Method do_setup

examples/graph_vgg_vdsr.cpp:52–149

    GraphVDSRExample &operator=(const GraphVDSRExample &) = delete;
    ~GraphVDSRExample() override                          = default;
    bool do_setup(int argc, char **argv) override
    {
        // Parse arguments
        cmd_parser.parse(argc, argv);
        cmd_parser.validate();

        // Consume common parameters
        common_params = consume_common_graph_parameters(common_opts);

        // Return when help menu is requested
        if (common_params.help)
        {
            cmd_parser.print_help(argv[0]);
            return false;
        }

        // Get input image width and height
        const unsigned int image_width  = model_input_width->value();
        const unsigned int image_height = model_input_height->value();

        // Print parameter values
        std::cout << common_params << std::endl;
        std::cout << "Image width: " << image_width << std::endl;
        std::cout << "Image height: " << image_height << std::endl;

        // Get trainable parameters data path
        const std::string data_path  = common_params.data_path;
        const std::string model_path = "/cnn_data/vdsr_model/";

        // Create a preprocessor object
        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();

        // Create input descriptor
        const TensorShape tensor_shape =
            permute_shape(TensorShape(image_width, image_height, 1U, common_params.batches), DataLayout::NCHW,
                          common_params.data_layout);
        TensorDescriptor input_descriptor =
            TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);

        // Set weights trained layout
        const DataLayout weights_layout = DataLayout::NCHW;

        // Note: Quantization info are random and used only for benchmarking purposes
        graph << common_params.target << common_params.fast_math_hint
              << InputLayer(input_descriptor.set_quantization_info(QuantizationInfo(0.0078125f, 128)),
                            get_input_accessor(common_params, std::move(preprocessor), false));

        SubStream left(graph);
        SubStream right(graph);

        // Layer 1
        right << ConvolutionLayer(3U, 3U, 64U, get_weights_accessor(data_path, "conv0_w.npy", weights_layout),
                                  get_weights_accessor(data_path, "conv0_b.npy"), PadStrideInfo(1, 1, 1, 1), 1,
                                  QuantizationInfo(0.031778190285f, 156), QuantizationInfo(0.0784313753247f, 128))
                     .set_name("conv0")
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
                     .set_name("conv0/Relu");