text: """
  Simple tester for the vgg16
  """
  import tensorflow as tf
  import vgg16.vgg16 as vgg16
  from dataSetGenerator import picShow
  from numpy import load, random
  from os import environ
  import argparse
  
  parser = argparse.ArgumentParser(prog="Test vgg16",description="Simple tester for the vgg16_trainable")
  parser.add_argument('--dataset', metavar='dataset', type=str,required=True,
                      help='DataSet Name')
  parser.add_argument('--batch', metavar='batch', type=int, default=12, help='batch size ')
  
  args = parser.parse_args()
  
  
  classes_name = args.dataset
  batch_size = args.batch
  
  # batch_size = 12
  # classes_name = "RSSCN7"
  # classes_name = "SIRI-WHU"
  # classes_name = "UCMerced_LandUse"
  
  classes = load("DataSets/{0}/{0}_classes.npy".format(classes_name))
  batch = load("DataSets/{0}/{0}_dataTest.npy".format(classes_name)) # read one picture
  label =load("DataSets/{0}/{0}_labelsTest.npy".format(classes_name))
  
  rib = batch.shape[1]
  data = random.randint(batch.shape[0], size=batch_size)
  
  # with tf.Session(config=tf.ConfigProto(gpu_options=(tf.GPUOptions(per_process_gpu_memory_fraction=0.7)))) as sess:
  with tf.device('/cpu:0'):
      with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=int(environ['NUMBER_OF_PROCESSORS']))) as sess:
          images = tf.placeholder(tf.float32, [None, rib, rib, 3])
  
          vgg = vgg16.Vgg16("Weights/VGG16_{}.npy".format(classes_name)) #set the path
          with tf.name_scope("content_vgg"):
              vgg.build(images)
  
          prob = sess.run(vgg.prob, {images: batch[data]})
          picShow(batch[data], label[data], classes, None, prob, Save_as="test19_{}.png".format(classes_name))