Don't always import tensorflow of pytorch in TMVA_CNN_Classification.py

tutorials/machine_learning/TMVA_CNN_Classification.py

@@ -22,48 +22,18 @@
 ## The difference between signal and background is in the gaussian width.
 ## The width for the background gaussian is slightly larger than the signal width by few % values
 
 import ROOT
 
 import os
 import importlib.util
 
-useKerasCNN = False
-
-if ROOT.gSystem.GetFromPipe("root-config --has-tmva-pymva") == "yes":
-    useKerasCNN = True
-
 opt = [1, 1, 1, 1, 1]
 useTMVACNN = opt[0] if len(opt) > 0  else False
-useKerasCNN = opt[1] if len(opt) > 1 else useKerasCNN
+useKerasCNN = opt[1] if len(opt) > 1 else False
 useTMVADNN = opt[2] if len(opt) > 2 else False
 useTMVABDT = opt[3] if len(opt) > 3 else False
 usePyTorchCNN = opt[4] if len(opt) > 4 else False
 
-if useKerasCNN:
-    try:
-      import tensorflow
-    except:
-      ROOT.Warning("TMVA_CNN_Classification", "Skip using Keras since tensorflow cannot be imported")
-      useKerasCNN = False
-
-# PyTorch has to be imported before ROOT to avoid crashes because of clashing
-# std::regexp symbols that are exported by cppyy.
-# See also: https://github.com/wlav/cppyy/issues/227
-torch_spec = importlib.util.find_spec("torch")
-if torch_spec is None:
-    usePyTorchCNN = False
-    print("TMVA_CNN_Classificaton","Skip using PyTorch since torch is not installed")
-else:
-    try:
-      import torch
-    except:
-      ROOT.Warning("TMVA_CNN_Classification", "Skip using PyTorch since it cannot be imported")
-      usePyTorchCNN = False
-
-
-import ROOT
-
-
 TMVA = ROOT.TMVA
 TFile = ROOT.TFile
 
@@ -120,7 +90,7 @@
         h2.FillRandom(f2, nRndmEvts)
 
         for k in range(nh):
             for l in range(nw):
                 m = k * nw + l
                 # add some noise in each bin
                 x1[m] = h1.GetBinContent(k + 1, l + 1) + ROOT.gRandom.Gaus(0, pixelNoise)
@@ -147,7 +117,7 @@
     useTMVACNN = False
     useTMVADNN = False
 
 if not "tmva-pymva" in ROOT.gROOT.GetConfigFeatures():
     useKerasCNN = False
     usePyTorchCNN = False
 else:
@@ -475,13 +445,13 @@
     ROOT.Info("TMVA_CNN_Classification", "Building convolutional keras model")
     # create python script which can be executed
     # create 2 conv2d layer + maxpool + dense
     import tensorflow
     from tensorflow.keras.models import Sequential
     from tensorflow.keras.optimizers import Adam
 
     # from keras.initializers import TruncatedNormal
     # from keras import initializations
     from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
 
     # from keras.callbacks import ReduceLROnPlateau
     model = Sequential()