title: How can I inference with multiple input network on TensorRT?
body: <p>I would like to test <code>GQ-CNN</code> which is network in <code>Dex-Net</code> on <code>tensorRT</code>.
  I successfully converted tflite file to uff file but when I tried to inference with that network, there is an error I couldn't figure out.</p>
  
  <pre><code>[TensorRT] ERROR: Parameter check failed at: ../builder/Network.cpp::addLRN::149, condition: lrnWindow &amp; 0x1
  python3: uff/orders.cpp:330: void UffParser::addTranspose(ParserLayer&amp;, std::vector&lt;int&gt;): Assertion `outputs.size() == 1' failed.
  </code></pre>
  
  <p>The error is appeared when building model.
  I tried to find clue from google but there are no codes and no references.</p>
  
  <p>There's only different thing compare with example code that works well.
  (I wrote captions which codes I added. If I remove that codes and replace model file to single input network, it works well.)</p>
  
  <p>I registered input twice like below code because GQ-CNN has multiple input.
  So I guess that registering multiple input using uffparser could be the main reason of that error.</p>
  
  <pre><code>class ModelData(object):
      MODEL_FILE = "./gqcnn.uff"
      INPUT_NAME_1 = "Placeholder"
      INPUT_SHAPE_1 = (1, 32, 32)
      INPUT_NAME_2 = "Placeholder_1"
      INPUT_SHAPE_2 = (2,)
      OUTPUT_NAME = "softmax/Softmax"
  
  def build_engine(model_file):
      # For more information on TRT basics, refer to the introductory samples.
      with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:
          builder.max_workspace_size = common.GiB(1)
          builder.fp16_mode = True
          #builder.int8_mode = True
  
          # Parse the Uff Network
          parser.register_input(ModelData.INPUT_NAME_1, ModelData.INPUT_SHAPE_1)
          parser.register_input(ModelData.INPUT_NAME_2, ModelData.INPUT_SHAPE_2) # added code
          parser.register_output(ModelData.OUTPUT_NAME)
          parser.parse(model_file, network)
  
          # Build and return an engine.
          return builder.build_cuda_engine(network)
  
  # do inference
  with build_engine(ModelData.MODEL_FILE) as engine:
      # Build an engine, allocate buffers and create a stream.
      # For more information on buffer allocation, refer to the introductory samples.
      inputs, outputs, bindings, stream = common.allocate_buffers(engine)
      with engine.create_execution_context() as context:
          for idx in range(len(val_images)) :
              start = time.time()
              val_image = val_images[idx]
              val_pose = val_poses[idx] # added code
              np.copyto(inputs[0].host, val_image)
              np.copyto(inputs[1].host, val_pose) # added code
              [prediction] = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
  </code></pre>
  
  <p>Is there anyone who succeeded to inference with multiple input model?</p>