Conversion from torch.sensor to models.common.Detection

Issue Overview

Users of the Nvidia Jetson Orin Nano Developer Kit are experiencing difficulties when attempting to convert outputs from YOLOv5, a popular object detection model, into a format compatible with motpy, a tracking library. The specific symptoms include errors related to data type conversion between "torch tensor" and "models.common.Detections." This issue arises during the execution of YOLOv5, which is designed to run on CUDA for GPU acceleration, resulting in outputs as "torch tensor." In contrast, when running on CPU, the output is of type "Detections." The users are operating on Ubuntu 20.04 with JetPack 5.12, CUDA 11.4, and Python 3.8.10. The problem appears to be consistent, impacting the ability to effectively utilize both YOLOv5 and motpy in their applications.

Possible Causes

• Data Type Mismatch: The primary cause is the difference in output data types when running YOLOv5 on CPU versus GPU. The GPU version returns a "torch tensor," which is not directly usable by motpy.
• Library Compatibility: There may be underlying compatibility issues between the versions of YOLOv5 and motpy being used.
• Configuration Errors: Incorrect configurations in either library could lead to improper handling of output data types.
• User Misunderstanding: Users may not fully understand how to manipulate tensor outputs in PyTorch.

Troubleshooting Steps, Solutions & Fixes

1. Check Output Type:

   • Verify the output type from YOLOv5 when using CUDA. Use the following command to inspect the type:

     print(type(output))
     

2. Convert Tensor to CPU:

   • If you need the CPU copy of the output tensor, use:

     cpu_output = output.cpu()
     

   • This step is crucial for converting "torch tensor" outputs into a format that can be processed further.

3. Convert Tensor to Detections:

   • After obtaining the CPU output, convert it into the required format for motpy. Ensure you follow the appropriate conversion method as per motpy’s documentation.

4. Update Libraries:

   • Ensure that both YOLOv5 and motpy are updated to their latest versions to mitigate any compatibility issues.
   • Check for any specific notes regarding version compatibility in their respective documentation.

5. Testing with Different Configurations:

   • If issues persist, attempt running YOLOv5 on CPU instead of CUDA to see if it resolves the problem.
   • Test different versions of CUDA or JetPack if feasible.

6. Documentation and Community Support:

   • Refer to the PyTorch documentation for more details on tensor operations.
   • Engage with community forums or GitHub issues related to YOLOv5 and motpy for additional insights or similar experiences.

7. Best Practices:

   • Regularly check for updates and community discussions regarding common issues faced by users of the Jetson Orin Nano.
   • Maintain backups of working configurations and document any changes made during troubleshooting.

By following these steps, users should be able to resolve their issues related to converting outputs from YOLOv5 for use with motpy effectively.