def generate(self, z_var, pg=1, t=False, alpha_trans=0.0):
with tf.variable_scope('generator') as scope:
de = tf.reshape(Pixl_Norm(z_var), [self.batch_size, 1, 1, int(self.get_nf(1))])
de = conv2d(de, output_dim=self.get_nf(1), k_h=4, k_w=4, d_w=1, d_h=1, use_wscale=self.use_wscale, gain=np.sqrt(2)/4, padding='Other', name='gen_n_1_conv')
de = Pixl_Norm(lrelu(de))
de = tf.reshape(de, [self.batch_size, 4, 4, int(self.get_nf(1))])
de = conv2d(de, output_dim=self.get_nf(1), d_w=1, d_h=1, use_wscale=self.use_wscale, name='gen_n_2_conv')
de = Pixl_Norm(lrelu(de))
for i in range(pg - 1):
if i == pg - 2 and t:
#To RGB
de_iden = conv2d(de, output_dim=2, k_w=1, k_h=1, d_w=1, d_h=1, use_wscale=self.use_wscale,
name='gen_y_rgb_conv_{}'.format(de.shape[1]))
de_iden = upscale(de_iden, 2)
de = upscale(de, 2)
de = Pixl_Norm(lrelu(
conv2d(de, output_dim=self.get_nf(i + 1), d_w=1, d_h=1, use_wscale=self.use_wscale, name='gen_n_conv_1_{}'.format(de.shape[1]))))
de = Pixl_Norm(lrelu(
conv2d(de, output_dim=self.get_nf(i + 1), d_w=1, d_h=1, use_wscale=self.use_wscale, name='gen_n_conv_2_{}'.format(de.shape[1]))))
#To RGB
de = conv2d(de, output_dim=2, k_w=1, k_h=1, d_w=1, d_h=1, use_wscale=self.use_wscale, gain=1, name='gen_y_rgb_conv_{}'.format(de.shape[1]))
if pg == 1: return de
if t: de = (1 - alpha_trans) * de_iden + alpha_trans*de
else: de = de
return de
def generate(self, z_var, t_text_embedding, pg=1, t=False, alpha_trans=0.0):
with tf.variable_scope('generator') as scope:
reduced_text_embedding = lrelu( linear(t_text_embedding, self.tdim, 'g_embeddings') )
z_concat = tf.concat([z_var,reduced_text_embedding], 1)
de = tf.reshape(z_concat, [self.batch_size, 1, 1, tf.cast(self.get_nf(1),tf.int32)])
de = conv2d(de, output_dim= self.get_nf(1), k_h=4, k_w=4, d_w=1, d_h=1, padding='Other', name='gen_n_1_conv')
de = Pixl_Norm(lrelu(de))
de = tf.reshape(de, [self.batch_size, 4, 4, tf.cast(self.get_nf(1),tf.int32)])
de = conv2d(de, output_dim=self.get_nf(1), d_w=1, d_h=1, name='gen_n_2_conv')
de = Pixl_Norm(lrelu(de))
for i in range(pg - 1):
if i == pg - 2 and t:
#To RGB
de_iden = conv2d(de, output_dim=3, k_w=1, k_h=1, d_w=1, d_h=1,
name='gen_y_rgb_conv_{}'.format(de.shape[1]))
de_iden = upscale(de_iden, 2)
de = upscale(de, 2)
de = Pixl_Norm(lrelu(
conv2d(de, output_dim=self.get_nf(i + 1), d_w=1, d_h=1, name='gen_n_conv_1_{}'.format(de.shape[1]))))
de = Pixl_Norm(lrelu(
conv2d(de, output_dim=self.get_nf(i + 1), d_w=1, d_h=1, name='gen_n_conv_2_{}'.format(de.shape[1]))))
#To RGB
de = conv2d(de, output_dim=3, k_w=1, k_h=1, d_w=1, d_h=1, name='gen_y_rgb_conv_{}'.format(de.shape[1]))
if pg == 1:
return de
if t:
de = (1 - alpha_trans) * de_iden + alpha_trans*de
else:
de = de
return de #tanh given in text to image code. will this work?
