def g_block(inp, style, noise, fil, u = True):
b = Dense(fil)(style)
b = Reshape([1, 1, fil])(b)
g = Dense(fil)(style)
g = Reshape([1, 1, fil])(g)
n = Conv2D(filters = fil, kernel_size = 1, padding = 'same', kernel_initializer = 'he_normal')(noise)
if u:
out = UpSampling2D(interpolation = 'bilinear')(inp)
out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)
else:
out = Activation('linear')(inp)
out = AdaInstanceNormalization()([out, b, g])
out = add([out, n])
out = LeakyReLU(0.01)(out)
b = Dense(fil)(style)
b = Reshape([1, 1, fil])(b)
g = Dense(fil)(style)
g = Reshape([1, 1, fil])(g)
n = Conv2D(filters = fil, kernel_size = 1, padding = 'same', kernel_initializer = 'he_normal')(noise)
out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)
out = AdaInstanceNormalization()([out, b, g])
out = add([out, n])
out = LeakyReLU(0.01)(out)
return out
def g_block(inp, style, noise, filter_size, upsample=True):
# b and g = beta and gamma. these are learnt transformations for the
# AdaIN operation. Both comprise 'A' in the paper diagram.
b = Dense(filter_size)(style)
b = Reshape([1, 1, filter_size])(b)
g = Dense(filter_size)(style)
g = Reshape([1, 1, filter_size])(g)
# add noise to block ('B' in the paper diagram)
n = Conv2D(filters=filter_size,
kernel_size=1,
padding='same',
kernel_initializer='he_normal')(noise)
# upsample if we need to.
# this part takes our input and puts it through the conv layer.
if upsample:
out = UpSampling2D(interpolation='bilinear')(inp)
out = Conv2D(filters=filter_size,
kernel_size=3,
padding='same',
kernel_initializer='he_normal')(out)
else:
out = inp
# add our output with the noise
out = add([out, n])
# AdaIN gets previous output + b and g (beta and gamma for transformation)
out = AdaInstanceNormalization()([out, b, g])
out = LeakyReLU(0.01)(out)
# the second half of the styleGAN generator block
# beta and gamma for our second AdaIN operations
b = Dense(filter_size)(style)
b = Reshape([1, 1, filter_size])(b)
g = Dense(filter_size)(style)
g = Reshape([1, 1, filter_size])(g)
# add noise again ('B' in the paper diagram)
n = Conv2D(filters=filter_size,
kernel_size=1,
padding='same',
kernel_initializer='he_normal')(noise)
# pass output through conv
out = Conv2D(filters=filter_size,
kernel_size=3,
padding='same',
kernel_initializer='he_normal')(out)
# apply AdaIN to output and thats it!
out = AdaInstanceNormalization()([out, b, g])
# add output and noise
out = add([out, n])
out = LeakyReLU(0.01)(out)
return out