[tmva][sofie] New SOFIE Keras Parser - Fixing Conv2D test failures

bindings/pyroot/pythonizations/CMakeLists.txt

@@ -58,7 +58,33 @@ if(tmva)
         ROOT/_pythonization/_tmva/_rtensor.py
         ROOT/_pythonization/_tmva/_tree_inference.py
         ROOT/_pythonization/_tmva/_utils.py
-        ROOT/_pythonization/_tmva/_gnn.py)
+        ROOT/_pythonization/_tmva/_gnn.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/__init__.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/generate_keras_functional.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/generate_keras_sequential.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/parser.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/parser_test_function.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/__init__.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/batchnorm.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/binary.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/concat.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/conv.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/dense.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/elu.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/flatten.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/identity.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/layernorm.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/leaky_relu.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/permute.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/pooling.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/reshape.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/relu.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/rnn.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/selu.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/sigmoid.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/softmax.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/swish.py
+        ROOT/_pythonization/_tmva/_sofie/_parser/_keras/layers/tanh.py)
     if(dataframe)
       list(APPEND PYROOT_EXTRA_PYTHON_SOURCES
           ROOT/_pythonization/_tmva/_batchgenerator.py)

bindings/pyroot/pythonizations/python/ROOT/_facade.py

@@ -427,6 +427,7 @@ def TMVA(self):
         from ._pythonization import _tmva  # noqa: F401
 
         ns = self._fallback_getattr("TMVA")
+        setattr(ns.Experimental.SOFIE, "PyKeras", _tmva.PyKeras)
         hasRDF = "dataframe" in self.gROOT.GetConfigFeatures()
         if hasRDF:
             try:

bindings/pyroot/pythonizations/python/ROOT/_pythonization/_tmva/__init__.py

@@ -44,6 +44,7 @@ def inject_rbatchgenerator(ns):
 
 
 from ._gnn import RModel_GNN, RModel_GraphIndependent
+from ._sofie._parser._keras.parser import PyKeras
 
 hasRDF = "dataframe" in cppyy.gbl.ROOT.GetROOT().GetConfigFeatures()
 if hasRDF:

bindings/pyroot/pythonizations/python/ROOT/_pythonization/_tmva/_sofie/_parser/_keras/__init__.py

@@ -0,0 +1,5 @@
+def get_keras_version() -> str:
+
+    import keras
+
+    return keras.__version__

bindings/pyroot/pythonizations/python/ROOT/_pythonization/_tmva/_sofie/_parser/_keras/generate_keras_functional.py

