Plugin library for machine-learning inference fitting. More...
Collaboration diagram for libFitEditorMLInfernence.so:
Classes | |
| class | ddastoys::Configuration |
| Manage fit configuration information. More... | |
| void | ddastoys::mlinference::performInference (FitInfo *pResult, std::vector< uint16_t > &trace, unsigned saturation, torch::jit::script::Module &module) |
| Perform ML inference to determine the pulse parameters. More... | |
Detailed Description
Plugin library for machine-learning inference fitting.
Function Documentation
◆ performInference()
| void ddastoys::mlinference::performInference | ( | FitInfo * | pResult, |
| std::vector< uint16_t > & | trace, | ||
| unsigned | saturation, | ||
| torch::jit::script::Module & | module | ||
| ) |
Perform ML inference to determine the pulse parameters.
- Parameters
-
[in,out] pResult Pointer to the fit results. [in] trace References the trace we're processing. [in] saturation ADC saturation value. Only samples below the saturation threshold are used to extrat the pulse parameters. [in] module References the inference model for this channel.
This is the interface to perform the machine-learning inference fit on a single trace analogous to the lmfit functions for iterative fitting. The steps are pretty simple:
- Preprocess the trace (offest removal and normalization).
- Perform inference on normalized input.
- Postprocess the trace (rescaling, extract and store fit parameters).
