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AnimeGANv2.py
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AnimeGANv2.py
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from tools.ops import *
from tools.utils import *
from glob import glob
import time
import numpy as np
from net import generator
from net.discriminator import D_net
from tools.data_loader import ImageGenerator
from tools.vgg19 import Vgg19
class AnimeGANv2(object) :
def __init__(self, sess, args):
self.model_name = 'AnimeGANv2'
self.sess = sess
self.checkpoint_dir = args.checkpoint_dir
self.log_dir = args.log_dir
self.dataset_name = args.dataset
self.epoch = args.epoch
self.init_epoch = args.init_epoch # args.epoch // 20
self.gan_type = args.gan_type
self.batch_size = args.batch_size
self.save_freq = args.save_freq
self.init_lr = args.init_lr
self.d_lr = args.d_lr
self.g_lr = args.g_lr
""" Weight """
self.g_adv_weight = args.g_adv_weight
self.d_adv_weight = args.d_adv_weight
self.con_weight = args.con_weight
self.sty_weight = args.sty_weight
self.color_weight = args.color_weight
self.tv_weight = args.tv_weight
self.training_rate = args.training_rate
self.ld = args.ld
self.img_size = args.img_size
self.img_ch = args.img_ch
""" Discriminator """
self.n_dis = args.n_dis
self.ch = args.ch
self.sn = args.sn
self.sample_dir = os.path.join(args.sample_dir, self.model_dir)
check_folder(self.sample_dir)
self.real = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch], name='real_A')
self.anime = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch], name='anime_A')
self.anime_smooth = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch], name='anime_smooth_A')
self.test_real = tf.placeholder(tf.float32, [1, None, None, self.img_ch], name='test_input')
self.anime_gray = tf.placeholder(tf.float32, [self.batch_size, self.img_size[0], self.img_size[1], self.img_ch],name='anime_B')
self.real_image_generator = ImageGenerator('./dataset/train_photo', self.img_size, self.batch_size)
self.anime_image_generator = ImageGenerator('./dataset/{}'.format(self.dataset_name + '/style'), self.img_size, self.batch_size)
self.anime_smooth_generator = ImageGenerator('./dataset/{}'.format(self.dataset_name + '/smooth'), self.img_size, self.batch_size)
self.dataset_num = max(self.real_image_generator.num_images, self.anime_image_generator.num_images)
self.vgg = Vgg19()
print()
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("# init_epoch : ", self.init_epoch)
print("# training image size [H, W] : ", self.img_size)
print("# g_adv_weight,d_adv_weight,con_weight,sty_weight,color_weight,tv_weight : ", self.g_adv_weight,self.d_adv_weight,self.con_weight,self.sty_weight,self.color_weight,self.tv_weight)
print("# init_lr,g_lr,d_lr : ", self.init_lr,self.g_lr,self.d_lr)
print(f"# training_rate G -- D: {self.training_rate} : 1" )
print()
##################################################################################
# Generator
##################################################################################
def generator(self, x_init, reuse=False, scope="generator"):
with tf.variable_scope(scope, reuse=reuse):
G = generator.G_net(x_init)
return G.fake
##################################################################################
# Discriminator
##################################################################################
def discriminator(self, x_init, reuse=False, scope="discriminator"):
D = D_net(x_init, self.ch, self.n_dis, self.sn, reuse=reuse, scope=scope)
return D
##################################################################################
# Model
##################################################################################
def gradient_panalty(self, real, fake, scope="discriminator"):
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, _= self.discriminator(interpolated, reuse=True, scope=scope)
grad = tf.gradients(logit, interpolated)[0] # gradient of D(interpolated)
grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm
GP = 0
# WGAN - LP
if self.gan_type.__contains__('lp'):
GP = self.ld * tf.reduce_mean(tf.square(tf.maximum(0.0, grad_norm - 1.)))
elif self.gan_type.__contains__('gp') or self.gan_type == 'dragan' :
GP = self.ld * tf.reduce_mean(tf.square(grad_norm - 1.))
return GP
def build_model(self):
""" Define Generator, Discriminator """
self.generated = self.generator(self.real)
self.test_generated = self.generator(self.test_real, reuse=True)
anime_logit = self.discriminator(self.anime)
anime_gray_logit = self.discriminator(self.anime_gray, reuse=True)
generated_logit = self.discriminator(self.generated, reuse=True)
smooth_logit = self.discriminator(self.anime_smooth, reuse=True)
""" Define Loss """
if self.gan_type.__contains__('gp') or self.gan_type.__contains__('lp') or self.gan_type.__contains__('dragan') :
GP = self.gradient_panalty(real=self.anime, fake=self.generated)
else :
GP = 0.0
# init pharse
init_c_loss = con_loss(self.vgg, self.real, self.generated)
init_loss = self.con_weight * init_c_loss
self.init_loss = init_loss
# gan
c_loss, s_loss = con_sty_loss(self.vgg, self.real, self.anime_gray, self.generated)
tv_loss = self.tv_weight * total_variation_loss(self.generated)
t_loss = self.con_weight * c_loss + self.sty_weight * s_loss + color_loss(self.real,self.generated) * self.color_weight + tv_loss
g_loss = self.g_adv_weight * generator_loss(self.gan_type, generated_logit)
d_loss = self.d_adv_weight * discriminator_loss(self.gan_type, anime_logit, anime_gray_logit, generated_logit, smooth_logit) + GP
self.Generator_loss = t_loss + g_loss
self.Discriminator_loss = d_loss
""" 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.init_optim = tf.train.AdamOptimizer(self.init_lr, beta1=0.5, beta2=0.999).minimize(self.init_loss, var_list=G_vars)
self.G_optim = tf.train.AdamOptimizer(self.g_lr , beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.d_lr , beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars)
"""" Summary """
self.G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss)
self.G_gan = tf.summary.scalar("G_gan", g_loss)
self.G_vgg = tf.summary.scalar("G_vgg", t_loss)
self.G_init_loss = tf.summary.scalar("G_init", init_loss)
self.V_loss_merge = tf.summary.merge([self.G_init_loss])
self.G_loss_merge = tf.summary.merge([self.G_loss, self.G_gan, self.G_vgg, self.G_init_loss])
self.D_loss_merge = tf.summary.merge([self.D_loss])
def train(self):
# initialize all variables
self.sess.run(tf.global_variables_initializer())
# saver to save model
self.saver = tf.train.Saver(max_to_keep=self.epoch)
# summary writer
self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_dir, self.sess.graph)
""" Input Image"""
real_img_op, anime_img_op, anime_smooth_op = self.real_image_generator.load_images(), self.anime_image_generator.load_images(), self.anime_smooth_generator.load_images()
# restore check-point if it exits
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
start_epoch = checkpoint_counter + 1
print(" [*] Load SUCCESS")
else:
start_epoch = 0
print(" [!] Load failed...")
