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from ugatit.ops import *
from utils import *
from glob import glob
import time
from tensorflow.contrib.data import prefetch_to_device, shuffle_and_repeat, map_and_batch
import numpy as np
class UGATIT(object) :
def __init__(self, sess, args):
self.light = args.light
if self.light :
self.model_name = 'UGATIT_light'
else :
self.model_name = 'UGATIT'
self.sess = sess
self.phase = args.phase
self.checkpoint_dir = args.checkpoint_dir
self.result_dir = args.result_dir
self.log_dir = args.log_dir
self.dataset_name = args.dataset
self.augment_flag = args.augment_flag
self.epoch = args.epoch
self.iteration = args.iteration
self.decay_flag = args.decay_flag
self.decay_epoch = args.decay_epoch
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.init_lr = args.lr
self.ch = args.ch
""" Weight """
self.adv_weight = args.adv_weight
self.cycle_weight = args.cycle_weight
self.identity_weight = args.identity_weight
self.cam_weight = args.cam_weight
self.ld = args.GP_ld
self.smoothing = args.smoothing
""" Generator """
self.n_res = args.n_res
""" Discriminator """
self.n_dis = args.n_dis
self.n_critic = args.n_critic
self.sn = args.sn
self.img_size = args.img_size
self.img_ch = args.img_ch
self.sample_dir = os.path.join(args.sample_dir, self.model_dir)
check_folder(self.sample_dir)
# self.trainA, self.trainB = prepare_data(dataset_name=self.dataset_name, size=self.img_size
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()
print("##### Information #####")
print("# light : ", self.light)
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("# smoothing : ", self.smoothing)
print()
print("##### Generator #####")
print("# residual blocks : ", self.n_res)
print()
print("##### Discriminator #####")
print("# discriminator layer : ", self.n_dis)
print("# the number of critic : ", self.n_critic)
print("# spectral normalization : ", self.sn)
print()
print("##### Weight #####")
print("# adv_weight : ", self.adv_weight)
print("# cycle_weight : ", self.cycle_weight)
print("# identity_weight : ", self.identity_weight)
print("# cam_weight : ", self.cam_weight)
##################################################################################
# Generator
##################################################################################
def generator(self, x_init, reuse=False, scope="generator"):
channel = self.ch
with tf.variable_scope(scope, reuse=reuse) :
x = conv(x_init, channel, kernel=7, stride=1, pad=3, pad_type='reflect', scope='conv')
x = instance_norm(x, scope='ins_norm')
x = relu(x)
# Down-Sampling
for i in range(2) :
x = conv(x, channel*2, kernel=3, stride=2, pad=1, pad_type='reflect', scope='conv_'+str(i))
x = instance_norm(x, scope='ins_norm_'+str(i))
x = relu(x)
channel = channel * 2
# Down-Sampling Bottleneck
for i in range(self.n_res):
x = resblock(x, channel, scope='resblock_' + str(i))
# Class Activation Map
cam_x = global_avg_pooling(x)
cam_gap_logit, cam_x_weight = fully_connected_with_w(cam_x, scope='CAM_logit')
x_gap = tf.multiply(x, cam_x_weight)
cam_x = global_max_pooling(x)
cam_gmp_logit, cam_x_weight = fully_connected_with_w(cam_x, reuse=True, scope='CAM_logit')
x_gmp = tf.multiply(x, cam_x_weight)
cam_logit = tf.concat([cam_gap_logit, cam_gmp_logit], axis=-1)
x = tf.concat([x_gap, x_gmp], axis=-1)
x = conv(x, channel, kernel=1, stride=1, scope='conv_1x1')
x = relu(x)
heatmap = tf.squeeze(tf.reduce_sum(x, axis=-1))
# Gamma, Beta block
gamma, beta = self.MLP(x, reuse=reuse)
# Up-Sampling Bottleneck
for i in range(self.n_res):
x = adaptive_ins_layer_resblock(x, channel, gamma, beta, smoothing=self.smoothing, scope='adaptive_resblock' + str(i))
# Up-Sampling
for i in range(2) :
x = up_sample(x, scale_factor=2)
x = conv(x, channel//2, kernel=3, stride=1, pad=1, pad_type='reflect', scope='up_conv_'+str(i))
x = layer_instance_norm(x, scope='layer_ins_norm_'+str(i))
x = relu(x)
channel = channel // 2
x = conv(x, channels=3, kernel=7, stride=1, pad=3, pad_type='reflect', scope='G_logit')
x = tanh(x)
return x, cam_logit, heatmap
def MLP(self, x, use_bias=True, reuse=False, scope='MLP'):
channel = self.ch * self.n_res
if self.light :
x = global_avg_pooling(x)
with tf.variable_scope(scope, reuse=reuse):
for i in range(2) :
x = fully_connected(x, channel, use_bias, scope='linear_' + str(i))
x = relu(x)
gamma = fully_connected(x, channel, use_bias, scope='gamma')
beta = fully_connected(x, channel, use_bias, scope='beta')
