text: _base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
  model = dict(
      rpn_head=dict(
          _delete_=True,
          type='GARPNHead',
          in_channels=256,
          feat_channels=256,
          approx_anchor_generator=dict(
              type='AnchorGenerator',
              octave_base_scale=8,
              scales_per_octave=3,
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          square_anchor_generator=dict(
              type='AnchorGenerator',
              ratios=[1.0],
              scales=[8],
              strides=[4, 8, 16, 32, 64]),
          anchor_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[0.07, 0.07, 0.14, 0.14]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[0.07, 0.07, 0.11, 0.11]),
          loc_filter_thr=0.01,
          loss_loc=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
      # model training and testing settings
      train_cfg=dict(
          rpn=dict(
              ga_assigner=dict(
                  type='ApproxMaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.3,
                  min_pos_iou=0.3,
                  ignore_iof_thr=-1),
              ga_sampler=dict(
                  type='RandomSampler',
                  num=256,
                  pos_fraction=0.5,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=False),
              allowed_border=-1,
              center_ratio=0.2,
              ignore_ratio=0.5)))
  optimizer_config = dict(
      _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
  