TensorFlow基础笔记(6) 图像风格化实验

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:

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 os

import numpy as np
import scipy.misc

from stylize import stylize

import 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 parser


def 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 parser


def 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 vgg

import tensorflow as tf
import numpy as np

from sys import stderr

CONTENT_LAYER = 'relu4_2'
STYLE_LAYERS = ('relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1')


try:
    reduce
except NameError:
    from functools import reduce


def 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
' % (i + 1, iterations))
            if last or (print_iterations and i % print_iterations == 0):
                stderr.write('  content loss: %g
' % content_loss.eval())
                stderr.write('    style loss: %g
' % style_loss.eval())
                stderr.write('       tv loss: %g
' % tv_loss.eval())
                stderr.write('    total loss: %g
' % 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.io


def 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_pixel


def _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_pixel


def unprocess(image, mean_pixel):
    return image + mean_pixel
【推广】 免费学中医，健康全家人
原文地址：https://www.cnblogs.com/adong7639/p/7652862.html