ONNX export Path to str()

[Doquey]

🤖 Generated by Copilot at 929bc87

Summary

🐛🔧📝

Fix a bug in export.py that caused a TypeError when exporting models to ONNX format. Convert the f variable to a string before passing it to torch.onnx.export.

f becomes a string
To avoid TypeError
Winter bug is fixed

Walkthrough

• Fix a bug that causes a TypeError when exporting to ONNX format (link)

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Enhanced export compatibility for ONNX files in 'ultralytics/yolov5'.

📊 Key Changes

• Altered the file path conversion to a string when defining the .onnx file name during export.

🎯 Purpose & Impact

• Purpose: This change ensures consistent handling of file paths across different environments, potentially preventing issues related to file path representations.
• Impact: Users exporting their YOLOv5 models to ONNX format will benefit from improved reliability, with reduced risk of export-related errors or compatibility issues across various platforms. 🔄📈

[github-actions]

👋 Hello @Doquey, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:

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[Doquey]

Its a very simple fix to a annoying bug that was happening when I was trying to export my yolov5m.pt model into a .onnx model.

ONNX: export failure ❌ 0.0s: Function torch.onnx.utils.export() parameter f="PosixPath('best.onnx')" violates type hint typing.Union[str, _io.BytesIO], as <protocol "pathlib.PosixPath"> "PosixPath('best.onnx')" not str or <class "_io.BytesIO">.

The only thing I did was go into the function that was trying to export the model in .onnx and add a str() to the f variable before pasing it to the onnx.utils.export() function

[glenn-jocher]

@Doquey thank you for bringing this bug to our attention and for providing a simple fix. We appreciate your contribution!

We understand how frustrating it can be when encountering errors like this during the model export process. Your fix of converting the f variable to a string before passing it to the torch.onnx.export function is indeed the correct solution.

We value your input and appreciate your effort in helping us improve YOLOv5. Your contribution will help ensure a smoother experience for all users.

Thank you once again for your contribution to the YOLOv5 project.