Optimize find_decision_points in agglomeration pipeline. - #124
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We optimized the decision point identification code to resolve slowness in the pipeline. The main bottlenecks were CPU-bound Python operations on large 3D arrays in a subvolume. Bottlenecks and Fixes: - **Vectorized Relabeling**: Replaced the dict-based list comprehension in `relabel` (connectomics/segmentation/labels.py) with a vectorized implementation using `np.searchsorted`. This reduced the final relabeling time for a 12.5M voxel subvolume from ~16.7s to ~0.2s (78x speedup). - **Slicing Optimization**: Replaced `ndimage.shift` and `np.roll` with NumPy slicing views in the neighbor-checking loop (ffn/utils/decision_point.py), reducing loop time from ~2.1s to ~1.1s and avoiding memory copying. - **DataFrame Aggregation**: Collected NumPy arrays in lists and created a single DataFrame at the end of the loop, reducing pandas overhead. Overall performance for `find_decision_points` on a representative dummy subvolume improved from **26.83s to 7.85s (3.4x speedup)**. PiperOrigin-RevId: 964798774
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Optimize find_decision_points in agglomeration pipeline.
We optimized the decision point identification code to resolve slowness in the pipeline. The main bottlenecks were CPU-bound Python operations on large 3D arrays in a subvolume.
Bottlenecks and Fixes:
relabel(connectomics/segmentation/labels.py) with a vectorized implementation usingnp.searchsorted. This reduced the final relabeling time for a 12.5M voxel subvolume from ~16.7s to ~0.2s (78x speedup).ndimage.shiftandnp.rollwith NumPy slicing views in the neighbor-checking loop (ffn/utils/decision_point.py), reducing loop time from ~2.1s to ~1.1s and avoiding memory copying.Overall performance for
find_decision_pointson a representative dummy subvolume improved from 26.83s to 7.85s (3.4x speedup).