text: # --coding=utf-8--
  
  _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
  
  
  model = dict(
          bbox_head=dict(
              num_classes=5,
              anchor_generator=dict(ratios=[0.2, 0.5, 1.0, 2.0, 5.0])
              )
              )
  
  
  dataset_type = 'COCODataset'
  # classes = ("DiaoChe", "ShiGongJiXie", "YanHuo", "TaDiao", "DaoXianYiWu")
  classes = ("DaoXianYiWu", "DiaoChe", "ShiGongJiXie", "TaDiao", "YanHuo")
  data = dict(
      samples_per_gpu=16,
      workers_per_gpu=16,
      persistent_workers=True,
      train=dict(
          img_prefix='data/transmission_line_detection/train/',
          classes=classes,
          ann_file='data/transmission_line_detection/annotations/instances_train.json'),
      val=dict(
          img_prefix='data/transmission_line_detection/train/',
          classes=classes,
          ann_file='data/transmission_line_detection/annotations/instances_train.json'),
      test=dict(
          img_prefix='data/transmission_line_detection/test/',
          classes=classes,
          ann_file='data/transmission_line_detection/annotations/instances_test.json'))
  
  
  # load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'
  load_from = 'checkpoints/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
  optimizer = dict(type='SGD', lr=0.05*4, momentum=0.9, weight_decay=0.0001)
  # optimizer = dict(_delete_=True, type='AdamW', lr=0.0005*4, weight_decay=0.01)
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=200,
      warmup_ratio=0.001,
      step=[60, 80])
  runner = dict(type='EpochBasedRunner', max_epochs=100)
  checkpoint_config = dict(interval=10)
  evaluation = dict(interval=10, metric='bbox')
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  