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model.py
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model.py
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from keras import Model
from keras.initializers import RandomNormal, Zeros
from keras.layers import Input, ZeroPadding2D, Conv2D, BatchNormalization, Activation, Dropout, Add, Conv2DTranspose, \
LeakyReLU, Concatenate
from config import *
def residual_block(feature, dropout=False):
x = Conv2D(256, kernel_size=3, strides=1, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(feature)
x = BatchNormalization()(x)
x = Activation('relu')(x)
if dropout:
x = Dropout(0.5)(x)
x = Conv2D(256, kernel_size=3, strides=1, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return Add()([feature, x])
def conv_block(feature, out_channel, downsample=True, dropout=False):
if downsample:
x = Conv2D(out_channel, kernel_size=4, strides=2, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(feature)
else:
x = Conv2DTranspose(out_channel, kernel_size=4, strides=2, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(feature)
x = BatchNormalization()(x)
x = Activation('relu')(x)
if dropout:
x = Dropout(0.5)(x)
return x
def get_generator(n_block=3):
input = Input(shape=(image_size, image_size, input_channel))
x = Conv2D(64, kernel_size=7, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(input) # use reflection padding instead
x = BatchNormalization()(x)
x = Activation('relu')(x)
# downsample
x = Conv2D(128, kernel_size=3, strides=2, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# downsample
x = Conv2D(256, kernel_size=3, strides=2, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
for i in range(n_block):
x = residual_block(x)
# upsample
x = Conv2DTranspose(128, kernel_size=3, strides=2, padding='same',
kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# upsample
x = Conv2DTranspose(64, kernel_size=3, strides=2, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# out
x = Conv2D(output_channel, kernel_size=7, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x) # use reflection padding instead
x = BatchNormalization()(x)
x = Activation('tanh')(x)
generator = Model(inputs=input, outputs=x)
return generator
def get_generator_unet(n_block=3):
input = Input(shape=(image_size, image_size, input_channel))
# encoder
e0 = Conv2D(64, kernel_size=4, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(input) # use reflection padding instead
e0 = BatchNormalization()(e0)
e0 = Activation('relu')(e0)
e1 = conv_block(e0, 128, downsample=True, dropout=False) # 1/2
e2 = conv_block(e1, 256, downsample=True, dropout=False) # 1/4
e3 = conv_block(e2, 512, downsample=True, dropout=False) # 1/8
e4 = conv_block(e3, 512, downsample=True, dropout=False) # 1/16
e5 = conv_block(e4, 512, downsample=True, dropout=False) # 1/32
e6 = conv_block(e5, 512, downsample=True, dropout=False) # 1/64
e7 = conv_block(e6, 512, downsample=True, dropout=False) # 1/128
# decoder
d0 = conv_block(e7, 512, downsample=False, dropout=True) # 1/64
d1 = Concatenate(axis=-1)([d0, e6])
d1 = conv_block(d1, 512, downsample=False, dropout=True) # 1/32
d2 = Concatenate(axis=-1)([d1, e5])
d2 = conv_block(d2, 512, downsample=False, dropout=True) # 1/16
d3 = Concatenate(axis=-1)([d2, e4])
d3 = conv_block(d3, 512, downsample=False, dropout=True) # 1/8
d4 = Concatenate(axis=-1)([d3, e3])
d4 = conv_block(d4, 256, downsample=False, dropout=True) # 1/4
d5 = Concatenate(axis=-1)([d4, e2])
d5 = conv_block(d5, 128, downsample=False, dropout=True) # 1/2
d6 = Concatenate(axis=-1)([d5, e1])
d6 = conv_block(d6, 64, downsample=False, dropout=True) # 1
# out
x = Conv2D(output_channel, kernel_size=3, padding='same', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(d6) # use reflection padding instead
x = BatchNormalization()(x)
x = Activation('tanh')(x)
generator = Model(inputs=input, outputs=x)
return generator
def get_generator_training_model(generator, discriminator):
imgA = Input(shape=(image_size, image_size, input_channel))
imgB = Input(shape=(image_size, image_size, input_channel))
fakeB = generator(imgA)
# discriminator.trainable=False
realA_fakeB = Concatenate()([imgA, fakeB])
pred_fake = discriminator(realA_fakeB)
generator_training_model = Model(inputs=[imgA, imgB], outputs=[pred_fake, fakeB])
return generator_training_model
def get_discriminator(n_layers=4, use_sigmoid=True):
input = Input(shape=(image_size, image_size, input_channel + output_channel))
x = Conv2D(64, kernel_size=4, padding='same', strides=2, kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(input)
x = LeakyReLU(alpha=0.2)(x)
for i in range(1, n_layers):
x = Conv2D(64 * 2 ** i, kernel_size=4, padding='same', strides=2, kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(64 * 2 ** n_layers, kernel_size=4, padding='same', strides=1, kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(1, kernel_size=4, padding='same', strides=1, kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=Zeros())(x)
if use_sigmoid:
x = Activation('sigmoid')(x)
discriminator = Model(inputs=input, outputs=x)
return discriminator