def transform(input_,
normalizer_fn=None,
normalizer_params=None,
reuse=False,
trainable=True,
is_training=True,
alpha=1.0):
"""Maps content images to stylized images.
Args:
input_: Tensor. Batch of input images.
normalizer_fn: normalization layer function for applying style
normalization.
normalizer_params: dict of parameters to pass to the style normalization op.
reuse: bool. Whether to reuse model parameters. Defaults to False.
trainable: bool. Should the parameters be marked as trainable?
is_training: bool. Is it training phase or not?
alpha: float. Width multiplier to reduce the number of filters used in the
model and slim it down. Defaults to 1.0, which results
in the hyper-parameters used in the published paper.
Returns:
Tensor. The output of the transformer network.
"""
with tf.variable_scope('transformer', reuse=reuse):
with slim.arg_scope([slim.conv2d],
activation_fn=tf.nn.relu,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params,
weights_initializer=tf.random_normal_initializer(
0.0, 0.01),
biases_initializer=tf.constant_initializer(0.0),
trainable=trainable):
with slim.arg_scope([slim.conv2d],
normalizer_fn=slim.batch_norm,
normalizer_params=None,
trainable=trainable):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
trainable=trainable):
with tf.variable_scope('contract'):
h = model_util.conv2d(input_, 9, 1, int(alpha * 32), 'conv1')
h = model_util.conv2d(h, 3, 2, int(alpha * 64), 'conv2')
h = model_util.conv2d(h, 3, 2, int(alpha * 128), 'conv3')
with tf.variable_scope('residual'):
h = model_util.residual_block(h, 3, 'residual1')
h = model_util.residual_block(h, 3, 'residual2')
h = model_util.residual_block(h, 3, 'residual3')
h = model_util.residual_block(h, 3, 'residual4')
h = model_util.residual_block(h, 3, 'residual5')
with tf.variable_scope('expand'):
h = model_util.upsampling(h, 3, 2, int(alpha * 64), 'conv1')
h = model_util.upsampling(h, 3, 2, int(alpha * 32), 'conv2')
return model_util.upsampling(
h, 9, 1, 3, 'conv3', activation_fn=tf.nn.sigmoid)
def transform(input_, normalizer_fn=None, normalizer_params=None,
reuse=False, trainable=True, is_training=True):
"""Maps content images to stylized images.
Args:
input_: Tensor. Batch of input images.
normalizer_fn: normalization layer function for applying style
normalization.
normalizer_params: dict of parameters to pass to the style normalization op.
reuse: bool. Whether to reuse model parameters. Defaults to False.
trainable: bool. Should the parameters be marked as trainable?
is_training: bool. Is it training phase or not?
Returns:
Tensor. The output of the transformer network.
"""
with tf.variable_scope('transformer', reuse=reuse):
with slim.arg_scope(
[slim.conv2d],
activation_fn=tf.nn.relu,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params,
weights_initializer=tf.random_normal_initializer(0.0, 0.01),
biases_initializer=tf.constant_initializer(0.0),
trainable=trainable):
with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm,
normalizer_params=None,
trainable=trainable):
with slim.arg_scope([slim.batch_norm], is_training=is_training,
trainable=trainable):
with tf.variable_scope('contract'):
h = model_util.conv2d(input_, 9, 1, 32, 'conv1')
h = model_util.conv2d(h, 3, 2, 64, 'conv2')
h = model_util.conv2d(h, 3, 2, 128, 'conv3')
with tf.variable_scope('residual'):
h = model_util.residual_block(h, 3, 'residual1')
h = model_util.residual_block(h, 3, 'residual2')
h = model_util.residual_block(h, 3, 'residual3')
h = model_util.residual_block(h, 3, 'residual4')
h = model_util.residual_block(h, 3, 'residual5')
with tf.variable_scope('expand'):
h = model_util.upsampling(h, 3, 2, 64, 'conv1')
h = model_util.upsampling(h, 3, 2, 32, 'conv2')
return model_util.upsampling(
h, 9, 1, 3, 'conv3', activation_fn=tf.nn.sigmoid)