TensorRT Inference Issues on Nvidia Jetson Orin Nano

Issue Overview

Users are experiencing issues with the Nvidia Jetson Orin Nano when attempting to run inference using TensorRT for YOLO models. The primary symptom reported is that the output image remains unchanged after running the inference command, indicating that no objects were detected. This issue occurs during the execution of the following command:

python trt.py -e yolox_s.trt -i src/1.jpg -o yolox-1.jpg --end2end

In this case, the input image (1.jpg) contains a person, but the output image (yolox-1.jpg) is identical to the input, suggesting a failure in the detection process. Users have tested both default and custom images, with consistent results showing no detection. The issue seems to be reproducible across different setups, indicating a potential problem with either the TensorRT model or its configuration.

Possible Causes

Several potential causes for this issue have been identified:

• Model Conversion Issues: Errors may occur during the conversion of the .pth file to an ONNX format and subsequently to a TensorRT engine (.trt), leading to an ineffective model.
• Library Compatibility: The TensorRT library version may not be compatible with the YOLO model being used, affecting inference accuracy.
• Configuration Errors: Incorrect parameters or flags in the inference command could lead to improper execution.
• Environmental Factors: Insufficient resources (e.g., GPU memory) or incorrect environment settings might hinder model performance.
• Input Image Quality: The quality or format of the input image may not meet expectations for effective detection.

Troubleshooting Steps, Solutions & Fixes

To address the issue effectively, follow these detailed troubleshooting steps:

1. Verify Model Conversion:

   • Ensure that the conversion from .pth to ONNX and then to .trt was successful. Check for any errors during this process.
   • Use a known working model from GitHub to verify if the issue persists with different models.

2. Check TensorRT Compatibility:

   • Confirm that you are using a compatible version of TensorRT for your YOLO model. Refer to TensorRT documentation for compatibility details.

3. Run Basic Inference Tests:

   • Test with a simple image provided in the repository (as suggested by forum users) to determine if the issue lies with specific images or configurations.

4. Inspect Command Parameters:

   • Review and modify command parameters as needed. For example, ensure flags like --end2end are appropriate for your use case.

5. Check Resource Availability:

   • Monitor GPU memory usage during inference using nvidia-smi. Ensure there are sufficient resources available for processing.

6. Update Libraries and Drivers:

   • Ensure that all relevant libraries (CUDA, cuDNN, TensorRT) are up-to-date. Use SDK Manager to install updates if necessary.

7. Use Serial Console for Debugging:

   • If issues persist, connect via serial console to gather logs during execution. This can provide insights into any underlying errors that may not be visible through standard output.

8. Seek Community Support:

   • If unresolved, consider posting detailed logs and descriptions of your setup on forums like NVIDIA Developer Forums for community assistance.

9. Documentation Reference:

   • Consult relevant documentation for additional troubleshooting steps specific to your hardware configuration and software versions.

10. Preventive Best Practices:

    • Regularly update your development environment and maintain compatibility between libraries.
    • Test models thoroughly before deployment in production environments.

By following these steps, users should be able to diagnose and potentially resolve issues related to TensorRT inference on their Jetson Orin Nano devices.