ACGAN

再通过一张图了解ACGAN（Auxiliary Classifier GAN）的原理

和CGAN不同的是， C 不直接输入 D 。 D 不仅需要判断每个样本的真假，还需要完成一个分类任务即预测 C ，通过增加一个辅助分类器实现

对 D 而言，损失函数如下

L_{adv}(D)=-mathbb{E}_{xsim p_{data}}[log D(x)]-mathbb{E}_{zsim p_z,csim p_{c}}[log(1-D(G(z,c)))]

L_{cls}(D)=mathbb{E}_{xsim p_{data}}[L_D(c_x|x)]

对 G 而言，损失函数如下

L_{adv}(G)=mathbb{E}_{zsim p_z,csim p_{c}}[log(1-D(G(z,c)))]

L_{cls}(G)=mathbb{E}_{zsim p_z,csim p_{c}}[L_D(c|G(z,c))]

还是以CelebA的Male作为条件，在WGAN的基础上实现ACGAN

载入库

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

import tensorflow as tf
import numpy as np
import os
import matplotlib.pyplot as plt
%matplotlib inline
from imageio import imread, imsave, mimsave
import cv2
import glob
from tqdm import tqdm

载入图片

images = glob.glob(celeba/*.jpg)
print(len(images))

读取图片的Male标签

tags = {}
target = Male
with open(list_attr_celeba.txt, r) as fr:
lines = fr.readlines()
all_tags = lines[0].strip(
).split()
for i in range(1, len(lines)):
line = lines[i].strip(
).split()
if int(line[all_tags.index(target) + 1]) == 1:
tags[line[0]] = [1, 0] # 男
else:
tags[line[0]] = [0, 1] # 女
print(len(tags))
print(all_tags)

定义一些常量、网路输入、辅助函数

batch_size = 100
z_dim = 100
WIDTH = 64
HEIGHT = 64
LABEL = 2
LAMBDA = 10
DIS_ITERS = 3 # 5

OUTPUT_DIR = samples
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)

X = tf.placeholder(dtype=tf.float32, shape=[batch_size, HEIGHT, WIDTH, 3], name=X)
Y = tf.placeholder(dtype=tf.float32, shape=[batch_size, LABEL], name=Y)
noise = tf.placeholder(dtype=tf.float32, shape=[batch_size, 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)

判别器部分，去掉条件输入，增加分类输出

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 = lrelu(tf.layers.conv2d(h0, kernel_size=5, filters=128, strides=2, padding=same))

h2 = lrelu(tf.layers.conv2d(h1, kernel_size=5, filters=256, strides=2, padding=same))

h3 = lrelu(tf.layers.conv2d(h2, kernel_size=5, filters=512, strides=2, padding=same))

h4 = tf.contrib.layers.flatten(h3)
Y_ = tf.layers.dense(h4, units=LABEL)
h4 = tf.layers.dense(h4, units=1)
return h4, Y_

生成器部分，没有任何改动

def generator(z, label, is_training=is_training):
momentum = 0.9
with tf.variable_scope(generator, reuse=None):
d = 4
z = tf.concat([z, label], axis=1)
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

定义损失函数，加上分类部分对应的损失。理论上分类问题应该用交叉熵作为损失函数，这里使用MSE效果也不错

g = generator(noise, Y)
d_real, y_real = discriminator(X)
d_fake, y_fake = discriminator(g, reuse=True)

loss_d_real = -tf.reduce_mean(d_real)
loss_d_fake = tf.reduce_mean(d_fake)

loss_cls_real = tf.losses.mean_squared_error(Y, y_real)
loss_cls_fake = tf.losses.mean_squared_error(Y, y_fake)

loss_d = loss_d_real + loss_d_fake + loss_cls_real
loss_g = -tf.reduce_mean(d_fake) + loss_cls_fake

alpha = tf.random_uniform(shape=[batch_size, 1, 1, 1], minval=0., maxval=1.)
interpolates = alpha * X + (1 - alpha) * g
grad = tf.gradients(discriminator(interpolates, reuse=True), [interpolates])[0]
slop = tf.sqrt(tf.reduce_sum(tf.square(grad), axis=[1]))
gp = tf.reduce_mean((slop - 1.) ** 2)
loss_d += LAMBDA * gp

