text: _base_ = [
      '../../_base_/models/faster_rcnn_r50_dc5.py',
      '../../_base_/datasets/imagenet_vid_fgfa_style.py',
      '../../_base_/default_runtime.py'
  ]
  model = dict(
      type='SELSA',
      pretrains=None,
      detector=dict(
          roi_head=dict(
              type='SelsaRoIHead',
              bbox_head=dict(
                  type='SelsaBBoxHead',
                  num_shared_fcs=2,
                  aggregator=dict(
                      type='SelsaAggregator',
                      in_channels=1024,
                      num_attention_blocks=16)))))
  
  # dataset settings
  data = dict(
      val=dict(
          ref_img_sampler=dict(
              _delete_=True,
              num_ref_imgs=14,
              frame_range=[-7, 7],
              method='test_with_adaptive_stride')),
      test=dict(
          ref_img_sampler=dict(
              _delete_=True,
              num_ref_imgs=14,
              frame_range=[-7, 7],
              method='test_with_adaptive_stride')))
  
  # optimizer
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(
      _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=1.0 / 3,
      step=[2, 5])
  # runtime settings
  total_epochs = 7
  evaluation = dict(metric=['bbox'], interval=7)
  