text: # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import numpy as np
  import cv2
  import copy
  import paddle.fluid as fluid
  from paddlex.cv.transforms import arrange_transforms
  
  
  def interpretation_predict(model, images):
      images = images.astype('float32')
      arrange_transforms(
          model.model_type,
          model.__class__.__name__,
          transforms=model.test_transforms,
          mode='test')
      tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
      model.test_transforms.transforms = model.test_transforms.transforms[-2:]
  
      new_imgs = []
      for i in range(images.shape[0]):
          images[i] = cv2.cvtColor(images[i], cv2.COLOR_RGB2BGR)
          new_imgs.append(model.test_transforms(images[i])[0])
  
      new_imgs = np.array(new_imgs)
      with fluid.scope_guard(model.scope):
          out = model.exe.run(
              model.test_prog,
              feed={'image': new_imgs},
              fetch_list=list(model.interpretation_feats.values()))
  
      model.test_transforms.transforms = tmp_transforms
  
      return out
  