def update_moving_average_of_w(w_broadcasted, w_avg, w_ema_decay):
with tf.variable_scope('wAvg'):
batch_avg = tf.reduce_mean(w_broadcasted[:, 0], axis=0)
update_op = tf.assign(w_avg, lerp(batch_avg, w_avg, w_ema_decay))
with tf.control_dependencies([update_op]):
w_broadcasted = tf.identity(w_broadcasted)
return w_broadcasted
def truncation_trick(n_broadcast, w_broadcasted, w_avg, truncation_psi, truncation_cutoff):
with tf.variable_scope('Truncation'):
layer_indices = np.arange(n_broadcast)[np.newaxis, :, np.newaxis]
ones = np.ones(layer_indices.shape, dtype=np.float32)
coefs = tf.where(layer_indices < truncation_cutoff, truncation_psi * ones, ones)
w_broadcasted = lerp(w_avg, w_broadcasted, coefs)
return w_broadcasted
def smooth_crossfade(images, alpha):
s = tf.shape(images)
y = tf.reshape(images, [-1, s[1], s[2] // 2, 2, s[3] // 2, 2])
y = tf.reduce_mean(y, axis=[3, 5], keepdims=True)
y = tf.tile(y, [1, 1, 1, 2, 1, 2])
y = tf.reshape(y, [-1, s[1], s[2], s[3]])
images = lerp(images, y, alpha)
return images
def test8():
from network.common_ops import lerp
w_dim = 5
n_broadcast = 18
truncation_psi = 0.7
truncation_cutoff = 8
w_broadcasted = tf.constant(1.0, dtype=tf.float32, shape=[1, n_broadcast, w_dim])
w_avg = tf.constant(0.5, dtype=tf.float32, shape=[w_dim])
layer_idx = np.arange(n_broadcast)[np.newaxis, :, np.newaxis]
ones = np.ones(layer_idx.shape, dtype=np.float32)
coefs = tf.where(layer_idx < truncation_cutoff, truncation_psi * ones, ones)
w_broadcasted = lerp(w_avg, w_broadcasted, coefs)
print('layer_idx: {}'.format(layer_idx))
print('ones: {}'.format(ones))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
coefs_out, out = sess.run([coefs, w_broadcasted])
print(coefs_out)
print(out)
return