⚡ Bolt: vectorize BasicEstimator.predict#31
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💡 What: Vectorized the `predict` method in `BasicEstimator` using the squared distance expansion formula ($||a-b||^2 = ||a||^2 + ||b||^2 - 2ab^T$) and optimized the `fit` method to pre-calculate squared norms of fitted embeddings. 🎯 Why: The previous implementation used a Python loop to calculate distances for each query embedding, which was inefficient for batch processing and didn't take full advantage of NumPy's optimized matrix operations. 📊 Impact: Provides a ~4x speedup for batch predictions (benchmarked 500 queries against 2000 fitted embeddings: ~0.218s -> ~0.054s). 🔬 Measurement: Verified using a comparison script that ensures the new implementation's output matches the original loop-based logic within floating-point precision limits. Run `PYTHONPATH=. python3 -m unittest tests/test_face_engine_models.py` to ensure core functionality is preserved. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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⚡ Bolt: Vectorized
BasicEstimator.predictThis PR introduces a significant performance optimization to the
BasicEstimatorby vectorizing the face recognition prediction logic.💡 What's changed?
predict()with a matrix-based approach using the expansion formulafit()method now pre-calculates and stores the squared norms of fitted embeddings (self.norms_sq), avoiding redundant calculations during every prediction call.np.maximum(dists_sq, 0)to ensure numerical stability andgetattrchecks to maintain backward compatibility with models pickled before this change.🎯 Why optimize this?
Prediction is the most frequently called operation in production. By moving from Python-level loops to NumPy/BLAS-optimized matrix operations, we drastically reduce the overhead for batch processing (e.g., when recognizing multiple faces in a video frame or processing a batch of images).
📊 Performance Impact
🔬 How to verify?
pip install numpy.PYTHONPATH=. python3 -m unittest discover tests.⚡ Bolt's Journal entry added:
Documented the numerical precision considerations when using the squared distance expansion formula in
.jules/bolt.md.PR created automatically by Jules for task 17004290814529122327 started by @guesswh0