PyTorch: Speed up PAF cost computation - #3117
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AlexEMG merged 6 commits intoOct 24, 2025
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Improve PAF performance by performing affinity computation on the GPU with advanced indexing. - Affinities are now calculated using torch operations. - The cost per batch dictionary is created more efficiently.
arashsm79
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October 14, 2025 15:32
maximpavliv
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October 14, 2025 15:33
maximpavliv
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Good job! ✅
- Code is clearer, docstrings are much more detailed, and variable naming is improved (batch_size, paf_limb_inds).
- GPU usage is now more efficient, avoiding unnecessary early CPU transfers → speed is improved.
- The inference results slighly differ from expected results.
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AlexEMG
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deruyter92
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This commit updates the PAF predictor to follow the DeepLabCut implementation in version 3.0.0.rc13. See DeepLabCut/DeepLabCut#3117
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* DEKRPredictor: add non-maximum suppression (NMS) This commit Updates the DEKR predictor to follow the DeepLabCut implementation in version 3.0.0rc7, see DeepLabCut/DeepLabCut#2907 * DEKRPredictor: speed up with vectorized operations This commit updates the DEKRPredictor to follow the DeepLabCut implementation in version 3.0.0rc13. see DeepLabCut/DeepLabCut#3121 * PartAffinityFieldPredictor (PAF): Speed up cost computation This commit updates the PAF predictor to follow the DeepLabCut implementation in version 3.0.0.rc13. See DeepLabCut/DeepLabCut#3117 * HeatmapPredictor (single animal): speed up with vecorized operations This commit updates the `HeatmapPredictor` in single_predictor.py to follow the implementation in DeepLabCut 3.0.0rc13. See DeepLabCut/DeepLabCut#3110
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Summary
Improve PAF performance by performing affinity computation on the GPU with advanced indexing.
Details
This implementation tries to delegate the parts that can be parallelized to the GPU by using torch operations instead of numpy ones. (thanks to @maximpavliv for running the benchmark)
The figure below shows the parts of the execution that can be optimized.
The part outlined by the red rectangle concerning compute_peaks_and_costs is now as optimized as I could make it.
The blue rectangle is concerned with the assembly procedure which did not get into in this PR. There is a lot of room for optimization in there as well, which may require a lot of refactoring/changes.