codes/net.py [187:217]:
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                pred_false = pred_false_scores.mean(dim=-1)
                pred_scores = torch.cat([pred_true, pred_false_scores], dim=0)
            else:
                pred_true, pred_false_scores = self.calc_nce_scores(batch)
                pred_scores = torch.cat([pred_true] + pred_false_scores, dim=0)
                true_weight = torch.ones_like(pred_true)
                true_weight = true_weight * len(pred_false_scores)
                target = torch.cat(
                    [torch.ones_like(pred_true)]
                    + [torch.zeros_like(pf) for pf in pred_false_scores],
                    dim=0,
                )
                pred_false = torch.cat(pred_false_scores, dim=-1).mean(dim=-1)
            loss = F.binary_cross_entropy(pred_scores, target, reduction="mean")
        else:
            if self.hparams.train_mode == "ref_score":
                pred_true, pred_false = self.calc_ref_scores(batch)
            else:
                pred_true, pred_false = self.calc_cont_scores(batch)
            device = pred_true.device
            t_loss = self.loss_fn(
                pred_true, torch.ones(pred_true.size(0), 1).to(device)
            )
            f_loss = self.loss_fn(
                pred_false, torch.zeros(pred_false.size(0), 1).to(device)
            )
            loss = t_loss + f_loss
        torch.cuda.empty_cache()
        return OrderedDict(
            {"val_loss": loss, "true_score": pred_true, "false_score": pred_false}
        )
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codes/net.py [270:300]:
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                    pred_false = pred_false_scores.mean(dim=-1)
                    pred_scores = torch.cat([pred_true, pred_false_scores], dim=0)
                else:
                    pred_true, pred_false_scores = self.calc_nce_scores(batch)
                    pred_scores = torch.cat([pred_true] + pred_false_scores, dim=0)
                    true_weight = torch.ones_like(pred_true)
                    true_weight = true_weight * len(pred_false_scores)
                    target = torch.cat(
                        [torch.ones_like(pred_true)]
                        + [torch.zeros_like(pf) for pf in pred_false_scores],
                        dim=0,
                    )
                    pred_false = torch.cat(pred_false_scores, dim=-1).mean(dim=-1)
                loss = F.binary_cross_entropy(pred_scores, target, reduction="mean")
            else:
                if self.hparams.train_mode == "ref_score":
                    pred_true, pred_false = self.calc_ref_scores(batch)
                else:
                    pred_true, pred_false = self.calc_cont_scores(batch)
                device = pred_true.device
                t_loss = self.loss_fn(
                    pred_true, torch.ones(pred_true.size(0), 1).to(device)
                )
                f_loss = self.loss_fn(
                    pred_false, torch.zeros(pred_false.size(0), 1).to(device)
                )
                loss = t_loss + f_loss
            torch.cuda.empty_cache()
            return OrderedDict(
                {"val_loss": loss, "true_score": pred_true, "false_score": pred_false}
            )
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