深度有趣 | 08 DCGAN人臉圖片生成

摘要： 使用兩個資料集 LFW：vis-www.cs.umass.edu/lfw/ ，Labeled Faces in the Wild，超過1.3W張圖片，其中1680人擁有超過兩張或以上圖片 CelebA：mmlab.ie.cuhk.edu...

使用兩個資料集

• LFW：ofollow,noindex">vis-www.cs.umass.edu/lfw/ ，Labeled Faces in the Wild，超過1.3W張圖片，其中1680人擁有超過兩張或以上圖片
• CelebA：mmlab.ie.cuhk.edu.hk/projects/Ce… ，CelebFaces Attributes Dataset，包括10177人共計超過20W張圖片，並且每張圖片還包括人臉的5個關鍵點位置和40個屬性的01標註，例如是否有眼鏡、帽子、鬍子等

實現

和上節課的程式碼差不多，根據彩色圖片進行適當調整即可

載入庫

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import urllib
import tarfile
import os
import matplotlib.pyplot as plt
%matplotlib inline
from imageio import imread, imsave, mimsave
from scipy.misc import imresize
import glob
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下載LFW資料並解壓處理，CelebA資料已經準備好

url = 'http://vis-www.cs.umass.edu/lfw/lfw.tgz'
filename = 'lfw.tgz'
directory = 'lfw_imgs'
new_dir = 'lfw_new_imgs'

if not os.path.isdir(new_dir):
os.mkdir(new_dir)

if not os.path.isdir(directory):
if not os.path.isfile(filename):
urllib.request.urlretrieve(url, filename)
tar = tarfile.open(filename, 'r:gz')
tar.extractall(path=directory)
tar.close()

count = 0
for dir_, _, files in os.walk(directory):
for file_ in files:
img = imread(os.path.join(dir_, file_))
imsave(os.path.join(new_dir, '%d.png' % count), img)
count += 1
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設定用於生成人臉的資料集

# dataset = 'lfw_new_imgs' # LFW
dataset = 'celeba' # CelebA
images = glob.glob(os.path.join(dataset, '*.*')) 
print(len(images))
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定義一些常量、網路輸入、輔助函式

batch_size = 100
z_dim = 100
WIDTH = 64
HEIGHT = 64

OUTPUT_DIR = 'samples_' + dataset
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)

X = tf.placeholder(dtype=tf.float32, shape=[None, HEIGHT, WIDTH, 3], name='X')
noise = tf.placeholder(dtype=tf.float32, shape=[None, z_dim], name='noise')
is_training = tf.placeholder(dtype=tf.bool, name='is_training')

def lrelu(x, leak=0.2):
return tf.maximum(x, leak * x)

def sigmoid_cross_entropy_with_logits(x, y):
return tf.nn.sigmoid_cross_entropy_with_logits(logits=x, labels=y)
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判別器部分

def discriminator(image, reuse=None, is_training=is_training):
momentum = 0.9
with tf.variable_scope('discriminator', reuse=reuse):
h0 = lrelu(tf.layers.conv2d(image, kernel_size=5, filters=64, strides=2, padding='same'))

h1 = tf.layers.conv2d(h0, kernel_size=5, filters=128, strides=2, padding='same')
h1 = lrelu(tf.contrib.layers.batch_norm(h1, is_training=is_training, decay=momentum))

h2 = tf.layers.conv2d(h1, kernel_size=5, filters=256, strides=2, padding='same')
h2 = lrelu(tf.contrib.layers.batch_norm(h2, is_training=is_training, decay=momentum))

h3 = tf.layers.conv2d(h2, kernel_size=5, filters=512, strides=2, padding='same')
h3 = lrelu(tf.contrib.layers.batch_norm(h3, is_training=is_training, decay=momentum))

h4 = tf.contrib.layers.flatten(h3)
h4 = tf.layers.dense(h4, units=1)
return tf.nn.sigmoid(h4), h4
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生成器部分

