9dbf8ff1a0bf28ab804185f641cbce846a339c68,examples/mxnet_imagenet_resnet50.py,,train_gluon,#,297

Before Change

        for nbatch, batch in enumerate(train_data, start=1):
            data, label = batch_fn(batch, [context])
            with autograd.record():
                outputs = [net(x.astype(args.dtype, copy=False)) for x in data]
                loss = [loss_fn(yhat, y) for yhat, y in zip(outputs, label)]
            for l in loss:
                l.backward()
            trainer.step(batch_size)

After Change

            loss.backward()
            trainer.step(batch_size)

            metric.update([label], [output])
            if args.log_interval and nbatch % args.log_interval == 0:
                name, acc = metric.get()
                logging.info("Epoch[%d] Rank[%d] Batch[%d]\t%s=%f\tlr=%f",
                             epoch, rank, nbatch, name, acc, trainer.learning_rate)

Instances