text: # -*- coding: utf-8 -*-
  from __future__ import division, print_function, absolute_import
  
  import numpy as np
  
  from alpharotate.utils.pretrain_zoo import PretrainModelZoo
  from configs._base_.models.retinanet_r50_fpn import *
  from configs._base_.datasets.dota_detection import *
  from configs._base_.schedules.schedule_1x import *
  
  # schedule
  BATCH_SIZE = 1  # r3det only support 1
  GPU_GROUP = '0,1,2'
  NUM_GPU = len(GPU_GROUP.strip().split(','))
  SAVE_WEIGHTS_INTE = 27000 * 2
  DECAY_STEP = np.array(DECAY_EPOCH, np.int32) * SAVE_WEIGHTS_INTE
  MAX_ITERATION = SAVE_WEIGHTS_INTE * MAX_EPOCH
  WARM_SETP = int(WARM_EPOCH * SAVE_WEIGHTS_INTE)
  
  # dataset
  DATASET_NAME = 'HRSC2016'
  IMG_SHORT_SIDE_LEN = 512
  IMG_MAX_LENGTH = 512
  CLASS_NUM = 1
  
  # data augmentation
  IMG_ROTATE = True
  RGB2GRAY = True
  VERTICAL_FLIP = True
  HORIZONTAL_FLIP = True
  IMAGE_PYRAMID = False
  
  # model
  pretrain_zoo = PretrainModelZoo()
  PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
  TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
  
  # bbox head
  NUM_REFINE_STAGE = 1
  
  # sample
  REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
  REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]
  
  # loss
  CLS_WEIGHT = 1.0
  REG_WEIGHT = 2.0
  USE_IOU_FACTOR = False
  
  GWD_TAU = 2.0
  GWD_FUNC = tf.sqrt
  
  VERSION = 'RetinaNet_HRSC2016_R3Det_GWD_2x_20201228'
  
  """
  r3det+gwd (only refine stage) + sqrt tau=2
  cls : ship|| Recall: 0.9641693811074918 || Precison: 0.3954575818303273|| AP: 0.8943491430116616
  F1:0.9233921715798948 P:0.944633730834753 R:0.9030944625407166
  mAP is : 0.8943491430116616
  
  89.43	89.32	88.89	87.45	78.3	65.88	45.47	15.02	0.92	0.06	56.07
  """
  
  