def main():
# Define Data for training
dataX = glob(
os.path.join("./data", FLAGS.datasetX, FLAGS.input_fname_pattern))
dataY = glob(
os.path.join("./data", FLAGS.datasetY, FLAGS.input_fname_pattern))
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
dcgan = DCGAN(sess,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
z_dim=FLAGS.generate_test_images,
c_dim=FLAGS.c_dim,
checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.train:
dcgan.train(FLAGS, dataX, dataY)
else: # INFERENCE
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# Render samples to "samples" folder
# Option 1 render manifold of samples with dim = n*n = number_of_samples
# Option 2 render imagens one by one
dcgan.get_samples(sample_dir=FLAGS.sample_dir, option=1)
print("====DONE=====")
