code: import argparse
  import scipy.io
  import torch
  import numpy as np
  import os
  from torchvision import datasets
  import matplotlib
  matplotlib.use('agg')
  import matplotlib.pyplot as plt
  #######################################################################
  # Evaluate
  parser = argparse.ArgumentParser(description='Demo')
  parser.add_argument('--query_index', default=777, type=int, help='test_image_index')
  parser.add_argument('--test_dir',default='/mnt/hdd_data/Dataset/market1501_ss/pytorch',type=str, help='./test_data')
  opts = parser.parse_args()
  
  data_dir = opts.test_dir
  image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ) for x in ['gallery','query']}
  
  #####################################################################
  #Show result
  def imshow(path, title=None):
      """Imshow for Tensor."""
      im = plt.imread(path)
      plt.imshow(im)
      if title is not None:
          plt.title(title)
      plt.pause(0.001)  # pause a bit so that plots are updated
  
  ######################################################################
  result = scipy.io.loadmat('pytorch_result.mat')
  query_feature = torch.FloatTensor(result['query_f'])
  query_cam = result['query_cam'][0]
  query_label = result['query_label'][0]
  gallery_feature = torch.FloatTensor(result['gallery_f'])
  gallery_cam = result['gallery_cam'][0]
  gallery_label = result['gallery_label'][0]
  
  multi = os.path.isfile('multi_query.mat')
  
  if multi:
      m_result = scipy.io.loadmat('multi_query.mat')
      mquery_feature = torch.FloatTensor(m_result['mquery_f'])
      mquery_cam = m_result['mquery_cam'][0]
      mquery_label = m_result['mquery_label'][0]
      mquery_feature = mquery_feature.cuda()
  
  query_feature = query_feature.cuda()
  gallery_feature = gallery_feature.cuda()
  
  #######################################################################
  # sort the images
  def sort_img(qf, ql, qc, gf, gl, gc):
      query = qf.view(-1,1)
      # print(query.shape)
      score = torch.mm(gf,query)
      score = score.squeeze(1).cpu()
      score = score.numpy()
      # predict index
      index = np.argsort(score)  #from small to large
      index = index[::-1]
      # index = index[0:2000]
      # good index
      query_index = np.argwhere(gl==ql)
      #same camera
      camera_index = np.argwhere(gc==qc)
  
      #good_index = np.setdiff1d(query_index, camera_index, assume_unique=True)
      junk_index1 = np.argwhere(gl==-1)
      junk_index2 = np.intersect1d(query_index, camera_index)
      junk_index = np.append(junk_index2, junk_index1) 
  
      mask = np.in1d(index, junk_index, invert=True)
      index = index[mask]
      return index
  
  i = opts.query_index
  index = sort_img(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)
  
  ########################################################################
  # Visualize the rank result
  
  query_path, _ = image_datasets['query'].imgs[i]
  query_label = query_label[i]
  print(query_path)
  print('Top 10 images are as follow:')
  try: # Visualize Ranking Result 
      # Graphical User Interface is needed
      fig = plt.figure(figsize=(16,4))
      ax = plt.subplot(1,11,1)
      ax.axis('off')
      imshow(query_path,'query')
      for i in range(10):
          ax = plt.subplot(1,11,i+2)
          ax.axis('off')
          img_path, _ = image_datasets['gallery'].imgs[index[i]]
          label = gallery_label[index[i]]
          imshow(img_path)
          if label == query_label:
              ax.set_title('%d'%(i+1), color='green')
          else:
              ax.set_title('%d'%(i+1), color='red')
          print(img_path)
  except RuntimeError:
      for i in range(10):
          img_path = image_datasets.imgs[index[i]]
          print(img_path[0])
      print('If you want to see the visualization of the ranking result, graphical user interface is needed.')
  
  fig.savefig("show.png")
  
apis: matplotlib.pyplot.imread torch.FloatTensor matplotlib.pyplot.pause numpy.append numpy.argwhere numpy.intersect1d matplotlib.pyplot.figure torch.mm numpy.argsort numpy.in1d matplotlib.pyplot.title matplotlib.pyplot.imshow matplotlib.pyplot.subplot matplotlib.use