🔨 Refactor Export

[ashwinvaidya17]

📝 Description

• Provide a clear summary of the changes and the issue that has been addressed.
• 🛠️ Fixes 🔨 Move export functionality to base anomaly module #1752

✨ Changes

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• 🐞 Bug fix (non-breaking change which fixes an issue)
• 🔨 Refactor (non-breaking change which refactors the code base)
• 🚀 New feature (non-breaking change which adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📚 Documentation update
• 🔒 Security update

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[samet-akcay]

lgtm thanks

[codecov]

Codecov Report

Attention: Patch coverage is 96.59091% with 3 lines in your changes are missing coverage. Please review.

Project coverage is 81.22%. Comparing base (e95dbe8) to head (b283e78).
Report is 1 commits behind head on main.

Files	Patch %	Lines
...rc/anomalib/models/components/base/export_mixin.py	96.20%	3 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #2057   +/-   ##
=======================================
  Coverage   81.21%   81.22%           
=======================================
  Files         225      226    +1     
  Lines       10233    10245   +12     
=======================================
+ Hits         8311     8321   +10     
- Misses       1922     1924    +2     

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[CarlosNacher]

Hi @ashwinvaidya17 @samet-akcay
One question, perhaps is a Pytorch general doubt, when you model.to_onnx (or to_torch for example), the model you are exporting does use the best weights or the last ones? And when in Anomalib we use the export method of the Engine class, what happens? If the last ones, how should we act to export the best weights taking advantage of the Anomalib's methods?

Thank you so much.

[ashwinvaidya17]

@CarlosNacher can you create a discussion for this (https://github.com/openvinotoolkit/anomalib/discussions) so that it does not get lost in the PRs.

[CarlosNacher]

@ashwinvaidya17 got it, thank you!

Dicussion: #2110