执行了释放资源操作但是内存没有释放

[wangdada-love]

我的模型初始化和模型释放代码分别如下：
int OBJDetecor::ModelInit(){

    // ModelPath_ = "/***/***.mnn";
    std::cout << "Current state: " << stateToString(getCurrentState()) << std::endl;
    if (!transitionTo(current_state_, ModelState::INITIALIZING)){
        std::lock_guard<std::mutex> lock(mutex_);
        DoInit = 0;
        return -1;
    }

    auto cleanupOnFailure = [this]() {
        // 释放手动创建的资源
        if (nhwcTensor_) {
            // 假设 MNN::Tensor 有对应的释放函数
            // 如果没有，需要确保正确的释放方式
            // nhwcTensor_.reset();
            MNN::Tensor::destroy(nhwcTensor_);
            nhwcTensor_ = nullptr;
        }
        // 状态转换回 UNINITIALIZED
        transitionTo(current_state_, ModelState::UNINITIALIZED);
    };

    try{

        // 创建解释器
        auto new_net = std::shared_ptr<MNN::Interpreter>(
            MNN::Interpreter::createFromFile(ModelPath_.c_str()));
        if (!new_net) {
            std::cerr << "Failed to create  interpreter. Model file might be invalid or not found: " << ModelPath_ << std::endl;
            cleanupOnFailure();
            return -1;
        }

        // 配置后端
        MNN::ScheduleConfig NetConfig_;
        MNN::BackendConfig BackConfig_;
        NetConfig_.type = MNN_FORWARD_OPENCL;
        NetConfig_.numThread = 1;
        BackConfig_.precision = static_cast<MNN::BackendConfig::PrecisionMode>(
            MNN::BackendConfig::Precision_Low);
        NetConfig_.backendConfig = &BackConfig_;

        std::cout << "create session..." << std::endl;

        // 创建会话
        new_net->setSessionMode(MNN::Interpreter::SessionMode::Session_Release);
        auto new_session = new_net->createSession(NetConfig_);
        if (!new_session) {
            std::cerr << "Failed to create  MNN session. Check backend configuration." << std::endl;
            cleanupOnFailure();
            return -1;
        }

        std::cout << "create session finished..." << std::endl;

        // 获取输入输出张量
        auto new_input_tensor = new_net->getSessionInput(new_session, "input");
        if (!new_input_tensor) {
            std::cerr << "Failed to get  input tensor with name 'input'" << std::endl;
            cleanupOnFailure();
            return -1;
        }
        std::cout << "get input tensor finished..." << std::endl;
        auto shape = new_input_tensor->shape();
        std::cout << "InputTensor_ dimensions: ["
                << shape[0] << ", " << shape[1] << ", " << shape[2] << ", " << shape[3] << "]" 
                << std::endl;

        auto new_output_tensor_p3 = new_net->getSessionOutput(new_session, "tf.concat_2");
        if (!new_output_tensor_p3) {
            std::cerr << "Failed to get  output tensor p3" << std::endl;
            cleanupOnFailure();
            return -1;
        }

        auto new_output_tensor_p4 = new_net->getSessionOutput(new_session, "tf.concat_5");
        if (!new_output_tensor_p4) {
            std::cerr << "Failed to get  output tensor p4" << std::endl;
            cleanupOnFailure();
            return -1;
        }

        auto new_output_tensor_p5 = new_net->getSessionOutput(new_session, "tf.concat_8");
        if (!new_output_tensor_p5) {
            std::cerr << "Failed to get  output tensor p5" << std::endl;
            cleanupOnFailure();
            return -1;
        }

        std::cout << "get output tensor finished..." << std::endl;

        // 创建处理用的张量
        std::vector<int> dims{1, ModelSize_, ModelSize_, 3};
        auto new_nhwc_tensor = MNN::Tensor::create<float>(dims, nullptr, MNN::Tensor::CAFFE);

        if (!new_nhwc_tensor) {
            std::cerr << "Failed to create nhwcTensor_" << std::endl;
            cleanupOnFailure();
            return -1;
        }
        std::cout << "init nhwc tensor tensor finished..." << std::endl;


        // 所有步骤成功，更新成员变量
        OBJNet_ = std::move(new_net);
        Session_ = new_session; // 注意: 这是原始指针，生命周期由 OBJNet_ 管理
        InputTensor_ = new_input_tensor;
        HeadTensorP3_ = new_output_tensor_p3;
        HeadTensorP4_ = new_output_tensor_p4;
        HeadTensorP5_ = new_output_tensor_p5;
        nhwcTensor_ = std::move(new_nhwc_tensor);
        std::cout << "modify local variables finished..." << std::endl;

       // 状态转换到 READY
        if (!transitionTo(current_state_, ModelState::READY)) {
            cleanupOnFailure();
            return -1;
        }

        {
            std::lock_guard<std::mutex> lock(mutex_);
            DoInit = 0;
        }
        std::cout << "Finish OBJdetector configuration" << std::endl;

        return 0;
    }catch(...){
        std::cerr << "OBJDetector init failed!!! unknown error!!!" << std::endl;
        cleanupOnFailure();
        return -1;
    }
}

int OBJDetecor::ModelRelease(){
std::cout << "model release......, Current state: " << stateToString(getCurrentState()) << std::endl;
// 检查并转换状态
if (!transitionTo(current_state_, ModelState::RELEASING)) {
std::lock_guardstd::mutex lock(mutex_);
DoRelease = 0;
return -1;
}

    try{
       
       // 1. 释放会话
        if (Session_) {
            std::cout << "Releasing session..." << std::endl;
            OBJNet_->releaseSession(Session_);
            Session_ = nullptr;
        }

        if (nhwcTensor_) {
            std::cout << "Deleting nhwcTensor_..." << std::endl;
            // delete nhwcTensor_;
            MNN::Tensor::destroy(nhwcTensor_);
            nhwcTensor_ = nullptr;
        }
        InputTensor_ = nullptr;
        HeadTensorP3_ = nullptr;
        HeadTensorP4_ = nullptr;
        HeadTensorP5_ = nullptr;

        if(OBJNet_){
            OBJNet_->releaseModel();
        }

        // 状态转换到 UNINITIALIZED
        transitionTo(current_state_, ModelState::UNINITIALIZED);
        {
            std::lock_guard<std::mutex> lock(mutex_);
            DoRelease = 0;
        }
        return 0;
    }catch(...){
        std::cerr << "model release failed! unknow error!" << std::endl;
        transitionTo(current_state_, ModelState::UNINITIALIZED);
        return -1;
    }

}

但是在项目实际运行过程中，我发先，虽然执行了我的ModelRelease函数，但是使用命令cat /proc/pid/status 查看VmRSS内存占用并没有减少，其中pid是进程id。
请问我的释放代码是否有问题，如果有问题，请告诉我是哪里的问题，如果没问题，我想知道为什么似乎释放操作并没有生效？