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[GSoC][TMVA][SOFIE] PyTorch Parser TMVA #8684
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Can one of the admins verify this patch? |
@phsft-bot build just on ROOT-ubuntu2004/default with flags -Dtmva-sofie=On |
Starting build on |
Build failed on ROOT-ubuntu2004/default. Errors:
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@phsft-bot build just on ROOT-ubuntu2004/default with flags -Dtmva-sofie=On |
Starting build on |
Build failed on ROOT-ubuntu2004/default. Errors:
|
@phsft-bot build just on ROOT-ubuntu2004/default with flags -Dtmva-sofie=On |
Starting build on |
Build failed on ROOT-ubuntu2004/default. Warnings:
|
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Thank you @sanjibansg for adding the documentation.
The PR is ready to be merged after the Keras converter
@phsft-bot build just on ROOT-ubuntu2004/default with flags -Dtmva-sofie=On |
Starting build on |
Build failed on ROOT-ubuntu2004/default. Warnings:
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The PR looks good, we need just to apply the same fixes done in the Keras converters
1) Added support for Tensors of Dynamic Axes 2) Modified Map for DType for PyTorch
1) Fix : segmentation violation by removing the Py_XDECREF() 2) Remove : RModelParser_Common.h and moved helper functions to SOFIE_common & under namespace SOFIE::PyTorch 3) Modify : Casting and usage of PyArrayObject* (now a float* is used instead of RTensor) 4) Add : Runtime error catch if Python String is not successfully run 5) Modify : Minor improvements and modifications in code structure and naming conventions
1) Added GTests for RModelParser_PyTorch 2) Added EmitFromPyTorch to generate header files 3) Configured CMake for tests
1. Refactor RModelParser_PyTorch 2. Call to AddBlasRoutines() while adding Gemm Node
@phsft-bot build! |
Starting build on |
Build failed on mac11.0/cxx17. Failing tests: |
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Thank you for the changes. The PR is now ready to be merged
Description
Development of the functionality for parsing a PyTorch PT file to ROOT format using the Fast Inference System SOFIE for the ROOT Storage of Deep Learning Models Project of Google Summer of Code'21.
Project Link: https://summerofcode.withgoogle.com/projects/#5424575602491392
Progress
Interface
The parser requires the shape and data type of the input tensors. Shape is a mandatory parameter, whereas the function defaults to Float data-type for the input tensors, if not explicitly mentioned.
Example usage
Checklist:
Tests
Documentation