Import Geant4 11.1.0.beta source tree
This commit is contained in:
Gabriele Cosmo
@@ -12,7 +12,8 @@
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and LWTNN [2] libraries.
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The model used in this example was trained externally (in Python) on data
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from this examples' full simulation and can be applied to perform fast simulation
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from this examples' full simulation and can be applied to perform fast simulation.
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The python scripts are availbale in the training folder.
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The geometry used in the example is a cylindrical setup of layers: tungsten
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absorber and silicon as the active material. 3D readout geometry (cylindrical)
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@@ -107,10 +108,10 @@
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The execution of the program (examplePar04) produces an output with histograms.
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Ntuples are also stored. They are not merged if the application is run on multiple threads.
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The macro file examplePar04.in is used to run full simulation. It will simulate 100
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The macro file examplePar04.mac is used to run full simulation. It will simulate 100
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events, for single 10 GeV electron beams.
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If CMake is able to find inference libraries (lwtnn and/or ONNX Runtime), a configuration
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macro will be available for that library (examplePar04_lwtnn.in and/or examplePar04_onnx.in).
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macro will be available for that library (examplePar04_lwtnn.mac and/or examplePar04_onnx.mac).
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It will use a trained model to run inference and create showers in the detector by directly
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depositing energy.
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@@ -124,18 +125,22 @@
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% make
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- Execute the application (in batch mode):
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% ./examplePar04 -m examplePar04.in
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% ./examplePar04 -m examplePar04.mac
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which produces two root file for full simulation.
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- Execute the application (in interactive mode):
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% ./examplePar04
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which allows to visualize hits.
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% ./examplePar04 -i -m vis.mac
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which allows to visualize hits (from full simulation).
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- If ONNX Runtime is available:
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% ./examplePar04 -m examplePar04_onnx.in
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% ./examplePar04 -m examplePar04_onnx.mac
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For interactive mode with visualization:
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% ./examplePar04 -i -m vis_onnx.mac
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- If LWTNN is available:
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% ./examplePar04 -m examplePar04_lwtnn.in
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% ./examplePar04 -m examplePar04_lwtnn.mac
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For interactive mode with visualization:
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% ./examplePar04 -i -m vis_lwtnn.mac
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By default, CMake will attempt to build fast simulation with ONNX Runtime and LWTNN. However, if none
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of those libraries is found, it will proceed with full simulation only. The search can be switched
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@@ -144,16 +149,24 @@
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9. Macros
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---------
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vis.mac - Allows to run visualization. It will be automatically run in interactive mode, if no
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argument is passed to the executable (examplePar04)
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vis.mac - Allows to run visualization. Pass it to the example in interactive mode ("-i" passed to the executable).
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It can be used to visualize full simulation.
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examplePar04.in - Runs full simulation. It will run 100 events with single electrons, 10 GeV and
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vis_onnx.mac - Allows to run visualization with ONNX Runtime inference. Pass it to the example in interactive mode
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("-i" passed to the executable). It contains ecessary settings of the inference, and it treats full
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calorimeter as sensitive material (due to deposition of hits regardless of the volume).
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vis_lwtnn.mac - Allows to run visualization with LWTNN inference. Pass it to the example in interactive mode
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("-i" passed to the executable). It contains ecessary settings of the inference, and it treats full
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calorimeter as sensitive material (due to deposition of hits regardless of the volume).
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examplePar04.mac - Runs full simulation. It will run 100 events with single electrons, 10 GeV and
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along y axis.
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examplePar04_onnx.in - Available only if ONNX Runtime is found by CMake. Runs fast simulation with
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examplePar04_onnx.mac - Available only if ONNX Runtime is found by CMake. Runs fast simulation with
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a NN stored in onnx file.
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examplePar04_lwtnn.in - Available only if LWTNN is found by CMake. Runs fast simulation with
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examplePar04_lwtnn.mac - Available only if LWTNN is found by CMake. Runs fast simulation with
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a NN stored in json file.
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10. UI commands
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@@ -198,3 +211,54 @@
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/Par04/inference/setNbOfRhoCells 18
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/Par04/inference/setNbOfPhiCells 50
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/Par04/inference/setNbOfZCells 45
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11. Python scripts for training
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--------------
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The scripts available in the training folder were used to train
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the VAE model of this example.
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- model: defines the VAE model as a class which contains the architecture,
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the loss function and the training function.
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- utils: defines the data loading and preprocessing function and returns
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the preprocessed array of shower energies and the condition arrays of energy,
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angle and geometry. The input is expected to be in local directory 'detector_*'.
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In this example, the model is trained on 2 detector geometries and the input
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directories are 'detector_SiW' and 'detector_SciPb'. Each directory contains the
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HDF5 files for each primary particle energy and angle.
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- train: defines data loading parameters and calls the data preprocessing function.
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It defines the model parameters and instantiates the VAE model, performs the training
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and then coverts the model into an ONNX format.
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A history object is returned from the training function of the VAE. The history is a callback
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object registered during the training which records metrics for each epoch such as the loss.
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The user can list the metrics collected in the history object using:
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% print(history.history.keys())
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The data collected in the history object can be used to create plots such as the loss function
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as function of the epochs. If the loss metric collected in the history object is called loss,
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then to plot it using for example the matplotlib library:
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% matplotlib.pyplot.plot(history.history['loss'])
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The traing function can also generate intermediate files representing checkpoints of the model
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that can be used for validation purposes. These checkpoints are generated if the early stopping flag
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of the training is off, which means to train the model for the predefined number of epochs. The weights
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of the model are saved every 100 epochs.
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After the training, the model (only the decoder part) is saved as an HDF5 file and then is converted
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into an ONNX format. The final output model called Generator.onnx can be used to perform the inference
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in this example.
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To perform the training run:
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% python train.py
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12. Public data
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--------------
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Data generated with full simulation with this example has been published on zenodo:
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https://doi.org/10.5281/zenodo.6082201
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