def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
if FLAGS.dataset == 'mnist':
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, y_dim=10, output_size=28, c_dim=1,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
else:
dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)
if FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
print(dcgan.evaluate(6400))
if FLAGS.visualize:
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
def main(_):
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.result_dir):
os.makedirs(FLAGS.result_dir)
dataset = FlowersData(subset=FLAGS.subset)
assert dataset.data_files()
dcgan = DCGAN(z_dim=200, dataset=dataset)
dcgan.evaluate()
if FLAGS.visualize:
# Below is codes for visualization
OPTION = 2
visualize(dcgan, FLAGS, OPTION)
def main(_):
if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height
FLAGS.out_name = '{}-{}x{}-bn{}'.format(FLAGS.dataset, FLAGS.output_height,
FLAGS.output_width,
FLAGS.batch_size)
# FLAGS.out_name = '{}-{}x{}'.format(FLAGS.dataset, FLAGS.output_height,FLAGS.output_width)
FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name)
FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir)
FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
if FLAGS.train:
dcgan.train(FLAGS)
else:
# dcgan.genPics(FLAGS)
dcgan.evaluate(FLAGS)
