a37dcd46cba88dfd0478b601fb8fbf9b425780c0,example/automatic-mixed-precision/amp_model_conversion.py,,,#,50

Before Change

        if args.run_dummy_inference:
            logger.info("Running inference on the mixed precision model with dummy inputs, batch size: 1")
            result_after = net.forward(mx.nd.zeros((1, 3, 224, 224), dtype=np.float32, ctx=mx.gpu(0)))
            result_after = net.forward(mx.nd.zeros((1, 3, 224, 224), dtype=np.float32, ctx=mx.gpu(0)))
            logger.info("Inference run successfully")

After Change

                                            If you want to use symbolic model instead, remove --use-gluon-model when running the script".format(gluon_models)
        shape = None
        if args.model in segmentation_models:
            shape = (1, 3, 480, 480)
        elif args.model in calib_ssd_models:
            shape = (1, 3, 512, 544)
        elif args.model in calib_inception_models:
            shape = (1, 3, 299, 299)
        else:
            shape = (1, 3, 224, 224)
        net = gluoncv.model_zoo.get_model(args.model, pretrained=True)
        net.hybridize()
        result_before1 = net.forward(mx.nd.random.uniform(shape=shape))
        net.export("{}".format(args.model))
        net = amp.convert_hybrid_block(net, cast_optional_params=args.cast_optional_params)
        net.export("{}-amp".format(args.model), remove_amp_cast=False)

Instances