text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """Config parameters for Darknet based yolov4_cspdarknet53 models."""
  
  
  class ConfigYOLOV4CspDarkNet53:
      """
      Config parameters for the yolov4_cspdarknet53.
  
      Examples:
          ConfigYOLOV4CspDarkNet53()
      """
      # train_param
      # data augmentation related
      hue = 0.1
      saturation = 1.5
      value = 1.5
      jitter = 0.3
  
      resize_rate = 10
      multi_scale = [[416, 416],
                     [448, 448],
                     [480, 480],
                     [512, 512],
                     [544, 544],
                     [576, 576],
                     [608, 608],
                     [640, 640],
                     [672, 672],
                     [704, 704],
                     [736, 736]
                     ]
  
      num_classes = 80
      max_box = 90
  
      backbone_input_shape = [32, 64, 128, 256, 512]
      backbone_shape = [64, 128, 256, 512, 1024]
      backbone_layers = [1, 2, 8, 8, 4]
  
      # confidence under ignore_threshold means no object when training
      ignore_threshold = 0.7
  
      # h->w
      anchor_scales = [(12, 16),
                       (19, 36),
                       (40, 28),
                       (36, 75),
                       (76, 55),
                       (72, 146),
                       (142, 110),
                       (192, 243),
                       (459, 401)]
      out_channel = 3 * (num_classes + 5)
  
      # test_param
      test_img_shape = [608, 608]
  