def structureModel(self):
Inputs = layers.Input(shape=self._inputShape, batch_size=self._iBatchSize)
Con1 = layers.Conv2D(64, (3, 3), name='Con1', activation='relu', padding='SAME', input_shape=self._inputShape, strides=1)(Inputs)
Con2 = layers.Conv2D(64, (3, 3), name='Con2', activation='relu', padding='SAME', strides=1)(Con1)
Side1 = sideBranch(Con2, 1)
MaxPooling1 = layers.MaxPooling2D((2, 2), name='MaxPooling1', strides=2, padding='SAME')(Con2)
# outputs1
Con3 = layers.Conv2D(128, (3, 3), name='Con3', activation='relu', padding='SAME', strides=1)(MaxPooling1)
Con4 = layers.Conv2D(128, (3, 3), name='Con4', activation='relu', padding='SAME', strides=1)(Con3)
Side2 = sideBranch(Con4, 2)
MaxPooling2 = layers.MaxPooling2D((2, 2), name='MaxPooling2', strides=2, padding='SAME')(Con4)
# outputs2
Con5 = layers.Conv2D(256, (3, 3), name='Con5', activation='relu', padding='SAME', strides=1)(MaxPooling2)
Con6 = layers.Conv2D(256, (3, 3), name='Con6', activation='relu', padding='SAME', strides=1)(Con5)
Con7 = layers.Conv2D(256, (3, 3), name='Con7', activation='relu', padding='SAME', strides=1)(Con6)
Side3 = sideBranch(Con7, 4)
MaxPooling3 = layers.MaxPooling2D((2, 2), name='MaxPooling3', strides=2, padding='SAME')(Con7)
# outputs3
Con8 = layers.Conv2D(512, (3, 3), name='Con8', activation='relu', padding='SAME', strides=1)(MaxPooling3)
Con9 = layers.Conv2D(512, (3, 3), name='Con9', activation='relu', padding='SAME', strides=1)(Con8)
Con10 = layers.Conv2D(512, (3, 3), name='Con10', activation='relu', padding='SAME', strides=1)(Con9)
Side4 = sideBranch(Con10, 8)
MaxPooling4 = layers.MaxPooling2D((2, 2), name='MaxPooling4', strides=2, padding='SAME')(Con10)
# outputs4
Con11 = layers.Conv2D(512, (3, 3), name='Con11', activation='relu', padding='SAME', strides=1)(MaxPooling4)
Con12 = layers.Conv2D(512, (3, 3), name='Con12', activation='relu', padding='SAME', strides=1)(Con11)
Con13 = layers.Conv2D(512, (3, 3), name='Con13', activation='relu', padding='SAME', strides=1)(Con12)
Side5 = sideBranch(Con13, 16)
Fuse = layers.Concatenate(axis=-1)([Side1, Side2, Side3, Side4, Side5])
# learn fusion weight
Fuse = layers.Conv2D(1, (1, 1), name='Fuse', padding='SAME', use_bias=False, activation=None)(Fuse)
output1 = layers.Activation('sigmoid', name='output1')(Side1)
output2 = layers.Activation('sigmoid', name='output2')(Side2)
output3 = layers.Activation('sigmoid', name='output3')(Side3)
output4 = layers.Activation('sigmoid', name='output4')(Side4)
output5 = layers.Activation('sigmoid', name='output5')(Side5)
output6 = layers.Activation('sigmoid', name='output6')(Fuse)
outputs = [output1, output2, output3, output4, output5, output6]
self._pModel = Model(inputs=Inputs, outputs=outputs)
pAdam = optimizers.adam(lr=0.0001)
self._pModel.compile(loss={'output1': classBalancedSigmoidCrossEntropy,
'output2': classBalancedSigmoidCrossEntropy,
'output3': classBalancedSigmoidCrossEntropy,
'output4': classBalancedSigmoidCrossEntropy,
'output5': classBalancedSigmoidCrossEntropy,
'output6': classBalancedSigmoidCrossEntropy
}, optimizer=pAdam)
def structureModel(self):
weightDecay = 0.00001
Inputs = layers.Input(shape=self._inputShape,
batch_size=self._iBatchSize)
Con1 = layers.Conv2D(64, (3, 3),
name='Con1',
activation='relu',
padding='SAME',
input_shape=self._inputShape,
strides=1,
kernel_regularizer=l2(weightDecay))(Inputs)
Con2 = layers.Conv2D(64, (3, 3),
