def compute(self, inputs, outputs):
    """
    Run one iteration, profiling it if requested.

    :param inputs: (dict) mapping region input names to numpy.array values
    :param outputs: (dict) mapping region output names to numpy.arrays that 
           should be populated with output values by this method
    """

    # Uncomment this to find out who is generating divide by 0, or other numpy warnings
    # numpy.seterr(divide='raise', invalid='raise', over='raise')

    # Modify this line to turn on profiling for a given node. The results file
    #  ('hotshot.stats') will be sensed and printed out by the vision framework's
    #  RunInference.py script at the end of inference.
    # Also uncomment the hotshot import at the top of this file.
    if False and self.learningMode \
        and self._iterations > 0 and self._iterations <= 10:

      import hotshot
      if self._iterations == 10:
        print "\n  Collecting and sorting internal node profiling stats generated by hotshot..."
        stats = hotshot.stats.load("hotshot.stats")
        stats.strip_dirs()
        stats.sort_stats('time', 'calls')
        stats.print_stats()

      # The guts of the compute are contained in the _compute() call so that we
      # can profile it if requested.
      if self._profileObj is None:
        print "\n  Preparing to capture profile using hotshot..."
        if os.path.exists('hotshot.stats'):
          # There is an old hotshot stats profile left over, remove it.
          os.remove('hotshot.stats')
        self._profileObj = hotshot.Profile("hotshot.stats", 1, 1)
                                          # filename, lineevents, linetimings
      self._profileObj.runcall(self._compute, *[inputs, outputs])
    else:
      self._compute(inputs, outputs)
def compute(self, inputs, outputs):
    """
    Run one iteration, profiling it if requested.

    :param inputs: (dict) mapping region input names to numpy.array values
    :param outputs: (dict) mapping region output names to numpy.arrays that 
           should be populated with output values by this method
    """

    # Uncomment this to find out who is generating divide by 0, or other numpy warnings
    # numpy.seterr(divide='raise', invalid='raise', over='raise')

    # Modify this line to turn on profiling for a given node. The results file
    #  ('hotshot.stats') will be sensed and printed out by the vision framework's
    #  RunInference.py script at the end of inference.
    # Also uncomment the hotshot import at the top of this file.
    if False and self.learningMode \
        and self._iterations > 0 and self._iterations <= 10:

      import hotshot
      if self._iterations == 10:
        print "\n  Collecting and sorting internal node profiling stats generated by hotshot..."
        stats = hotshot.stats.load("hotshot.stats")
        stats.strip_dirs()
        stats.sort_stats('time', 'calls')
        stats.print_stats()

      # The guts of the compute are contained in the _compute() call so that we
      # can profile it if requested.
      if self._profileObj is None:
        print "\n  Preparing to capture profile using hotshot..."
        if os.path.exists('hotshot.stats'):
          # There is an old hotshot stats profile left over, remove it.
          os.remove('hotshot.stats')
        self._profileObj = hotshot.Profile("hotshot.stats", 1, 1)
                                          # filename, lineevents, linetimings
      self._profileObj.runcall(self._compute, *[inputs, outputs])
    else:
      self._compute(inputs, outputs)
def compute ( self , inputs , outputs ) : # Uncomment this to find out who is generating divide by 0, or other numpy warnings # numpy.seterr(divide='raise', invalid='raise', over='raise') # Modify this line to turn on profiling for a given node. The results file #  ('hotshot.stats') will be sensed and printed out by the vision framework's #  RunInference.py script at the end of inference. # Also uncomment the hotshot import at the top of this file. if False and self . learningMode and self . _iterations > 0 and self . _iterations <= 10 : import hotshot if self . _iterations == 10 : print "\n  Collecting and sorting internal node profiling stats generated by hotshot..." stats = hotshot . stats . load ( "hotshot.stats" ) stats . strip_dirs ( ) stats . sort_stats ( 'time' , 'calls' ) stats . print_stats ( ) # The guts of the compute are contained in the _compute() call so that we # can profile it if requested. if self . _profileObj is None : print "\n  Preparing to capture profile using hotshot..." if os . path . exists ( 'hotshot.stats' ) : # There is an old hotshot stats profile left over, remove it. os . remove ( 'hotshot.stats' ) self . _profileObj = hotshot . Profile ( "hotshot.stats" , 1 , 1 ) # filename, lineevents, linetimings self . _profileObj . runcall ( self . _compute , * [ inputs , outputs ] ) else : self . _compute ( inputs , outputs )
Run one iteration, profiling it if requested.

    :param inputs: (dict) mapping region input names to numpy.array values
    :param outputs: (dict) mapping region output names to numpy.arrays that 
           should be populated with output values by this method
Run one iteration profiling it if requested .