def _resnet(self, input):
with tf.variable_scope(self.name, reuse=self._reuse):
num_filters = [128, 256, 512, 512]
if self._image_size == 256:
num_filters.append(512)
E = input
E = ops.conv_block(E,
64,
'C{}_{}'.format(64, 0),
4,
2,
self._is_train,
self._reuse,
norm=None,
activation='leaky',
bias=True)
for i, n in enumerate(num_filters):
E = ops.residual(E,
n,
'res{}_{}'.format(n, i + 1),
self._is_train,
self._reuse,
norm=self._norm,
bias=True)
E = tf.nn.avg_pool(E, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
E = tf.nn.relu(E)
E = tf.nn.avg_pool(E, [1, 8, 8, 1], [1, 8, 8, 1], 'SAME')
E = ops.flatten(E)
mu = ops.mlp(E,
self._latent_dim,
'FC8_mu',
self._is_train,
self._reuse,
norm=None,
activation=None)
log_sigma = ops.mlp(E,
self._latent_dim,
'FC8_sigma',
self._is_train,
self._reuse,
norm=None,
activation=None)
z = mu + tf.random_normal(
shape=tf.shape(self._latent_dim)) * tf.exp(log_sigma)
self._reuse = True
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
self.name)
return z, mu, log_sigma
def _convnet(self, input):
with tf.variable_scope(self.name, reuse=self._reuse):
num_filters = [64, 128, 256, 512, 512, 512, 512]
if self._image_size == 256:
num_filters.append(512)
E = input
for i, n in enumerate(num_filters):
E = ops.conv_block(E,
n,
'C{}_{}'.format(n, i),
4,
2,
self._is_train,
self._reuse,
norm=self._norm if i else None,
activation='leaky')
E = ops.flatten(E)
mu = ops.mlp(E,
self._latent_dim,
'FC8_mu',
self._is_train,
self._reuse,
norm=None,
activation=None)
log_sigma = ops.mlp(E,
self._latent_dim,
'FC8_sigma',
self._is_train,
self._reuse,
norm=None,
activation=None)
z = mu + tf.random_normal(
shape=tf.shape(self._latent_dim)) * tf.exp(log_sigma)
self._reuse = True
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
self.name)
return z, mu, log_sigma