name='Con2',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con1)
Side1 = sideBranch(Con2, 1)
MaxPooling1 = layers.MaxPooling2D((2, 2),
name='MaxPooling1',
strides=2,
padding='SAME')(Con2)
# outputs1
Con3 = layers.Conv2D(128, (3, 3),
name='Con3',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(MaxPooling1)
Con4 = layers.Conv2D(128, (3, 3),
name='Con4',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con3)
Side2 = sideBranch(Con4, 2)
MaxPooling2 = layers.MaxPooling2D((2, 2),
name='MaxPooling2',
strides=2,
padding='SAME')(Con4)
# outputs2
Con5 = layers.Conv2D(256, (3, 3),
name='Con5',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(MaxPooling2)
Con6 = layers.Conv2D(256, (3, 3),
name='Con6',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con5)
Con7 = layers.Conv2D(256, (3, 3),
name='Con7',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con6)
Side3 = sideBranch(Con7, 4)
MaxPooling3 = layers.MaxPooling2D((2, 2),
name='MaxPooling3',
strides=2,
padding='SAME')(Con7)
# outputs3
Con8 = layers.Conv2D(512, (3, 3),
name='Con8',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(MaxPooling3)
Con9 = layers.Conv2D(512, (3, 3),
name='Con9',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con8)
Con10 = layers.Conv2D(512, (3, 3),
name='Con10',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con9)
Side4 = sideBranch(Con10, 8)
MaxPooling4 = layers.MaxPooling2D((2, 2),
name='MaxPooling4',
strides=2,
padding='SAME')(Con10)
# outputs4
Con11 = layers.Conv2D(512, (3, 3),
name='Con11',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(MaxPooling4)
Con12 = layers.Conv2D(512, (3, 3),
name='Con12',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con11)
Con13 = layers.Conv2D(512, (3, 3),
name='Con13',
activation='relu',
padding='SAME',
strides=1,
kernel_regularizer=l2(weightDecay))(Con12)
Side5 = sideBranch(Con13, 16)
Fuse = layers.Concatenate(axis=-1)([Side1, Side2, Side3, Side4, Side5])
# learn fusion weight
fuseInitWeight = initializers.constant(0.2)
Fuse = layers.Conv2D(1, (1, 1),
name='Fuse',
padding='SAME',
use_bias=False,
activation=None,
kernel_initializer=fuseInitWeight,
kernel_regularizer=l1(weightDecay))(Fuse)
# output1 = layers.Activation('sigmoid', name='output1')(Side1)
# output2 = layers.Activation('sigmoid', name='output2')(Side2)
# output3 = layers.Activation('sigmoid', name='output3')(Side3)
# output4 = layers.Activation('sigmoid', name='output4')(Side4)
# output5 = layers.Activation('sigmoid', name='output5')(Side5)
output6 = layers.Activation('sigmoid', name='output6')(Fuse)
outputs = [output6
] # [output1, output2, output3, output4, output5, output6]
self._pModel = Model(inputs=Inputs, outputs=outputs)
pOptimizer = optimizers.adam(lr=0.0001)
pOptimizer = optimizers.SGD(lr=0.000001, decay=0., momentum=0.9)
pOptimizer = tf.optimizers.SGD(lr=0.5, decay=0., momentum=0.9)
# pOptimizer = monitorSGD(lr=0.000001, decay=0., momentum=0.9)
# grads = tf.gradients(classBalancedSigmoidCrossEntropy, self._pModel.trainable_weights)
# pSGD = optimizers.SGD()
self._pModel.compile(
loss={
# 'output1': classBalancedSigmoidCrossEntropy,
# 'output2': classBalancedSigmoidCrossEntropy,
# 'output3': classBalancedSigmoidCrossEntropy,
# 'output4': classBalancedSigmoidCrossEntropy,
# 'output5': classBalancedSigmoidCrossEntropy,
'output6': classBalancedSigmoidCrossEntropy
},
optimizer=pOptimizer)