class_docstr: This class loads in image and sentence features for testing.
code: def __init__(self, path):
      self.path = path
      neon_logger.display(
          "Reading test images and sentences from %s" % self.path
      )
      # Load vocab using training set and then load test set
      self.read_images("train")
      self.load_vocab()
      self.read_images("test")
  
      trainIter = self.iterImageSentenceGroup()
      trainSents, trainImgs = [], []
      for i, img_sent in enumerate(trainIter):
          trainImgs.append(img_sent["image"])
          trainSents.append(
              [
                  " ".join(sent["tokens"])
                  for sent in img_sent["sentences"]
              ]
          )
  
      self.nbatches = len(trainImgs) // self.be.bsz
      self.ndata = self.nbatches * self.be.bsz
  
      self.images = np.vstack(trainImgs)
  
      self.ref_sents = trainSents
  
imports: from collections import Counter
  import numpy
  import os
  from neon import logger
  from neon.data.dataiterator import NervanaDataIterator
  from neon.data.datasets import Dataset
  from neon.util.compat import xrange
label_desc: Use image node_id 1 to create a new ImageCaptionTest object from inherited base classes: ImageCaption with example: obj = ImageCaptionTest(path)