Method _run

test/expr/ModuleTest.cpp:917–985

class MemeoryUsageTest : public MNNTestCase {
public:
    bool _run(int precision, bool lazy) {
        auto func = [precision](VARP y, float limit) {
            flatbuffers::FlatBufferBuilder builderOutput(1024);
            {
                std::unique_ptr<MNN::NetT> net(new NetT);
                Variable::save({y}, net.get());
                auto len = MNN::Net::Pack(builderOutput, net.get());
                builderOutput.Finish(len);
            }
            int sizeOutput    = builderOutput.GetSize();
            auto bufferOutput = builderOutput.GetBufferPointer();
            std::shared_ptr<Interpreter> net(Interpreter::createFromBuffer((void*)bufferOutput, sizeOutput), Interpreter::destroy);
            ScheduleConfig config;
            BackendConfig bnConfig;
            bnConfig.precision = (MNN::BackendConfig::PrecisionMode)precision;
            config.numThread = 1;
            config.type = ExecutorScope::Current()->getAttr()->firstType;
            config.backendConfig = &bnConfig;
            auto s1 = net->createSession(config);
            float memory = 0.0f;
            net->getSessionInfo(s1, MNN::Interpreter::MEMORY, &memory);
            if (memory < 0.01f) {
                FUNC_PRINT(precision);
                return false;
            }
            if (memory > limit) {
                MNN_ERROR("memory %f larger than limit: %f, precision=%d\n", memory, limit, precision);
                return false;
            }
            FUNC_PRINT_ALL(memory, f);
            return true;
        };
        auto y = _mobileNetV1Expr();
        bool res = func(y, 62.0f);
        if (!res) {
            return false;
        }
        auto x = _Input({1, 3, 1024, 1024}, NCHW);
        y = _Sigmoid(x);
        res = func(y, 35.0f);
        if (!res) {
            return false;
        }
        auto weightVar = MNN::Express::_Const(0.0f, {100, 10000}, NCHW);
        x = MNN::Express::_Input({1, 100}, NCHW);
        auto x2 = MNN::Express::_Input({1, 10000}, NCHW);
        y = MNN::Express::_MatMul(x, weightVar);
        auto weightVar2 = MNN::Express::_Const(0.0f, {10000, 100}, NCHW);
        y = MNN::Express::_MatMul(y, weightVar2);
        res = func(y, 8.0f);
        if (!res) {
            return false;
        }
        weightVar = MNN::Express::_Const(0.0f, {100, 10000, 1, 1}, NC4HW4);
        x = MNN::Express::_Input({100, 10000, 1, 1}, NC4HW4);
        y = MNN::Express::_Add(x, weightVar);
        res = func(y, 12.0f);
        if (!res) {