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,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir)
else:
dcgan = DCGAN(sess,
image_size=FLAGS.image_size,
batch_size=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
is_crop=FLAGS.is_crop,
checkpoint_dir=FLAGS.checkpoint_dir)
if FLAGS.is_infer:
dcgan.infer(FLAGS)
elif FLAGS.metalconv is None and FLAGS.is_train:
dcgan.train(FLAGS)
else:
dcgan.load(FLAGS.checkpoint_dir)
if FLAGS.metalconv is not None:
dcgan.convertToMPS(FLAGS.metalconv)
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)
