text: # Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions
  # are met:
  #  * Redistributions of source code must retain the above copyright
  #    notice, this list of conditions and the following disclaimer.
  #  * Redistributions in binary form must reproduce the above copyright
  #    notice, this list of conditions and the following disclaimer in the
  #    documentation and/or other materials provided with the distribution.
  #  * Neither the name of NVIDIA CORPORATION nor the names of its
  #    contributors may be used to endorse or promote products derived
  #    from this software without specific prior written permission.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
  # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
  # PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
  # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
  # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
  # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  from threading import Thread
  
  import numpy as np
  
  from ga3c.Config import Config
  
  
  class ThreadPredictor(Thread):
      def __init__(self, server, id):
          super(ThreadPredictor, self).__init__()
          self.setDaemon(True)
  
          self.id = id
          self.server = server
          self.exit_flag = False
  
      def run(self):
          ids = np.zeros(Config.PREDICTION_BATCH_SIZE, dtype=np.uint16)
          states = np.zeros(
              (Config.PREDICTION_BATCH_SIZE, Config.IMAGE_HEIGHT, Config.IMAGE_WIDTH, Config.STACKED_FRAMES),
              dtype=np.float32)
  
          while not self.exit_flag:
              ids[0], states[0] = self.server.prediction_q.get()
  
              size = 1
              while size < Config.PREDICTION_BATCH_SIZE and not self.server.prediction_q.empty():
                  ids[size], states[size] = self.server.prediction_q.get()
                  size += 1
  
              batch = states[:size]
              p, v = self.server.model.predict_p_and_v(batch)
  
              for i in range(size):
                  if ids[i] < len(self.server.agents):
                      self.server.agents[ids[i]].wait_q.put((p[i], v[i]))
  