text: #!/usr/bin/env python
  # -*- encoding: utf-8 -*-
  """predict"""
  
  import os
  import mindspore.dataset as ds
  from mindspore import Tensor
  from mindspore import load_checkpoint, load_param_into_net
  from networks.model import RetinexLieIqaNetVgg
  from utils.datasets import LieIqaDataset
  from utils.tools import create_iqa_testset
  
  
  ds.config.set_seed(58)
  root_dir = os.path.dirname(os.path.realpath(__file__))
  lledataset = LieIqaDataset(root_dir=os.path.join(root_dir, 'images'), txt_file='test_file.txt')
  lledataset = ds.GeneratorDataset(lledataset, ['x', 'y', 'x_name'], shuffle=False)
  lledataset = create_iqa_testset(lledataset, batch_size=1)
  dataset_iter = lledataset.create_dict_iterator(output_numpy=True)
  
  model = RetinexLieIqaNetVgg(load_weight=True)
  net_dict = load_checkpoint(os.path.join(root_dir, 'checkpoint/Retinex_LIEIQANet.ckpt'))
  # net_dict = load_checkpoint(os.path.join(root_dir, 'checkpoint/Retinex_ComIQANet.ckpt'))
  param_not_load = load_param_into_net(model, net_dict, strict_load=True)
  print('params not load to net: ', param_not_load)
  
  for _, data in enumerate(dataset_iter):
      lle_img = Tensor(data['x'])
      ref_img = Tensor(data['y'])
      lle_name = str(data['x_name'][0], encoding='utf-8')
      score = model(lle_img, ref_img)
      score = score.asnumpy()
      print('{:32}: {:.3f} '.format(lle_name, score))
  