text: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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 sys
  from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize, Base64ToImage
  try:
      from paddle_serving_server_gpu.web_service import WebService, Op
  except ImportError:
      from paddle_serving_server.web_service import WebService, Op
  import logging
  import numpy as np
  import base64, cv2
  
  
  class ImagenetOp(Op):
      def init_op(self):
          self.seq = Sequential([
              Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
              Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
                                  True)
          ])
          self.label_dict = {}
          label_idx = 0
          with open("imagenet.label") as fin:
              for line in fin:
                  self.label_dict[label_idx] = line.strip()
                  label_idx += 1
  
      def preprocess(self, input_dicts, data_id, log_id):
          (_, input_dict), = input_dicts.items()
          batch_size = len(input_dict.keys())
          imgs = []
          for key in input_dict.keys():
              data = base64.b64decode(input_dict[key].encode('utf8'))
              data = np.fromstring(data, np.uint8)
              im = cv2.imdecode(data, cv2.IMREAD_COLOR)
              img = self.seq(im)
              imgs.append(img[np.newaxis, :].copy())
          input_imgs = np.concatenate(imgs, axis=0)
          return {"inputs": input_imgs}, False, None, ""
  
      def postprocess(self, input_dicts, fetch_dict, log_id):
          score_list = fetch_dict["save_infer_model/scale_0.tmp_1"]
          result = {"label": [], "prob": []}
          for score in score_list:
              score = score.tolist()
              max_score = max(score)
              result["label"].append(self.label_dict[score.index(max_score)]
                                     .strip().replace(",", ""))
              result["prob"].append(max_score)
          result["label"] = str(result["label"])
          result["prob"] = str(result["prob"])
          return result, None, ""
  
  
  class ImageService(WebService):
      def get_pipeline_response(self, read_op):
          image_op = ImagenetOp(name="imagenet", input_ops=[read_op])
          return image_op
  
  
  uci_service = ImageService(name="imagenet")
  uci_service.prepare_pipeline_config("config.yml")
  uci_service.run_service()
  