简介使用TensorFlow实现快速图像风格迁移（Fast Neural Style Transfer）
原理在之前介绍的图像风格迁移中，我们根据内容图片和风格图片优化输入图片，使得内容损失函数和风格损失函数尽可能小
和DeepDream一样，属于网络参数不变，根据损失函数调整输入数据，因此每生成一张图片都相当于训练一个模型，需要很长时间
训练模型需要很长时间，而使用训练好的模型进行推断则很快
使用快速图像风格迁移可大大缩短生成一张迁移图片所需的时间，其模型结构如下，包括转换网络和损失网络
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风格图片是固定的，而内容图片是可变的输入，因此以上模型用于将任意图片快速转换为指定风格的图片
- 转换网络：参数需要训练，将内容图片转换成迁移图片
- 损失网络：计算迁移图片和风格图片之间的风格损失，以及迁移图片和原始内容图片之间的内容损失
经过训练后，转换网络所生成的迁移图片，在内容上和输入的内容图片相似，在风格上和指定的风格图片相似
进行推断时，仅使用转换网络，输入内容图片，即可得到对应的迁移图片
如果有多个风格图片，对每个风格分别训练一个模型即可
实现基于以下两个项目进行修改，github.com/lengstrom/f…、github.com/hzy46/fast-…
依然通过之前用过的imagenet-vgg-verydeep-19.mat计算内容损失函数和风格损失函数
需要一些图片作为输入的内容图片，对图片具体内容没有任何要求，也不需要任何标注，这里选择使用MSCOCO数据集的train2014部分，cocodataset.org/#download，共82612张图片
加载库
# -*- coding: utf-8 -*-import tensorflow as tfimport numpy as npimport cv2from imageio import imread, imsaveimport scipy.ioimport osimport globfrom tqdm import tqdmimport matplotlib.pyplot as plt%matplotlib inline复制代码查看风格图片，共10张
style_images = glob.glob('styles/*.jpg')print(style_images)复制代码加载内容图片，去掉黑白图片，处理成指定大小，暂时不进行归一化，像素值范围为0至255之间
def resize_and_crop(image, image_size): h = image.shape[0] w = image.shape[1] if h > w: image = image[h // 2 - w // 2: h // 2 + w // 2, :, :] else: image = image[:, w // 2 - h // 2: w // 2 + h // 2, :] image = cv2.resize(image, (image_size, image_size)) return imageX_data = []image_size = 256paths = glob.glob('train2014/*.jpg')for i in tqdm(range(len(paths))): path = paths image = imread(path) if len(image.shape) < 3: continue X_data.append(resize_and_crop(image, image_size))X_data = np.array(X_data)print(X_data.shape)复制代码加载vgg19模型，并定义一个函数，对于给定的输入，返回vgg19各个层的输出值，就像在GAN中那样，通过variable_scope重用实现网络的重用
vgg = scipy.io.loadmat('imagenet-vgg-verydeep-19.mat')vgg_layers = vgg['layers']def vgg_endpoints(inputs, reuse=None): with tf.variable_scope('endpoints', reuse=reuse): def _weights(layer, expected_layer_name): W = vgg_layers[0][layer][0][0][2][0][0] b = vgg_layers[0][layer][0][0][2][0][1] layer_name = vgg_layers[0][layer][0][0][0][0] assert layer_name == expected_layer_name return W, b def _conv2d_relu(prev_layer, layer, layer_name): W, b = _weights(layer, layer_name) W = tf.constant(W) b = tf.constant(np.reshape(b, (b.size))) return tf.nn.relu(tf.nn.conv2d(prev_layer, filter=W, strides=[1, 1, 1, 1], padding='SAME') + b) def _avgpool(prev_layer): return tf.nn.avg_pool(prev_layer, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') graph = {} graph['conv1_1'] = _conv2d_relu(inputs, 0, 'conv1_1') graph['conv1_2'] = _conv2d_relu(graph['conv1_1'], 2, 'conv1_2') graph['avgpool1'] = _avgpool(graph['conv1_2']) graph['conv2_1'] = _conv2d_relu(graph['avgpool1'], 5, 'conv2_1') graph['conv2_2'] = _conv2d_relu(graph['conv2_1'], 7, 'conv2_2') graph['avgpool2'] = _avgpool(graph['conv2_2']) graph['conv3_1'] = _conv2d_relu(graph['avgpool2'], 10, 'conv3_1') graph['conv3_2'] = _conv2d_relu(graph['conv3_1'], 12, 'conv3_2') graph['conv3_3'] = _conv2d_relu(graph['conv3_2'], 14, 'conv3_3') graph['conv3_4'] = _conv2d_relu(graph['conv3_3'], 16, 'conv3_4') graph['avgpool3'] = _avgpool(graph['conv3_4']) graph['conv4_1'] = _conv2d_relu(graph['avgpool3'], 19, 'conv4_1') graph['conv4_2'] = _conv2d_relu(graph['conv4_1'], 21, 'conv4_2') graph['conv4_3'] = _conv2d_relu(graph['conv4_2'], 23, 'conv4_3') graph['conv4_4'] = _conv2d_relu(graph['conv4_3'], 25, 'conv4_4') graph['avgpool4'] = _avgpool(graph['conv4_4']) graph['conv5_1'] = _conv2d_relu(graph['avgpool4'], 28, 'conv5_1') graph['conv5_2'] = _conv2d_relu(graph['conv5_1'], 30, 'conv5_2') graph['conv5_3'] = _conv2d_relu(graph['conv5_2'], 32, 'conv5_3') graph['conv5_4'] = _conv2d_relu(graph['conv5_3'], 34, 'conv5_4') graph['avgpool5'] = _avgpool(graph['conv5_4']) return graph复制代码选择一张风格图，减去通道颜色均值后，得到风格图片在vgg19各个层的输出值，计算四个风格层对应的Gram矩阵
