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To avoid random behavior when application of model, the origin model, saved by torch.jit.trace is replaced by the model, saved by torch.jit.script.
The engine is updated to process 6 sectors as batch. Also, translator for input and output of the model is updated.
As test in clara workflow, run time is improved from ~150 ms/event/thread to ~90 ms/event/thread.

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baltzell commented Dec 2, 2025

What's the speed for the c++ denoiser?

@baltzell baltzell changed the title Iss956 Make denoising deterministic and speedup with batching Dec 2, 2025
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What's the speed for the c++ denoiser?

About 150 ms/event/thread

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baltzell commented Dec 2, 2025

FYI, here's the coatjava gitlab "model registry", started playing with it just now and we should switch to it for the next network:

https://code.jlab.org/hallb/clas12/coatjava/coatjava/-/ml/models?first=30&orderBy=created_at&sort=desc#/

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baltzell commented Dec 2, 2025

Or, rather, let's use another git repo for now, see #970.

@baltzell baltzell changed the title Make denoising deterministic and speedup with batching Denoising: make it deterministic and speed it up Dec 3, 2025
@baltzell baltzell changed the title Denoising: make it deterministic and speed it up Make denoising deterministic and speed it up Dec 3, 2025
@baltzell baltzell added the ai label Dec 3, 2025
@baltzell baltzell linked an issue Dec 3, 2025 that may be closed by this pull request
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baltzell commented Dec 3, 2025

Note it's still looping over the input (and output) bank 6 times, before constructing the batch.

@baltzell baltzell merged commit 180bf20 into development Dec 3, 2025
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@baltzell baltzell deleted the iss956 branch December 3, 2025 20:45
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PyTorch script versus trace

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