02ef279b852bad53771e02435d0caa0a64d17343,baseline/tf/classify/training/distributed.py,ClassifyTrainerDistributedTf,_test,#ClassifyTrainerDistributedTf#Any#Any#,142

Before Change

        strategy = self.strategy
        cm = ConfusionMatrix(self.model.labels)
        nc = len(self.model.labels)

        def _replica_test_step(inputs):
            features, y = inputs
            y = tf.cast(y, tf.int64)

After Change

        with strategy.scope():

            total_loss = tf.Variable(0.0)
            total_acc = tf.Variable(0.0)
            total_norm = tf.Variable(0.0)

            SET_TRAIN_FLAG(False)
            test_iter = iter(loader)

            for i in range(steps):
                //step_loss, step_batchsz, distributed_cm = _distributed_test_step(next(test_iter))
                step_loss, step_batchsz, distributed_acc = _distributed_test_step(next(test_iter))

                total_loss.assign_add(step_loss)
                total_norm.assign_add(step_batchsz)
                total_acc.assign_add(distributed_acc)
                //cm._cm += distributed_cm.numpy()

            //metrics = cm.get_all_metrics()
            total_loss = total_loss.numpy()
            total_norm = total_norm.numpy()
            total_acc = total_acc.numpy()
            metrics = {}
            metrics["avg_loss"] = total_loss / float(total_norm)
            metrics["acc"] = total_acc / float(total_norm)
            //verbose_output(verbose, cm)

            return metrics

Instances