# loop for epoch
init_mean_loss = []
mean_loss = []
# training times , G : D = self.training_rate : 1
j = self.training_rate
for epoch in range(start_epoch, self.epoch):
for idx in range(int(self.dataset_num / self.batch_size)):
anime, anime_smooth, real = self.sess.run([anime_img_op, anime_smooth_op, real_img_op])
train_feed_dict = {
self.real:real[0],
self.anime:anime[0],
self.anime_gray:anime[1],
self.anime_smooth:anime_smooth[1]
}
if epoch < self.init_epoch :
# Init G
start_time = time.time()
real_images, generator_images, _, v_loss, summary_str = self.sess.run([self.real, self.generated,
self.init_optim,
self.init_loss, self.V_loss_merge], feed_dict = train_feed_dict)
self.writer.add_summary(summary_str, epoch)
init_mean_loss.append(v_loss)
print("Epoch: %3d Step: %5d / %5d time: %f s init_v_loss: %.8f mean_v_loss: %.8f" % (epoch, idx,int(self.dataset_num / self.batch_size), time.time() - start_time, v_loss, np.mean(init_mean_loss)))
if (idx+1)%200 ==0:
init_mean_loss.clear()
else :
start_time = time.time()
if j == self.training_rate:
# Update D
_, d_loss, summary_str = self.sess.run([self.D_optim, self.Discriminator_loss, self.D_loss_merge],
feed_dict=train_feed_dict)
self.writer.add_summary(summary_str, epoch)
# Update G
real_images, generator_images, _, g_loss, summary_str = self.sess.run([self.real, self.generated,self.G_optim,
self.Generator_loss, self.G_loss_merge], feed_dict = train_feed_dict)
self.writer.add_summary(summary_str, epoch)
mean_loss.append([d_loss, g_loss])
if j == self.training_rate:
print(
"Epoch: %3d Step: %5d / %5d time: %f s d_loss: %.8f, g_loss: %.8f -- mean_d_loss: %.8f, mean_g_loss: %.8f" % (
epoch, idx, int(self.dataset_num / self.batch_size), time.time() - start_time, d_loss, g_loss, np.mean(mean_loss, axis=0)[0],
np.mean(mean_loss, axis=0)[1]))
else:
print(
"Epoch: %3d Step: %5d / %5d time: %f s , g_loss: %.8f -- mean_g_loss: %.8f" % (
epoch, idx, int(self.dataset_num / self.batch_size), time.time() - start_time, g_loss, np.mean(mean_loss, axis=0)[1]))
if (idx + 1) % 200 == 0:
mean_loss.clear()
j = j - 1
if j < 1:
j = self.training_rate
if (epoch + 1) >= self.init_epoch and np.mod(epoch + 1, self.save_freq) == 0:
self.save(self.checkpoint_dir, epoch)
if epoch >= self.init_epoch -1:
""" Result Image """
val_files = glob('./dataset/{}/*.*'.format('val'))
save_path = './{}/{:03d}/'.format(self.sample_dir, epoch)
check_folder(save_path)
for i, sample_file in enumerate(val_files):
print('val: '+ str(i) + sample_file)
sample_image = np.asarray(load_test_data(sample_file, self.img_size))
test_real,test_generated = self.sess.run([self.test_real,self.test_generated],feed_dict = {self.test_real:sample_image} )
save_images(test_real, save_path+'{:03d}_a.jpg'.format(i), None)
save_images(test_generated, save_path+'{:03d}_b.jpg'.format(i), None)
@property
def model_dir(self):
return "{}_{}_{}_{}_{}_{}_{}_{}_{}".format(self.model_name, self.dataset_name,
self.gan_type,
int(self.g_adv_weight), int(self.d_adv_weight),
int(self.con_weight), int(self.sty_weight),
int(self.color_weight), int(self.tv_weight))
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) # checkpoint file information
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path) # first line
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
counter = int(ckpt_name.split('-')[-1])
print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name)))
return True, counter
else:
print(" [*] Failed to find a checkpoint")
return False, 0