def generator_single_chart(self, noise):
with tf.variable_scope("generator", reuse=tf.AUTO_REUSE):
depths = self.config.depths
spatial = self.config.spatial
output = layers.linear.Linear('input_FC', self.config.latent_vec_dim, (spatial[0]**2)*depths[0], noise)
output = tf.nn.relu(output)
output = tf.reshape(output, [-1, depths[0], spatial[0], spatial[0]])
# convolution layers
output = Conv2D('conv_in_{0}-{1}_{2}'.format(depths[0], depths[0], spatial[0]),
depths[0], depths[0], 3, output, spatial[0]) # N x dim x 4 x 4
output = tf.nn.relu(output)
for ind, depth in enumerate(depths[:-1]):
output, _, _ = layers.conv_block.GenConvBlock('conv__{3}_{0}-{1}_{2}'.format(depth, depths[ind+1], spatial[ind], ind),
depth, depths[ind+1], 3, output, spatial[ind])
output = Conv2D('conv_out', depths[ind+1], self.config.depth_dim, 1, output, self.config.spatial_dim)
# enforce toric symmetry
output = toric_symmetry(output, self.config.spatial_dim)
# reduce mean
if self.config.normalize_charts:
data_mean, _ = tf.nn.moments(output, axes=[2, 3], keep_dims=True)
output = tf.subtract(output, data_mean)
return output
def generator(self, noise):
if self.config.model == 'single_chart':
return self.generator_single_chart(noise)
else:
with tf.variable_scope("generator", reuse=tf.AUTO_REUSE):
depths = self.config.depths
spatial = self.config.spatial
output = layers.linear.Linear('input_FC', self.config.latent_vec_dim, (spatial[0]**2)*depths[0], noise)
output = tf.nn.relu(output)
output = tf.reshape(output, [-1, depths[0], spatial[0], spatial[0]])
# convolution layers
output = Conv2D('conv_in_{0}-{1}_{2}'.format(depths[0], depths[0], spatial[0]),
depths[0], depths[0], 3, output, spatial[0]) # N x dim x 4 x 4
output = tf.nn.relu(output)
for ind, depth in enumerate(depths[:-1]):
output, _, _ = layers.conv_block.GenConvBlock('conv_{3}_{0}-{1}_{2}'.format(depth, depths[ind+1], spatial[ind], ind),
depth, depths[ind+1], 3, output, spatial[ind])
output = Conv2D('conv_out', depths[ind+1], self.config.depth_dim, 1, output, self.config.spatial_dim)
# enforce toric symmetry
output = toric_symmetry(output, self.config.spatial_dim)
# landmark consistency layer
def project_ST_op():
if self.config.gamma_decay is not None:
gamma = tf.multiply(tf.pow(self.config.gamma_decay,tf.cast(tf.subtract(self.cur_epoch_tensor,self.config.kick_projection),dtype='float32')),self.config.gamma)
else:
gamma = self.config.gamma
op1 = project_ST(self, output, gamma)
with tf.control_dependencies([op1]):
return tf.identity(op1)
# the landmark consistency layer kicks in from a certain given epoch
output = tf.cond(tf.greater(self.cur_epoch_tensor, self.config.kick_projection),
project_ST_op,
lambda: output)
# reduce mean
if self.config.normalize_charts:
data_mean, _ = tf.nn.moments(output, axes=[2, 3], keep_dims=True)
output = tf.subtract(output, data_mean)
return output