class_docstr: Trains post-hoc detection SVMs for all classes using the algorithm
  and hyper-parameters of traditional R-CNN.
class_docstr_tok: ["Trains","post-hoc","detection","SVMs","for","all","classes","using","the","algorithm","and","hyper-parameters","of","traditional","R-CNN","."]
code: def _get_pos_counts(self):
      counts = np.zeros((len(self.imdb.classes)), dtype=np.int)
      roidb = self.imdb.roidb
      for i in range(len(roidb)):
          for j in range(1, self.imdb.num_classes):
              I = np.where(roidb[i]["gt_classes"] == j)[0]
              counts[j] += len(I)
  
      for j in range(1, self.imdb.num_classes):
          print(
              "class {:s} has {:d} positives".format(
                  self.imdb.classes[j], counts[j]
              )
          )
  
      return counts
  
imports: from timer import Timer
  from sklearn import svm
  import numpy
label_desc: Use image node_id 3 for calling the SVMTrainer obj's underlying member method code with example usage: obj._get_pos_counts() and returns: counts