Freeze graph about real-time-self-adaptive-deep-stereo HOT 1 CLOSED

left_img_batch = tf.placeholder(tf.float32,shape=[1,384,1280,3], name='left_input')
right_img_batch = tf.placeholder(tf.float32, shape=[1,384,1280,3], name='right_input')

with tf.variable_scope('model'):
    net_args = {}
    net_args['left_img'] = left_img_batch
    net_args['right_img'] = right_img_batch
    net_args['split_layers'] = [None]
    net_args['sequence'] = True
    net_args['train_portion'] = 'BEGIN'
    net_args['bulkhead'] = False
    stereo_net = Nets.get_stereo_net('MADNet', net_args)
    predictions = stereo_net.get_disparities()

full_res_disp = tf.identity(predictions[-1], name='full_res_disp')