8bafae2ee7044529543768eec63d8460d894f5c6,train.py,,validate,#Any#Any#Any#Any#Any#Any#,163

Before Change

            t.set_postfix(loss="{:.2f}".format(loss_meter.avg), refresh=False)

        val_loss = loss_meter.avg
        t.write(desc + " | valid loss {:2.2f} | valid ppl {:3.2f}"
                .format(val_loss, math.pow(2, val_loss)))

    // update and return the learning rate
    return val_loss

After Change

                             max_positions=args.max_positions,
                             skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test)
    loss_meter = AverageMeter()
    extra_meters = collections.defaultdict(lambda: AverageMeter())

    desc = "| epoch {:03d} | valid on \"{}\" subset".format(epoch, subset)
    with progress_bar(itr, desc, leave=False) as t:
        for _, sample in data.skip_group_enumerator(t, ngpus):
            loss_dict = trainer.valid_step(sample)
            loss = loss_dict["loss"]
            del loss_dict["loss"]  // don"t include in extra_meters or extra_postfix

            ntokens = sum(s["ntokens"] for s in sample)
            loss_meter.update(loss, ntokens)

            extra_postfix = []
            for k, v in loss_dict.items():
                extra_meters[k].update(v)
                extra_postfix.append((k, "{:.4f}".format(extra_meters[k].avg)))

            t.set_postfix(collections.OrderedDict([
                ("loss", "{:.2f}".format(loss_meter.avg)),
            ] + extra_postfix), refresh=False)

        val_loss = loss_meter.avg
        fmt = desc + " | valid loss {:2.2f} | valid ppl {:3.2f}".format(
            val_loss, math.pow(2, val_loss))
        fmt += "".join(
            " | {} {:.4f}".format(k, meter.avg)
            for k, meter in extra_meters.items()
        )
        t.write(fmt)

    // update and return the learning rate
    return val_loss

Instances