style_index = 1X_style_data = resize_and_crop(imread(style_images[style_index]), image_size)X_style_data = np.expand_dims(X_style_data, 0)print(X_style_data.shape)MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))X_style = tf.placeholder(dtype=tf.float32, shape=X_style_data.shape, name='X_style')style_endpoints = vgg_endpoints(X_style - MEAN_VALUES)STYLE_LAYERS = ['conv1_2', 'conv2_2', 'conv3_3', 'conv4_3']style_features = {}sess = tf.Session()for layer_name in STYLE_LAYERS: features = sess.run(style_endpoints[layer_name], feed_dict={X_style: X_style_data}) features = np.reshape(features, (-1, features.shape[3])) gram = np.matmul(features.T, features) / features.size style_features[layer_name] = gram复制代码定义转换网络，典型的卷积、残差、逆卷积结构，内容图片输入之前也需要减去通道颜色均值
batch_size = 4X = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3], name='X')k_initializer = tf.truncated_normal_initializer(0, 0.1)def relu(x): return tf.nn.relu(x)def conv2d(inputs, filters, kernel_size, strides): p = int(kernel_size / 2) h0 = tf.pad(inputs, [[0, 0], [p, p], [p, p], [0, 0]], mode='reflect') return tf.layers.conv2d(inputs=h0, filters=filters, kernel_size=kernel_size, strides=strides, padding='valid', kernel_initializer=k_initializer)def deconv2d(inputs, filters, kernel_size, strides): shape = tf.shape(inputs) height, width = shape[1], shape[2] h0 = tf.image.resize_images(inputs, [height * strides * 2, width * strides * 2], tf.image.ResizeMethod.NEAREST_NEIGHBOR) return conv2d(h0, filters, kernel_size, strides) def instance_norm(inputs): return tf.contrib.layers.instance_norm(inputs)def residual(inputs, filters, kernel_size): h0 = relu(conv2d(inputs, filters, kernel_size, 1)) h0 = conv2d(h0, filters, kernel_size, 1) return tf.add(inputs, h0)with tf.variable_scope('transformer', reuse=None): h0 = tf.pad(X - MEAN_VALUES, [[0, 0], [10, 10], [10, 10], [0, 0]], mode='reflect') h0 = relu(instance_norm(conv2d(h0, 32, 9, 1))) h0 = relu(instance_norm(conv2d(h0, 64, 3, 2))) h0 = relu(instance_norm(conv2d(h0, 128, 3, 2))) for i in range(5): h0 = residual(h0, 128, 3) h0 = relu(instance_norm(deconv2d(h0, 64, 3, 2))) h0 = relu(instance_norm(deconv2d(h0, 32, 3, 2))) h0 = tf.nn.tanh(instance_norm(conv2d(h0, 3, 9, 1))) h0 = (h0 + 1) / 2 * 255. shape = tf.shape(h0) g = tf.slice(h0, [0, 10, 10, 0], [-1, shape[1] - 20, shape[2] - 20, -1], name='g')复制代码将转换网络的输出即迁移图片，以及原始内容图片都输入到vgg19，得到各自对应层的输出，计算内容损失函数
CONTENT_LAYER = 'conv3_3'content_endpoints = vgg_endpoints(X - MEAN_VALUES, True)g_endpoints = vgg_endpoints(g - MEAN_VALUES, True)def get_content_loss(endpoints_x, endpoints_y, layer_name): x = endpoints_x[layer_name] y = endpoints_y[layer_name] return 2 * tf.nn.l2_loss(x - y) / tf.to_float(tf.size(x))content_loss = get_content_loss(content_endpoints, g_endpoints, CONTENT_LAYER)复制代码根据迁移图片和风格图片在指定风格层的输出，计算风格损失函数
style_loss = []for layer_name in STYLE_LAYERS: layer = g_endpoints[layer_name] shape = tf.shape(layer) bs, height, width, channel = shape[0], shape[1], shape[2], shape[3] features = tf.reshape(layer, (bs, height * width, channel)) gram = tf.matmul(tf.transpose(features, (0, 2, 1)), features) / tf.to_float(height * width * channel) style_gram = style_features[layer_name] style_loss.append(2 * tf.nn.l2_loss(gram - style_gram) / tf.to_float(tf.size(layer)))style_loss = tf.reduce_sum(style_loss)复制代码计算全变差正则，得到总的损失函数
def get_total_variation_loss(inputs): h = inputs[:, :-1, :, :] - inputs[:, 1:, :, :] w = inputs[:, :, :-1, :] - inputs[:, :, 1:, :] return tf.nn.l2_loss(h) / tf.to_float(tf.size(h)) + tf.nn.l2_loss(w) / tf.to_float(tf.size(w)) total_variation_loss = get_total_variation_loss(g)content_weight = 1style_weight = 250total_variation_weight = 0.01loss = content_weight * content_loss + style_weight * style_loss + total_variation_weight * total_variation_loss复制代码定义优化器，通过调整转换网络中的参数降低总损失
