6aad1de658a933d3fa376f7fe9abf419da8a8bd2,python/baseline/pytorch/seq2seq/train.py,Seq2SeqTrainerPyTorch,test,#Seq2SeqTrainerPyTorch#Any#Any#Any#,32

Before Change

        pg.done()

        self.log.debug({"phase": phase, "time": time.time() - start})
        avg_loss = float(total_loss)/total
        metrics["avg_loss"] = avg_loss
        metrics["perplexity"] = np.exp(avg_loss)
        for reporting in reporting_fns:
            reporting(metrics, epochs, phase)
        return metrics

After Change

        for batch_dict in pg(vs):
            input_ = self._input(batch_dict)
            tgt = input_["tgt"]
            tgt_lens = batch_dict["tgt_lengths"]
            pred = self.model(input_)
            loss = self.crit(pred, tgt)
            toks = self._num_toks(tgt_lens)
            total_loss += loss.item() * toks
            total_toks += toks

        metrics = self.calc_metrics(total_loss, total_toks)
        self.report(
            epochs, metrics, start,
            phase, "EPOCH", reporting_fns
        )
        return metrics

    def train(self, ts, reporting_fns):
        self.model.train()

Instances