gamma = tf.reshape(gamma, shape=[self.batch_size, 1, 1, channel])
beta = tf.reshape(beta, shape=[self.batch_size, 1, 1, channel])
return gamma, beta
##################################################################################
# Discriminator
##################################################################################
def discriminator(self, x_init, reuse=False, scope="discriminator"):
D_logit = []
D_CAM_logit = []
with tf.variable_scope(scope, reuse=reuse) :
local_x, local_cam, local_heatmap = self.discriminator_local(x_init, reuse=reuse, scope='local')
global_x, global_cam, global_heatmap = self.discriminator_global(x_init, reuse=reuse, scope='global')
D_logit.extend([local_x, global_x])
D_CAM_logit.extend([local_cam, global_cam])
return D_logit, D_CAM_logit, local_heatmap, global_heatmap
def discriminator_global(self, x_init, reuse=False, scope='discriminator_global'):
with tf.variable_scope(scope, reuse=reuse):
channel = self.ch
x = conv(x_init, channel, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_0')
x = lrelu(x, 0.2)
for i in range(1, self.n_dis - 1):
x = conv(x, channel * 2, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_' + str(i))
x = lrelu(x, 0.2)
channel = channel * 2
x = conv(x, channel * 2, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='conv_last')
x = lrelu(x, 0.2)
channel = channel * 2
cam_x = global_avg_pooling(x)
cam_gap_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, scope='CAM_logit')
x_gap = tf.multiply(x, cam_x_weight)
cam_x = global_max_pooling(x)
cam_gmp_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, reuse=True, scope='CAM_logit')
x_gmp = tf.multiply(x, cam_x_weight)
cam_logit = tf.concat([cam_gap_logit, cam_gmp_logit], axis=-1)
x = tf.concat([x_gap, x_gmp], axis=-1)
x = conv(x, channel, kernel=1, stride=1, scope='conv_1x1')
x = lrelu(x, 0.2)
heatmap = tf.squeeze(tf.reduce_sum(x, axis=-1))
x = conv(x, channels=1, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='D_logit')
return x, cam_logit, heatmap
def discriminator_local(self, x_init, reuse=False, scope='discriminator_local'):
with tf.variable_scope(scope, reuse=reuse) :
channel = self.ch
x = conv(x_init, channel, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_0')
x = lrelu(x, 0.2)
for i in range(1, self.n_dis - 2 - 1):
x = conv(x, channel * 2, kernel=4, stride=2, pad=1, pad_type='reflect', sn=self.sn, scope='conv_' + str(i))
x = lrelu(x, 0.2)
channel = channel * 2
x = conv(x, channel * 2, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='conv_last')
x = lrelu(x, 0.2)
channel = channel * 2
cam_x = global_avg_pooling(x)
cam_gap_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, scope='CAM_logit')
x_gap = tf.multiply(x, cam_x_weight)
cam_x = global_max_pooling(x)
cam_gmp_logit, cam_x_weight = fully_connected_with_w(cam_x, sn=self.sn, reuse=True, scope='CAM_logit')
x_gmp = tf.multiply(x, cam_x_weight)
cam_logit = tf.concat([cam_gap_logit, cam_gmp_logit], axis=-1)
x = tf.concat([x_gap, x_gmp], axis=-1)
x = conv(x, channel, kernel=1, stride=1, scope='conv_1x1')
x = lrelu(x, 0.2)
heatmap = tf.squeeze(tf.reduce_sum(x, axis=-1))
x = conv(x, channels=1, kernel=4, stride=1, pad=1, pad_type='reflect', sn=self.sn, scope='D_logit')
return x, cam_logit, heatmap
##################################################################################
# Model
##################################################################################
def generate_a2b(self, x_A, reuse=False):
out, cam, _ = self.generator(x_A, reuse=reuse, scope="generator_B")
return out, cam
def generate_b2a(self, x_B, reuse=False):
out, cam, _ = self.generator(x_B, reuse=reuse, scope="generator_A")
return out, cam
def discriminate_real(self, x_A, x_B):
real_A_logit, real_A_cam_logit, _, _ = self.discriminator(x_A, scope="discriminator_A")
real_B_logit, real_B_cam_logit, _, _ = self.discriminator(x_B, scope="discriminator_B")
return real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit
def discriminate_fake(self, x_ba, x_ab):
fake_A_logit, fake_A_cam_logit, _, _ = self.discriminator(x_ba, reuse=True, scope="discriminator_A")
fake_B_logit, fake_B_cam_logit, _, _ = self.discriminator(x_ab, reuse=True, scope="discriminator_B")
return fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit
def gradient_panalty(self, real, fake, scope="discriminator_A"):
if self.gan_type.__contains__('dragan'):
eps = tf.random_uniform(shape=tf.shape(real), minval=0., maxval=1.)
