new_contents: import time
  
  from core.detection_module import DetModule
  
  import argparse
  import importlib
  import mxnet as mx
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Test detector inference speed')
      # general
      parser.add_argument('--config', help='config file path', type=str, required=True)
      parser.add_argument('--shape', help='specify input 2d image shape', metavar=('SHORT', 'LONG'), type=int, nargs=2, required=True)
      parser.add_argument('--gpu', help='GPU index', type=int, default=0)
      parser.add_argument('--count', help='number of runs, final result will be averaged', type=int, default=100)
      args = parser.parse_args()
  
      config = importlib.import_module(args.config.replace('.py', '').replace('/', '.'))
      return config, args.gpu, args.shape, args.count
  
  
  if __name__ == "__main__":
      config, gpu, shape, count = parse_args()
  
      pGen, pKv, pRpn, pRoi, pBbox, pDataset, pModel, pOpt, pTest, \
          transform, data_name, label_name, metric_list = config.get_config(is_train=False)
  
      sym = pModel.test_symbol
  
      # create dummy data batch
      data = mx.nd.ones(shape=[1, 3] + shape)
      im_info = mx.nd.array([x / 2.0 for x in shape] + [2.0]).reshape(1, 3)
      im_id = mx.nd.array([1])
      rec_id = mx.nd.array([1])
      data_names = ["data", "im_info", "im_id", "rec_id"]
      data_shape = [[1, 3] + shape, [1, 3], [1], [1]]
      data_shape = [(name, shape) for name, shape in zip(data_names, data_shape)]
      data_batch = mx.io.DataBatch(data=[data, im_info, im_id, rec_id])
  
      ctx = mx.gpu(gpu)
      mod = DetModule(sym, data_names=data_names, context=ctx)
      mod.bind(data_shapes=data_shape, for_training=False)
      mod.set_params({}, {}, True)
  
      # let AUTOTUNE run for once
      mod.forward(data_batch, is_train=False)
      for output in mod.get_outputs():
          output.wait_to_read()
  
      tic = time.time()
      for _ in range(count):
          mod.forward(data_batch, is_train=False)
          for output in mod.get_outputs():
              output.wait_to_read()
      toc = time.time()
  
      print((toc - tic) / count * 1000)
  
  
subject: add infer speed script (#102)