class_docstr: build SiamRPNTracker.predicted tracking result.and according to
  tracking result, product window penalty and smooth bbox
  
  Parameters
  ----------
      PENALTY_K : float
          Scale penalty
      WINDOW_INFLUENCE :float
          Window influence
      LR : float
          Interpolation learning rate
      EXEMPLAR_SIZE : int
          EXEMPLAR_SIZE
      INSTANCE_SIZE : int
          INSTANCE_SIZE
      BASE_SIZE : int
          Base size
      CONTEXT_AMOUNT : float
          Context amount
      STRIDE : int
          Anchor stride
      RATIOS : tuple
          Anchor ratios
      SCALES : tuple
          Anchor scales
class_docstr_tok: ["build","SiamRPNTracker.predicted","tracking","result.and","according","to","tracking","result",",","product","window","penalty","and","smooth","bbox","Parameters","--","--","--","--","--","PENALTY_K",":","float","Scale","penalty","WINDOW_INFLUENCE",":","float","Window","influence","LR",":","float","Interpolation","learning","rate","EXEMPLAR_SIZE",":","int","EXEMPLAR_SIZE","INSTANCE_SIZE",":","int","INSTANCE_SIZE","BASE_SIZE",":","int","Base","size","CONTEXT_AMOUNT",":","float","Context","amount","STRIDE",":","int","Anchor","stride","RATIOS",":","tuple","Anchor","ratios","SCALES",":","tuple","Anchor","scales"]
code: def __init__(
      self,
      model,
      PENALTY_K=0.16,
      WINDOW_INFLUENCE=0.40,
      LR=0.30,
      EXEMPLAR_SIZE=127,
      INSTANCE_SIZ=287,
      BASE_SIZE=0,
      CONTEXT_AMOUNT=0.5,
      STRIDE=8,
      RATIOS=(0.33, 0.5, 1, 2, 3),
      SCALES=(8,),
  ):
      super(SiamRPNTracker, self).__init__()
      self.PENALTY_K = PENALTY_K
      self.WINDOW_INFLUENCE = WINDOW_INFLUENCE
      self.LR = LR
      self.EXEMPLAR_SIZE = EXEMPLAR_SIZE
      self.INSTANCE_SIZE = INSTANCE_SIZ
      self.BASE_SIZE = BASE_SIZE
      self.CONTEXT_AMOUNT = CONTEXT_AMOUNT
      self.STRIDE = STRIDE
      self.RATIOS = list(RATIOS)
      self.SCALES = list(SCALES)
      self.score_size = (
          (self.INSTANCE_SIZE - self.EXEMPLAR_SIZE) // self.STRIDE
          + 1
          + self.BASE_SIZE
      )
      self.anchor_num = len(self.RATIOS) * len(self.SCALES)
      hanning = np.hanning(self.score_size)
      window = np.outer(hanning, hanning)
      self.window = np.tile(window.flatten(), self.anchor_num)
      self.anchors = self.generate_anchor(self.score_size)
      self.model = model
      self.channel_average = None
      self.size = None
      self.center_pos = None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import math
  from collections import namedtuple
  import numpy
  import mxnet
  from mxnet import nd
  from gluoncv.utils.filesystem import try_import_cv2
label_desc: Use image node_id 1 to create a new SiamRPNTracker object from inherited base classes: BaseTracker with example: obj = SiamRPNTracker(model, PENALTY_K, WINDOW_INFLUENCE, LR, EXEMPLAR_SIZE, INSTANCE_SIZ, BASE_SIZE, CONTEXT_AMOUNT, STRIDE, RATIOS, SCALES)