Function test_output_no_copy

lite/test/test_network.cpp:595–635

namespace {

void test_output_no_copy(int record) {
    Config config;
    config.options.force_output_use_user_specified_memory = true;
    config.options.comp_node_seq_record_level = record;
    auto tensor = get_input_data("./input_data.npy");
    std::string model_path = "./shufflenet.mge";
    std::string input_name = "data";
    auto result_mgb = mgb_lar(model_path, config, input_name, tensor);

    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);

    auto src_ptr = tensor->get_memory_ptr();
    auto src_layout = tensor->get_layout();
    input_tensor->reset(src_ptr, src_layout);

    std::shared_ptr<Tensor> output_tensor = network->get_output_tensor(0);
    size_t times = 5;
    std::vector<std::shared_ptr<Tensor>> result_tensors;
    for (size_t i = 0; i < times; i++) {
        auto tmp = std::make_shared<Tensor>(
                LiteDeviceType::LITE_CPU,
                Layout{{1, 1000}, 2, LiteDataType::LITE_FLOAT});
        result_tensors.push_back(tmp);
    }

    for (size_t i = 0; i < times; i++) {
        void* out_data = result_tensors[i]->get_memory_ptr();
        output_tensor->reset(out_data, result_tensors[i]->get_layout());

        network->forward();
        network->wait();
        ASSERT_EQ(output_tensor->get_memory_ptr(), out_data);
        compare_lite_tensor<float>(output_tensor, result_mgb);
    }
    for (size_t i = 0; i < times; i++) {
        compare_lite_tensor<float>(result_tensors[i], result_mgb);
    }
}

void test_input_no_copy(int record) {
    Config config;