LR = FLAGS.lr
MOMENTUM = FLAGS.momentum
ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
LOG_PATH = os.path.join(ROOT_PATH, FLAGS.log_dir)
if not os.path.exists(LOG_PATH): os.mkdir(LOG_PATH)
acc_count = 0
while True:
if os.path.exists(os.path.join(LOG_PATH, 'log_%02d.txt' % acc_count)):
acc_count += 1
else:
break
LOG_FNAME = 'log_%02d.txt' % acc_count
LOG_FOUT = open(os.path.join(LOG_PATH, LOG_FNAME), 'w')
(train_images, train_labels,
train_att), train_iters = data.data_train(REAL_PATH, TRAIN_LABEL, BATCH_SIZE)
(fake_images, fake_labels,
fake_att), fake_iters = data.data_train(FAKE_PATH, TRAIN_LABEL, BATCH_SIZE)
(valid_images, valid_labels,
valid_att), valid_iters = data.data_test(REAL_PATH, VALID_LABEL,
BATCH_SIZE * 10)
(test_images, test_labels,
test_att), test_iters = data.data_test(REAL_PATH, TEST_LABEL, BATCH_SIZE)
####################################################
def log_string(out_str):
LOG_FOUT.write(out_str + '\n')
LOG_FOUT.flush()
print(out_str)
VALID_LABEL = FLAGS.valid_label
BATCH_SIZE = FLAGS.batch_size
N_SAMPLE = FLAGS.sample_size
N_EPOCH = FLAGS.n_epoch
N_ADV = FLAGS.n_adv
N_CLASS = FLAGS.n_class
LR = FLAGS.lr
MOMENTUM = FLAGS.momentum
ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
LOG_PATH = os.path.join(ROOT_PATH, FLAGS.log_dir)
OUT_PATH = os.path.join(ROOT_PATH, FLAGS.output_dir)
if not os.path.exists(LOG_PATH): os.mkdir(LOG_PATH)
if not os.path.exists(OUT_PATH): os.mkdir(OUT_PATH)
(train_images,
train_labels), train_iters = data.data_train(IMG_PATH, TRAIN_LABEL,
BATCH_SIZE)
(valid_images,
valid_labels), valid_iters = data.data_test(IMG_PATH, VALID_LABEL, N_SAMPLE)
Genc = model.Genc()
Gdec = model.Gdec()
D = model.D()
Adv = model.Adv_cls()
####################################################
def V_graph(sess, phv):
real_labels = valid_labels * 2 - 1
fake_labels = -real_labels
u = Genc.build(valid_images, phv['is_training_v'])
'Wavy_Hair': 33,
'Wearing_Earrings': 34,
'Wearing_Hat': 35,
'Wearing_Lipstick': 36,
'Wearing_Necklace': 37,
'Wearing_Necktie': 38,
'Young': 39
}
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu
# tf.set_random_seed(0)# 0 for 512
tf.set_random_seed(100)
(train_images, train_labels,
train_att), train_iters = data.data_train(FLAGS.real_path, FLAGS.train_label,
64)
(fake_images, fake_labels,
fake_att), fake_iters = data.data_fake(FLAGS.fake_path, FLAGS.train_label, 64)
(valid_images, valid_labels,
valid_att), valid_iters = data.data_test(FLAGS.real_path, FLAGS.valid_label,
FLAGS.batch_size)
(test_images, test_labels,
test_att), test_iters = data.data_test(FLAGS.real_path, FLAGS.test_label,
FLAGS.batch_size)
batch_images = tf.placeholder(tf.float32, [None, 128, 128, 3])
batch_labels = tf.placeholder(tf.int32, [
None,
])
is_training = tf.placeholder(tf.bool)
lr_ph = tf.placeholder(tf.float32)
N_EPISODE = FLAGS.n_episode
N_CLASS = FLAGS.n_class
N_ACTION = FLAGS.n_action
LR = FLAGS.lr
MOMENTUM = FLAGS.momentum
ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
LOG_PATH = os.path.join(ROOT_PATH, FLAGS.log_dir)
if not os.path.exists(LOG_PATH): os.mkdir(LOG_PATH)
acc_count = 0
while True:
if os.path.exists(os.path.join(LOG_PATH, 'log_%02d.txt' % acc_count)): acc_count += 1
else: break
LOG_FNAME = 'log_%02d.txt' % acc_count
LOG_FOUT = open(os.path.join(LOG_PATH, LOG_FNAME), 'w')
(train_images, train_labels, train_att), train_iters = data.data_train(REAL_PATH, TRAIN_LABEL, BATCH_SIZE)
(fake_images, fake_labels, fake_att), fake_iters = data.data_train(FAKE_PATH, TRAIN_LABEL, BATCH_SIZE)
(valid_images, valid_labels, valid_att), valid_iters = data.data_test(REAL_PATH, VALID_LABEL, BATCH_SIZE)
(test_images, test_labels, test_att), test_iters = data.data_test(REAL_PATH, TEST_LABEL, BATCH_SIZE)
####################################################
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def choose_action(prob_actions):
actions = []
for i in range(prob_actions.shape[0]):
action = np.random.choice(range(prob_actions.shape[1]), p=prob_actions[i])
