Function test_input_no_copy

lite/test/test_network.cpp:637–677

}

void test_input_no_copy(int record) {
    Config config;
    config.options.force_output_use_user_specified_memory = true;
    config.options.comp_node_seq_record_level = record;
    std::string model_path = "./shufflenet.mge";
    std::string input_name = "data";

    Layout layout_in{{1, 3, 224, 224}, 4};
    std::vector<std::shared_ptr<Tensor>> inputs;
    std::vector<std::shared_ptr<Tensor>> outputs;
    for (int i = 0; i < 3; i++) {
        auto tmp_in = std::make_shared<Tensor>(LiteDeviceType::LITE_CPU, layout_in);

        auto ptr = static_cast<float*>(tmp_in->get_memory_ptr());
        for (size_t id = 0; id < 2 * 224 * 224; id++) {
            ptr[id] = i + 1;
        }
        inputs.push_back(tmp_in);
        outputs.push_back(mgb_lar(model_path, config, input_name, tmp_in));
    }

    std::shared_ptr<Network> network = std::make_shared<Network>(config);

    network->load_model(model_path);
    std::shared_ptr<Tensor> input_tensor = network->get_io_tensor(input_name);
    std::shared_ptr<Tensor> output_tensor = network->get_output_tensor(0);

    for (int i = 0; i < 3; i++) {
        auto ptr = inputs[i]->get_memory_ptr();
        input_tensor->reset(ptr, layout_in);

        auto tmp_out = std::make_shared<Tensor>(
                LiteDeviceType::LITE_CPU,
                Layout{{1, 1000}, 2, LiteDataType::LITE_FLOAT});
        output_tensor->reset(tmp_out->get_memory_ptr(), output_tensor->get_layout());

        network->forward();
        network->wait();
        compare_lite_tensor<float>(output_tensor, outputs[i]);
    }
}

void test_io_no_copy_ax(std::string model_name, int record = 1) {
    std::string model_path = model_name;