def __init__(self):
super(Decom, self).__init__()
self.conv_1 = keras.layers.Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', name='conv_1',
activation=tf.nn.leaky_relu)
self.pool_1 = keras.layers.MaxPool2D(name='pool_1')
self.conv_2 = keras.layers.Conv2D(filters=64, kernel_size=3, strides=1,
padding='same', name='conv_2',
activation=tf.nn.leaky_relu)
self.pool_2 = keras.layers.MaxPool2D(name='pool_2')
self.conv_3 = keras.layers.Conv2D(filters=128, kernel_size=3, strides=1,
padding='same', name='conv_3',
activation=tf.nn.leaky_relu)
# self.pool_3 = keras.layers.MaxPool2D(name='pool_3')
self.up_1 = Layer.Conv_Upsample_Concat(kernel_num_1=64, kernel_num_2=64,
kernel_size_1=3, kernel_size_2=3,
activation='leaky_relu',
name='up_1')
self.up_2 = Layer.Conv_Upsample_Concat(kernel_num_1=32, kernel_num_2=32,
kernel_size_1=3, kernel_size_2=3,
activation='leaky_relu',
name='up_2')
self.relfer = keras.layers.Conv2D(filters=3, kernel_size=1,
strides=1, padding='same',
name='relfer')
self.illum_1 = keras.layers.Conv2D(filters=32, kernel_size=3,
strides=1, padding='same',
activation=tf.nn.leaky_relu,
name='illum_1')
self.illum_2 = keras.layers.Conv2D(filters=1, kernel_size=1,
strides=1, padding='same',
name='illum_2')
def __init__(self):
super(Restor, self).__init__()
self.conv_1_1 = keras.layers.Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', name='conv_1_1',
activation=tf.nn.leaky_relu)
self.conv_1_2 = keras.layers.Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', name='conv_1_2',
activation=tf.nn.leaky_relu)
self.pool_1 = keras.layers.MaxPool2D(name='pool_1')
self.conv_2_1 = keras.layers.Conv2D(filters=64, kernel_size=3, strides=1,
padding='same', name='conv_2_1',
activation=tf.nn.leaky_relu)
self.conv_2_2 = keras.layers.Conv2D(filters=64, kernel_size=3, strides=1,
padding='same', name='conv_2_2',
activation=tf.nn.leaky_relu)
self.pool_2 = keras.layers.MaxPool2D(name='pool_2')
self.conv_3_1 = keras.layers.Conv2D(filters=128, kernel_size=3, strides=1,
padding='same', name='conv_3_1',
activation=tf.nn.leaky_relu)
self.conv_3_2 = keras.layers.Conv2D(filters=128, kernel_size=3, strides=1,
padding='same', name='conv_3_2',
activation=tf.nn.leaky_relu)
self.pool_3 = keras.layers.MaxPool2D(name='pool_3')
self.conv_4_1 = keras.layers.Conv2D(filters=256, kernel_size=3, strides=1,
padding='same', name='conv_4_1',
activation=tf.nn.leaky_relu)
self.conv_4_2 = keras.layers.Conv2D(filters=256, kernel_size=3, strides=1,
padding='same', name='conv_4_2',
activation=tf.nn.leaky_relu)
self.pool_4 = keras.layers.MaxPool2D(name='pool_4')
self.conv_5_1 = keras.layers.Conv2D(filters=512, kernel_size=3, strides=1,
padding='same', name='conv_5_1',
activation=tf.nn.leaky_relu)
self.conv_5_2 = keras.layers.Conv2D(filters=512, kernel_size=3, strides=1,
padding='same', name='conv_5_2',
activation=tf.nn.leaky_relu)
# self.pool_5 = keras.layers.MaxPool2D(name='pool_5')
self.up_1 = Layer.Conv_Upsample_Concat(kernel_num_1=256, kernel_num_2=256,
kernel_size_1=3, kernel_size_2=3,
activation='leaky_relu',
name='up_1')
self.up_1_conv = keras.layers.Conv2D(filters=256, kernel_size=3, strides=1,
padding='same', name='up_1_conv',
activation=tf.nn.leaky_relu)
self.up_2 = Layer.Conv_Upsample_Concat(kernel_num_1=128, kernel_num_2=128,
kernel_size_1=3, kernel_size_2=3,
activation='leaky_relu',
name='up_2')
self.up_2_conv = keras.layers.Conv2D(filters=128, kernel_size=3, strides=1,
padding='same', name='up_2_conv',
activation=tf.nn.leaky_relu)
self.up_3 = Layer.Conv_Upsample_Concat(kernel_num_1=64, kernel_num_2=64,
kernel_size_1=3, kernel_size_2=3,
activation='leaky_relu',
name='up_3')
self.up_3_conv = keras.layers.Conv2D(filters=64, kernel_size=3, strides=1,
padding='same', name='up_3_conv',
activation=tf.nn.leaky_relu)
self.up_4 = Layer.Conv_Upsample_Concat(kernel_num_1=32, kernel_num_2=32,
kernel_size_1=3, kernel_size_2=3,
activation='leaky_relu',
name='up_4')
self.up_4_conv = keras.layers.Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', name='up_4_conv',
activation=tf.nn.leaky_relu)
self.restor = keras.layers.Conv2D(filters=3, kernel_size=3, strides=1,
padding='same', name='restor',
activation=tf.nn.leaky_relu)
