Function async_forward

lite/example/cpp_example/mge/basic.cpp:246–303

}

bool async_forward(const Args& args) {
    std::string network_path = args.model_path;
    std::string input_path = args.input_path;
    Config config;
    config.options.var_sanity_check_first_run = false;

    //! create and load the network
    std::shared_ptr<Network> network = std::make_shared<Network>(config);

    network->load_model(network_path);

    //! set input data to input tensor
    std::shared_ptr<Tensor> input_tensor = network->get_input_tensor(0);
    //! copy or forward data to network
    size_t length = input_tensor->get_tensor_total_size_in_byte();
    void* dst_ptr = input_tensor->get_memory_ptr();
    auto src_tensor = parse_npy(input_path);
    void* src = src_tensor->get_memory_ptr();
    memcpy(dst_ptr, src, length);

    //! set async mode and callback
    volatile bool finished = false;
    network->set_async_callback([&finished]() {
#if !__DEPLOY_ON_XP_SP2__
        std::cout << "worker thread_id:" << std::this_thread::get_id() << std::endl;
#endif
        finished = true;
    });

#if !__DEPLOY_ON_XP_SP2__
    std::cout << "out thread_id:" << std::this_thread::get_id() << std::endl;
#endif

    //! forward
    network->forward();
    size_t count = 0;
    while (finished == false) {
        count++;
    }
    printf("Forward finish, count is %zu\n", count);

    //! get the output data or read tensor set in network_in
    std::shared_ptr<Tensor> output_tensor = network->get_output_tensor(0);
    void* out_data = output_tensor->get_memory_ptr();
    size_t out_length = output_tensor->get_tensor_total_size_in_byte() /
                        output_tensor->get_layout().get_elem_size();
    printf("length=%zu\n", length);
    float max = -1.0f;
    float sum = 0.0f;
    for (size_t i = 0; i < out_length; i++) {
        float data = static_cast<float*>(out_data)[i];
        sum += data;
        if (max < data)
            max = data;
    }
    printf("max=%e, sum=%e\n", max, sum);
    return true;
}