Import Geant4 11.0.0 source tree
This commit is contained in:
Gabriele Cosmo
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-------------------------------------------------------------------
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=========================================================
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Geant4 - an Object-Oriented Toolkit for Simulation in HEP
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=========================================================
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Example Par04
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-------------
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This example demonstrates how to use the Machine Learning (ML) inference
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to create energy deposits as a fast simulation model using
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<a href="https://github.com/microsoft/onnxruntime">ONNX runtime</a>
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and <a href="https://github.com/lwtnn/lwtnn">LWTNN</a> 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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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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is defined dynamically, based on the particle direction at the entrance to the
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calorimeter. This is set using a fast simulation model that is triggered at
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detector entrance. Analysis of energy deposits is done in the event action,
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ntuple with hits is stored.
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## 1. Detector description
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The detector consists of cylindrical layers of passive and active material,
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tungsten and silicon, respectively.
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Fast simulation is attached to the region of the detector.
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Input macro can specify which layer is considered an active layer (sensitive
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detector is attached to it). For fast simulation both layers should be marked
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as sensitive. It is connected to the wway the deposits are created: position is
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centre of the layer, which may often fall within the absorber (which is thicker
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than the active material). In a realistic detector setup, the positions used in
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fast simulation would be calculated properly, to deposit energy within the active
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material.
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## 2. Sensitive detector
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Sensitive detector inherits from both base classes:
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- G4VSensitiveDetector: for processing of detailed/non-fast simulation hits
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- G4VFastSimSensitiveDetector: for processing of fast sim (G4FastSim) hits.
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Hits are placed in the same hit collection, with a different flag to distinguish
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between those originated in the full simulation, and those from the fast
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simulation.
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During visualisation, hits are represented as volumes of different colour:
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green for full simulation and red for fast simulation.
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## 3. Primary generation
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Particle gun is used as a primary generator. 10 GeV electron is used by default.
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By default particles are generated along y axis. Those values
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can be changed using /gun/ UI commands.
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## 4. Physics List
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FTFP_BERT modular physics list is used. On top of it, fast simulation physics
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is registered for selected particles (electrons, positrons).
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## 5. User actions
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- Par04RunAction : run action used for initialization and termination
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of the run. Histograms for analysis of shower development
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in the detector are created.
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- Par04EventAction : event action used for initialization and termination
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of the event. Analysis of shower development is performed
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on event-by-event basis.
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## 6. ML Inference
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- Par04MLFastSimModel : model used for parametrisation of źelectrons, positrons,
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and gammas. Energy is deposited and
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distributed according to inferred values from the ML model.
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This class triggers the inference setup, asks for values,
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and deposits energies at given positions.
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- Par04InferenceSetup : this class is used to initialize the inference parameters
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(user application specific) such as the inference library,
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the path and name of the inference model and the size of
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the input inference vector(latent dimension and and condition size).
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This class constructs this vector and triggers the interface
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corresponding to the specified input inference library.
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After the inference, the post processing step consists of
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scaling back inferred values to the original range.
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- Par04InferenceInterface : is a base class that allows to read in the ML model, configure
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and execute inference.
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- Par04OnnxInference and Par04LWTNNInference : inference library specific classes that inherit
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from the base class Par04InferenceInterface.
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## 7. Output
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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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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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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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## 8. How to build and run the example
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- LWTNN and ONNX Runtime are available on LCG. In order to use them, one can setup the envirnment:
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% source /cvmfs/sft.cern.ch/lcg/views/LCG_100/x86_64-centos7-gcc10-opt/setup.sh
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- Compile and link to generate the executable (in your CMAKE build directory):
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% cmake <Par04_SOURCE>
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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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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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- If ONNX Runtime is available:
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% ./examplePar04 -m examplePar04_onnx.in
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- If LWTNN is available:
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% ./examplePar04 -m examplePar04_lwtnn.in
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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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off manually switching CMake flag INFERENCE_LIB to OFF (-DINFERENCE_LIB=OFF)
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## 9. Macros
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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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examplePar04.in - 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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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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a NN stored in json file.