def generator(z, is_training=is_training):
momentum = 0.9
with tf.variable_scope('generator', reuse=None):
d = 4
h0 = tf.layers.dense(z, units=d * d * 512)
h0 = tf.reshape(h0, shape=[-1, d, d, 512])
h0 = tf.nn.relu(tf.contrib.layers.batch_norm(h0, is_training=is_training, decay=momentum))

h1 = tf.layers.conv2d_transpose(h0, kernel_size=5, filters=256, strides=2, padding='same')
h1 = tf.nn.relu(tf.contrib.layers.batch_norm(h1, is_training=is_training, decay=momentum))

h2 = tf.layers.conv2d_transpose(h1, kernel_size=5, filters=128, strides=2, padding='same')
h2 = tf.nn.relu(tf.contrib.layers.batch_norm(h2, is_training=is_training, decay=momentum))

h3 = tf.layers.conv2d_transpose(h2, kernel_size=5, filters=64, strides=2, padding='same')
h3 = tf.nn.relu(tf.contrib.layers.batch_norm(h3, is_training=is_training, decay=momentum))

h4 = tf.layers.conv2d_transpose(h3, kernel_size=5, filters=3, strides=2, padding='same', activation=tf.nn.tanh, name='g')
return h4
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損失函式

g = generator(noise)
d_real, d_real_logits = discriminator(X)
d_fake, d_fake_logits = discriminator(g, reuse=True)

vars_g = [var for var in tf.trainable_variables() if var.name.startswith('generator')]
vars_d = [var for var in tf.trainable_variables() if var.name.startswith('discriminator')]

loss_d_real = tf.reduce_mean(sigmoid_cross_entropy_with_logits(d_real_logits, tf.ones_like(d_real)))
loss_d_fake = tf.reduce_mean(sigmoid_cross_entropy_with_logits(d_fake_logits, tf.zeros_like(d_fake)))
loss_g = tf.reduce_mean(sigmoid_cross_entropy_with_logits(d_fake_logits, tf.ones_like(d_fake)))
loss_d = loss_d_real + loss_d_fake
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優化函式

update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer_d = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(loss_d, var_list=vars_d)
optimizer_g = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(loss_g, var_list=vars_g)
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讀取圖片的函式

def read_image(path, height, width):
image = imread(path)
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 = imresize(image, (height, width))
return image / 255.
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合成圖片的函式

def montage(images):
if isinstance(images, list):
images = np.array(images)
img_h = images.shape[1]
img_w = images.shape[2]
n_plots = int(np.ceil(np.sqrt(images.shape[0])))
if len(images.shape) == 4 and images.shape[3] == 3:
m = np.ones(
(images.shape[1] * n_plots + n_plots + 1,
images.shape[2] * n_plots + n_plots + 1, 3)) * 0.5
elif len(images.shape) == 4 and images.shape[3] == 1:
m = np.ones(
(images.shape[1] * n_plots + n_plots + 1,
images.shape[2] * n_plots + n_plots + 1, 1)) * 0.5
elif len(images.shape) == 3:
m = np.ones(
(images.shape[1] * n_plots + n_plots + 1,
images.shape[2] * n_plots + n_plots + 1)) * 0.5
else:
raise ValueError('Could not parse image shape of {}'.format(images.shape))
for i in range(n_plots):
for j in range(n_plots):
this_filter = i * n_plots + j
if this_filter < images.shape[0]:
this_img = images[this_filter]
m[1 + i + i * img_h:1 + i + (i + 1) * img_h,
1 + j + j * img_w:1 + j + (j + 1) * img_w] = this_img
return m
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模型的訓練

sess = tf.Session()
sess.run(tf.global_variables_initializer())
z_samples = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32)
samples = []
loss = {'d': [], 'g': []}

offset = 0
for i in range(60000):
n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32)

offset = (offset + batch_size) % len(images)
batch = np.array([read_image(img, HEIGHT, WIDTH) for img in images[offset: offset + batch_size]])
batch = (batch - 0.5) * 2

