Update model loading and add new features

[gray-wei]

This PR includes improvements to model loading with best checkpoint preference and other updates.

[claude]

Claude finished @gray-wei's task —— View job

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Pull Request Review

Reviewing PR #2: "Update model loading and add new features"

Review Checklist

• Read and analyze .gitignore changes
• Read and analyze geort/dataset.py changes
• Read and analyze geort/export.py changes
• Check for code quality and best practices
• Identify potential bugs or issues
• Assess performance considerations
• Review security concerns
• Evaluate test coverage implications
• Provide comprehensive feedback

Code Review Feedback

✅ Positive Aspects

1. Clean .gitignore improvements - The addition of checkpoint/* and data/* entries properly excludes model checkpoints and datasets from version control, which is essential for ML projects.

2. Better model loading logic - The enhanced load_model() function with use_best=True parameter is a significant improvement that allows preferential loading of the best performing model checkpoint.

3. Reduced verbosity - Commenting out debug print statements in both dataset.py:50 and export.py:25 improves production readiness.

4. Comprehensive documentation - The updated docstring for load_model() clearly explains parameters and behavior.

⚠️ Areas for Improvement

geort/export.py:64-76

if use_best and (checkpoint_root / "best.pth").exists():
    model_path = checkpoint_root / "best.pth"
    # Load training history to show best model info
    history_path = checkpoint_root / "training_history.json"
    if history_path.exists():
        with open(history_path, 'r') as f:
            history = json.load(f)
            print(f"Loading best model from epoch {history['best_epoch']} with validation loss: {history['best_val_loss']:.4f}")

Issues identified:

1. Missing exception handling - The JSON loading could fail if the file is corrupted or has invalid JSON. Should wrap in try-except block.

2. Missing key validation - No validation that required keys (best_epoch, best_val_loss) exist in the history JSON.

3. File handle not explicitly closed - While with open() handles this, consider explicit error handling.

geort/export.py:53-56

for checkpoint in all_checkpoints:
    if tag in checkpoint:
        checkpoint_name = checkpoint
        break 

Potential issue: This uses substring matching which could match unintended checkpoint names. Consider more precise matching.

🔒 Security Considerations

• JSON loading vulnerability: Loading arbitrary JSON files without validation could be risky if the training history files are user-controlled.
• Path traversal: While using Path(), ensure checkpoint directories are properly sandboxed.

⚡ Performance Considerations

• The changes don't introduce performance regressions
• JSON parsing is minimal and only occurs when loading models
• File existence checks are efficient

🧪 Test Coverage Implications

The load_model() function now has multiple code paths:

• epoch > 0 path
• use_best=True with existing best.pth
• use_best=True with missing best.pth
• use_best=False path
• Missing training history edge case

Recommendation: Add unit tests to cover these scenarios.

🛠 Suggested Improvements

1. Add error handling in geort/export.py:67-71:

try:
    with open(history_path, 'r') as f:
        history = json.load(f)
        if 'best_epoch' in history and 'best_val_loss' in history:
            print(f"Loading best model from epoch {history['best_epoch']} with validation loss: {history['best_val_loss']:.4f}")
        else:
            print("Loading best model (training history incomplete)")
except (json.JSONDecodeError, IOError) as e:
    print(f"Loading best model (could not read training history: {e})")

2. Improve checkpoint matching:

checkpoint_name = next((cp for cp in all_checkpoints if tag in cp), '')

📋 Summary

This is a solid improvement that enhances model loading functionality while cleaning up debug output. The core logic is sound, but adding error handling around JSON operations would make it production-ready. The changes align well with ML best practices.

Overall Rating: ✅ Approve with minor suggestions

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