def grow(res_increase, res_decrease):
x = lambda: from_rgb(layers.downscale2d(inputs, 2**res_decrease), res_increase)
if res_decrease > 0:
x = utils.cset(
x,
(res_training > res_increase),
lambda: grow(res_increase+1, res_decrease-1))
x = block(x(), res_increase); y = lambda: x
if res_increase > 2:
y = utils.cset(
y,
(res_training < res_increase),
lambda: utils.lerp(x, from_rgb(layers.downscale2d(inputs, 2**(res_decrease+1)), res_increase-1), res_increase-res_training))
return y()
def grow(x, res_increase, res_decrease):
y = block(x, res_increase)
img = lambda: layers.upscale2d(to_rgb(y, res_increase-2), 2**res_decrease)
if res_increase > 2:
img = utils.cset(
img,
(res_training < res_increase),
lambda: layers.upscale2d(utils.lerp(to_rgb(y, res_increase-2), layers.upscale2d(to_rgb(x, res_increase-3)), res_increase-res_training), 2**res_decrease))
if res_decrease > 0:
img = utils.cset(
img,
(res_training > res_increase),
lambda: grow(y, res_increase + 1, res_decrease - 1))
return img()
def GradientPenalty(discriminator,
image_inputs,
fake_images,
minibatch_size,
wgan_target=1.0,
wgan_lambda=10.0):
with tf.compat.v1.name_scope('GradientPenalty'):
mixing_factors = tf.compat.v1.random_uniform([minibatch_size, 1, 1, 1],
0.0,
1.0,
dtype=tf.float32)
mixed_images = utils.lerp(image_inputs, fake_images, mixing_factors)
mixed_loss = tf.compat.v1.math.reduce_sum(discriminator(mixed_images))
mixed_grads = tf.compat.v1.gradients(mixed_loss, [mixed_images])
mixed_norms = tf.compat.v1.math.sqrt(
tf.compat.v1.math.reduce_sum(tf.compat.v1.math.square(mixed_grads),
axis=[1, 2, 3]))
gradient_penalty = tf.compat.v1.math.square(mixed_norms - wgan_target)
return gradient_penalty * (wgan_lambda / (wgan_target**2))
