参考 http://blog.csdn.net/wspba/article/details/53994649
https://www.ctolib.com/AdaIN-style.html
Acknowledgement
This project is inspired by many existing style transfer methods and their open-source implementations, including:
- Image Style Transfer Using Convolutional Neural Networks, Gatys et al. [code (by Johnson)]
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Johnson et al. [code]
- Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis, Ulyanov et al. [code]
- A Learned Representation For Artistic Style, Dumoulin et al. [code]
- Fast Patch-based Style Transfer of Arbitrary Style, Chen and Schmidt [code]
- Controlling Perceptual Factors in Neural Style Transfer, Gatys et al. [code]
注意点：
初始化了一个TensorFlow的变量，即为我们需要训练的对象。
image = tf.Variable(initial)
注意这里我们训练的对象是一张图像，而不是weight和bias。
#optimizer setup
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
train_step.run()
执行后，开始不断进行改变变量值image使得loss变小，达到优化目标在minist分类实验中
#这里的x和y并不是特定的值，相反，他们都只是一个占位符，可以在TensorFlow运行某一计算时根据该占位符输入具体的值。
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))#需要进行优化的变量
b = tf.Variable(tf.zeros([10]))#需要进行优化的变量
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x,W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
执行后，开始不断进行改变变量值W和b使得cross_entropy变小，达到优化目标
neural_style.py
# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3.import osimport numpy as np
import scipy.miscfrom stylize import stylizeimport math
from argparse import ArgumentParser#python neural_style.py --content gril.jpg --styles 3dtest.jpg --out output.jpg# default arguments
CONTENT_WEIGHT = 5e0
STYLE_WEIGHT = 1e2
TV_WEIGHT = 1e2
LEARNING_RATE = 1e2
STYLE_SCALE = 1.0
ITERATIONS = 1000
VGG_PATH = './imagenet-vgg-verydeep-19.mat'
content = './image/content.jpg'
styles = './image/styles.jpg'
out = './image/output.jpg'def build_parser():
parser = ArgumentParser()
parser.add_argument('--content',
dest='content', help='content image',
metavar='CONTENT', default=content)
parser.add_argument('--styles',
dest='styles',
nargs='+', help='one or more style images',
metavar='STYLE', default=styles)
parser.add_argument('--output',
dest='output', help='output path',
metavar='OUTPUT', default=out)
parser.add_argument('--checkpoint-output',
dest='checkpoint_output', help='checkpoint output format',
metavar='OUTPUT')
parser.add_argument('--iterations', type=int,
dest='iterations', help='iterations (default %(default)s)',
metavar='ITERATIONS', default=ITERATIONS)
parser.add_argument('--width', type=int,
dest='width', help='output width',
metavar='WIDTH')
parser.add_argument('--style-scales', type=float,
dest='style_scales',
nargs='+', help='one or more style scales',
metavar='STYLE_SCALE')
parser.add_argument('--network',
dest='network', help='path to network parameters (default %(default)s)',
metavar='VGG_PATH', default=VGG_PATH)
parser.add_argument('--content-weight', type=float,
dest='content_weight', help='content weight (default %(default)s)',
metavar='CONTENT_WEIGHT', default=CONTENT_WEIGHT)
parser.add_argument('--style-weight', type=float,
dest='style_weight', help='style weight (default %(default)s)',
metavar='STYLE_WEIGHT', default=STYLE_WEIGHT)
parser.add_argument('--style-blend-weights', type=float,
dest='style_blend_weights', help='style blending weights',
nargs='+', metavar='STYLE_BLEND_WEIGHT')
parser.add_argument('--tv-weight', type=float,
dest='tv_weight', help='total variation regularization weight (default %(default)s)',
metavar='TV_WEIGHT', default=TV_WEIGHT)
parser.add_argument('--learning-rate', type=float,
dest='learning_rate', help='learning rate (default %(default)s)',
metavar='LEARNING_RATE', default=LEARNING_RATE)
parser.add_argument('--initial',
dest='initial', help='initial image',
metavar='INITIAL')
parser.add_argument('--print-iterations', type=int,
dest='print_iterations', help='statistics printing frequency',
metavar='PRINT_ITERATIONS')
parser.add_argument('--checkpoint-iterations', type=int,
dest='checkpoint_iterations', help='checkpoint frequency',
metavar='CHECKPOINT_ITERATIONS')
return parserdef build_parser_src():
parser = ArgumentParser()
parser.add_argument('--content',
dest='content', help='content image',
metavar='CONTENT', required=True)
parser.add_argument('--styles',
dest='styles',
nargs='+', help='one or more style images',
metavar='STYLE', required=True)
parser.add_argument('--output',
dest='output', help='output path',
metavar='OUTPUT', required=True)
parser.add_argument('--checkpoint-output',
dest='checkpoint_output', help='checkpoint output format',
metavar='OUTPUT')
parser.add_argument('--iterations', type=int,
dest='iterations', help='iterations (default %(default)s)',
metavar='ITERATIONS', default=ITERATIONS)
