def total_loss(content_inputs, style_inputs, stylized_inputs, content_weights,
style_weights, total_variation_weight, reuse=False):
"""Computes the total loss function.
The total loss function is composed of a content, a style and a total
variation term.
Args:
content_inputs: Tensor. The input images.
style_inputs: Tensor. The input images.
stylized_inputs: Tensor. The stylized input images.
content_weights: dict mapping layer names to their associated content loss
weight. Keys that are missing from the dict won't have their content
loss computed.
style_weights: dict mapping layer names to their associated style loss
weight. Keys that are missing from the dict won't have their style
loss computed.
total_variation_weight: float. Coefficient for the total variation part of
the loss.
reuse: bool. Whether to reuse model parameters. Defaults to False.
Returns:
Tensor for the total loss, dict mapping loss names to losses.
"""
# Propagate the input and its stylized version through VGG16.
with tf.name_scope('content_endpoints'):
content_end_points = vgg.vgg_16(content_inputs, reuse=reuse)
with tf.name_scope('style_endpoints'):
style_end_points = vgg.vgg_16(style_inputs, reuse=True)
with tf.name_scope('stylized_endpoints'):
stylized_end_points = vgg.vgg_16(stylized_inputs, reuse=True)
# Compute the content loss
with tf.name_scope('content_loss'):
total_content_loss, content_loss_dict = content_loss(
content_end_points, stylized_end_points, content_weights)
# Compute the style loss
with tf.name_scope('style_loss'):
total_style_loss, style_loss_dict = style_loss(
style_end_points, stylized_end_points, style_weights)
# Compute the total variation loss
with tf.name_scope('total_variation_loss'):
tv_loss, total_variation_loss_dict = learning_utils.total_variation_loss(
stylized_inputs, total_variation_weight)
# Compute the total loss
with tf.name_scope('total_loss'):
loss = total_content_loss + total_style_loss + tv_loss
loss_dict = {'total_loss': loss}
loss_dict.update(content_loss_dict)
loss_dict.update(style_loss_dict)
loss_dict.update(total_variation_loss_dict)
return loss, loss_dict
def total_loss(content_inputs, style_inputs, stylized_inputs, content_weights,
style_weights, total_variation_weight, reuse=False):
"""Computes the total loss function.
The total loss function is composed of a content, a style and a total
variation term.
Args:
content_inputs: Tensor. The input images.
style_inputs: Tensor. The input images.
stylized_inputs: Tensor. The stylized input images.
content_weights: dict mapping layer names to their associated content loss
weight. Keys that are missing from the dict won't have their content
loss computed.
style_weights: dict mapping layer names to their associated style loss
weight. Keys that are missing from the dict won't have their style
loss computed.
total_variation_weight: float. Coefficient for the total variation part of
the loss.
reuse: bool. Whether to reuse model parameters. Defaults to False.
Returns:
Tensor for the total loss, dict mapping loss names to losses.
"""
# Propagate the input and its stylized version through VGG16.
with tf.name_scope('content_endpoints'):
content_end_points = vgg.vgg_16(content_inputs, reuse=reuse)
with tf.name_scope('style_endpoints'):
style_end_points = vgg.vgg_16(style_inputs, reuse=True)
with tf.name_scope('stylized_endpoints'):
stylized_end_points = vgg.vgg_16(stylized_inputs, reuse=True)
# Compute the content loss
with tf.name_scope('content_loss'):
total_content_loss, content_loss_dict = content_loss(
content_end_points, stylized_end_points, content_weights)
# Compute the style loss
with tf.name_scope('style_loss'):
total_style_loss, style_loss_dict = style_loss(
style_end_points, stylized_end_points, style_weights)
# Compute the total variation loss
with tf.name_scope('total_variation_loss'):
tv_loss, total_variation_loss_dict = learning_utils.total_variation_loss(
stylized_inputs, total_variation_weight)
# Compute the total loss
with tf.name_scope('total_loss'):
loss = total_content_loss + total_style_loss + tv_loss
loss_dict = {'total_loss': loss}
loss_dict.update(content_loss_dict)
loss_dict.update(style_loss_dict)
loss_dict.update(total_variation_loss_dict)
return loss, loss_dict