def generate(self, z_var, pg=1, t=False, alpha_trans=0.0):
with tf.variable_scope('generator') as scope:
#
de = tf.reshape(Pixl_Norm(z_var), [self.batch_size, 1, 1, int(self.get_nf(1))])
de = conv2d(de, output_dim=self.get_nf(1), k_h=4, k_w=4, d_w=1, d_h=1, use_wscale=self.use_wscale, gain=np.sqrt(2)/4, padding='Other', name='gen_n_1_conv')
de = Pixl_Norm(lrelu(de))
de = tf.reshape(de, [self.batch_size, 4, 4, int(self.get_nf(1))])
de = conv2d(de, output_dim=self.get_nf(1), d_w=1, d_h=1, use_wscale=self.use_wscale, name='gen_n_2_conv')
de = Pixl_Norm(lrelu(de))
# pg 代表当前 pixel 上升至哪个档位
# pg=1，即第一层的training不需要进入，不需要提升pixel并进行一系列操作
for i in range(pg - 1):
# t 代表是否进行training，如果进行training则进行下述操作
# 最后一层循环，进入
if i == pg - 2 and t:
# To RGB
de_iden = conv2d(de, output_dim=3, k_w=1, k_h=1, d_w=1, d_h=1, use_wscale=self.use_wscale,
name='gen_y_rgb_conv_{}'.format(de.shape[1]))
de_iden = upscale(de_iden, 2)
de = upscale(de, 2)
de = Pixl_Norm(lrelu(
conv2d(de, output_dim=self.get_nf(i + 1), d_w=1, d_h=1, use_wscale=self.use_wscale, name='gen_n_conv_1_{}'.format(de.shape[1]))))
de = Pixl_Norm(lrelu(
conv2d(de, output_dim=self.get_nf(i + 1), d_w=1, d_h=1, use_wscale=self.use_wscale, name='gen_n_conv_2_{}'.format(de.shape[1]))))
# To RGB
de = conv2d(de, output_dim=3, k_w=1, k_h=1, d_w=1, d_h=1, use_wscale=self.use_wscale, gain=1, name='gen_y_rgb_conv_{}'.format(de.shape[1]))
if pg == 1: return de
# 如果是training，通过 alpha_trans 来调控和线性组合图片
if t:
de = (1 - alpha_trans) * de_iden + alpha_trans*de
else:
de = de
return de
def generate(self, z_var, pg=1, t=False, alpha_trans=0.0):
with tf.variable_scope('generator') as scope:
# latent vector(batch_size, 512)를 (batch_size, 1, 1, 512)로 바꿈
de = tf.reshape(
z_var,
[self.batch_size, 1, 1,
tf.cast(self.get_nf(1), tf.int32)])
de = conv2d(de,
output_dim=self.get_nf(1),
k_h=4,
k_w=4,
d_w=1,
d_h=1,
padding='Other',
name='gen_n_1_conv')
de = Pixl_Norm(lrelu(de))
# [batch size, 4, 4, 512]로 바꿈
de = tf.reshape(
de, [self.batch_size, 4, 4,
tf.cast(self.get_nf(1), tf.int32)])
de = conv2d(de,
output_dim=self.get_nf(1),
d_w=1,
d_h=1,
name='gen_n_2_conv')
de = Pixl_Norm(lrelu(de))
#pg=2일때 i=0, for문 1번 실행
#pg=3일때 i=0,1 for문 2번 실행
for i in range(pg - 1):
#pg=2이고 i=0, t=True일때 실행
#pg=2이고 i=0, t=False일때 실행 x
#pg=3이고 i=0, t=True 실행x
#pg=3이고 i=1, t=True 실행
if i == pg - 2 and t:
#To RGB
# 논문에선 upscale을 먼저하고 conv2d(toRGB)를 하도록 되어 있는데 텐서플로우 변수 이름의 중복때문에 어쩔수 없이 conv를 먼저
de_iden = conv2d(de,
output_dim=3,
k_w=1,
k_h=1,
d_w=1,
d_h=1,
name='gen_y_rgb_conv_{}'.format(
de.shape[1]))
de_iden = upscale(de_iden, 2)
de = upscale(de, 2)
# i=0일때 get_nf(0+1) = 512
# i=1일때 get_ng(1+1) = 256
de = Pixl_Norm(
lrelu(
conv2d(de,
output_dim=self.get_nf(i + 1),
d_w=1,
d_h=1,
name='gen_n_conv_1_{}'.format(de.shape[1]))))
de = Pixl_Norm(
lrelu(
conv2d(de,
output_dim=self.get_nf(i + 1),
d_w=1,
d_h=1,
name='gen_n_conv_2_{}'.format(de.shape[1]))))
#To RGB
de = conv2d(de,
output_dim=3,
k_w=1,
k_h=1,
d_w=1,
d_h=1,
name='gen_y_rgb_conv_{}'.format(de.shape[1]))
if pg == 1:
return de
# transition이 True일때
if t:
de = (1 - alpha_trans) * de_iden + alpha_trans * de
else:
de = de
return de
def generator(hparams, z_var, train, reuse):
with tf.variable_scope("generator") as scope:
if reuse:
tf.get_variable_scope().reuse_variables()
use_wscale = True
de = tf.reshape(Pixl_Norm(z_var), [hparams.batch_size, 1, 1, 128])
de = conv2d(de, output_dim=128, k_h=4, k_w=4, d_w=1, d_h=1, use_wscale=use_wscale, gain=np.sqrt(2)/4, padding='Other', name='gen_n_1_conv')
de = Pixl_Norm(lrelu(de))
de = tf.reshape(de, [hparams.batch_size, 4, 4, 128])
de = conv2d(de, output_dim=128, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_2_conv')
de = Pixl_Norm(lrelu(de))
de = upscale(de, 2)
de = Pixl_Norm(lrelu(conv2d(de, output_dim=128, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_1_8')))
de = Pixl_Norm(lrelu(conv2d(de, output_dim=128, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_2_8')))
de = upscale(de, 2)
de = Pixl_Norm(lrelu(conv2d(de, output_dim=64, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_1_16')))
de = Pixl_Norm(lrelu(conv2d(de, output_dim=64, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_2_16')))
de = upscale(de, 2)
de = Pixl_Norm(lrelu(conv2d(de, output_dim=32, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_1_32')))
de = Pixl_Norm(lrelu(conv2d(de, output_dim=32, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_2_32')))
de_iden = conv2d(de, output_dim=2, k_w=1, k_h=1, d_w=1, d_h=1, use_wscale=use_wscale,name='gen_y_rgb_conv_32')
de_iden = upscale(de_iden, 2)
de = upscale(de, 2)
de = Pixl_Norm(lrelu(conv2d(de, output_dim=16, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_1_64')))
de = Pixl_Norm(lrelu(conv2d(de, output_dim=16, d_w=1, d_h=1, use_wscale=use_wscale, name='gen_n_conv_2_64')))
de = conv2d(de, output_dim=2, k_w=1, k_h=1, d_w=1, d_h=1, use_wscale=use_wscale, gain=1, name='gen_y_rgb_conv_64')
return de