds[i][0] for i in range(batchsize)]
        return Output(preds, cand_preds)

    def _build_candidates(self, batch, source, mode):

Instances