text: _base_ = [
      '../_base_/datasets/dota.py',
      '../_base_/schedules/schedule_1x.py',
      '../../_base_/default_runtime.py'
  ]
  
  
  model = dict(
      type='RoITransformer',
      
         backbone=dict(
          type='SwinTransformer',
          embed_dims=96,
          depths=[2, 2, 6, 2],
          num_heads=[3, 6, 12, 24],
          window_size=7,
          mlp_ratio=4,
          qkv_bias=True,
          qk_scale=None,
          drop_rate=0.,
          attn_drop_rate=0.,
          drop_path_rate=0.2,
          patch_norm=True,
          out_indices=(0, 1, 2, 3),
          with_cp=False,
          convert_weights=True,
          init_cfg=dict(type='Pretrained', checkpoint='swin_tiny_patch4_window7_224.pth')),
      
      neck=dict(
          type='FPN',
          in_channels=[96, 192, 384, 768],
          out_channels=256,
          num_outs=5),
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              scales=[6],
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[1.0, 1.0, 1.0, 1.0]),
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
      roi_head=dict(
          type='RoITransRoIHead',
          num_stages=2,
          stage_loss_weights=[1, 1],
          bbox_roi_extractor=[
              dict(
                  type='SingleRoIExtractor',
                  roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
                  out_channels=256,
                  featmap_strides=[4, 8, 16, 32]),
              dict(
                  type='OBBSingleRoIExtractor',
                  roi_layer=dict(type='RoIAlignRotated', out_size=7, sample_num=2),
                  out_channels=256,
                  extend_factor=(1.4, 1.2),
                  featmap_strides=[4, 8, 16, 32]),
          ],
          bbox_head=[
              dict(
                  type='OBBShared2FCBBoxHead',
                  start_bbox_type='hbb',
                  end_bbox_type='obb',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=37,
                  bbox_coder=dict(
                      type='HBB2OBBDeltaXYWHTCoder',
                      target_means=[0., 0., 0., 0., 0.],
                      target_stds=[0.1, 0.1, 0.2, 0.2, 1]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                                 loss_weight=1.0)),
              dict(
                  type='OBBShared2FCBBoxHead',
                  start_bbox_type='obb',
                  end_bbox_type='obb',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=37,
                  bbox_coder=dict(
                      type='OBB2OBBDeltaXYWHTCoder',
                      target_means=[0., 0., 0., 0., 0.],
                      target_stds=[0.05, 0.05, 0.1, 0.1, 0.5]),
                  reg_class_agnostic=False,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      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(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              match_low_quality=True,
              gpu_assign_thr=200,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=1000,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          allowed_border=0,
          pos_weight=-1,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=5000,
          nms_post=5000,
          max_num=5000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=[
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  match_low_quality=False,
                  ignore_iof_thr=-1,
                  iou_calculator=dict(type='BboxOverlaps2D')),
              sampler=dict(
                  type='RandomSampler',
                  num=2000,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=-1,
              debug=False),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  match_low_quality=False,
                  ignore_iof_thr=-1,
                  iou_calculator=dict(type='OBBOverlaps')),
              sampler=dict(
                  type='OBBRandomSampler',
                  num=2000,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=-1,
              debug=False)])
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=5000,
          nms_post=5000,
          max_num=5000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=0.005,
          score_weights=[0, 1],
          nms=dict(type='obb_nms', iou_thr=0.1), max_per_img=2000))
  
  optimizer = dict(
      _delete_=True,
      type='AdamW',
      lr=0.0001,
      betas=(0.9, 0.999),
      weight_decay=0.05,
      paramwise_cfg=dict(
          custom_keys={
              'absolute_pos_embed': dict(decay_mult=0.),
              'relative_position_bias_table': dict(decay_mult=0.),
              'norm': dict(decay_mult=0.)
          }))
  lr_config = dict(warmup_iters=1000, step=[9, 11])
  total_epochs = 12
  
  