def style_transfer(self, inputs, styles, style_layers=None):
"""style transfer via recursive feature transforms
Args:
inputs: input images [batch_size, height, width, channel]
styles: input styles [1 or batch_size, height, width, channel]
style_layers: a list of enforced style layer ids, default is None that
applies all style layers as self.style_loss_layers
Returns:
outputs: the stylized images [batch_size, height, width, channel]
"""
# get the style features for the styles
styles_image_features = losses.extract_image_features(
styles, self.network_name)
styles_features = losses.compute_content_features(
styles_image_features, self.style_loss_layers)
# construct the recurrent modules
selected_style_layers = self.style_loss_layers
if style_layers:
selected_style_layers = [
selected_style_layers[i] for i in style_layers
]
else:
style_layers = range(len(selected_style_layers))
outputs = tf.identity(inputs)
num_modules = len(selected_style_layers)
for i in range(num_modules, 0, -1):
starting_layer = selected_style_layers[i - 1]
# encoding the inputs
contents_image_features = losses.extract_image_features(
outputs, self.network_name)
content_features = losses.compute_content_features(
contents_image_features, [starting_layer])
# feature transformation
transformed_features = feature_transform(
content_features[starting_layer],
styles_features[starting_layer])
if self.blending_weight:
transformed_features = self.blending_weight * transformed_features + \
(1-self.blending_weight) * content_features[starting_layer]
# decoding the contents
with slim.arg_scope(vgg_decoder.vgg_decoder_arg_scope()):
outputs = vgg_decoder.vgg_decoder(transformed_features,
self.network_name,
starting_layer,
reuse=True,
scope='decoder_%d' %
style_layers[i - 1])
outputs = preprocessing.batch_mean_image_subtraction(outputs)
print('Finish the module of [%s]' % starting_layer)
# recover the outputs
return preprocessing.batch_mean_image_summation(outputs)
def style_transfer(self, inputs, styles, style_layers=(2, )):
"""style transfer via patch swapping
Args:
inputs: input images [batch_size, height, width, channel]
styles: input styles [1, height, width, channel]
style_layers: the list of layers to perform style swapping, default is None
that applied all style layers as self.style_loss_layers
Returns:
outputs: the stylized images [batch_size, height, width, channel]s
"""
styles_image_features = losses.extract_image_features(
styles, self.network_name)
styles_features = losses.compute_content_features(
styles_image_features, self.style_loss_layers)
# construct the recurrent modules
selected_style_layers = self.style_loss_layers
if style_layers:
selected_style_layers = [
selected_style_layers[i] for i in style_layers
]
else:
style_layers = range(len(selected_style_layers))
# input preprocessing
outputs = tf.identity(inputs)
# start style transfer
num_modules = len(selected_style_layers)
for i in range(num_modules, 0, -1):
starting_layer = selected_style_layers[i - 1]
# encoding the inputs
contents_image_features = losses.extract_image_features(
outputs, self.network_name)
contents_features = losses.compute_content_features(
contents_image_features, [starting_layer])
# feature transformation
transformed_features = feature_transform(
contents_features[starting_layer],
styles_features[starting_layer],
patch_size=self.patch_size)
# decoding the contents
with slim.arg_scope(vgg_decoder.vgg_decoder_arg_scope()):
outputs = vgg_decoder.vgg_decoder(transformed_features,
self.network_name,
starting_layer,
scope='decoder_%d' %
style_layers[i - 1])
outputs = preprocessing.batch_mean_image_subtraction(outputs)
print('Finish the module of [%s]' % starting_layer)
# recover the outputs
return preprocessing.batch_mean_image_summation(outputs)
def auto_encoder(self, inputs, content_layer=2, reuse=True):
# extract the content features
image_features = losses.extract_image_features(inputs, self.network_name)
content_features = losses.compute_content_features(image_features, self.content_layers)
# used content feature
selected_layer = self.content_layers[content_layer]
content_feature = content_features[selected_layer]
input_content_features = {selected_layer: content_feature}
# reconstruct the images
with slim.arg_scope(vgg_decoder.vgg_decoder_arg_scope(self.weight_decay)):
outputs = vgg_decoder.vgg_decoder(
content_feature,
self.network_name,
selected_layer,
reuse=reuse,
scope='decoder_%d' % content_layer)
return outputs, input_content_features