How do I run ONNX model on Simulink?

Question

翼 2025-1-17

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2172983-how-do-i-run-onnx-model-on-simulink

编辑： Manish 2025-5-13

I created a simple onnx model by Pytorch as below code.

import torch
import torch.nn as nn
class EmptyModel(nn.Module):
    def __init__(self):
        super(EmptyModel, self).__init__()
        # No trainable parameters, but add a linear layer to match Simulink requirements
        self.linear = nn.Linear(7, 2, bias=False) 
        with torch.no_grad():
          self.linear.weight.fill_(0.0)  
    def forward(self, x):
        # Returns the first two elements of the input as is, without any computation
        return x[:, :2] 
model = EmptyModel()
dummy_input = torch.randn(1, 7, dtype=torch.float32)
torch.onnx.export(
    model,  
    dummy_input, 
    "empty_model.onnx", 
    export_params=True, 
    opset_version=11,
    do_constant_folding=True,  
    input_names=["input"], 
    output_names=["output"],  
    dynamic_axes={
        "input": {0: "batch_size"}, 
        "output": {0: "batch_size"},  
    },
)

It is an ONNX model that performs model inference on 7 input data and returns 2 output data that are the results of the inference.

I would like to incorporate this ONNX model in Simulink and run the simulation.

Since I value performance aspects such as execution speed, I also used ONNX Model Predict( Deep Learing toolbox), but the performance aspect was not so good, so I am looking for a better way.

Do you have any ideas?

I know that there are ways such as using S-Function, but I would like to know if there are any other ways that can be used. If possible, I would appreciate it if you could also tell me how to implement it.

Actually, I just tried below on my own.

I build the below C++ code (File name: smex.cpp) with mex command into MEX file.

Then I tried to call that MEX file in S-Function like this, however MATLAB crashes.

This MEX file (smex.mexw64) is placed in the same folder as this .slx file, but It doesn't work.

I'm using Windows.

Also, I downloaded (onnxruntime-win-x64-1.20.1.zip) from this link https://github.com/microsoft/onnxruntime/releases

then set up ONNX Runtime C++ API environment with Visual Studio 2022.

I couldn't figure out why this error happened about this matter....

#define S_FUNCTION_NAME  smex
#define S_FUNCTION_LEVEL 2
#include "simstruc.h"
#include <onnxruntime_cxx_api.h>
#include <vector>
#include <memory>
static std::unique_ptr<Ort::Env> g_env;
static std::unique_ptr<Ort::Session> g_session;
static std::vector<const char*> g_input_node_names;
static std::vector<const char*> g_output_node_names;
#ifdef __cplusplus
extern "C" {
#endif
static void mdlInitializeSizes(SimStruct *S)
{
    if (!ssSetNumInputPorts(S, 1)) return;
    ssSetInputPortWidth(S, 0, 7);
    ssSetInputPortDataType(S, 0, SS_DOUBLE);
    ssSetInputPortDirectFeedThrough(S, 0, 1);
    ssSetInputPortRequiredContiguous(S, 0, 1);
    if (!ssSetNumOutputPorts(S, 1)) return;
    ssSetOutputPortWidth(S, 0, 2);
    ssSetOutputPortDataType(S, 0, SS_DOUBLE);
    ssSetNumSampleTimes(S, 1);
    ssSetOptions(S, SS_OPTION_EXCEPTION_FREE_CODE);
}
static void mdlInitializeSampleTimes(SimStruct *S)
{
    ssSetSampleTime(S, 0, INHERITED_SAMPLE_TIME);
    ssSetOffsetTime(S, 0, 0.0);
}
#define MDL_START
static void mdlStart(SimStruct *S)
{
    try {
        g_env = std::make_unique<Ort::Env>(ORT_LOGGING_LEVEL_WARNING, "test");
        
        Ort::SessionOptions session_options;
        session_options.SetIntraOpNumThreads(1);
        session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
        const wchar_t* model_path = L"empty_model.onnx";
        g_session = std::make_unique<Ort::Session>(*g_env, model_path, session_options);
        Ort::AllocatorWithDefaultOptions allocator;
        
        size_t num_input_nodes = g_session->GetInputCount();
        g_input_node_names.resize(num_input_nodes);
        for (size_t i = 0; i < num_input_nodes; i++) {
            auto input_name = g_session->GetInputNameAllocated(i, allocator);
            g_input_node_names[i] = input_name.get();
        }
        size_t num_output_nodes = g_session->GetOutputCount();
        g_output_node_names.resize(num_output_nodes);
        for (size_t i = 0; i < num_output_nodes; i++) {
            auto output_name = g_session->GetOutputNameAllocated(i, allocator);
            g_output_node_names[i] = output_name.get();
        }
    }
    catch (const Ort::Exception& ex) {
        mexErrMsgIdAndTxt("myOnnxSfunc:InitError", " %s", ex.what());
    }
}
static void mdlOutputs(SimStruct *S, int_T tid)
{
    try {
        const real_T *u = ssGetInputPortRealSignal(S, 0);
        std::vector<float> input_data(7);
        for (int i = 0; i < 7; i++) {
            input_data[i] = static_cast<float>(u[i]);
        }
        std::vector<int64_t> input_shape = { 1, 7 };
        Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
        Ort::Value input_tensor = Ort::Value::CreateTensor<float>(
            memory_info,
            input_data.data(),
            input_data.size(),
            input_shape.data(),
            input_shape.size()
        );
        auto output_tensors = g_session->Run(
            Ort::RunOptions{ nullptr },
            g_input_node_names.data(),
            &input_tensor,
            1,
            g_output_node_names.data(),
            g_output_node_names.size()
        );
        float* output_data = output_tensors[0].GetTensorMutableData<float>();
        auto type_info = output_tensors[0].GetTensorTypeAndShapeInfo();
        auto output_shape = type_info.GetShape();
        if (output_shape.size() != 2 || output_shape[0] != 1 || output_shape[1] < 2) {
            mexErrMsgIdAndTxt("myOnnxSfunc:OutputError", 
                             "(%lld, %lld)", 
                             output_shape[0], output_shape[1]);
        }
        real_T *y = ssGetOutputPortRealSignal(S, 0);
        y[0] = static_cast<double>(output_data[0]);
        y[1] = static_cast<double>(output_data[1]);
    }
    catch (const Ort::Exception& ex) {
        mexErrMsgIdAndTxt("myOnnxSfunc:RuntimeError", "%s", ex.what());
    }
}
static void mdlTerminate(SimStruct *S)
{
    g_session.reset();
    g_env.reset();
    g_input_node_names.clear();
    g_output_node_names.clear();
}
#ifdef __cplusplus
}
#endif
#ifdef MATLAB_MEX_FILE
#include "simulink.c"
#else
#include "cg_sfun.h"
#endif

Best,

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Answer 1

Manish 2025-5-13

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2172983-how-do-i-run-onnx-model-on-simulink#answer_1565145

编辑：Manish 2025-5-13

在 MATLAB Online 中打开

Hi,

I understand that you want to import an ONNX model into Simulink. This can be achieved with the help of the 'importNetworkfromOnnx' function and the 'exportNetworktoSimulink' function.

Refer to the links below for more information about these functions:

First, load the ONNX model using 'importNetworkfromOnnx', and then pass the output to 'exportNetworktoSimulink'.

Here is the sample code:

net = importNetworkFromONNX("simplenet.onnx");
exportNetworkToSimulink(net);

For better understanding, refer to the link below:

https://www.mathworks.com/help/deeplearning/ug/inference-comparison-between-onnx-and-imported-network-for-image-classification.html

Hope it helps!

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