def Q(x, reuse=False):
with tf.variable_scope('infer', reuse=reuse):
feamap0 = lrelu(
conv2d(x, channelne[1], k=kersizee[0], s=1, name="conv2d_0"))
feamap1 = block_down(feamap0,
channelne[1],
k=kersizee[1],
s=1,
name='block1') # 32x32
feamap2 = resblock_down(feamap1,
channelne[2],
k=kersizee[2],
s=1,
name='block2') # 16x16
feamap3 = block_down(feamap2,
channelne[3],
k=kersizee[3],
s=1,
name='block3') # 8x8
feamap4 = block_down(feamap3,
channelne[4],
k=kersizee[4],
s=1,
name='block4') # 4x4
#feamap5 = lrelu(conv2d(feamap4, channelne[5],k=kersizee[4], s=1,name="conv2d_5")) # 4x4
#feamap5 = lrelu(conv2d(feamap5, channelne[5],k=4, s=1,padding='VALID',name="conv2d_6")) # 1x1
feamap5 = tf.reshape(feamap4,
[BATCH_SIZE, channelne[5] * fme[5] * fme[5]])
za = tf.nn.tanh(dense(feamap5, z_dim, gain=1, name='fully_f1'))
return za
def descriptor(x, reuse=False):
with tf.variable_scope('des', reuse=reuse):
feamap0 = lrelu(
conv2d(x, channelndes[1], k=kersizedes[0], s=1, name="conv2d_0"))
feamap1 = block_down(feamap0,
channelndes[1],
k=kersizedes[1],
s=1,
name='block1') # 32x32
feamap2 = resblock_down(feamap1,
channelndes[2],
k=kersizedes[2],
s=1,
name='block2') # 16x16
feamap3 = block_down(feamap2,
channelndes[3],
k=kersizedes[3],
s=1,
name='block3') # 8x8
feamap4 = block_down(feamap3,
channelndes[4],
k=kersizedes[4],
s=1,
name='block4') # 4x4
feamap4 = minibatch_stddev_layer(feamap4, 10)
feamap5 = lrelu(
conv2d(feamap4,
channelndes[5],
k=kersizedes[4],
s=1,
name="conv2d_5")) # 4x4
#feamap5 = lrelu(conv2d(feamap5, channelndes[5],k=4, s=1,padding='VALID',name="conv2d_6")) # 1x1
feamap5 = tf.reshape(
feamap5, [BATCH_SIZE, channelndes[5] * fmdes[5] * fmdes[5]])
out = dense(feamap5, 1, gain=1, name='fully_f')
return out