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: Collect trainable params.
  
  This function serves as a help utility function to return only
  trainable parameters if predefined by experienced developer/researcher.
  For example, if cross-device BatchNorm is not enabled, we will definitely
  want to fix BatchNorm statistics to avoid scaling problem because RCNN training
  batch size is usually very small.
  
  Parameters
  ----------
  select : select : str
      Regular expressions for parameter match pattern
  
  Returns
  -------
  The selected :py:class:`mxnet.gluon.ParameterDict`
docstr_tok: ["Collect","trainable","params",".","This","function","serves","as","a","help","utility","function","to","return","only","trainable","parameters","if","predefined","by","experienced","developer\/researcher",".","For","example",",","if","cross-device","BatchNorm","is","not","enabled",",","we","will","definitely","want","to","fix","BatchNorm","statistics","to","avoid","scaling","problem","because","RCNN","training","batch","size","is","usually","very","small",".","Parameters","--","--","--","--","--","select",":","select",":","str","Regular","expressions","for","parameter","match","pattern","Returns","--","--","--","-","The","selected",":","py",":","class",":","`","mxnet.gluon.ParameterDict","`"]
code: def collect_train_params(self, select=None):
      """Collect trainable params.
  
      This function serves as a help utility function to return only
      trainable parameters if predefined by experienced developer/researcher.
      For example, if cross-device BatchNorm is not enabled, we will definitely
      want to fix BatchNorm statistics to avoid scaling problem because RCNN training
      batch size is usually very small.
  
      Parameters
      ----------
      select : select : str
          Regular expressions for parameter match pattern
  
      Returns
      -------
      The selected :py:class:`mxnet.gluon.ParameterDict`
  
      """
      if select is None:
          return self.collect_params(self.train_patterns)
      return self.collect_params(select)
  
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 2 for calling the RCNN obj's underlying member method code with example usage: obj.collect_train_params(select) and returns: self, self