d_ls, g_ls = sess.run([loss_d, loss_g], feed_dict={X: batch, noise: n, is_training: True})
loss['d'].append(d_ls)
loss['g'].append(g_ls)

sess.run(optimizer_d, feed_dict={X: batch, noise: n, is_training: True})
sess.run(optimizer_g, feed_dict={X: batch, noise: n, is_training: True})
sess.run(optimizer_g, feed_dict={X: batch, noise: n, is_training: True})

if i % 500 == 0:
print(i, d_ls, g_ls)
gen_imgs = sess.run(g, feed_dict={noise: z_samples, is_training: False})
gen_imgs = (gen_imgs + 1) / 2
imgs = [img[:, :, :] for img in gen_imgs]
gen_imgs = montage(imgs)
plt.axis('off')
plt.imshow(gen_imgs)
imsave(os.path.join(OUTPUT_DIR, 'sample_%d.jpg' % i), gen_imgs)
plt.show()
samples.append(gen_imgs)

plt.plot(loss['d'], label='Discriminator')
plt.plot(loss['g'], label='Generator')
plt.legend(loc='upper right')
plt.savefig(os.path.join(OUTPUT_DIR, 'Loss.png'))
plt.show()
mimsave(os.path.join(OUTPUT_DIR, 'samples.gif'), samples, fps=10)
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LFW人臉生成結果如下

CelebA人臉生成結果如下

儲存模型，便於後續使用

saver = tf.train.Saver()
saver.save(sess, os.path.join(OUTPUT_DIR, 'dcgan_' + dataset), global_step=60000)
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在單機上使用模型生成人臉圖片

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os

batch_size = 100
z_dim = 100
dataset = 'lfw_new_imgs'
# dataset = 'celeba'

def montage(images):
if isinstance(images, list):
images = np.array(images)
img_h = images.shape[1]
img_w = images.shape[2]
n_plots = int(np.ceil(np.sqrt(images.shape[0])))
if len(images.shape) == 4 and images.shape[3] == 3:
m = np.ones(
(images.shape[1] * n_plots + n_plots + 1,
images.shape[2] * n_plots + n_plots + 1, 3)) * 0.5
elif len(images.shape) == 4 and images.shape[3] == 1:
m = np.ones(
(images.shape[1] * n_plots + n_plots + 1,
images.shape[2] * n_plots + n_plots + 1, 1)) * 0.5
elif len(images.shape) == 3:
m = np.ones(
(images.shape[1] * n_plots + n_plots + 1,
images.shape[2] * n_plots + n_plots + 1)) * 0.5
else:
raise ValueError('Could not parse image shape of {}'.format(images.shape))
for i in range(n_plots):
for j in range(n_plots):
this_filter = i * n_plots + j
if this_filter < images.shape[0]:
this_img = images[this_filter]
m[1 + i + i * img_h:1 + i + (i + 1) * img_h,
1 + j + j * img_w:1 + j + (j + 1) * img_w] = this_img
return m

sess = tf.Session()
sess.run(tf.global_variables_initializer())

saver = tf.train.import_meta_graph(os.path.join('samples_' + dataset, 'dcgan_' + dataset + '-60000.meta'))
saver.restore(sess, tf.train.latest_checkpoint('samples_' + dataset))
graph = tf.get_default_graph()
g = graph.get_tensor_by_name('generator/g/Tanh:0')
noise = graph.get_tensor_by_name('noise:0')
is_training = graph.get_tensor_by_name('is_training:0')

n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32)
gen_imgs = sess.run(g, feed_dict={noise: n, is_training: False})
gen_imgs = (gen_imgs + 1) / 2
imgs = [img[:, :, :] for img in gen_imgs]
gen_imgs = montage(imgs)
gen_imgs = np.clip(gen_imgs, 0, 1)
plt.figure(figsize=(8, 8))
plt.axis('off')
plt.imshow(gen_imgs)
plt.show()
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