深度有趣 | 30 快速影象風格遷移
摘要：
在之前介紹的影象風格遷移中，我們根據內容圖片和風格圖片優化輸入圖片，使得內容損失函式和風格損失函式儘可能小
和DeepDream一樣，屬於網路引數不變，根據損失函式調整輸入資料，因此每生成一張圖片都相當於訓練一個模型，需要很長時間
訓練模型需要很長時間，而使用訓練好的模型進行推斷則很快...
在之前介紹的影象風格遷移中，我們根據內容圖片和風格圖片優化輸入圖片，使得內容損失函式和風格損失函式儘可能小
和DeepDream一樣，屬於網路引數不變，根據損失函式調整輸入資料，因此每生成一張圖片都相當於訓練一個模型，需要很長時間
訓練模型需要很長時間，而使用訓練好的模型進行推斷則很快
使用快速影象風格遷移可大大縮短生成一張遷移圖片所需的時間，其模型結構如下，包括轉換網路和損失網路
風格圖片是固定的，而內容圖片是可變的輸入，因此以上模型用於將任意圖片快速轉換為指定風格的圖片
- 轉換網路：引數需要訓練，將內容圖片轉換成遷移圖片
- 損失網路：計算遷移圖片和風格圖片之間的風格損失，以及遷移圖片和原始內容圖片之間的內容損失
經過訓練後，轉換網路所生成的遷移圖片，在內容上和輸入的內容圖片相似，在風格上和指定的風格圖片相似
進行推斷時，僅使用轉換網路，輸入內容圖片，即可得到對應的遷移圖片
如果有多個風格圖片，對每個風格分別訓練一個模型即可
實現
基於以下兩個專案進行修改， ofollow,noindex">github.com/lengstrom/f… 、 github.com/hzy46/fast-…
依然通過之前用過的 imagenet-vgg-verydeep-19.mat 計算內容損失函式和風格損失函式
需要一些圖片作為輸入的內容圖片，對圖片具體內容沒有任何要求，也不需要任何標註，這裡選擇使用MSCOCO資料集的train2014部分， cocodataset.org/#download ，共82612張圖片
載入庫
# -*- coding: utf-8 -*- import tensorflow as tf import numpy as np import cv2 from imageio import imread, imsave import scipy.io import os import glob from tqdm import tqdm import matplotlib.pyplot as plt %matplotlib inline 複製程式碼
檢視風格圖片，共10張
style_images = glob.glob('styles/*.jpg')
print(style_images)
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載入內容圖片，去掉黑白圖片，處理成指定大小，暫時不進行歸一化，畫素值範圍為0至255之間
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 image
X_data = []
image_size = 256
paths = glob.glob('train2014/*.jpg')
for i in tqdm(range(len(paths))):
path = paths[i]
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)
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載入vgg19模型，並定義一個函式，對於給定的輸入，返回vgg19各個層的輸出值，就像在GAN中那樣，通過 variable_scope 重用實現網路的重用
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
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選擇一張風格圖，減去通道顏色均值後，得到風格圖片在vgg19各個層的輸出值，計算四個風格層對應的Gram矩陣
style_index = 1
X_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
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定義轉換網路，典型的卷積、殘差、逆卷積結構，內容圖片輸入之前也需要減去通道顏色均值
batch_size = 4 X = 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') 複製程式碼
將轉換網路的輸出即遷移圖片，以及原始內容圖片都輸入到vgg19，得到各自對應層的輸出，計算內容損失函式
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) 複製程式碼
根據遷移圖片和風格圖片在指定風格層的輸出，計算風格損失函式
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) 複製程式碼
計算全變差正則，得到總的損失函式
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 = 1 style_weight = 250 total_variation_weight = 0.01 loss = content_weight * content_loss + style_weight * style_loss + total_variation_weight * total_variation_loss 複製程式碼
定義優化器，通過調整轉換網路中的引數降低總損失
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)
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訓練模型，每輪訓練結束後，用一張測試圖片進行測試，並且將一些tensor的值寫入events檔案，便於使用tensorboard檢視
style_name = style_images[style_index]
style_name = style_name[style_name.find('/') + 1:].rstrip('.jpg')
OUTPUT_DIR = 'samples_%s' % style_name
if 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 = 2
X_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)
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儲存模型
saver = tf.train.Saver() saver.save(sess, os.path.join(OUTPUT_DIR, 'fast_style_transfer')) 複製程式碼
測試圖片依舊是之前用過的交大廟門
風格遷移結果
訓練過程中可以使用tensorboard檢視訓練過程
tensorboard --logdir=samples_starry 複製程式碼
在單機上使用以下程式碼即可快速完成風格遷移，在CPU上也只需要10秒左右
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
from imageio import imread, imsave
import os
import time
def the_current_time():
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(time.time()))))
style = 'wave'
model = 'samples_%s' % style
content_image = 'sjtu.jpg'
result_image = 'sjtu_%s.jpg' % style
X_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()
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對於其他風格圖片，用相同方法訓練對應模型即可
