def my_train():
with tf.Graph().as_default():
sess = tf.Session(config=config)
model = FaceAging(sess=sess,
lr=FLAGS.learning_rate,
keep_prob=1.,
model_num=FLAGS.model_index,
batch_size=FLAGS.batch_size,
age_loss_weight=FLAGS.age_loss_weight,
gan_loss_weight=FLAGS.gan_loss_weight,
fea_loss_weight=FLAGS.fea_loss_weight,
tv_loss_weight=FLAGS.tv_loss_weight)
imgs = tf.placeholder(
tf.float32,
[FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3])
true_label_features_128 = tf.placeholder(
tf.float32, [FLAGS.batch_size, 128, 128, FLAGS.age_groups])
true_label_features_64 = tf.placeholder(
tf.float32, [FLAGS.batch_size, 64, 64, FLAGS.age_groups])
false_label_features_64 = tf.placeholder(
tf.float32, [FLAGS.batch_size, 64, 64, FLAGS.age_groups])
age_label = tf.placeholder(tf.int32, [FLAGS.batch_size])
source_img_227, source_img_128, face_label = load_source_batch3(
FLAGS.source_file, FLAGS.root_folder, FLAGS.batch_size)
model.train_age_lsgan_transfer(source_img_227, source_img_128, imgs,
true_label_features_128,
true_label_features_64,
false_label_features_64,
FLAGS.fea_layer_name, age_label)
ge_samples = model.generate_images(imgs,
true_label_features_128,
reuse=True,
mode='train')
# Create a saver.
model.saver = tf.train.Saver(model.save_d_vars + model.save_g_vars,
max_to_keep=200)
model.alexnet_saver = tf.train.Saver(model.alexnet_vars)
model.age_saver = tf.train.Saver(model.age_vars)
d_error = model.d_loss / model.gan_loss_weight
g_error = model.g_loss / model.gan_loss_weight
fea_error = model.fea_loss / model.fea_loss_weight
age_error = model.age_loss / model.age_loss_weight
# Start running operations on the Graph.
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess)
model.alexnet_saver.restore(sess, FLAGS.alexnet_pretrained_model)
model.age_saver.restore(sess, FLAGS.age_pretrained_model)
if model.load(FLAGS.checkpoint_dir, model.saver):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
print("{} Start training...")
# Loop over max_steps
for step in range(FLAGS.max_steps):
images, t_label_features_128, t_label_features_64, f_label_features_64, age_labels = \
train_generator.next_target_batch_transfer2()
dict = {
imgs: images,
true_label_features_128: t_label_features_128,
true_label_features_64: t_label_features_64,
false_label_features_64: f_label_features_64,
age_label: age_labels
}
for i in range(d_iter):
_, d_loss = sess.run([model.d_optim, d_error], feed_dict=dict)
for i in range(g_iter):
_, g_loss, fea_loss, age_loss = sess.run(
[model.g_optim, g_error, fea_error, age_error],
feed_dict=dict)
format_str = (
'%s: step %d, d_loss = %.3f, g_loss = %.3f, fea_loss=%.3f, age_loss=%.3f'
)
print(format_str %
(datetime.now(), step, d_loss, g_loss, fea_loss, age_loss))
# Save the model checkpoint periodically.
if step % SAVE_INTERVAL == SAVE_INTERVAL - 1 or (
step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.checkpoint_dir)
model.save(checkpoint_path, step, 'acgan')
if step % VAL_INTERVAL == VAL_INTERVAL - 1:
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
path = os.path.join(FLAGS.sample_dir, str(step))
if not os.path.exists(path):
os.makedirs(path)
source = sess.run(source_img_128)
save_source(source, [4, 8], os.path.join(path, 'source.jpg'))
for j in range(train_generator.n_classes):
true_label_fea = train_generator.label_features_128[j]
dict = {
imgs: source,
true_label_features_128: true_label_fea
}
samples = sess.run(ge_samples, feed_dict=dict)
save_images(samples, [4, 8],
'./{}/test_{:01d}.jpg'.format(path, j))
def my_train():
with tf.Graph().as_default():
#기본 작업 : 플레이스 홀더(변수) 선언 및 하이퍼 파라미터 전달 !
