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MUNIT.py
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MUNIT.py
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from ops import *
from utils import *
from glob import glob
import time
from tensorflow.contrib.data import batch_and_drop_remainder
class MUNIT(object) :
def __init__(self, sess, args):
self.model_name = 'MUNIT'
self.sess = sess
self.checkpoint_dir = args.checkpoint_dir
self.result_dir = args.result_dir
self.log_dir = args.log_dir
self.sample_dir = args.sample_dir
self.dataset_name = args.dataset
self.augment_flag = args.augment_flag
self.epoch = args.epoch
self.iteration = args.iteration
self.gan_type = args.gan_type
self.batch_size = args.batch_size
self.print_freq = args.print_freq
self.save_freq = args.save_freq
self.num_style = args.num_style # for test
self.guide_img = args.guide_img
self.direction = args.direction
self.img_h = args.img_h
self.img_w = args.img_w
self.img_ch = args.img_ch
self.init_lr = args.lr
self.ch = args.ch
""" Weight """
self.gan_w = args.gan_w
self.recon_x_w = args.recon_x_w
self.recon_s_w = args.recon_s_w
self.recon_c_w = args.recon_c_w
self.recon_x_cyc_w = args.recon_x_cyc_w
""" Generator """
self.n_res = args.n_res
self.mlp_dim = pow(2, args.n_sample) * args.ch # default : 256
self.n_downsample = args.n_sample
self.n_upsample = args.n_sample
self.style_dim = args.style_dim
""" Discriminator """
self.n_dis = args.n_dis
self.n_scale = args.n_scale
self.sample_dir = os.path.join(args.sample_dir, self.model_dir)
check_folder(self.sample_dir)
self.trainA_dataset = glob('./dataset/{}/*.*'.format(self.dataset_name + '/trainA'))
self.trainB_dataset = glob('./dataset/{}/*.*'.format(self.dataset_name + '/trainB'))
self.dataset_num = max(len(self.trainA_dataset), len(self.trainB_dataset))
print("##### Information #####")
print("# gan type : ", self.gan_type)
print("# dataset : ", self.dataset_name)
print("# max dataset number : ", self.dataset_num)
print("# batch_size : ", self.batch_size)
print("# epoch : ", self.epoch)
print("# iteration per epoch : ", self.iteration)
print("# style in test phase : ", self.num_style)
print()
print("##### Generator #####")
print("# residual blocks : ", self.n_res)
print("# Style dimension : ", self.style_dim)
print("# MLP dimension : ", self.mlp_dim)
print("# Down sample : ", self.n_downsample)
print("# Up sample : ", self.n_upsample)
print()
print("##### Discriminator #####")
print("# Discriminator layer : ", self.n_dis)
print("# Multi-scale Dis : ", self.n_scale)
##################################################################################
# Encoder and Decoders
##################################################################################
def Style_Encoder(self, x, reuse=False, scope='style_encoder'):
# IN removes the original feature mean and variance that represent important style information
channel = self.ch
with tf.variable_scope(scope, reuse=reuse) :
x = conv(x, channel, kernel=7, stride=1, pad=3, pad_type='reflect', scope='conv_0')
x = relu(x)
for i in range(2) :
x = conv(x, channel*2, kernel=4, stride=2, pad=1, pad_type='reflect', scope='conv_'+str(i+1))
x = relu(x)
channel = channel * 2
for i in range(2) :
x = conv(x, channel, kernel=4, stride=2, pad=1, pad_type='reflect', scope='down_conv_'+str(i))
x = relu(x)
