Import Geant4 11.1.0.beta source tree

2022-07-01 10:44:02 +02:00
parent b3bf75a2a1
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+"""
+** train **
+    - defines data loading parameters and calls the data preprocessing function 
+    - defines the model parameters and instantiates the VAE model
+    - performs the training
+"""
+
+# 1. Data loading/preprocessing
+from utils import *
+# Directory where the HDF5 files are saved
+init_dir = './detector_'
+# Number of calorimeter layers 
+nCells_z = 45
+# Segmentation in the r,phi direction
+nCells_r = 18
+nCells_phi = 50
+# Total number of readout cells (represents the number of nodes in the input/output layers of the model)
+original_dim = nCells_z*nCells_r*nCells_phi
+# Minimum and maximum primary particle energy to consider for training in GeV units 
+min_energy = 1
+max_energy = 1024
+# Minimum and maximum primary particle angle to consider for training in degrees units 
+min_angle = 50
+max_angle = 90
+# The preprocess function reads the data and performs preprocessing and encoding for the values of energy, angle and geometry
+energies_Train,condE_Train,condAngle_Train,condGeo_Train = preprocess(init_dir,original_dim,min_angle,max_angle,min_energy,max_energy)
+
+# 2. Model architecture
+import model
+# Instantiate a VAE model and define all the parameters
+vae = model.VAE(batch_size=100 ,
+          original_dim=original_dim,
+          intermediate_dim1=100,  
+          intermediate_dim2=50,
+          intermediate_dim3=20,
+          intermediate_dim4=10+4,
+          latent_dim=10,
+          epsilon_std=1.,
+          mu=0,
+          epochs=10000,
+          lr=0.001,
+          activ=tf.keras.layers.LeakyReLU(),  
+          outActiv='sigmoid',  
+          validation_split=0.05,
+          wReco=original_dim,
+          wkl=0.5,
+          optimizer=optimizers.Adam(),
+          ki='RandomNormal',
+          bi='Zeros',
+          earlyStop=False,
+          checkpoint_dir = "."
+          )
+
+# 3. Model training
+history = vae.train(energies_Train,
+        condE_Train,
+        condAngle_Train,
+        condGeo_Train
+        )
+
+# 4. Save the model (ony the decoder part) after traing 
+self.vae.decoder.save("decoder.h5")
+
+# 5. Convert the model to ONNX format
+import keras2onnx
+import tensorflow
+# Create the Keras model and convert itinto an ONNX model
+kerasModel = tensorflow.keras.models.load_model("decoder.h5") 
+onnxModel = keras2onnx.convert_keras(kerasModel,"name")
+# Save the ONNX model. Generator.onnx can then be used to perform the inference in the example
+keras2onnx.save_model(onnxModel,"Generator.onnx")
+
+"""
+# In order to convert the model into a format that can be used with the LWTNN library
+# 1. After training :
+# serialize model to JSON
+json_model = self.vae.decoder.to_json()
+with open("decoder.json", "w") as json_file:
+    json_file.write(json_model)
+# serialize weights to HDF5
+self.vae.decoder.save_weights("decoder.h5")
+# 2. Externally, after building the LWTNN code available at https://github.com/lwtnn/lwtnn 
+#   2.1 Run the kerasfunc2json python script (available in lwtnn/ converters/) to generate a template file of your functional model input variables by calling:
+#       $ kerasfunc2json.py decoder.json decoder.h5 > inputs.json
+#   2.2 Run again kerasfunc2json script to get your output file that would be used for the inference in the example
+#       $ kerasfunc2json.py decoder.json decoder.h5 inputs.json > Generator.json
+"""