🐞 fix(data): Perlin noise generator masks

[ashwinvaidya17]

📝 Description

• Additionally, it upgrades the torchmetric version to include the fix in fix(pr-curve): set precision to NaN when TP+FP == 0 Lightning-AI/torchmetrics#3227

✨ Changes

Select what type of change your PR is:

• 🚀 New feature (non-breaking change which adds functionality)
• 🐞 Bug fix (non-breaking change which fixes an issue)
• 🔄 Refactor (non-breaking change which refactors the code base)
• ⚡ Performance improvements
• 🎨 Style changes (code style/formatting)
• 🧪 Tests (adding/modifying tests)
• 📚 Documentation update
• 📦 Build system changes
• 🚧 CI/CD configuration
• 🔧 Chore (general maintenance)
• 🔒 Security update
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)

✅ Checklist

Before you submit your pull request, please make sure you have completed the following steps:

• 📚 I have made the necessary updates to the documentation (if applicable).
• 🧪 I have written tests that support my changes and prove that my fix is effective or my feature works (if applicable).
• 🏷️ My PR title follows conventional commit format.

For more information about code review checklists, see the Code Review Checklist.

[rajeshgangireddy]

i was checking if perlin noise is used anywhere else. Supersimplenet and DSR uses generate_perlin_noise.

Both use the noise (from generate_perlin_noise()) and then compare with a certain threshold to create binary masks.
We might need to apply the same fix on both models or move the normalisation inside the method. Although the probability of all of Perlin noise being below threshold is rare.

Let me know what you think.