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## 10. UI commands
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UI commands useful in this example:
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- activation/disactivation of the fast simulation model:
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/param/ActivateModel inferenceModel
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/param/InActivateModel inferenceModel
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- particle gun commands
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/gun/particle e-
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/gun/energy 10 GeV
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/gun/direction 0 1 0
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/gun/position 0 0 0
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UI commands defined in this example:
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- detector settings
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/Par04/detector/setDetectorInnerRadius 80 cm
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/Par04/detector/setDetectorLength 2 m
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/Par04/detector/setNbOfLayers 90
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/Par04/detector/setAbsorber 0 G4_W 1.4 mm false
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/Par04/detector/setAbsorber 1 G4_Si 0.3 mm true
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- readout mesh
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/Par04/mesh/setSizeOfRhoCells 2.325 mm
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/Par04/mesh/setSizeOfZCells 3.4 mm
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/Par04/mesh/setNbOfRhoCells 18
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/Par04/mesh/setNbOfPhiCells 50
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/Par04/mesh/setNbOfZCells 45
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- inference setup
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/Par04/inference/setSizeLatentVector 10
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/Par04/inference/setSizeConditionVector 4
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/Par04/inference/setModelPathName MLModels/Generator.onnx
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/Par04/inference/setProfileFlag 0
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/Par04/inference/setOptimizationFlag 0
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/Par04/inference/setInferenceLibrary ONNX
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/Par04/inference/setSizeOfRhoCells 2.325 mm
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/Par04/inference/setSizeOfZCells 3.4 mm
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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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@@ -0,0 +1,200 @@
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-------------------------------------------------------------------
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=========================================================
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Geant4 - an Object-Oriented Toolkit for Simulation in HEP
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=========================================================
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Example Par04
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-------------
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This example demonstrates how to use the Machine Learning (ML) inference
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to create energy deposits as a fast simulation model using ONNX runtime [1]
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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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|
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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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is defined dynamically, based on the particle direction at the entrance to the
|
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calorimeter. This is set using a fast simulation model that is triggered at
|
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detector entrance. Analysis of energy deposits is done in the event action,
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ntuple with hits is stored.
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[1]: https://github.com/microsoft/onnxruntime
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[2]: https://github.com/lwtnn/lwtnn
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1. Detector description
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-----------------------
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The detector consists of cylindrical layers of passive and active material,
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tungsten and silicon, respectively.
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|
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Fast simulation is attached to the region of the detector.
|
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|
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Input macro can specify which layer is considered an active layer (sensitive
|
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detector is attached to it). For fast simulation both layers should be marked
|
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as sensitive. It is connected to the wway the deposits are created: position is
|
||||
centre of the layer, which may often fall within the absorber (which is thicker
|
||||
than the active material). In a realistic detector setup, the positions used in
|
||||
fast simulation would be calculated properly, to deposit energy within the active
|
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material.
|
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2. Sensitive detector
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-----------------------
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Sensitive detector inherits from both base classes:
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- G4VSensitiveDetector: for processing of detailed/non-fast simulation hits
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- G4VFastSimSensitiveDetector: for processing of fast sim (G4FastSim) hits.
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Hits are placed in the same hit collection, with a different flag to distinguish
|
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between those originated in the full simulation, and those from the fast
|
||||
simulation.
|
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During visualisation, hits are represented as volumes of different colour:
|
||||
green for full simulation and red for fast simulation.
|
||||
|
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3. Primary generation
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---------------------
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|
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Particle gun is used as a primary generator. 10 GeV electron is used by default.
|
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By default particles are generated along y axis. Those values
|
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can be changed using /gun/ UI commands.
|
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|
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4. Physics List
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---------------
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FTFP_BERT modular physics list is used. On top of it, fast simulation physics
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is registered for selected particles (electrons, positrons).
|
||||
|
||||
|
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5. User actions
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----------------------------------------------------------
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- Par04RunAction : run action used for initialization and termination
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of the run. Histograms for analysis of shower development
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in the detector are created.
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- Par04EventAction : event action used for initialization and termination
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of the event. Analysis of shower development is performed
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on event-by-event basis.