x = adaptive_avg_pooling(x) # global average pooling
x = conv(x, self.style_dim, kernel=1, stride=1, scope='SE_logit')
return x
def Content_Encoder(self, x, reuse=False, scope='content_encoder'):
channel = self.ch
with tf.variable_scope(scope, reuse=reuse) :
x = conv(x, channel, kernel=7, stride=1, pad=3, pad_type='reflect', scope='conv_0')
x = instance_norm(x, scope='ins_0')
x = relu(x)
for i in range(self.n_downsample) :
x = conv(x, channel*2, kernel=4, stride=2, pad=1, pad_type='reflect', scope='conv_'+str(i+1))
x = instance_norm(x, scope='ins_'+str(i+1))
x = relu(x)
channel = channel * 2
for i in range(self.n_res) :
x = resblock(x, channel, scope='resblock_'+str(i))
return x
def generator(self, contents, style, reuse=False, scope="decoder"):
channel = self.mlp_dim
with tf.variable_scope(scope, reuse=reuse) :
mu, var = self.MLP(style)
x = contents
for i in range(self.n_res) :
idx = 2 * i
x = adaptive_resblock(x, channel, mu[idx], var[idx], mu[idx + 1], var[idx + 1], scope='adaptive_resblock_'+str(i))
for i in range(self.n_upsample) :
# # IN removes the original feature mean and variance that represent important style information
x = up_sample(x, scale_factor=2)
x = conv(x, channel//2, kernel=5, stride=1, pad=2, pad_type='reflect', scope='conv_'+str(i))
x = layer_norm(x, scope='layer_norm_'+str(i))
x = relu(x)
channel = channel // 2
x = conv(x, channels=self.img_ch, kernel=7, stride=1, pad=3, pad_type='reflect', scope='G_logit')
x = tanh(x)
return x
def MLP(self, style, scope='MLP'):
channel = self.mlp_dim
with tf.variable_scope(scope) :
x = style
for i in range(2):
x = fully_connected(x, channel, scope='FC_' + str(i))
x = relu(x)
mu_list = []
var_list = []
for i in range(self.n_res * 2):
mu = fully_connected(x, channel, scope='FC_mu_' + str(i))
var = fully_connected(x, channel, scope='FC_var_' + str(i))
mu = tf.reshape(mu, shape=[-1, 1, 1, channel])
var = tf.reshape(var, shape=[-1, 1, 1, channel])
mu_list.append(mu)
var_list.append(var)
return mu_list, var_list
##################################################################################
# Discriminator
##################################################################################
def discriminator(self, x_init, reuse=False, scope="discriminator"):
D_logit = []
with tf.variable_scope(scope, reuse=reuse) :
for scale in range(self.n_scale) :
channel = self.ch
x = conv(x_init, channel, kernel=4, stride=2, pad=1, pad_type='reflect', scope='ms_' + str(scale) + 'conv_0')
x = lrelu(x, 0.2)
for i in range(1, self.n_dis):
x = conv(x, channel * 2, kernel=4, stride=2, pad=1, pad_type='reflect', scope='ms_' + str(scale) +'conv_' + str(i))
x = lrelu(x, 0.2)
channel = channel * 2
x = conv(x, channels=1, kernel=1, stride=1, scope='ms_' + str(scale) + 'D_logit')
D_logit.append(x)
x_init = down_sample(x_init)
return D_logit
##################################################################################
# Model
##################################################################################
def Encoder_A(self, x_A, reuse=False):
style_A = self.Style_Encoder(x_A, reuse=reuse, scope='style_encoder_A')
content_A = self.Content_Encoder(x_A, reuse=reuse, scope='content_encoder_A')
return content_A, style_A
def Encoder_B(self, x_B, reuse=False):
style_B = self.Style_Encoder(x_B, reuse=reuse, scope='style_encoder_B')
content_B = self.Content_Encoder(x_B, reuse=reuse, scope='content_encoder_B')
