text: # -*- coding: utf-8 -*-
  from __future__ import division, print_function, absolute_import
  
  import numpy as np
  
  from configs._base_.models.retinanet_r50_fpn import *
  from configs._base_.datasets.dota_detection import *
  from configs._base_.schedules.schedule_1x import *
  from alpharotate.utils.pretrain_zoo import PretrainModelZoo
  
  # schedule
  BATCH_SIZE = 1
  GPU_GROUP = "0,1,2"
  NUM_GPU = len(GPU_GROUP.strip().split(','))
  LR = 1e-3
  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
  
  # model
  # backbone
  pretrain_zoo = PretrainModelZoo()
  PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
  TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
  
  # loss
  CLS_WEIGHT = 1.0
  REG_WEIGHT = 1.0
  
  VERSION = 'RetinaNet_DOTA_2x_20210128'
  
  """
  RSDet-8p 20 epoch
  FLOPs: 865678510;    Trainable params: 33148131
  This is your result for task 1:
  
  mAP: 0.6720081911437207
  ap of each class:
  plane:0.884237723275074,
  baseball-diamond:0.729444339641071,
  bridge:0.43210254062916365,
  ground-track-field:0.681519345646089,
  small-vehicle:0.7078486578377088,
  large-vehicle:0.5469995892993882,
  ship:0.7267624293020835,
  tennis-court:0.8949116521292252,
  basketball-court:0.787517553471782,
  storage-tank:0.7972219799903847,
  soccer-ball-field:0.5521436820074317,
  roundabout:0.6207868886053054,
  harbor:0.6099057077868252,
  swimming-pool:0.6401352757788455,
  helicopter:0.46858550175543046
  
  The submitted information is :
  
  Description: RetinaNet_DOTA_2x_20210128_70.2w
  Username: SJTU-Det
  Institute: SJTU
  Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
  TeamMembers: yangxue
  
  """
  