Trtexec Use for Nvidia Jetson Orin Nano: UnicodeDecodeError Issue

Issue Overview

Users are encountering a UnicodeDecodeError while attempting to run TensorRT (trt) output generated by the trtexec tool on the Nvidia Jetson Orin Nano Dev board. The specific error message states: “unicodedecodeerror: ‘utf-8’ codec can’t decode byte 0xe8 in position 4: invalid continuation byte.” This issue arises during the execution of an engine file that was created from an ONNX model using the following command:

/usr/src/tensorrt/bin/trtexec --onnx=v8.onnx --saveEngine=v8.engine

The problem seems to occur when users try to utilize the generated engine file in their code, specifically when converting the YOLOv8 model. Users have reported that while they can successfully generate the engine file, they do not achieve the expected performance improvements when running it.

The issue is consistently reported by multiple users, indicating a potential commonality in either the conversion process or the handling of the resulting engine file. The impact of this problem significantly affects user experience, as it hampers their ability to leverage TensorRT for optimized inference on their applications.

Possible Causes

• Encoding/Decoding Issues: The error message suggests that there may be a problem with how strings are encoded or decoded within the application using the engine file. This could be due to non-UTF-8 encoded characters being present in the data being processed.

• Incorrect Command Usage: Users may be using incorrect or incomplete commands when attempting to run or convert their models, leading to unexpected errors.

• Software Bugs: There may be bugs within TensorRT or related libraries that cause issues when handling certain model configurations or data types.

• Model Compatibility: The ONNX model being converted might not be fully compatible with TensorRT, leading to issues during conversion or runtime.

• Configuration Errors: There could be misconfigurations in the environment setup or in how dependencies are managed, which might lead to runtime errors.

Troubleshooting Steps, Solutions & Fixes

1. Verify Command Syntax:

   • Ensure that the command used for generating the engine file is correct and complete. Compare it with official documentation.

2. Check Encoding Settings:

   • Investigate if any strings or data being processed contain non-UTF-8 characters. Ensure that all text data is properly encoded before processing.

3. Use Alternative Conversion Methods:

   • If converting directly from ONNX to TensorRT is causing issues, consider using different methods or tools for conversion, such as using other versions of TensorRT or modifying model parameters before conversion.

4. Consult Related Issues:

   • Review similar issues reported on forums or GitHub repositories related to YOLOv8 and TensorRT. For example, a related issue can be found at:
     • YOLO v8 model conversion in tensorrt

5. Test with Different Models:

   • Try converting and running a different ONNX model to determine if the issue is specific to the YOLOv8 model or a broader problem with TensorRT on your setup.

6. Update Software and Drivers:

   • Ensure that you are using the latest versions of TensorRT and any relevant drivers. Check for updates regularly as they may contain fixes for known issues.

7. Isolate Environmental Factors:

   • Test your setup under different conditions (e.g., different power supplies, temperatures) to rule out environmental factors affecting performance.

8. Seek Community Help:

   • If problems persist, consider asking for assistance in forums dedicated to Nvidia Jetson products or TensorRT, providing detailed information about your setup and steps taken so far.

9. Document Findings:

   • Keep track of any changes made and their effects on resolving the issue for future reference and community sharing.

By following these steps, users should be able to diagnose and potentially resolve the UnicodeDecodeError encountered when using trtexec with the Nvidia Jetson Orin Nano Dev board.