vars_t = [var for var in tf.trainable_variables() if var.name.startswith('transformer')]optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss, var_list=vars_t)复制代码训练模型，每轮训练结束后，用一张测试图片进行测试，并且将一些tensor的值写入events文件，便于使用tensorboard查看
style_name = style_images[style_index]style_name = style_name[style_name.find('/') + 1:].rstrip('.jpg')OUTPUT_DIR = 'samples_%s' % style_nameif not os.path.exists(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)tf.summary.scalar('losses/content_loss', content_loss)tf.summary.scalar('losses/style_loss', style_loss)tf.summary.scalar('losses/total_variation_loss', total_variation_loss)tf.summary.scalar('losses/loss', loss)tf.summary.scalar('weighted_losses/weighted_content_loss', content_weight * content_loss)tf.summary.scalar('weighted_losses/weighted_style_loss', style_weight * style_loss)tf.summary.scalar('weighted_losses/weighted_total_variation_loss', total_variation_weight * total_variation_loss)tf.summary.image('transformed', g)tf.summary.image('origin', X)summary = tf.summary.merge_all()writer = tf.summary.FileWriter(OUTPUT_DIR)sess.run(tf.global_variables_initializer())losses = []epochs = 2X_sample = imread('sjtu.jpg')h_sample = X_sample.shape[0]w_sample = X_sample.shape[1]for e in range(epochs): data_index = np.arange(X_data.shape[0]) np.random.shuffle(data_index) X_data = X_data[data_index] for i in tqdm(range(X_data.shape[0] // batch_size)): X_batch = X_data[i * batch_size: i * batch_size + batch_size] ls_, _ = sess.run([loss, optimizer], feed_dict={X: X_batch}) losses.append(ls_) if i > 0 and i % 20 == 0: writer.add_summary(sess.run(summary, feed_dict={X: X_batch}), e * X_data.shape[0] // batch_size + i) writer.flush() print('Epoch %d Loss %f' % (e, np.mean(losses))) losses = [] gen_img = sess.run(g, feed_dict={X: [X_sample]})[0] gen_img = np.clip(gen_img, 0, 255) result = np.zeros((h_sample, w_sample * 2, 3)) result[:, :w_sample, :] = X_sample / 255. result[:, w_sample:, :] = gen_img[:h_sample, :w_sample, :] / 255. plt.axis('off') plt.imshow(result) plt.show() imsave(os.path.join(OUTPUT_DIR, 'sample_%d.jpg' % e), result)复制代码保存模型
saver = tf.train.Saver()saver.save(sess, os.path.join(OUTPUT_DIR, 'fast_style_transfer'))复制代码测试图片依旧是之前用过的交大庙门
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风格迁移结果
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训练过程中可以使用tensorboard查看训练过程
tensorboard --logdir=samples_starry复制代码
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在单机上使用以下代码即可快速完成风格迁移，在CPU上也只需要10秒左右
# -*- coding: utf-8 -*-import tensorflow as tfimport numpy as npfrom imageio import imread, imsaveimport osimport timedef the_current_time(): print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(time.time()))))style = 'wave'model = 'samples_%s' % stylecontent_image = 'sjtu.jpg'result_image = 'sjtu_%s.jpg' % styleX_image = imread(content_image)sess = tf.Session()sess.run(tf.global_variables_initializer())saver = tf.train.import_meta_graph(os.path.join(model, 'fast_style_transfer.meta'))saver.restore(sess, tf.train.latest_checkpoint(model))graph = tf.get_default_graph()X = graph.get_tensor_by_name('X:0')g = graph.get_tensor_by_name('transformer/g:0')the_current_time()gen_img = sess.run(g, feed_dict={X: [X_image]})[0]gen_img = np.clip(gen_img, 0, 255) / 255.imsave(result_image, gen_img)the_current_time()复制代码对于其他风格图片，用相同方法训练对应模型即可
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参考
链接：https://juejin.im/post/5ba4e029e51d450e9874eaf3
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