_, x_var = tf.nn.moments(real, axes=[0, 1, 2, 3])
x_std = tf.sqrt(x_var) # magnitude of noise decides the size of local region
fake = real + 0.5 * x_std * eps
alpha = tf.random_uniform(shape=[self.batch_size, 1, 1, 1], minval=0., maxval=1.)
interpolated = real + alpha * (fake - real)
logit, cam_logit, _, _ = self.discriminator(interpolated, reuse=True, scope=scope)
GP = []
cam_GP = []
for i in range(2) :
grad = tf.gradients(logit[i], interpolated)[0] # gradient of D(interpolated)
grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm
# WGAN - LP
if self.gan_type == 'wgan-lp' :
GP.append(self.ld * tf.reduce_mean(tf.square(tf.maximum(0.0, grad_norm - 1.))))
elif self.gan_type == 'wgan-gp' or self.gan_type == 'dragan':
GP.append(self.ld * tf.reduce_mean(tf.square(grad_norm - 1.)))
for i in range(2) :
grad = tf.gradients(cam_logit[i], interpolated)[0] # gradient of D(interpolated)
grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm
# WGAN - LP
if self.gan_type == 'wgan-lp' :
cam_GP.append(self.ld * tf.reduce_mean(tf.square(tf.maximum(0.0, grad_norm - 1.))))
elif self.gan_type == 'wgan-gp' or self.gan_type == 'dragan':
cam_GP.append(self.ld * tf.reduce_mean(tf.square(grad_norm - 1.)))
return sum(GP), sum(cam_GP)
def build_model(self):
if self.phase == 'train' :
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, 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)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))
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 Generator, Discriminator """
x_ab, cam_ab = self.generate_a2b(self.domain_A) # real a
x_ba, cam_ba = self.generate_b2a(self.domain_B) # real b
x_aba, _ = self.generate_b2a(x_ab, reuse=True) # real b
x_bab, _ = self.generate_a2b(x_ba, reuse=True) # real a
x_aa, cam_aa = self.generate_b2a(self.domain_A, reuse=True) # fake b
x_bb, cam_bb = self.generate_a2b(self.domain_B, reuse=True) # fake a
real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit = self.discriminate_real(self.domain_A, self.domain_B)
fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit = self.discriminate_fake(x_ba, x_ab)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan' :
GP_A, GP_CAM_A = self.gradient_panalty(real=self.domain_A, fake=x_ba, scope="discriminator_A")
GP_B, GP_CAM_B = self.gradient_panalty(real=self.domain_B, fake=x_ab, scope="discriminator_B")
else :
GP_A, GP_CAM_A = 0, 0
GP_B, GP_CAM_B = 0, 0
G_ad_loss_A = (generator_loss(self.gan_type, fake_A_logit) + generator_loss(self.gan_type, fake_A_cam_logit))
G_ad_loss_B = (generator_loss(self.gan_type, fake_B_logit) + generator_loss(self.gan_type, fake_B_cam_logit))
D_ad_loss_A = (discriminator_loss(self.gan_type, real_A_logit, fake_A_logit) + discriminator_loss(self.gan_type, real_A_cam_logit, fake_A_cam_logit) + GP_A + GP_CAM_A)
D_ad_loss_B = (discriminator_loss(self.gan_type, real_B_logit, fake_B_logit) + discriminator_loss(self.gan_type, real_B_cam_logit, fake_B_cam_logit) + GP_B + GP_CAM_B)
reconstruction_A = L1_loss(x_aba, self.domain_A) # reconstruction
reconstruction_B = L1_loss(x_bab, self.domain_B) # reconstruction
identity_A = L1_loss(x_aa, self.domain_A)
identity_B = L1_loss(x_bb, self.domain_B)
cam_A = cam_loss(source=cam_ba, non_source=cam_aa)
cam_B = cam_loss(source=cam_ab, non_source=cam_bb)
Generator_A_gan = self.adv_weight * G_ad_loss_A
Generator_A_cycle = self.cycle_weight * reconstruction_B
Generator_A_identity = self.identity_weight * identity_A
Generator_A_cam = self.cam_weight * cam_A
Generator_B_gan = self.adv_weight * G_ad_loss_B
Generator_B_cycle = self.cycle_weight * reconstruction_A
Generator_B_identity = self.identity_weight * identity_B
Generator_B_cam = self.cam_weight * cam_B
Generator_A_loss = Generator_A_gan + Generator_A_cycle + Generator_A_identity + Generator_A_cam
Generator_B_loss = Generator_B_gan + Generator_B_cycle + Generator_B_identity + Generator_B_cam
Discriminator_A_loss = self.adv_weight * D_ad_loss_A
Discriminator_B_loss = self.adv_weight * D_ad_loss_B
self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss('generator')
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss('discriminator')
""" Result Image """
self.fake_A = x_ba
self.fake_B = x_ab
self.real_A = self.domain_A
self.real_B = self.domain_B
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' 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_A_gan = tf.summary.scalar("G_A_gan", Generator_A_gan)
