print 'NUM_CRITIC: ', NUM_CRITIC
print 'LOAD_MODEL: ', LOAD_MODEL
print 'JITTER: ', JITTER
print 'SIZE: ', SIZE
print 'L1_WEIGHT: ', L1_WEIGHT
print 'L2_WEIGHT: ', L2_WEIGHT
print 'GAN_WEIGHT: ', GAN_WEIGHT
print
# global step that is saved with a model to keep track of how many steps/epochs
global_step = tf.Variable(0, name='global_step', trainable=False)
# load data
Data = data_ops.loadData(DATA_DIR,
DATASET,
BATCH_SIZE,
jitter=JITTER,
SIZE=SIZE)
# number of training images
num_train = Data.count
# The gray 'lightness' channel in range [-1, 1]
L_image = Data.inputs
# The color channels in [-1, 1] range
ab_image = Data.targets
lab_images = tf.concat([L_image, ab_image], axis=3)
if ARCHITECTURE == 'pix2pix':
# generated ab values from generator
ckpt = tf.train.get_checkpoint_state(experiment_dir)
if ckpt and ckpt.model_checkpoint_path:
print "Restoring previous model..."
try:
saver.restore(sess, ckpt.model_checkpoint_path)
print "Model restored"
except:
print "Could not restore model"
pass
step = int(sess.run(global_step))
merged_summary_op = tf.summary.merge_all()
real_images, synthetic_images = data_ops.loadData(dataset)
# just use the last 500 synthetic for testing
train_synthetic, test_synthetic = synthetic_images[:
10000], synthetic_images[
10000:]
real_len = len(real_images)
synth_len = len(train_synthetic)
num_test = len(test_synthetic)
num_train = real_len + synth_len
print 'train:', len(train_synthetic)
print 'test:', len(test_synthetic)
n_critic = 5
MAX_STEPS = a.MAX_STEPS
CHECKPOINT_DIR = 'checkpoints/DATASET_' + DATASET + '/SCALE_' + str(
SCALE) + '/NORM_' + str(NORM) + '/SELU_' + str(SELU) + '/'
IMAGES_DIR = CHECKPOINT_DIR + 'images/'
try:
os.makedirs(IMAGES_DIR)
except:
pass
# placeholders for data going into the network
global_step = tf.Variable(0, name='global_step', trainable=False)
z = tf.placeholder(tf.float32, shape=(BATCH_SIZE, 100), name='z')
train_images_list = data_ops.loadData(DATA_DIR, DATASET)
filename_queue = tf.train.string_input_producer(train_images_list)
real_images = data_ops.read_input_queue(filename_queue, BATCH_SIZE)
# generated images
gen_images = netG(z, BATCH_SIZE)
# get the output from D on the real and fake data
errD_real = netD(real_images, BATCH_SIZE, SELU, NORM)
errD_fake = netD(gen_images, BATCH_SIZE, SELU, NORM, reuse=True)
# cost functions
errD = tf.reduce_mean(errD_real) - tf.reduce_mean(errD_fake)
errG = tf.reduce_mean(errD_fake)
# gradient penalty
sys.path.insert(0, 'ops/') sys.path.insert(0, 'config/') import data_ops if __name__ == '__main__': CHECKPOINT_DIR = 'checkpoints/' IMAGES_DIR = CHECKPOINT_DIR+'images/' BATCH_SIZE=1 test_images = glob.glob(sys.argv[1]+'*.*') num_images = len(test_images) Data = data_ops.loadData(sys.argv[1], BATCH_SIZE, train=False) # The gray 'lightness' channel in range [-1, 1] gray_image = Data.inputs # The color channels in [-1, 1] range color_image = Data.targets # architecture from # http://hi.cs.waseda.ac.jp/~iizuka/projects/colorization/data/colorization_sig2016.pdf col_img = colorarch.architecture(gray_image, train=False) col_img = tf.image.convert_image_dtype(col_img, dtype=tf.uint8, saturate=True) saver = tf.train.Saver(max_to_keep=1) init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
exp_pkl = open(CHECKPOINT_DIR + 'info.pkl', 'wb')
data = pickle.dumps(exp_info)
exp_pkl.write(data)
exp_pkl.close()
print
print 'EPOCHS: ', EPOCHS
print 'DATA_DIR: ', DATA_DIR
print 'BATCH_SIZE: ', BATCH_SIZE
print
# global step that is saved with a model to keep track of how many steps/epochs
global_step = tf.Variable(0, name='global_step', trainable=False)
# load data
Data = data_ops.loadData(DATA_DIR, BATCH_SIZE)
num_train = Data.count
gray_image = Data.inputs
color_image = Data.targets
# architecture from
col_img = net.architecture(gray_image)
#loss = tf.reduce_mean((ab_image-col_img)**2)
loss = tf.reduce_mean(tf.nn.l2_loss(color_image - col_img))
train_op = tf.train.AdamOptimizer(learning_rate=1e-6).minimize(
loss, global_step=global_step)
saver = tf.train.Saver(max_to_keep=1)
# tensorboard summaries
