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Dorchies David authored
Saved work 5 May 2022 Refs HYCAR-Hydro/airgr#152, HYCAR-Hydro/airgr#153
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HYCAR-Hydro / airGR
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import tensorflow.keras as tfk
import tensorflow as tf
class CNN1D(tf.keras.Model):
def __init__(self, n_filters, suffix, dropout_rate = 0.0, hidden_activation='relu', name='CNN1D', **kwargs):
super(CNN1D, self).__init__(name=name, **kwargs)
self.conv1 = tfk.layers.Conv1D(filters=n_filters, kernel_size=3, padding='valid', name="conv1_"+suffix, activation="relu")
self.bn1 = tfk.layers.BatchNormalization(name="bn1_"+suffix)
self.do1 = tfk.layers.Dropout(rate=dropout_rate, name="dropOut1_"+suffix)
self.conv2 = tfk.layers.Conv1D(filters=n_filters, kernel_size=3, padding='valid', name="conv2_"+suffix, activation="relu")
self.bn2 = tfk.layers.BatchNormalization(name="bn2_"+suffix)
self.do2 = tfk.layers.Dropout(rate=dropout_rate, name="dropOut2_"+suffix)
self.conv3 = tfk.layers.Conv1D(filters=n_filters*2, kernel_size=3, padding='valid', name="conv3_"+suffix, activation="relu")
self.bn3 = tfk.layers.BatchNormalization(name="bn3_"+suffix)
self.do3 = tfk.layers.Dropout(rate=dropout_rate, name="dropOut3_"+suffix)
self.conv4 = tfk.layers.Conv1D(filters=n_filters*2, kernel_size=1, padding='valid', name="conv4_"+suffix, activation="relu")
self.bn4 = tfk.layers.BatchNormalization(name="bn4_"+suffix)
self.do4 = tfk.layers.Dropout(rate=dropout_rate, name="dropOut4_"+suffix)
self.pool = tfk.layers.GlobalAveragePooling1D()
@tf.function
def call(self, inputs, training=False):
conv1 = self.conv1(inputs)
#conv1 = self.bn1(conv1)#, training=training)
conv1 = self.do1(conv1, training=training)
conv2 = self.conv2(conv1)
#conv2 = self.bn2(conv2)#, training=training)
conv2 = self.do2(conv2, training=training)
conv3 = self.conv3(conv2)
#conv3 = self.bn3(conv3)#, training=training)
conv3 = self.do3(conv3, training=training)
conv4 = self.conv4(conv3)
#conv4 = self.bn4(conv4)#, training=training)
conv4 = self.do4(conv4, training=training)
pool = self.pool(conv4)
return pool
class TwoBranchModel(tf.keras.Model):
def __init__(self, n_filters, suffix, nb_classes, dropout_rate = 0.0, hidden_activation='relu', name='TwoBranchModel', **kwargs):
super(TwoBranchModel, self).__init__(name=name, **kwargs)
self.PixelBranch = CNN1D(n_filters, suffix, dropout_rate=dropout_rate)
self.ObjBranch = CNN1D(n_filters, suffix, dropout_rate=dropout_rate)
self.dense1 = tfk.layers.Dense(256, activation='relu')
self.drop1 = tfk.layers.Dropout(rate=dropout_rate)
self.bn1 = tfk.layers.BatchNormalization()
self.dense2 = tfk.layers.Dense(256, activation='relu')
self.classif = tfk.layers.Dense(nb_classes, activation='softmax')
self.classifPix = tfk.layers.Dense(nb_classes, activation='softmax')
self.classifObj = tfk.layers.Dense(nb_classes, activation='softmax')
@tf.function
def call(self, inputs, training=False):
pixel_inputs, obj_inputs = inputs
branchP = self.PixelBranch(pixel_inputs, training=training)
branchO = self.ObjBranch(obj_inputs, training=training)
classifP = self.classifPix(branchP)
classifO = self.classifObj(branchO)
#feat = branchP + branchO
feat = tf.concat([branchP, branchO], axis=1)
output = self.dense1(feat)
output = self.bn1(output, training=training)
output = self.drop1(output, training=training)
output = self.dense2(output)
classif = self.classif(output)
return classif, classifP, classifO