class_docstr: Postprocess the model outputs to get final prediction:
      1. Do NMS for heatmap to get top `max_per_img` bboxes.
      2. Decode bboxes using center offset and box size.
      3. Rescale decoded bboxes reference to the origin image shape.
  Args:
      max_per_img(int): the maximum number of predicted objects in a image,
          500 by default.
      down_ratio(int): the down ratio from images to heatmap, 4 by default.
      regress_ltrb (bool): whether to regress left/top/right/bottom or
          width/height for a box, true by default.
class_docstr_tok: ["Postprocess","the","model","outputs","to","get","final","prediction",":","1",".","Do","NMS","for","heatmap","to","get","top","`","max_per_img","`","bboxes",".","2",".","Decode","bboxes","using","center","offset","and","box","size",".","3",".","Rescale","decoded","bboxes","reference","to","the","origin","image","shape",".","Args",":","max_per_img","(","int",")",":","the","maximum","number","of","predicted","objects","in","a","image",",","500","by","default",".","down_ratio","(","int",")",":","the","down","ratio","from","images","to","heatmap",",","4","by","default",".","regress_ltrb","(","bool",")",":","whether","to","regress","left\/top\/right\/bottom","or","width\/height","for","a","box",",","true","by","default","."]
docstr: Use maxpool to filter the max score, get local peaks. 
code: def _simple_nms(self, heat, kernel=3):
      """Use maxpool to filter the max score, get local peaks."""
      pad = (kernel - 1) // 2
      hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad)
      keep = paddle.cast(hmax == heat, "float32")
      return heat * keep
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import nonempty_bbox
  from .transformers import bbox_cxcywh_to_xyxy
label_desc: Use image node_id 2 for calling the CenterNetPostProcess obj's underlying member method code with example usage: obj._simple_nms(heat, kernel) and returns: unknown