parser.add_argument('--width', type=int,
dest='width', help='output width',
metavar='WIDTH')
parser.add_argument('--style-scales', type=float,
dest='style_scales',
nargs='+', help='one or more style scales',
metavar='STYLE_SCALE')
parser.add_argument('--network',
dest='network', help='path to network parameters (default %(default)s)',
metavar='VGG_PATH', default=VGG_PATH)
parser.add_argument('--content-weight', type=float,
dest='content_weight', help='content weight (default %(default)s)',
metavar='CONTENT_WEIGHT', default=CONTENT_WEIGHT)
parser.add_argument('--style-weight', type=float,
dest='style_weight', help='style weight (default %(default)s)',
metavar='STYLE_WEIGHT', default=STYLE_WEIGHT)
parser.add_argument('--style-blend-weights', type=float,
dest='style_blend_weights', help='style blending weights',
nargs='+', metavar='STYLE_BLEND_WEIGHT')
parser.add_argument('--tv-weight', type=float,
dest='tv_weight', help='total variation regularization weight (default %(default)s)',
metavar='TV_WEIGHT', default=TV_WEIGHT)
parser.add_argument('--learning-rate', type=float,
dest='learning_rate', help='learning rate (default %(default)s)',
metavar='LEARNING_RATE', default=LEARNING_RATE)
parser.add_argument('--initial',
dest='initial', help='initial image',
metavar='INITIAL')
parser.add_argument('--print-iterations', type=int,
dest='print_iterations', help='statistics printing frequency',
metavar='PRINT_ITERATIONS')
parser.add_argument('--checkpoint-iterations', type=int,
dest='checkpoint_iterations', help='checkpoint frequency',
metavar='CHECKPOINT_ITERATIONS')
return parserdef main():
parser = build_parser()
options = parser.parse_args() if not os.path.isfile(options.network):
parser.error("Network %s does not exist. (Did you forget to download it?)" % options.network) content_image = imread(options.content)
#for style in options.styles:
# print(style)
#style_images = [imread(style) for style in options.styles]
style_images = [imread(options.styles)] width = options.width
if width is not None:
new_shape = (int(math.floor(float(content_image.shape[0]) / content_image.shape[1] * width)), width)
content_image = scipy.misc.imresize(content_image, new_shape)
target_shape = content_image.shape
for i in range(len(style_images)):
style_scale = STYLE_SCALE
if options.style_scales is not None:
style_scale = options.style_scales[i]
style_images[i] = scipy.misc.imresize(style_images[i], style_scale * target_shape[1] / style_images[i].shape[1]) style_blend_weights = options.style_blend_weights
if style_blend_weights is None:
# default is equal weights
style_blend_weights = [1.0/len(style_images) for _ in style_images]
else:
total_blend_weight = sum(style_blend_weights)
style_blend_weights = [weight/total_blend_weight
for weight in style_blend_weights] initial = options.initial
if initial is not None:
initial = scipy.misc.imresize(imread(initial), content_image.shape[:2]) if options.checkpoint_output and "%s" not in options.checkpoint_output:
parser.error("To save intermediate images, the checkpoint output "
"parameter must contain `%s` (e.g. `foo%s.jpg`)") for iteration, image in stylize(
network=options.network,
initial=initial,
content=content_image,
styles=style_images,
iterations=options.iterations,
content_weight=options.content_weight,
style_weight=options.style_weight,
style_blend_weights=style_blend_weights,
tv_weight=options.tv_weight,
learning_rate=options.learning_rate,
print_iterations=options.print_iterations,
checkpoint_iterations=options.checkpoint_iterations
):
output_file = None
if iteration is not None:
if options.checkpoint_output:
output_file = options.checkpoint_output % iteration
else:
output_file = options.output
if output_file:
imsave(output_file, image)def imread(path):
return scipy.misc.imread(path).astype(np.float)def imsave(path, img):
img = np.clip(img, 0, 255).astype(np.uint8)
scipy.misc.imsave(path, img)if __name__ == '__main__':
main()
stylize.py
# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3.import vggimport tensorflow as tf
import numpy as npfrom sys import stderrCONTENT_LAYER = 'relu4_2'
STYLE_LAYERS = ('relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1')try:
reduce
except NameError:
from functools import reducedef stylize(network, initial, content, styles, iterations,
content_weight, style_weight, style_blend_weights, tv_weight,
learning_rate, print_iterations=None, checkpoint_iterations=None):
"""
Stylize images. This function yields tuples (iteration, image); `iteration` is None
if this is the final image (the last iteration). Other tuples are yielded
every `checkpoint_iterations` iterations. :rtype: iterator[tuple[int|None,image]]
"""
shape = (1,) + content.shape
