class_docstr: RCNN network.
  
  Parameters
  ----------
  features : gluon.HybridBlock
      Base feature extractor before feature pooling layer.
  top_features : gluon.HybridBlock
      Tail feature extractor after feature pooling layer.
  classes : iterable of str
      Names of categories, its length is ``num_class``.
  box_features : gluon.HybridBlock
      feature head for transforming roi output for box prediction.
  short : int
      Input image short side size.
  max_size : int
      Maximum size of input image long side.
  train_patterns : str
      Matching pattern for trainable parameters.
  nms_thresh : float
      Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
  nms_topk : int
      Apply NMS to top k detection results, use -1 to disable so that every Detection
       result is used in NMS.
  roi_mode : str
      ROI pooling mode. Currently support 'pool' and 'align'.
  roi_size : tuple of int, length 2
      (height, width) of the ROI region.
  strides : int/tuple of ints
      Stride(s) of network features. Tuple for FPN.
  clip: float
      Clip bounding box target to this value.
  
  Attributes
  ----------
  classes : iterable of str
      Names of categories, its length is ``num_class``.
  num_class : int
      Number of positive categories.
  short : int
      Input image short side size.
  max_size : int
      Maximum size of input image long side.
  train_patterns : str
      Matching pattern for trainable parameters.
  nms_thresh : float
      Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
  nms_topk : int
      Apply NMS to top k detection results, use -1 to disable so that every Detection
       result is used in NMS.
  minimal_opset : bool
      We sometimes add special operators to accelerate training/inference, however, for exporting
      to third party compilers we want to utilize most widely used operators.
      If `minimal_opset` is `True`, the network will use a minimal set of operators good
      for e.g., `TVM`.
docstr: Set NMS parameters to the network.
  
  .. Note::
      If you are using hybrid mode, make sure you re-hybridize after calling
      ``set_nms``.
  
  Parameters
  ----------
  nms_thresh : float, default is 0.3.
      Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
  nms_topk : int, default is 400
      Apply NMS to top k detection results, use -1 to disable so that every Detection
       result is used in NMS.
  force_nms : bool, default is False
      Appy NMS to all categories, this is to avoid overlapping detection results
      from different categories.
  
  Returns
  -------
  None
docstr_tok: ["Set","NMS","parameters","to","the","network",".","..","Note",":",":","If","you","are","using","hybrid","mode",",","make","sure","you","re-hybridize","after","calling","``","set_nms","``",".","Parameters","--","--","--","--","--","nms_thresh",":","float",",","default","is","0.3",".","Non-maximum","suppression","threshold",".","You","can","specify","<","0","or",">","1","to","disable","NMS",".","nms_topk",":","int",",","default","is","400","Apply","NMS","to","top","k","detection","results",",","use","-1","to","disable","so","that","every","Detection","result","is","used","in","NMS",".","force_nms",":","bool",",","default","is","False","Appy","NMS","to","all","categories",",","this","is","to","avoid","overlapping","detection","results","from","different","categories",".","Returns","--","--","--","-","None"]
code: def set_nms(
      self, nms_thresh=0.3, nms_topk=400, force_nms=False, post_nms=100
  ):
      """Set NMS parameters to the network.
  
      .. Note::
          If you are using hybrid mode, make sure you re-hybridize after calling
          ``set_nms``.
  
      Parameters
      ----------
      nms_thresh : float, default is 0.3.
          Non-maximum suppression threshold. You can specify < 0 or > 1 to disable NMS.
      nms_topk : int, default is 400
          Apply NMS to top k detection results, use -1 to disable so that every Detection
           result is used in NMS.
      force_nms : bool, default is False
          Appy NMS to all categories, this is to avoid overlapping detection results
          from different categories.
  
      Returns
      -------
      None
  
      """
      self._clear_cached_op()
      self.nms_thresh = nms_thresh
      self.nms_topk = nms_topk
      self.force_nms = force_nms
      self.post_nms = post_nms
  
file_path: gluon-cv/gluoncv/model_zoo/rcnn/rcnn.py
imports: from __future__ import absolute_import
  import warnings
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from ...nn.coder import NormalizedBoxCenterDecoder, MultiPerClassDecoder
  from ...nn.feature import FPNFeatureExpander
label_desc: Use image node_id 3 for calling the RCNN obj's underlying member method code with example usage: obj.set_nms(nms_thresh, nms_topk, force_nms, post_nms) without return types