sess = tf.Session(config=config)
model = FaceAging(sess=sess,
lr=FLAGS.learning_rate,
keep_prob=1.,
model_num=FLAGS.model_index,
batch_size=FLAGS.batch_size,
age_loss_weight=FLAGS.age_loss_weight,
gan_loss_weight=FLAGS.gan_loss_weight,
fea_loss_weight=FLAGS.fea_loss_weight,
tv_loss_weight=FLAGS.tv_loss_weight)
imgs = tf.placeholder(
tf.float32,
[FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3
]) # (_,128,128,3)
true_label_features_128 = tf.placeholder(
tf.float32, [FLAGS.batch_size, 128, 128, FLAGS.age_groups])
true_label_features_64 = tf.placeholder(
tf.float32, [FLAGS.batch_size, 64, 64, FLAGS.age_groups])
false_label_features_64 = tf.placeholder(
tf.float32, [FLAGS.batch_size, 64, 64, FLAGS.age_groups])
age_label = tf.placeholder(tf.int32, [FLAGS.batch_size])
# _배치 1회! 저장 source_input.py ---------------------------------------------------------------------
source_img_227, source_img_128, face_label = load_source_batch3(
FLAGS.source_file, FLAGS.root_folder, FLAGS.batch_size)
# --------------------------------------------------------------------------------------------------
# with tf.Session():
# print(face_label.eval())
# source_img_227, source_img_128,및 placeholder 전달
model.train_age_lsgan_transfer(source_img_227, source_img_128, imgs,
true_label_features_128,
true_label_features_64,
false_label_features_64,
FLAGS.fea_layer_name, age_label)
# age_label 이아니라 face_label 사용해야 하는 거 아닌가 ?????????????
# placeholder 전달 ,ge_samples는 중간 과정 체크용 사진 생성
ge_samples = model.generate_images(imgs,
true_label_features_128,
reuse=True,
mode='train')
# Create a saver.
model.saver = tf.train.Saver(model.save_d_vars + model.save_g_vars,
max_to_keep=200)
model.alexnet_saver = tf.train.Saver(model.alexnet_vars)
model.age_saver = tf.train.Saver(model.age_vars)
d_error = model.d_loss / model.gan_loss_weight
g_error = model.g_loss / model.gan_loss_weight
fea_error = model.fea_loss / model.fea_loss_weight
age_error = model.age_loss / model.age_loss_weight
# Start running operations on the Graph.
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess)
model.alexnet_saver.restore(sess, FLAGS.alexnet_pretrained_model)
model.age_saver.restore(sess, FLAGS.age_pretrained_model)
if model.load(FLAGS.checkpoint_dir, model.saver):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
print("{} Start training...")
# tensorboard --logdir=./logs
writer = tf.summary.FileWriter("./logs", sess.graph)
'''
img : variable that contains img
'''
# Loop over max_steps
for step in range(FLAGS.max_steps): #200000
#-------------------------------------------------------------------------------------------------------
#data_generator.py 배치 200000번 저장!: images, t_label_features_128, t_label_features_64, f_label_features_64, age_labels:실제 데이터 배치 학습반복
images, t_label_features_128, t_label_features_64, f_label_features_64, age_labels = \
train_generator.next_target_batch_transfer2()
dict = {
imgs: images,
true_label_features_128:
t_label_features_128, #condition for resnet generator
true_label_features_64:
t_label_features_64, #condition for discriminator
false_label_features_64:
f_label_features_64, #condition for discriminator
age_label: age_labels
}
print(len(images))
print("train discriminator------------------------")
# import ipdb
# ipdb.set_trace()
for i in range(d_iter): # 1
_, d_loss = sess.run(
[model.d_optim, d_error],
feed_dict=dict) # dict가 위 placeholder 에 모두 feed?
print("train generator--------------------------")
for i in range(g_iter): # 1
_, g_loss, fea_loss, age_loss = sess.run(
[model.g_optim, g_error, fea_error, age_error],
feed_dict=dict)
format_str = (
'%s: step %d, d_loss = %.3f, g_loss = %.3f, fea_loss=%.3f, age_loss=%.3f'
)
print(format_str %
(datetime.now(), step, d_loss, g_loss, fea_loss, age_loss))
#---------------------------------------------------------------------------------------------------
# Save the model checkpoint periodically
if step % SAVE_INTERVAL == SAVE_INTERVAL - 1 or (
step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.checkpoint_dir)
model.save(checkpoint_path, step, 'acgan')
if step % VAL_INTERVAL == VAL_INTERVAL - 1:
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
path = os.path.join(FLAGS.sample_dir, str(step))
if not os.path.exists(path):
os.makedirs(path)
source = sess.run(source_img_128)
save_source(source, [4, 8], os.path.join(path, 'source.jpg'))
for j in range(train_generator.n_classes):
true_label_fea = train_generator.label_features_128[j]
dict = {
imgs: source,
true_label_features_128: true_label_fea
}
samples = sess.run(ge_samples, feed_dict=dict)
save_images(samples, [4, 8],
'./{}/test_{:01d}.jpg'.format(path, j))