yolo8 onnx in opencv about ultralytics

[github-actions]

👋 Hello @Etty-Cohen, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@Etty-Cohen hi there,

Thank you for reaching out and providing a detailed description of your issue along with the code snippet. It's great to see that you've already tried running your model on the CPU and confirmed that it works. Let's address the GPU issue.

Steps to Troubleshoot

1. Verify ONNX Model Compatibility:
   Ensure that the ONNX model is compatible with the OpenCV DNN module. Sometimes, certain layers or operations in the ONNX model might not be fully supported by OpenCV's DNN module, especially when using GPU acceleration.

2. Check CUDA Installation:
   Verify that your CUDA installation is correctly set up and that your GPU is CUDA-compatible. You can check this by running:

   nvidia-smi

   This command should display your GPU details and the CUDA version.

3. Update OpenCV:
   Make sure you are using the latest version of OpenCV, as newer versions often come with improved support for various backends and targets.

4. Debugging the Output:
   To better understand why the output is empty on GPU, you can add some debugging steps to print intermediate results or check for errors during the forward pass.

Example Code with Debugging

Here's an updated version of your code with additional checks and debugging steps:

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>

using namespace cv;
using namespace dnn;

int main() {
    // Load the ONNX model
    Net net = readNet("yolov8.onnx");
    net.setPreferableBackend(DNN_BACKEND_CUDA);
    net.setPreferableTarget(DNN_TARGET_CUDA);

    std::cout << "Model loaded successfully" << std::endl;

    // Read the input image
    Mat image = imread("path/to/your/image.jpg");
    if (image.empty()) {
        std::cerr << "Could not read the image" << std::endl;
        return -1;
    }

    // Prepare the input blob
    Mat blob = blobFromImage(image, 1.0, Size(640, 640), Scalar(0, 0, 0), true, false);
    net.setInput(blob);

    // Perform inference
    Mat prob = net.forward();

    // Check if the output is empty
    if (prob.empty()) {
        std::cerr << "Inference output is empty" << std::endl;
        return -1;
    }

    // Print the results
    for (int j = 0; j < prob.total(); ++j) {
        std::cout << prob.at<float>(0, j) << '\n';
    }

    return 0;
}

Additional Tips

• Ensure Correct Input Size: Double-check that the input size (640x640) matches the size expected by your YOLOv8 model.
• Check for Errors: Look for any error messages or warnings in the console that might give more insight into why the GPU inference is failing.
• Try Different Backends: If CUDA backend continues to cause issues, you might want to try other backends like DNN_BACKEND_DEFAULT to see if the problem persists.

If the issue still persists after these steps, please ensure you are using the latest versions of torch and ultralytics packages. If not, upgrade them and try again.

For further assistance, you can also refer to our Common Issues Guide.

I hope this helps! Feel free to reach out if you have any more questions. 😊

[Etty-Cohen]

@glenn-jocher Thank you for your answer.

if (prob.empty()) -> return false
prob.total() -> return 705600
About your tips:
Ensure Correct Input Size: I did. its OK
Check for Errors: I did. no errors
Try Different Backends: I did. all other gpu backands include DEFAULT you mentioned caused to runtime error

I am using the latest versions of torch and ultralytics packages

Update OpenCV: to which version?

[glenn-jocher]

Hi @Etty-Cohen,

Thank you for the detailed follow-up! It's great to hear that you've already tried various troubleshooting steps. Given that you're using the latest versions of torch and ultralytics, and have ensured the correct input size and checked for errors, let's focus on updating OpenCV.

Update OpenCV

To ensure compatibility with the latest models and CUDA backends, I recommend updating to the latest stable version of OpenCV. You can do this using the following command:

pip install --upgrade opencv-python-headless

Additional Debugging

Since prob.total() returns 705600, it indicates that the model is producing output, but there might be an issue with interpreting these results. Let's add some additional debugging to inspect the shape and type of the output:

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>

using namespace cv;
using namespace dnn;

int main() {
    // Load the ONNX model
    Net net = readNet("yolov8.onnx");
    net.setPreferableBackend(DNN_BACKEND_CUDA);
    net.setPreferableTarget(DNN_TARGET_CUDA);

    std::cout << "Model loaded successfully" << std::endl;

    // Read the input image
    Mat image = imread("path/to/your/image.jpg");
    if (image.empty()) {
        std::cerr << "Could not read the image" << std::endl;
        return -1;
    }

    // Prepare the input blob
    Mat blob = blobFromImage(image, 1.0, Size(640, 640), Scalar(0, 0, 0), true, false);
    net.setInput(blob);

    // Perform inference
    Mat prob = net.forward();

    // Check if the output is empty
    if (prob.empty()) {
        std::cerr << "Inference output is empty" << std::endl;
        return -1;
    }

    // Print the shape and type of the output
    std::cout << "Output shape: " << prob.size << std::endl;
    std::cout << "Output type: " << prob.type() << std::endl;

    // Print the first few results
    for (int j = 0; j < std::min(85, prob.total()); ++j) {
        std::cout << prob.at<float>(0, j) << '\n';
    }

    return 0;
}

This will help us understand the structure of the output and ensure that the data is being processed correctly.

Next Steps

1. Update OpenCV to the latest version.
2. Run the updated code to inspect the shape and type of the output.

Please let me know the results of these steps. If the issue persists, we can further investigate based on the new information.