def discriminator(self, inp, cond, stages, t, reuse=False):
alpha_trans = self.alpha_tra
with tf.variable_scope("d_net", reuse=reuse):
x_iden = None
if t:
x_iden = pool(inp, 2)
x_iden = self.from_rgb(x_iden, stages - 2)
x = self.from_rgb(inp, stages - 1)
for i in range(stages - 1, 0, -1):
with tf.variable_scope(self.get_conv_scope_name(i), reuse=reuse):
x = conv2d(x, f=self.get_dnf(i), ks=(3, 3), s=(1, 1), act=lrelu_act())
x = conv2d(x, f=self.get_dnf(i-1), ks=(3, 3), s=(1, 1), act=lrelu_act())
x = pool(x, 2)
if i == stages - 1 and t:
x = tf.multiply(alpha_trans, x) + tf.multiply(tf.subtract(1., alpha_trans), x_iden)
with tf.variable_scope(self.get_conv_scope_name(0), reuse=reuse):
# Real/False branch
cond_compress = fc(cond, units=128, act=lrelu_act())
concat = self.concat_cond4(x, cond_compress)
x_b1 = conv2d(concat, f=self.get_dnf(0), ks=(3, 3), s=(1, 1), act=lrelu_act())
x_b1 = conv2d(x_b1, f=self.get_dnf(0), ks=(4, 4), s=(1, 1), padding='VALID', act=lrelu_act())
output_b1 = fc(x_b1, units=1)
return output_b1
def build_down_block(self, inputs, name, down_outputs, first=False):
out_num = self.conf.start_channel_num if first else 2 * \
inputs.shape[self.channel_axis].value
conv1 = ops.conv(inputs, out_num, self.conv_size, name + '/conv1',
self.conf.data_type)
conv2 = ops.conv(conv1, out_num, self.conv_size, name + '/conv2',
self.conf.data_type)
down_outputs.append(conv2)
pool = ops.pool(conv2, self.pool_size, name + '/pool',
self.conf.data_type)
return pool