return content_B, style_B
def Decoder_A(self, content_B, style_A, reuse=False):
x_ba = self.generator(contents=content_B, style=style_A, reuse=reuse, scope='decoder_A')
return x_ba
def Decoder_B(self, content_A, style_B, reuse=False):
x_ab = self.generator(contents=content_A, style=style_B, reuse=reuse, scope='decoder_B')
return x_ab
def discriminate_real(self, x_A, x_B):
real_A_logit = self.discriminator(x_A, scope="discriminator_A")
real_B_logit = self.discriminator(x_B, scope="discriminator_B")
return real_A_logit, real_B_logit
def discriminate_fake(self, x_ba, x_ab):
fake_A_logit = self.discriminator(x_ba, reuse=True, scope="discriminator_A")
fake_B_logit = self.discriminator(x_ab, reuse=True, scope="discriminator_B")
return fake_A_logit, fake_B_logit
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_h, self.img_w, self.img_ch, self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
trainA = trainA.prefetch(self.batch_size).shuffle(self.dataset_num).map(Image_Data_Class.image_processing, num_parallel_calls=8).apply(batch_and_drop_remainder(self.batch_size)).repeat()
trainB = trainB.prefetch(self.batch_size).shuffle(self.dataset_num).map(Image_Data_Class.image_processing, num_parallel_calls=8).apply(batch_and_drop_remainder(self.batch_size)).repeat()
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.domain_A = trainA_iterator.get_next()
self.domain_B = trainB_iterator.get_next()
""" Define Encoder, Generator, Discriminator """
self.style_a = tf.placeholder(tf.float32, shape=[self.batch_size, 1, 1, self.style_dim], name='style_a')
self.style_b = tf.placeholder(tf.float32, shape=[self.batch_size, 1, 1, self.style_dim], name='style_b')
# encode
content_a, style_a_prime = self.Encoder_A(self.domain_A)
content_b, style_b_prime = self.Encoder_B(self.domain_B)
# decode (within domain)
x_aa = self.Decoder_A(content_B=content_a, style_A=style_a_prime)
x_bb = self.Decoder_B(content_A=content_b, style_B=style_b_prime)
# decode (cross domain)
x_ba = self.Decoder_A(content_B=content_b, style_A=self.style_a, reuse=True)
x_ab = self.Decoder_B(content_A=content_a, style_B=self.style_b, reuse=True)
# encode again
content_b_, style_a_ = self.Encoder_A(x_ba, reuse=True)
content_a_, style_b_ = self.Encoder_B(x_ab, reuse=True)
# decode again (if needed)
if self.recon_x_cyc_w > 0 :
x_aba = self.Decoder_A(content_B=content_a_, style_A=style_a_prime, reuse=True)
x_bab = self.Decoder_B(content_A=content_b_, style_B=style_b_prime, reuse=True)
cyc_recon_A = L1_loss(x_aba, self.domain_A)
cyc_recon_B = L1_loss(x_bab, self.domain_B)
else :
cyc_recon_A = 0.0
cyc_recon_B = 0.0
real_A_logit, real_B_logit = self.discriminate_real(self.domain_A, self.domain_B)
fake_A_logit, fake_B_logit = self.discriminate_fake(x_ba, x_ab)
""" Define Loss """
G_ad_loss_a = generator_loss(self.gan_type, fake_A_logit)
G_ad_loss_b = generator_loss(self.gan_type, fake_B_logit)
D_ad_loss_a = discriminator_loss(self.gan_type, real_A_logit, fake_A_logit)
D_ad_loss_b = discriminator_loss(self.gan_type, real_B_logit, fake_B_logit)
recon_A = L1_loss(x_aa, self.domain_A) # reconstruction
recon_B = L1_loss(x_bb, self.domain_B) # reconstruction
# The style reconstruction loss encourages
# diverse outputs given different style codes
recon_style_A = L1_loss(style_a_, self.style_a)
recon_style_B = L1_loss(style_b_, self.style_b)
