def main(_):
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
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(allow_soft_placement=True)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
dcgan = DCGAN(sess,
input_depth=FLAGS.input_depth,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_depth=FLAGS.output_depth,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=1,
c_dim=FLAGS.c_dim,
dataset_name=FLAGS.dataset,
data_type=FLAGS.data_type,
mode=FLAGS.mode,
checkpoint_dir=FLAGS.checkpoint_dir,
training=FLAGS.is_train)
if FLAGS.is_train:
dcgan.train(FLAGS)
exit(0)
else:
if not dcgan.load(FLAGS.checkpoint_dir):
raise Exception("[!] Train a model first, then run test mode")
sample_z = np.random.uniform(-1., 1.,
size=(10, 1, 100)).astype(np.float32)
dcgan.same(sample_z, radio=1)