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)]

定义优化器

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)

拼接图片的函数

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

整理数据

X_all = []
Y_all = []
for i in tqdm(range(len(images))):
image = imread(images[i])
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, (WIDTH, HEIGHT))
image = (image / 255. - 0.5) * 2
X_all.append(image)

image_name = images[i][images[i].find(/) + 1:]
Y_all.append(tags[image_name])

X_all = np.array(X_all)
Y_all = np.array(Y_all)
print(X_all.shape, Y_all.shape)

查看数据样例

for i in range(10):
plt.imshow((X_all[i, :, :, :] + 1) / 2)
plt.show()
print(Y_all[i, :])

定义随机产生批数据的函数

def get_random_batch():
data_index = np.arange(X_all.shape[0])
np.random.shuffle(data_index)
data_index = data_index[:batch_size]
X_batch = X_all[data_index, :, :, :]
Y_batch = Y_all[data_index, :]

return X_batch, Y_batch

训练模型，根据ACGAN作相应调整

sess = tf.Session()
sess.run(tf.global_variables_initializer())
zs = np.random.uniform(-1.0, 1.0, [batch_size // 2, z_dim]).astype(np.float32)
z_samples = []
y_samples = []
for i in range(batch_size // 2):
z_samples.append(zs[i, :])
y_samples.append([1, 0])
z_samples.append(zs[i, :])
y_samples.append([0, 1])
samples = []
loss = {d: [], g: []}

for i in tqdm(range(60000)):
for j in range(DIS_ITERS):
n = np.random.uniform(-1.0, 1.0, [batch_size, z_dim]).astype(np.float32)
X_batch, Y_batch = get_random_batch()
_, d_ls = sess.run([optimizer_d, loss_d], feed_dict={X: X_batch, Y: Y_batch, noise: n, is_training: True})

_, g_ls = sess.run([optimizer_g, loss_g], feed_dict={X: X_batch, Y: Y_batch, noise: n, is_training: True})

loss[d].append(d_ls)
loss[g].append(g_ls)

if i % 500 == 0:
print(i, d_ls, g_ls)
gen_imgs = sess.run(g, feed_dict={noise: z_samples, Y: y_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(Loss.png)
plt.show()
mimsave(os.path.join(OUTPUT_DIR, samples.gif), samples, fps=10)

结果如下，比CGAN的结果好一些，崩掉的情况比较少，而且人脸更真实更清晰

保存模型

saver = tf.train.Saver()
saver.save(sess, ./celeba_acgan, global_step=60000)

在单机上载入模型，进行以下两个尝试：

  • 固定噪音，渐变条件;
  • 固定条件，渐变噪音。

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

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

batch_size = 100
z_dim = 100
LABEL = 2

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(./celeba_acgan-60000.meta)
saver.restore(sess, tf.train.latest_checkpoint(./))

graph = tf.get_default_graph()
g = graph.get_tensor_by_name(generator/g/Tanh:0)
noise = graph.get_tensor_by_name(noise:0)
Y = graph.get_tensor_by_name(Y:0)
is_training = graph.get_tensor_by_name(is_training:0)

# 固定噪音，渐变条件
n = np.random.uniform(-1.0, 1.0, [10, z_dim]).astype(np.float32)
ns = []
y_samples = []
for i in range(100):
ns.append(n[i // 10, :])
y_samples.append([i % 10 / 9, 1 - i % 10 / 9])
gen_imgs = sess.run(g, feed_dict={noise: ns, Y: y_samples, 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()

# 固定条件，渐变噪音
n = np.random.uniform(-1.0, 1.0, [5, 2, z_dim]).astype(np.float32)
ns = []
y_samples = []
for i in range(5):
for k in range(2):
for j in range(10):
start = n[i, 0, :]
end = n[i, 1, :]
ns.append(start + j * (end - start) / 9)
if k == 0:
y_samples.append([0, 1])
else:
y_samples.append([1, 0])
gen_imgs = sess.run(g, feed_dict={noise: ns, Y: y_samples, 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()

由女变男的过程

人脸两两之间的渐变

参考

  • Conditional Generative Adversarial Nets：arxiv.org/abs/1411.1784
  • Conditional Image Synthesis With Auxiliary Classifier GANs：arxiv.org/abs/1610.0958

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