style_shapes = [(1,) + style.shape for style in styles]
content_features = {}
style_features = [{} for _ in styles] # compute content features in feedforward mode
g = tf.Graph()
with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:
image = tf.placeholder('float', shape=shape)
net, mean_pixel = vgg.net(network, image)
content_pre = np.array([vgg.preprocess(content, mean_pixel)])
content_features[CONTENT_LAYER] = net[CONTENT_LAYER].eval(
feed_dict={image: content_pre}) # compute style features in feedforward mode
for i in range(len(styles)):
g = tf.Graph()
with g.as_default(), g.device('/cpu:0'), tf.Session() as sess:
image = tf.placeholder('float', shape=style_shapes[i])
net, _ = vgg.net(network, image)
style_pre = np.array([vgg.preprocess(styles[i], mean_pixel)])
for layer in STYLE_LAYERS:
features = net[layer].eval(feed_dict={image: style_pre})
features = np.reshape(features, (-1, features.shape[3]))
gram = np.matmul(features.T, features) / features.size
style_features[i][layer] = gram # make stylized image using backpropogation
with tf.Graph().as_default():
if initial is None:
noise = np.random.normal(size=shape, scale=np.std(content) * 0.1)
initial = tf.random_normal(shape) * 0.256
else:
initial = np.array([vgg.preprocess(initial, mean_pixel)])
initial = initial.astype('float32')
image = tf.Variable(initial)
net, _ = vgg.net(network, image) # content loss
content_loss = content_weight * (2 * tf.nn.l2_loss(
net[CONTENT_LAYER] - content_features[CONTENT_LAYER]) /
content_features[CONTENT_LAYER].size)
# style loss
style_loss = 0
for i in range(len(styles)):
style_losses = []
for style_layer in STYLE_LAYERS:
layer = net[style_layer]
_, height, width, number = map(lambda i: i.value, layer.get_shape())
size = height * width * number
feats = tf.reshape(layer, (-1, number))
gram = tf.matmul(tf.transpose(feats), feats) / size
style_gram = style_features[i][style_layer]
style_losses.append(2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size)
style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses)
# total variation denoising
tv_y_size = _tensor_size(image[:,1:,:,:])
tv_x_size = _tensor_size(image[:,:,1:,:])
tv_loss = tv_weight * 2 * (
(tf.nn.l2_loss(image[:,1:,:,:] - image[:,:shape[1]-1,:,:]) /
tv_y_size) +
(tf.nn.l2_loss(image[:,:,1:,:] - image[:,:,:shape[2]-1,:]) /
tv_x_size))
# overall loss
loss = content_loss + style_loss + tv_loss # optimizer setup
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) def print_progress(i, last=False):
stderr.write('Iteration %d/%d\n' % (i + 1, iterations))
if last or (print_iterations and i % print_iterations == 0):
stderr.write(' content loss: %g\n' % content_loss.eval())
stderr.write(' style loss: %g\n' % style_loss.eval())
stderr.write(' tv loss: %g\n' % tv_loss.eval())
stderr.write(' total loss: %g\n' % loss.eval()) # optimization
best_loss = float('inf')
best = None
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for i in range(iterations):
last_step = (i == iterations - 1)
print_progress(i, last=last_step)
train_step.run() if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step:
this_loss = loss.eval()
if this_loss < best_loss:
best_loss = this_loss
best = image.eval()
yield (
(None if last_step else i),
vgg.unprocess(best.reshape(shape[1:]), mean_pixel)
)def _tensor_size(tensor):
from operator import mul
return reduce(mul, (d.value for d in tensor.get_shape()), 1)
vgg.py
# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3.import tensorflow as tf
import numpy as np
import scipy.iodef net(data_path, input_image):
layers = (
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
'relu5_3', 'conv5_4', 'relu5_4'
) data = scipy.io.loadmat(data_path)
mean = data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
weights = data['layers'][0] net = {}
current = input_image
for i, name in enumerate(layers):
kind = name[:4]
if kind == 'conv':
kernels, bias = weights[i][0][0][0][0]
# matconvnet: weights are [width, height, in_channels, out_channels]
# tensorflow: weights are [height, width, in_channels, out_channels]
kernels = np.transpose(kernels, (1, 0, 2, 3))
bias = bias.reshape(-1)
current = _conv_layer(current, kernels, bias)
elif kind == 'relu':
current = tf.nn.relu(current)
elif kind == 'pool':
current = _pool_layer(current)
net[name] = current assert len(net) == len(layers)
return net, mean_pixeldef _conv_layer(input, weights, bias):
conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
padding='SAME')
return tf.nn.bias_add(conv, bias)def _pool_layer(input):
return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
padding='SAME')def preprocess(image, mean_pixel):
return image - mean_pixeldef unprocess(image, mean_pixel):
return image + mean_pixel
