6aad1de658a933d3fa376f7fe9abf419da8a8bd2,python/baseline/pytorch/classify/train.py,ClassifyTrainerPyTorch,_test,#ClassifyTrainerPyTorch#Any#,32

Before Change

            loss = self.crit(pred, y)
            total_loss += loss.item()
            _add_to_cm(cm, y, pred)
            pg.update()
        pg.done()

        metrics = cm.get_all_metrics()
        metrics["avg_loss"] = total_loss/float(steps)
        verbose_output(verbose, cm)

After Change

        cm = ConfusionMatrix(self.labels)
        verbose = kwargs.get("verbose", None)

        for batch_dict in pg(loader):
            example = self._make_input(batch_dict)
            y = example.pop("y")
            pred = self.model(example)
            loss = self.crit(pred, y)
            batchsz = self._get_batchsz(batch_dict)
            total_loss += loss.item() * batchsz
            total_norm += batchsz
            _add_to_cm(cm, y, pred)

        metrics = cm.get_all_metrics()
        metrics["avg_loss"] = total_loss / float(total_norm)

Instances