# The content reconstruction loss encourages
# the translated image to preserve semantic content of the input image
recon_content_A = L1_loss(content_a_, content_a)
recon_content_B = L1_loss(content_b_, content_b)
Generator_A_loss = self.gan_w * G_ad_loss_a + \
self.recon_x_w * recon_A + \
self.recon_s_w * recon_style_A + \
self.recon_c_w * recon_content_A + \
self.recon_x_cyc_w * cyc_recon_A
Generator_B_loss = self.gan_w * G_ad_loss_b + \
self.recon_x_w * recon_B + \
self.recon_s_w * recon_style_B + \
self.recon_c_w * recon_content_B + \
self.recon_x_cyc_w * cyc_recon_B
Discriminator_A_loss = self.gan_w * D_ad_loss_a
Discriminator_B_loss = self.gan_w * D_ad_loss_b
self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss('encoder') + regularization_loss('decoder')
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss('discriminator')
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'decoder' in var.name or 'encoder' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
self.G_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars)
"""" Summary """
self.all_G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.G_loss = tf.summary.merge([self.G_A_loss, self.G_B_loss, self.all_G_loss])
self.D_loss = tf.summary.merge([self.D_A_loss, self.D_B_loss, self.all_D_loss])
""" Image """
self.fake_A = x_ba
self.fake_B = x_ab
self.real_A = self.domain_A
self.real_B = self.domain_B
""" Test """
self.test_image = tf.placeholder(tf.float32, [1, self.img_h, self.img_w, self.img_ch], name='test_image')
self.test_style = tf.placeholder(tf.float32, [1, 1, 1, self.style_dim], name='test_style')
test_content_a, _ = self.Encoder_A(self.test_image, reuse=True)
test_content_b, _ = self.Encoder_B(self.test_image, reuse=True)
self.test_fake_A = self.Decoder_A(content_B=test_content_b, style_A=self.test_style, reuse=True)
self.test_fake_B = self.Decoder_B(content_A=test_content_a, style_B=self.test_style, reuse=True)
""" Guided Image Translation """
self.content_image = tf.placeholder(tf.float32, [1, self.img_h, self.img_w, self.img_ch], name='content_image')
self.style_image = tf.placeholder(tf.float32, [1, self.img_h, self.img_w, self.img_ch], name='guide_style_image')
if self.direction == 'a2b' :
guide_content_A, guide_style_A = self.Encoder_A(self.content_image, reuse=True)
guide_content_B, guide_style_B = self.Encoder_B(self.style_image, reuse=True)
else :
guide_content_B, guide_style_B = self.Encoder_B(self.content_image, reuse=True)
guide_content_A, guide_style_A = self.Encoder_A(self.style_image, reuse=True)
self.guide_fake_A = self.Decoder_A(content_B=guide_content_B, style_A=guide_style_A, reuse=True)
self.guide_fake_B = self.Decoder_B(content_A=guide_content_A, style_B=guide_style_B, reuse=True)
def train(self):
# initialize all variables
tf.global_variables_initializer().run()
# saver to save model
self.saver = tf.train.Saver()
# summary writer
self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_dir, self.sess.graph)
# restore check-point if it exits
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
start_epoch = (int)(checkpoint_counter / self.iteration)
start_batch_id = checkpoint_counter - start_epoch * self.iteration
counter = checkpoint_counter
print(" [*] Load SUCCESS")
else:
start_epoch = 0
start_batch_id = 0
counter = 1
print(" [!] Load failed...")