@@ -0,0 +1,211 @@
+def generate_keras_functional(dst_dir):
+
+    from keras import models, layers
+    import numpy as np
+
+    # Helper training function
+    def train_and_save(keras_model, model_name):
+        # Handle multiple inputs dynamically
+        if isinstance(keras_model.input_shape, list):
+            x_train = [np.random.rand(32, *shape[1:]) for shape in keras_model.input_shape]
+        else:
+            x_train = np.random.rand(32, *keras_model.input_shape[1:])
+        y_train = np.random.rand(32, *keras_model.output_shape[1:])
+
+        try:
+            keras_model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])
+            keras_model.fit(x_train, y_train, epochs=1, verbose=0)
+            keras_model.save(f"{dst_dir}/Functional_{model_name}_test.keras")
+        except Exception as error:
+            print(f"Error while traning the keras_model {model_name}: {error}")
+
+    # Activation Functions
+    for act in ['relu', 'elu', 'leaky_relu', 'selu', 'sigmoid', 'softmax', 'swish', 'tanh']:
+        inp = layers.Input(shape=(10,))
+        out = layers.Activation(act)(inp)
+        keras_model = models.Model(inp, out)
+        train_and_save(keras_model, f"Activation_layer_{act.capitalize()}")
+    # Along with these, Keras allows explicit delcaration of activation layers such as:
+    # [ELU, ReLU, LeakyReLU, Softmax]
+
+    # Add
+    in1 = layers.Input(shape=(8,))
+    in2 = layers.Input(shape=(8,))
+    out = layers.Add()([in1, in2])
+    keras_model = models.Model([in1, in2], out)
+    train_and_save(keras_model, "Add")
+
+    # AveragePooling2D channels_first
+    inp = layers.Input(shape=(3, 8, 8))
+    out = layers.AveragePooling2D(pool_size=(2, 2), data_format='channels_first')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "AveragePooling2D_channels_first")
+
+    # AveragePooling2D channels_last
+    inp = layers.Input(shape=(8, 8, 3))
+    out = layers.AveragePooling2D(pool_size=(2, 2), data_format='channels_last')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "AveragePooling2D_channels_last")
+
+    # BatchNorm
+    inp = layers.Input(shape=(10, 3, 5))
+    out = layers.BatchNormalization(axis=2)(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "BatchNorm")
+
+    # Concat
+    in1 = layers.Input(shape=(8,))
+    in2 = layers.Input(shape=(8,))
+    out = layers.Concatenate()([in1, in2])
+    keras_model = models.Model([in1, in2], out)
+    train_and_save(keras_model, "Concat")
+
+    # Conv2D channels_first
+    inp = layers.Input(shape=(3, 8, 8))
+    out = layers.Conv2D(4, (3, 3), padding='same', data_format='channels_first', activation='relu')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Conv2D_channels_first")
+
+    # Conv2D channels_last
+    inp = layers.Input(shape=(8, 8, 3))
+    out = layers.Conv2D(4, (3, 3), padding='same', data_format='channels_last', activation='leaky_relu')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Conv2D_channels_last")
+
+    # Conv2D padding_same
+    inp = layers.Input(shape=(8, 8, 3))
+    out = layers.Conv2D(4, (3, 3), padding='same', data_format='channels_last')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Conv2D_padding_same")
+
+    # Conv2D padding_valid
+    inp = layers.Input(shape=(8, 8, 3))
+    out = layers.Conv2D(4, (3, 3), padding='valid', data_format='channels_last', activation='elu')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Conv2D_padding_valid")
+
+    # Dense
+    inp = layers.Input(shape=(10,))
+    out = layers.Dense(5, activation='tanh')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Dense")
+
+    # ELU
+    inp = layers.Input(shape=(10,))
+    out = layers.ELU(alpha=0.5)(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "ELU")
+
+    # Flatten
+    inp = layers.Input(shape=(4, 5))
+    out = layers.Flatten()(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Flatten")
+
+    # GlobalAveragePooling2D channels first
+    inp = layers.Input(shape=(3, 4, 6))
+    out = layers.GlobalAveragePooling2D(data_format='channels_first')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "GlobalAveragePooling2D_channels_first")
+
+    # GlobalAveragePooling2D channels last
+    inp = layers.Input(shape=(4, 6, 3))
+    out = layers.GlobalAveragePooling2D(data_format='channels_last')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "GlobalAveragePooling2D_channels_last")
+
+    # LayerNorm
+    inp = layers.Input(shape=(10, 3, 5))
+    out = layers.LayerNormalization(axis=-1)(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "LayerNorm")
+
+    # LeakyReLU
+    inp = layers.Input(shape=(10,))
+    out = layers.LeakyReLU()(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "LeakyReLU")
+
+    # MaxPooling2D channels_first
+    inp = layers.Input(shape=(3, 8, 8))
+    out = layers.MaxPooling2D(pool_size=(2, 2), data_format='channels_first')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "MaxPool2D_channels_first")
+
+    # MaxPooling2D channels_last
+    inp = layers.Input(shape=(8, 8, 3))
+    out = layers.MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "MaxPool2D_channels_last")
+
+    # Multiply
+    in1 = layers.Input(shape=(8,))
+    in2 = layers.Input(shape=(8,))
+    out = layers.Multiply()([in1, in2])
+    keras_model = models.Model([in1, in2], out)
+    train_and_save(keras_model, "Multiply")
+
+    # Permute
+    inp = layers.Input(shape=(3, 4, 5))
+    out = layers.Permute((2, 1, 3))(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Permute")
+
+    # ReLU
+    inp = layers.Input(shape=(10,))
+    out = layers.ReLU()(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "ReLU")
+
+    # Reshape
+    inp = layers.Input(shape=(4, 5))
+    out = layers.Reshape((2, 10))(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Reshape")
+
+    # Softmax
+    inp = layers.Input(shape=(10,))
+    out = layers.Softmax()(inp)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Softmax")
+
+    # Subtract
+    in1 = layers.Input(shape=(8,))
+    in2 = layers.Input(shape=(8,))
+    out = layers.Subtract()([in1, in2])
+    keras_model = models.Model([in1, in2], out)
+    train_and_save(keras_model, "Subtract")
+
+    # Layer Combination
+
+    inp = layers.Input(shape=(32, 32, 3))
+    x = layers.Conv2D(8, (3,3), padding="same", activation="relu")(inp)
+    x = layers.MaxPooling2D((2,2))(x)
+    x = layers.Reshape((16, 16, 8))(x)
+    x = layers.Permute((3, 1, 2))(x)
+    x = layers.Flatten()(x)
+    out = layers.Dense(10, activation="softmax")(x)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Layer_Combination_1")
+
+    inp = layers.Input(shape=(20,))
+    x = layers.Dense(32, activation="tanh")(inp)
+    x = layers.Dense(16)(x)
+    x = layers.ELU()(x)
+    x = layers.LayerNormalization()(x)
+    out = layers.Dense(5, activation="sigmoid")(x)
+    keras_model = models.Model(inp, out)
+    train_and_save(keras_model, "Layer_Combination_2")
+
+    inp1 = layers.Input(shape=(16,))
+    inp2 = layers.Input(shape=(16,))
+    d1 = layers.Dense(16, activation="relu")(inp1)
+    d2 = layers.Dense(16, activation="selu")(inp2)
+    add = layers.Add()([d1, d2])
+    sub = layers.Subtract()([d1, d2])
+    mul = layers.Multiply()([d1, d2])
+    merged = layers.Concatenate()([add, sub, mul])
+    merged = layers.LeakyReLU(alpha=0.1)(merged)
+    out = layers.Dense(4, activation="softmax")(merged)
+    keras_model = models.Model([inp1, inp2], out)
+    train_and_save(keras_model, "Layer_Combination_3")