TMVA_SOFIE_PyTorch_HiggsModel.py File Reference

Functions

 TMVA_SOFIE_PyTorch_HiggsModel.GenerateCode (modelFile="model.onnx")    TMVA_SOFIE_PyTorch_HiggsModel.PrepareData ()    TMVA_SOFIE_PyTorch_HiggsModel.TrainModel (x_train, y_train, x_check, name)      TMVA_SOFIE_PyTorch_HiggsModel.modelName = GenerateCode(modelFile)  Step 2 : Parse model and generate inference code with SOFIE.
   TMVA_SOFIE_PyTorch_HiggsModel.session = sofie.Session()    TMVA_SOFIE_PyTorch_HiggsModel.sofie = getattr(ROOT, "TMVA_SOFIE_" + modelName)  Step 3 : Compile the generated C++ model code.
  str TMVA_SOFIE_PyTorch_HiggsModel.TRAIN_SCRIPT    TMVA_SOFIE_PyTorch_HiggsModel.x_check = x_test[:10]    TMVA_SOFIE_PyTorch_HiggsModel.x_test  Step 1 : Create and train the model, export it to ONNX.
   TMVA_SOFIE_PyTorch_HiggsModel.x_train  Step 1 : Create and train the model, export it to ONNX.
   TMVA_SOFIE_PyTorch_HiggsModel.y = session.infer(x_check[i])    TMVA_SOFIE_PyTorch_HiggsModel.y_test  Step 1 : Create and train the model, export it to ONNX.
   TMVA_SOFIE_PyTorch_HiggsModel.y_train  Step 1 : Create and train the model, export it to ONNX.
   TMVA_SOFIE_PyTorch_HiggsModel.ytorch  

Detailed Description

This macro trains a simple deep neural network on the Higgs dataset with PyTorch, exports the model to ONNX and runs the SOFIE parser on it to generate and compile C++ inference code.

The trained model is saved as HiggsModel.onnx and is used as input by other SOFIE tutorials (e.g. TMVA_SOFIE_RDataFrame.C), so this macro needs to be run before them.

The PyTorch export and ROOT's SOFIE parser are both linked against protobuf, but usually against different versions, so loading them in the same process leads to a symbol clash. We therefore run the PyTorch training and ONNX export in a separate Python process and only use ROOT before and afterwards.

size of data 10000
Sequential(
  (0): Linear(in_features=7, out_features=64, bias=True)
  (1): ReLU()
  (2): Linear(in_features=64, out_features=64, bias=True)
  (3): ReLU()
  (4): Linear(in_features=64, out_features=1, bias=True)
  (5): Sigmoid()
)
Epoch 1/5 - average loss: 0.6782
Epoch 2/5 - average loss: 0.6556
Epoch 3/5 - average loss: 0.6431
Epoch 4/5 - average loss: 0.6339
Epoch 5/5 - average loss: 0.6293
calling torch.onnx.export with parameters {'input_names': ['input'], 'output_names': ['output'], 'external_data': False, 'dynamo': True}
[torch.onnx] Obtain model graph for `Sequential([...]` with `torch.export.export(..., strict=False)`...
[torch.onnx] Obtain model graph for `Sequential([...]` with `torch.export.export(..., strict=False)`... ✅
[torch.onnx] Run decompositions...
[torch.onnx] Run decompositions... ✅
[torch.onnx] Translate the graph into ONNX...
[torch.onnx] Translate the graph into ONNX... ✅
[torch.onnx] Optimize the ONNX graph...
[torch.onnx] Optimize the ONNX graph... ✅
model exported to ONNX as HiggsModel.onnx
input to model is  [1.3551283  1.0198661  0.98278755 0.5504138  1.2055093  0.91609305
 0.93084896] 
    -> output using SOFIE =  0.27042633295059204  using PyTorch =  0.27042633
input to model is  [1.0965776 0.9103265 1.9756684 1.3508093 1.3468878 1.4005579 1.1609015] 
    -> output using SOFIE =  0.39927685260772705  using PyTorch =  0.39927685
input to model is  [0.846992  0.9408182 0.98906   1.6148995 1.038698  1.2381754 1.0323234] 
    -> output using SOFIE =  0.6307218074798584  using PyTorch =  0.63072187
input to model is  [1.897264   1.234499   0.98704207 0.708829   0.7279103  0.9053675
 0.76521254] 
    -> output using SOFIE =  0.5393198728561401  using PyTorch =  0.5393199
input to model is  [0.791873   0.9792347  0.9924756  0.9159218  1.1000326  0.94064647
 0.79085195] 
    -> output using SOFIE =  0.5081952214241028  using PyTorch =  0.5081953
input to model is  [0.9692043  0.6372814  0.9850732  0.9201175  0.72131383 0.8001433
 0.7160924 ] 
    -> output using SOFIE =  0.5554959177970886  using PyTorch =  0.5554959
input to model is  [1.5037444 1.1279533 0.9814414 1.5327642 0.7886151 1.1838427 1.0383142] 
    -> output using SOFIE =  0.5453738570213318  using PyTorch =  0.54537386
input to model is  [1.2042431  1.0750061  1.5724212  1.1590953  1.367509   1.1043229
 0.99204683] 
    -> output using SOFIE =  0.3828982412815094  using PyTorch =  0.38289824
input to model is  [1.012179   0.76250947 0.9957243  0.48331824 0.4295301  0.55478483
 0.7100585 ] 
    -> output using SOFIE =  0.3829401731491089  using PyTorch =  0.38294017
input to model is  [0.8616951  1.1908345  0.99018383 1.2523934  1.1448306  1.0246366
 0.9923519 ] 
    -> output using SOFIE =  0.5372459292411804  using PyTorch =  0.5372459
OK

Definition in file TMVA_SOFIE_PyTorch_HiggsModel.py.