ONNX Runtime 1.12 Error: Access Violation In C++ Class

Aug 14, 2025 by Pedro Alvarez 55 views

ONNX Runtime 1.12 Error: Access Violation with Class Member Method

Hey guys! Ever faced a super tricky error that just makes you scratch your head? I recently ran into one while working with ONNX Runtime 1.12 and encapsulating it within a C++ class. It was one of those situations where one method worked perfectly, while another, seemingly identical one, kept throwing an access violation. Talk about frustrating! I'm going to walk you through the issue, the debugging process, and the solution I finally arrived at. If you're knee-deep in deep learning, C++14, and ONNX Runtime, this might just save you some serious time and headaches.

The Problem: ONNX Runtime and Class Encapsulation

The core of the issue revolved around using ONNX Runtime 1.12 (and even 1.20) within a C++ class. The goal was to encapsulate the ONNX inference engine to make it more user-friendly and manageable. Imagine wrapping all the ONNX session creation, input binding, and output extraction logic into a neat little class. Sounds great, right? Well, it was until I hit a snag.

I had two inference methods within my class. One worked flawlessly, churning out predictions like a champ. The other? It consistently threw an "Access violation reading location" error. The bizarre part was that the methods appeared almost identical. Both were designed to take input data, feed it to the ONNX model, and retrieve the results. So, what was the difference? This mystery is exactly what I aimed to figure out.

Digging Deeper: Debugging the Access Violation

When you encounter an "Access violation," it's like a flashing red light screaming, "Memory problem!" In essence, your program is trying to read memory it doesn't have permission to access, or memory that's no longer valid. This can happen for a myriad of reasons, from dangling pointers to buffer overflows. In this particular scenario, with ONNX Runtime involved, it suggested something was amiss in how the inference session or the input/output bindings were being managed within the class.

The first step was to break out the debugging tools. I meticulously examined the call stack, the values of variables, and the flow of execution in both methods – the one that worked and the one that crashed. I compared the memory addresses involved, looking for any clues about where the access violation might be originating. This was like being a detective, piecing together fragments of evidence to uncover the culprit. The more I delved into it, the clearer it became that the issue was related to how the ONNX session was being handled within the class context.

The Solution: Memory Management and ONNX Sessions

After hours of debugging and experimentation, the root cause finally emerged: memory management within the class. The ONNX Runtime session, an Ort::Session object, was being created and used in a way that led to it being deallocated prematurely in one of the methods. When the second method tried to use this deallocated session, it resulted in the dreaded access violation.

The key was to ensure that the Ort::Session object had a lifetime that spanned the execution of both methods. This meant carefully managing the session's scope and making sure it wasn't destroyed before it was needed. The solution involved adjusting how the Ort::Session was stored and accessed within the class. By making sure the session object remained valid for the duration of the inference calls, the access violation vanished, and both methods started working harmoniously. This underscores the importance of understanding object lifecycles, especially when dealing with external libraries like ONNX Runtime that manage their own resources.

Code Snippets (Illustrative):

To give you a clearer picture, let's look at some simplified code snippets that highlight the issue and the solution (note: these are illustrative and might not be a direct copy of the original code):

Problematic Code (Illustrative)

class MyInferenceClass {
public:
    MyInferenceClass(const std::string& modelPath) {
        // Problem: session might be deallocated prematurely
        session_ = Ort::Session(env, modelPath, sessionOptions);
    }

    void inferMethod1(const std::vector<float>& input) {
        // ... uses session_
    }

    void inferMethod2(const std::vector<float>& input) {
        // ... might crash if session_ is deallocated
    }

private:
    Ort::Env env;
    Ort::SessionOptions sessionOptions;
    Ort::Session session_; // Session stored by value
};

In this problematic code, the Ort::Session is stored by value. If inferMethod2 is called after session_ has been implicitly deallocated or gone out of scope, it will likely lead to an access violation.

Corrected Code (Illustrative)

class MyInferenceClass {
public:
    MyInferenceClass(const std::string& modelPath) {
        // Solution: use a unique_ptr to manage session lifetime
        session_ = std::make_unique<Ort::Session>(env, modelPath, sessionOptions);
    }

    void inferMethod1(const std::vector<float>& input) {
        // ... uses session_
    }

    void inferMethod2(const std::vector<float>& input) {
        // ... now safe to use session_
    }

private:
    Ort::Env env;
    Ort::SessionOptions sessionOptions;
    std::unique_ptr<Ort::Session> session_; // Session managed by unique_ptr
};

Here, we've switched to using a std::unique_ptr to manage the lifetime of the Ort::Session. This ensures that the session object is properly deallocated when the MyInferenceClass object is destroyed, but remains valid as long as the class instance is alive. This eliminates the risk of premature deallocation and the associated access violation.

Lessons Learned: Memory Management is Key

This debugging adventure drove home a crucial lesson: memory management is paramount, especially when working with external libraries that have their own resource management mechanisms. When encapsulating libraries like ONNX Runtime within classes, you need to be extra vigilant about object lifetimes and scopes. Using smart pointers (like std::unique_ptr or std::shared_ptr) can be a lifesaver, helping you avoid memory leaks and access violations.

Another key takeaway is the importance of meticulous debugging. When faced with a cryptic error like an access violation, stepping through the code, examining memory addresses, and comparing execution paths can reveal subtle but critical differences. It's a bit like detective work, where you piece together clues until the whole picture emerges.

Additional Tips for Working with ONNX Runtime and C++

To help you further on your journey with ONNX Runtime and C++:

Use Smart Pointers: Seriously, embrace std::unique_ptr and std::shared_ptr. They simplify memory management and reduce the risk of leaks and dangling pointers.
Understand Object Lifecycles: Pay close attention to when objects are created and destroyed, especially when dealing with external libraries.
Check ONNX Runtime Documentation: The official ONNX Runtime documentation is your friend. It provides valuable insights into how to use the library correctly.
Simplify Your Code: When debugging, try to isolate the problem. Create minimal reproducible examples to narrow down the source of the issue.
Leverage Debugging Tools: Get comfortable with your debugger. Learn how to step through code, inspect variables, and examine the call stack.

Conclusion

So, that's the story of how I wrestled with an access violation while using ONNX Runtime 1.12 in a C++ class. It was a challenging but ultimately rewarding experience. By understanding memory management principles and employing careful debugging techniques, I was able to track down the root cause and implement a robust solution. I hope this helps you avoid similar pitfalls and makes your journey with ONNX Runtime a little smoother. Happy coding, and remember: memory management matters!

ONNX Runtime, C++14, Deep Learning, Access Violation, Memory Management, Debugging, Class Encapsulation, Ort::Session, Smart Pointers, std::unique_ptr