from keras.optimizers import Adam
from keras.initializers import RandomNormal
from keras.models import Model
from keras.models import Input
from keras.layers import Conv2D
from keras.layers import LeakyReLU
from tensorflow_addons.layers import InstanceNormalization


def define_discriminator(image_shape):
	# weight initialization
	init = RandomNormal(stddev=0.02)
	# source image input
	in_image = Input(shape=image_shape)
	# C64
	d = Conv2D(64, (4, 4), strides=(2, 2), padding='same', kernel_initializer=init)(in_image)
	d = LeakyReLU(alpha=0.2)(d)
	# C128
	d = Conv2D(128, (4, 4), strides=(2, 2), padding='same', kernel_initializer=init)(d)
	d = InstanceNormalization(axis=-1)(d)
	d = LeakyReLU(alpha=0.2)(d)
	# C256
	d = Conv2D(256, (4, 4), strides=(2, 2), padding='same', kernel_initializer=init)(d)
	d = InstanceNormalization(axis=-1)(d)
	d = LeakyReLU(alpha=0.2)(d)
	# C512
	d = Conv2D(512, (4, 4), strides=(2, 2), padding='same', kernel_initializer=init)(d)
	d = InstanceNormalization(axis=-1)(d)
	d = LeakyReLU(alpha=0.2)(d)
	# second last output layer
	d = Conv2D(512, (4, 4), padding='same', kernel_initializer=init)(d)
	d = InstanceNormalization(axis=-1)(d)
	d = LeakyReLU(alpha=0.2)(d)
	# patch output
	patch_out = Conv2D(1, (4, 4), padding='same', kernel_initializer=init)(d)
	# define model
	model = Model(in_image, patch_out)
	# compile model
	model.compile(loss='mse', optimizer=Adam(lr=0.0002, beta_1=0.5), loss_weights=[0.5])
	return model


if __name__ == '__main__':
	# define image shape
	image_shape = (256, 256, 3)
	# create the model
	model = define_discriminator(image_shape)
	# summarize the model
	model.summary()
