text: # Copyright (c) 2019 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.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os, sys
  # add python path of PadleDetection to sys.path
  parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
  if parent_path not in sys.path:
      sys.path.append(parent_path)
  
  import paddle.fluid as fluid
  
  from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results, json_eval_results
  import ppdet.utils.checkpoint as checkpoint
  from ppdet.utils.check import check_gpu, check_version, check_config
  
  from ppdet.data.reader import create_reader
  
  from ppdet.core.workspace import load_config, merge_config, create
  from ppdet.utils.cli import ArgsParser
  
  import logging
  FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
  logging.basicConfig(level=logging.INFO, format=FORMAT)
  logger = logging.getLogger(__name__)
  
  
  def main():
      """
      Main evaluate function
      """
      cfg = load_config(FLAGS.config)
      merge_config(FLAGS.opt)
      check_config(cfg)
      # check if set use_gpu=True in paddlepaddle cpu version
      check_gpu(cfg.use_gpu)
      # check if paddlepaddle version is satisfied
      check_version()
  
      main_arch = cfg.architecture
  
      multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
  
      # define executor
      place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      # build program
      model = create(main_arch)
      startup_prog = fluid.Program()
      eval_prog = fluid.Program()
      with fluid.program_guard(eval_prog, startup_prog):
          with fluid.unique_name.guard():
              inputs_def = cfg['EvalReader']['inputs_def']
              feed_vars, loader = model.build_inputs(**inputs_def)
              if multi_scale_test is None:
                  fetches = model.eval(feed_vars)
              else:
                  fetches = model.eval(feed_vars, multi_scale_test)
      eval_prog = eval_prog.clone(True)
  
      reader = create_reader(cfg.EvalReader, devices_num=1)
      loader.set_sample_list_generator(reader, place)
  
      dataset = cfg['EvalReader']['dataset']
  
      # eval already exists json file
      if FLAGS.json_eval:
          logger.info(
              "In json_eval mode, PaddleDetection will evaluate json files in "
              "output_eval directly. And proposal.json, bbox.json and mask.json "
              "will be detected by default.")
          json_eval_results(
              cfg.metric, json_directory=FLAGS.output_eval, dataset=dataset)
          return
  
      compile_program = fluid.compiler.CompiledProgram(
          eval_prog).with_data_parallel()
  
      assert cfg.metric != 'OID', "eval process of OID dataset \
                            is not supported."
  
      if cfg.metric == "WIDERFACE":
          raise ValueError("metric type {} does not support in tools/eval.py, "
                           "please use tools/face_eval.py".format(cfg.metric))
      assert cfg.metric in ['COCO', 'VOC'], \
              "unknown metric type {}".format(cfg.metric)
      extra_keys = []
  
      if cfg.metric == 'COCO':
          extra_keys = ['im_info', 'im_id', 'im_shape']
      if cfg.metric == 'VOC':
          extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
  
      keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
  
      # whether output bbox is normalized in model output layer
      is_bbox_normalized = False
      if hasattr(model, 'is_bbox_normalized') and \
              callable(model.is_bbox_normalized):
          is_bbox_normalized = model.is_bbox_normalized()
  
      sub_eval_prog = None
      sub_keys = None
      sub_values = None
      # build sub-program
      if 'Mask' in main_arch and multi_scale_test:
          sub_eval_prog = fluid.Program()
          with fluid.program_guard(sub_eval_prog, startup_prog):
              with fluid.unique_name.guard():
                  inputs_def = cfg['EvalReader']['inputs_def']
                  inputs_def['mask_branch'] = True
                  feed_vars, eval_loader = model.build_inputs(**inputs_def)
                  sub_fetches = model.eval(
                      feed_vars, multi_scale_test, mask_branch=True)
                  assert cfg.metric == 'COCO'
                  extra_keys = ['im_id', 'im_shape']
          sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
                                                  extra_keys)
          sub_eval_prog = sub_eval_prog.clone(True)
  
      #if 'weights' in cfg:
      #    checkpoint.load_params(exe, sub_eval_prog, cfg.weights)
  
      # load model
      exe.run(startup_prog)
      if 'weights' in cfg:
          checkpoint.load_params(exe, startup_prog, cfg.weights)
  
      resolution = None
      if 'Mask' in cfg.architecture:
          resolution = model.mask_head.resolution
      results = eval_run(exe, compile_program, loader, keys, values, cls, cfg,
                         sub_eval_prog, sub_keys, sub_values, resolution)
  
      #print(cfg['EvalReader']['dataset'].__dict__)
      # evaluation
      # if map_type not set, use default 11point, only use in VOC eval
      map_type = cfg.map_type if 'map_type' in cfg else '11point'
      save_only = getattr(cfg, 'save_prediction_only', False)
      eval_results(
          results,
          cfg.metric,
          cfg.num_classes,
          resolution,
          is_bbox_normalized,
          FLAGS.output_eval,
          map_type,
          dataset=dataset,
          save_only=save_only)
  
  
  if __name__ == '__main__':
      parser = ArgsParser()
      parser.add_argument(
          "--json_eval",
          action='store_true',
          default=False,
          help="Whether to re eval with already exists bbox.json or mask.json")
      parser.add_argument(
          "-f",
          "--output_eval",
          default=None,
          type=str,
          help="Evaluation file directory, default is current directory.")
      FLAGS = parser.parse_args()
      main()
  