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6. ML Inference
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----------------------------------------------------------
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- Par04MLFastSimModel : model used for parametrisation of źelectrons, positrons,
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and gammas. Energy is deposited and
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distributed according to inferred values from the ML model.
|
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This class triggers the inference setup, asks for values,
|
||||
and deposits energies at given positions.
|
||||
|
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- Par04InferenceSetup : this class is used to initialize the inference parameters
|
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(user application specific) such as the inference library,
|
||||
the path and name of the inference model and the size of
|
||||
the input inference vector(latent dimension and and condition size).
|
||||
This class constructs this vector and triggers the interface
|
||||
corresponding to the specified input inference library.
|
||||
After the inference, the post processing step consists of
|
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scaling back inferred values to the original range.
|
||||
|
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- Par04InferenceInterface : is a base class that allows to read in the ML model, configure
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and execute inference.
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- Par04OnnxInference and Par04LWTNNInference : inference library specific classes that inherit
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from the base class Par04InferenceInterface.
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7. Output
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---------
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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.
|
||||
|
||||
The macro file examplePar04.in is used to run full simulation. It will simulate 100
|
||||
events, for single 10 GeV electron beams.
|
||||
If CMake is able to find inference libraries (lwtnn and/or ONNX Runtime), a configuration
|
||||
macro will be available for that library (examplePar04_lwtnn.in and/or examplePar04_onnx.in).
|
||||
It will use a trained model to run inference and create showers in the detector by directly
|
||||
depositing energy.
|
||||
|
||||
8. How to build and run the example
|
||||
-----------------------------------
|
||||
- LWTNN and ONNX Runtime are available on LCG. In order to use them, one can setup the envirnment:
|
||||
% source /cvmfs/sft.cern.ch/lcg/views/LCG_100/x86_64-centos7-gcc10-opt/setup.sh
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- Compile and link to generate the executable (in your CMAKE build directory):
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% cmake <Par04_SOURCE>
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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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||||
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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- If ONNX Runtime is available:
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% ./examplePar04 -m examplePar04_onnx.in
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- If LWTNN is available:
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% ./examplePar04 -m examplePar04_lwtnn.in
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By default, CMake will attempt to build fast simulation with ONNX Runtime and LWTNN. However, if none
|
||||
of those libraries is found, it will proceed with full simulation only. The search can be switched
|
||||
off manually switching CMake flag INFERENCE_LIB to OFF (-DINFERENCE_LIB=OFF)
|
||||
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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
|
||||
argument is passed to the executable (examplePar04)
|
||||
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||||
examplePar04.in - 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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a NN stored in onnx file.
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||||
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examplePar04_lwtnn.in - 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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--------------
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||||
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||||
UI commands useful in this example:
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||||
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||||
- activation/disactivation of the fast simulation model:
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/param/ActivateModel inferenceModel
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/param/InActivateModel inferenceModel
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- particle gun commands
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/gun/particle e-
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/gun/energy 10 GeV
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/gun/direction 0 1 0
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/gun/position 0 0 0
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UI commands defined in this example:
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- detector settings
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/Par04/detector/setDetectorInnerRadius 80 cm
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/Par04/detector/setDetectorLength 2 m
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/Par04/detector/setNbOfLayers 90
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/Par04/detector/setAbsorber 0 G4_W 1.4 mm false
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/Par04/detector/setAbsorber 1 G4_Si 0.3 mm true
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- readout mesh
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/Par04/mesh/setSizeOfRhoCells 2.325 mm
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/Par04/mesh/setSizeOfZCells 3.4 mm
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/Par04/mesh/setNbOfRhoCells 18
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/Par04/mesh/setNbOfPhiCells 50
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/Par04/mesh/setNbOfZCells 45
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- inference setup
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/Par04/inference/setSizeLatentVector 10
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/Par04/inference/setSizeConditionVector 4
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/Par04/inference/setModelPathName MLModels/Generator.onnx
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/Par04/inference/setProfileFlag 0
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/Par04/inference/setOptimizationFlag 0
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/Par04/inference/setInferenceLibrary ONNX
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/Par04/inference/setSizeOfRhoCells 2.325 mm
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/Par04/inference/setSizeOfZCells 3.4 mm
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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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Reference in New Issue
Block a user