我尝试从opencv重新实现dasiamrpn tracker，但是使用openvino推理。在init方法中，我假设一些层参数已经被r1和cls1头产生的Tensor改变了

siamRPN.setInput(blob);
    cv::Mat out1;
    siamRPN.forward(out1, "63");

    siamKernelCL1.setInput(out1);
    siamKernelR1.setInput(out1);

    cv::Mat cls1 = siamKernelCL1.forward();
    cv::Mat r1 = siamKernelR1.forward();
    std::vector<int> r1_shape = { 20, 256, 4, 4 }, cls1_shape = { 10, 256, 4, 4 }; //same shape as conv layers 65 and 68

    siamRPN.setParam(siamRPN.getLayerId("65"), 0, r1.reshape(0, r1_shape));
    siamRPN.setParam(siamRPN.getLayerId("68"), 0, cls1.reshape(0, cls1_shape));

但是我在openvino中找不到一个API或者某种方法来实现这个。有人遇到过这样的问题吗？
what I'm trying to do
我假设权重存储在这两个节点中：

<layer id="31" name="new_layer_2.weight" type="Const" version="opset1">
        <data element_type="f32" shape="10, 256, 4, 4" offset="17349120" size="163840"/>
        <rt_info>
            <attribute name="fused_names" version="0" value="new_layer_2.weight"/>
        </rt_info>
        <output>
            <port id="0" precision="FP32" names="new_layer_2.weight">
                <dim>10</dim>
                <dim>256</dim>
                <dim>4</dim>
                <dim>4</dim>
            </port>
        </output>
    </layer>
    <layer id="38" name="new_layer_1.weight" type="Const" version="opset1">
        <data element_type="f32" shape="20, 256, 4, 4" offset="19873280" size="327680"/>
        <rt_info>
            <attribute name="fused_names" version="0" value="new_layer_1.weight"/>
        </rt_info>
        <output>
            <port id="0" precision="FP32" names="new_layer_1.weight">
                <dim>20</dim>
                <dim>256</dim>
                <dim>4</dim>
                <dim>4</dim>
            </port>
        </output>
    </layer>

我可以在模型操作中查看此节点

auto ops = model->get_ops();

但是我不知道如何改变它的重量数据。有没有办法在运行时改变它？