text: #!/usr/bin/env python
  
  """
  Copyright 2017-2018 Fizyr (https://fizyr.com)
  
  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 argparse
  import os
  import sys
  
  import keras
  import tensorflow as tf
  
  # Allow relative imports when being executed as script.
  if __name__ == "__main__" and __package__ is None:
      sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
      import keras_retinanet.bin  # noqa: F401
      __package__ = "keras_retinanet.bin"
  
  # Change these to absolute imports if you copy this script outside the keras_retinanet package.
  from .. import models
  from ..preprocessing.csv_generator import CSVGenerator
  from ..preprocessing.pascal_voc import PascalVocGenerator
  from ..utils.config import read_config_file, parse_anchor_parameters
  from ..utils.eval import evaluate
  from ..utils.keras_version import check_keras_version
  
  
  def get_session():
      """ Construct a modified tf session.
      """
      config = tf.ConfigProto()
      config.gpu_options.allow_growth = True
      return tf.Session(config=config)
  
  
  def create_generator(args):
      """ Create generators for evaluation.
      """
      if args.dataset_type == 'coco':
          # import here to prevent unnecessary dependency on cocoapi
          from ..preprocessing.coco import CocoGenerator
  
          validation_generator = CocoGenerator(
              args.coco_path,
              'val2017',
              image_min_side=args.image_min_side,
              image_max_side=args.image_max_side,
              config=args.config
          )
      elif args.dataset_type == 'pascal':
          validation_generator = PascalVocGenerator(
              args.pascal_path,
              'test',
              image_min_side=args.image_min_side,
              image_max_side=args.image_max_side,
              config=args.config
          )
      elif args.dataset_type == 'csv':
          validation_generator = CSVGenerator(
              args.annotations,
              args.classes,
              image_min_side=args.image_min_side,
              image_max_side=args.image_max_side,
              config=args.config
          )
      else:
          raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
  
      return validation_generator
  
  
  def parse_args(args):
      """ Parse the arguments.
      """
      parser     = argparse.ArgumentParser(description='Evaluation script for a RetinaNet network.')
      subparsers = parser.add_subparsers(help='Arguments for specific dataset types.', dest='dataset_type')
      subparsers.required = True
  
      coco_parser = subparsers.add_parser('coco')
      coco_parser.add_argument('coco_path', help='Path to dataset directory (ie. /tmp/COCO).')
  
      pascal_parser = subparsers.add_parser('pascal')
      pascal_parser.add_argument('pascal_path', help='Path to dataset directory (ie. /tmp/VOCdevkit).')
  
      csv_parser = subparsers.add_parser('csv')
      csv_parser.add_argument('annotations', help='Path to CSV file containing annotations for evaluation.')
      csv_parser.add_argument('classes', help='Path to a CSV file containing class label mapping.')
  
      parser.add_argument('model',              help='Path to RetinaNet model.')
      parser.add_argument('--convert-model',    help='Convert the model to an inference model (ie. the input is a training model).', action='store_true')
      parser.add_argument('--backbone',         help='The backbone of the model.', default='resnet50')
      parser.add_argument('--gpu',              help='Id of the GPU to use (as reported by nvidia-smi).')
      parser.add_argument('--score-threshold',  help='Threshold on score to filter detections with (defaults to 0.05).', default=0.05, type=float)
      parser.add_argument('--iou-threshold',    help='IoU Threshold to count for a positive detection (defaults to 0.5).', default=0.5, type=float)
      parser.add_argument('--max-detections',   help='Max Detections per image (defaults to 100).', default=100, type=int)
      parser.add_argument('--save-path',        help='Path for saving images with detections (doesn\'t work for COCO).')
      parser.add_argument('--image-min-side',   help='Rescale the image so the smallest side is min_side.', type=int, default=800)
      parser.add_argument('--image-max-side',   help='Rescale the image if the largest side is larger than max_side.', type=int, default=1333)
      parser.add_argument('--config',           help='Path to a configuration parameters .ini file (only used with --convert-model).')
  
      return parser.parse_args(args)
  
  
  def main(args=None):
      # parse arguments
      if args is None:
          args = sys.argv[1:]
      args = parse_args(args)
  
      # make sure keras is the minimum required version
      check_keras_version()
  
      # optionally choose specific GPU
      if args.gpu:
          os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
      keras.backend.tensorflow_backend.set_session(get_session())
  
      # make save path if it doesn't exist
      if args.save_path is not None and not os.path.exists(args.save_path):
          os.makedirs(args.save_path)
  
      # optionally load config parameters
      if args.config:
          args.config = read_config_file(args.config)
  
      # create the generator
      generator = create_generator(args)
  
      # optionally load anchor parameters
      anchor_params = None
      if args.config and 'anchor_parameters' in args.config:
          anchor_params = parse_anchor_parameters(args.config)
  
      # load the model
      print('Loading model, this may take a second...')
      model = models.load_model(args.model, backbone_name=args.backbone)
  
      # optionally convert the model
      if args.convert_model:
          model = models.convert_model(model, anchor_params=anchor_params)
  
      # print model summary
      # print(model.summary())
  
      # start evaluation
      if args.dataset_type == 'coco':
          from ..utils.coco_eval import evaluate_coco
          evaluate_coco(generator, model, args.score_threshold)
      else:
          average_precisions = evaluate(
              generator,
              model,
              # iou_threshold=0.1,
              iou_threshold=args.iou_threshold,
              score_threshold=args.score_threshold,
              max_detections=args.max_detections,
              save_path=args.save_path
          )
  
          # print evaluation
          total_instances = []
          precisions = []
          for label, (average_precision, num_annotations) in average_precisions.items():
              print('{:.0f} instances of class'.format(num_annotations),
                    generator.label_to_name(label), 'with average precision: {:.4f}'.format(average_precision))
              total_instances.append(num_annotations)
              precisions.append(average_precision)
  
          if sum(total_instances) == 0:
              print('No test instances found.')
              return
  
          print('mAP using the weighted average of precisions among classes: {:.4f}'.format(sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances)))
          print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))
  
  
  if __name__ == '__main__':
      main()
  