# loop for epoch
start_time = time.time()
for epoch in range(start_epoch, self.epoch):
lr = self.init_lr * pow(0.5, epoch)
for idx in range(start_batch_id, self.iteration):
style_a = np.random.normal(loc=0.0, scale=1.0, size=[self.batch_size, 1, 1, self.style_dim])
style_b = np.random.normal(loc=0.0, scale=1.0, size=[self.batch_size, 1, 1, self.style_dim])
train_feed_dict = {
self.style_a : style_a,
self.style_b : style_b,
self.lr : lr
}
# Update D
_, d_loss, summary_str = self.sess.run([self.D_optim, self.Discriminator_loss, self.D_loss], feed_dict = train_feed_dict)
self.writer.add_summary(summary_str, counter)
# Update G
batch_A_images, batch_B_images, fake_A, fake_B, _, g_loss, summary_str = self.sess.run([self.real_A, self.real_B, self.fake_A, self.fake_B, self.G_optim, self.Generator_loss, self.G_loss], feed_dict = train_feed_dict)
self.writer.add_summary(summary_str, counter)
# display training status
counter += 1
print("Epoch: [%2d] [%6d/%6d] time: %4.4f d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, self.iteration, time.time() - start_time, d_loss, g_loss))
if np.mod(idx+1, self.print_freq) == 0 :
save_images(batch_A_images, [self.batch_size, 1],
'./{}/real_A_{:02d}_{:06d}.jpg'.format(self.sample_dir, epoch, idx+1))
# save_images(batch_B_images, [self.batch_size, 1],
# './{}/real_B_{}_{:02d}_{:06d}.jpg'.format(self.sample_dir, gpu_id, epoch, idx+1))
# save_images(fake_A, [self.batch_size, 1],
# './{}/fake_A_{}_{:02d}_{:06d}.jpg'.format(self.sample_dir, gpu_id, epoch, idx+1))
save_images(fake_B, [self.batch_size, 1],
'./{}/fake_B_{:02d}_{:06d}.jpg'.format(self.sample_dir, epoch, idx+1))
if np.mod(idx+1, self.save_freq) == 0 :
self.save(self.checkpoint_dir, counter)
# After an epoch, start_batch_id is set to zero
# non-zero value is only for the first epoch after loading pre-trained model
start_batch_id = 0
# save model for final step
self.save(self.checkpoint_dir, counter)
@property
def model_dir(self):
return "{}_{}_{}".format(self.model_name, self.dataset_name, self.gan_type)
def save(self, checkpoint_dir, step):
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess, os.path.join(checkpoint_dir, self.model_name + '.model'), global_step=step)
def load(self, checkpoint_dir):
import re
print(" [*] Reading checkpoints...")
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
counter = int(next(re.finditer("(\d+)(?!.*\d)", ckpt_name)).group(0))
print(" [*] Success to read {}".format(ckpt_name))
return True, counter
else:
print(" [*] Failed to find a checkpoint")
return False, 0
def test(self):
tf.global_variables_initializer().run()
test_A_files = glob('./dataset/{}/*.*'.format(self.dataset_name + '/testA'))
test_B_files = glob('./dataset/{}/*.*'.format(self.dataset_name + '/testB'))
self.saver = tf.train.Saver()
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
self.result_dir = os.path.join(self.result_dir, self.model_dir)
check_folder(self.result_dir)
if could_load :
print(" [*] Load SUCCESS")
else :
print(" [!] Load failed...")
