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: 0.6715
Epoch 2/5 - average loss: 0.6471
Epoch 3/5 - average loss: 0.6369
Epoch 4/5 - average loss: 0.6282
Epoch 5/5 - average loss: 0.6301
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 =  0.32207491993904114  using PyTorch =  0.32207492
input to model is  [1.0965776 0.9103265 1.9756684 1.3508093 1.3468878 1.4005579 1.1609015] 
    -> output using SOFIE =  0.5153125524520874  using PyTorch =  0.51531255
input to model is  [0.846992  0.9408182 0.98906   1.6148995 1.038698  1.2381754 1.0323234] 
    -> output using SOFIE =  0.5877813100814819  using PyTorch =  0.5877813
input to model is  [1.897264   1.234499   0.98704207 0.708829   0.7279103  0.9053675
 0.76521254] 
    -> output using SOFIE =  0.5718671083450317  using PyTorch =  0.5718671
input to model is  [0.791873   0.9792347  0.9924756  0.9159218  1.1000326  0.94064647
 0.79085195] 
    -> output using SOFIE =  0.5007792115211487  using PyTorch =  0.5007792
input to model is  [0.9692043  0.6372814  0.9850732  0.9201175  0.72131383 0.8001433
 0.7160924 ] 
    -> output using SOFIE =  0.5492246747016907  using PyTorch =  0.5492246
input to model is  [1.5037444 1.1279533 0.9814414 1.5327642 0.7886151 1.1838427 1.0383142] 
    -> output using SOFIE =  0.5529407262802124  using PyTorch =  0.5529407
input to model is  [1.2042431  1.0750061  1.5724212  1.1590953  1.367509   1.1043229
 0.99204683] 
    -> output using SOFIE =  0.44583791494369507  using PyTorch =  0.44583791
input to model is  [1.012179   0.76250947 0.9957243  0.48331824 0.4295301  0.55478483
 0.7100585 ] 
    -> output using SOFIE =  0.32368960976600647  using PyTorch =  0.3236896
input to model is  [0.8616951  1.1908345  0.99018383 1.2523934  1.1448306  1.0246366
 0.9923519 ] 
    -> output using SOFIE =  0.524616003036499  using PyTorch =  0.524616
OK

Definition in file TMVA_SOFIE_PyTorch_HiggsModel.py.