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)