# write html for visual comparison
index_path = os.path.join(self.result_dir, 'index.html')
index = open(index_path, 'w')
index.write("<html><body><table><tr>")
index.write("<th>name</th><th>input</th><th>output</th></tr>")
for sample_file in test_A_files : # A -> B
print('Processing A image: ' + sample_file)
sample_image = np.asarray(load_test_data(sample_file, size_h=self.img_h, size_w=self.img_w))
file_name = os.path.basename(sample_file).split(".")[0]
file_extension = os.path.basename(sample_file).split(".")[1]
for i in range(self.num_style) :
test_style = np.random.normal(loc=0.0, scale=1.0, size=[1, 1, 1, self.style_dim])
image_path = os.path.join(self.result_dir, '{}_style{}.{}'.format(file_name, i, file_extension))
fake_img = self.sess.run(self.test_fake_B, feed_dict = {self.test_image : sample_image, self.test_style : test_style})
save_images(fake_img, [1, 1], image_path)
index.write("<td>%s</td>" % os.path.basename(image_path))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (sample_file if os.path.isabs(sample_file) else (
'../..' + os.path.sep + sample_file), self.img_w, self.img_h))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (image_path if os.path.isabs(image_path) else (
'../..' + os.path.sep + image_path), self.img_w, self.img_h))
index.write("</tr>")
for sample_file in test_B_files : # B -> A
print('Processing B image: ' + sample_file)
sample_image = np.asarray(load_test_data(sample_file, size_h=self.img_h, size_w=self.img_w))
file_name = os.path.basename(sample_file).split(".")[0]
file_extension = os.path.basename(sample_file).split(".")[1]
for i in range(self.num_style):
test_style = np.random.normal(loc=0.0, scale=1.0, size=[1, 1, 1, self.style_dim])
image_path = os.path.join(self.result_dir, '{}_style{}.{}'.format(file_name, i, file_extension))
fake_img = self.sess.run(self.test_fake_A, feed_dict={self.test_image: sample_image, self.test_style: test_style})
save_images(fake_img, [1, 1], image_path)
index.write("<td>%s</td>" % os.path.basename(image_path))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (sample_file if os.path.isabs(sample_file) else (
'../..' + os.path.sep + sample_file), self.img_w, self.img_h))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (image_path if os.path.isabs(image_path) else (
'../..' + os.path.sep + image_path), self.img_w, self.img_h))
index.write("</tr>")
index.close()
def style_guide_test(self):
tf.global_variables_initializer().run()
test_A_files = glob('./dataset/{}/*.*'.format(self.dataset_name + '/testA'))
test_B_files = glob('./dataset/{}/*.*'.format(self.dataset_name + '/testB'))
style_file = np.asarray(load_test_data(self.guide_img, size_h=self.img_h, size_w=self.img_w))
self.saver = tf.train.Saver()
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
self.result_dir = os.path.join(self.result_dir, self.model_dir, 'guide')
check_folder(self.result_dir)
if could_load:
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# write html for visual comparison
index_path = os.path.join(self.result_dir, 'index.html')
index = open(index_path, 'w')
index.write("<html><body><table><tr>")
index.write("<th>name</th><th>input</th><th>output</th></tr>")
if self.direction == 'a2b' :
for sample_file in test_A_files: # A -> B
print('Processing A image: ' + sample_file)
sample_image = np.asarray(load_test_data(sample_file, size_h=self.img_h, size_w=self.img_w))
image_path = os.path.join(self.result_dir, '{}'.format(os.path.basename(sample_file)))
fake_img = self.sess.run(self.guide_fake_B, feed_dict={self.content_image: sample_image, self.style_image : style_file})
save_images(fake_img, [1, 1], image_path)
index.write("<td>%s</td>" % os.path.basename(image_path))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (sample_file if os.path.isabs(sample_file) else (
'../../..' + os.path.sep + sample_file), self.img_w, self.img_h))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (image_path if os.path.isabs(image_path) else (
'../../..' + os.path.sep + image_path), self.img_w, self.img_h))
index.write("</tr>")
else :
for sample_file in test_B_files: # B -> A
print('Processing B image: ' + sample_file)
sample_image = np.asarray(load_test_data(sample_file, size_h=self.img_h, size_w=self.img_w))
image_path = os.path.join(self.result_dir, '{}'.format(os.path.basename(sample_file)))
fake_img = self.sess.run(self.guide_fake_A, feed_dict={self.content_image: sample_image, self.style_image : style_file})
save_images(fake_img, [1, 1], image_path)
index.write("<td>%s</td>" % os.path.basename(image_path))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (sample_file if os.path.isabs(sample_file) else (
'../../..' + os.path.sep + sample_file), self.img_w, self.img_h))
index.write("<td><img src='%s' width='%d' height='%d'></td>" % (image_path if os.path.isabs(image_path) else (
'../../..' + os.path.sep + image_path), self.img_w, self.img_h))
index.write("</tr>")
index.close()