self.G_A_cycle = tf.summary.scalar("G_A_cycle", Generator_A_cycle)
self.G_A_identity = tf.summary.scalar("G_A_identity", Generator_A_identity)
self.G_A_cam = tf.summary.scalar("G_A_cam", Generator_A_cam)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.G_B_gan = tf.summary.scalar("G_B_gan", Generator_B_gan)
self.G_B_cycle = tf.summary.scalar("G_B_cycle", Generator_B_cycle)
self.G_B_identity = tf.summary.scalar("G_B_identity", Generator_B_identity)
self.G_B_cam = tf.summary.scalar("G_B_cam", Generator_B_cam)
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.rho_var = []
for var in tf.trainable_variables():
if 'rho' in var.name:
self.rho_var.append(tf.summary.histogram(var.name, var))
self.rho_var.append(tf.summary.scalar(var.name + "_min", tf.reduce_min(var)))
self.rho_var.append(tf.summary.scalar(var.name + "_max", tf.reduce_max(var)))
self.rho_var.append(tf.summary.scalar(var.name + "_mean", tf.reduce_mean(var)))
g_summary_list = [self.G_A_loss, self.G_A_gan, self.G_A_cycle, self.G_A_identity, self.G_A_cam,
self.G_B_loss, self.G_B_gan, self.G_B_cycle, self.G_B_identity, self.G_B_cam,
self.all_G_loss]
g_summary_list.extend(self.rho_var)
d_summary_list = [self.D_A_loss, self.D_B_loss, self.all_D_loss]
self.G_loss = tf.summary.merge(g_summary_list)
self.D_loss = tf.summary.merge(d_summary_list)
else :
""" Test """
self.test_domain_A = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='test_domain_A')
self.test_domain_B = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='test_domain_B')
self.test_fake_B, _ = self.generate_a2b(self.test_domain_A)
self.test_fake_A, _ = self.generate_b2a(self.test_domain_B)
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()
past_g_loss = -1.
lr = self.init_lr
for epoch in range(start_epoch, self.epoch):
# lr = self.init_lr if epoch < self.decay_epoch else self.init_lr * (self.epoch - epoch) / (self.epoch - self.decay_epoch)
if self.decay_flag :
#lr = self.init_lr * pow(0.5, epoch // self.decay_epoch)
lr = self.init_lr if epoch < self.decay_epoch else self.init_lr * (self.epoch - epoch) / (self.epoch - self.decay_epoch)
for idx in range(start_batch_id, self.iteration):
train_feed_dict = {
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
g_loss = None
if (counter - 1) % self.n_critic == 0 :
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)
past_g_loss = g_loss
# display training status
counter += 1
if g_loss == None :
g_loss = past_g_loss
print("Epoch: [%2d] [%5d/%5d] 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_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))
# save_images(batch_B_images, [self.batch_size, 1],
# './{}/real_B_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))
# save_images(fake_A, [self.batch_size, 1],
# './{}/fake_A_{:03d}_{:05d}.png'.format(self.sample_dir, epoch, idx+1))
save_images(fake_B, [self.batch_size, 1],
'./{}/fake_B_{:03d}_{:05d}.png'.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):
n_res = str(self.n_res) + 'resblock'
n_dis = str(self.n_dis) + 'dis'
if self.smoothing :
smoothing = '_smoothing'
else :
smoothing = ''
if self.sn :
sn = '_sn'
else :
sn = ''
return "{}_{}_{}_{}_{}_{}_{}_{}_{}_{}{}{}".format(self.model_name, self.dataset_name,
self.gan_type, n_res, n_dis,
self.n_critic,
self.adv_weight, self.cycle_weight, self.identity_weight, self.cam_weight, sn, smoothing)
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):
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(ckpt_name.split('-')[-1])
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=self.img_size))
image_path = os.path.join(self.result_dir,'{0}'.format(os.path.basename(sample_file)))
fake_img = self.sess.run(self.test_fake_B, feed_dict = {self.test_domain_A : sample_image})
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_size, self.img_size))
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_size, self.img_size))
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=self.img_size))
image_path = os.path.join(self.result_dir,'{0}'.format(os.path.basename(sample_file)))
fake_img = self.sess.run(self.test_fake_A, feed_dict = {self.test_domain_B : sample_image})
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_size, self.img_size))
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_size, self.img_size))
index.write("</tr>")
index.close()
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