text: _base_ = ['./r3det_kfiou_ln_r50_fpn_1x_dota_oc.py']
  
  data_root = '../datasets/split_ms_dota1_0/'
  angle_version = 'le90'
  model = dict(
      backbone=dict(
          _delete_=True,
          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='torch/hub/checkpoints/'
              'swin_tiny_patch4_window7_224.pth')),
      neck=dict(
          _delete_=True,
          type='FPN',
          in_channels=[96, 192, 384, 768],
          out_channels=256,
          start_level=1,
          add_extra_convs='on_input',
          num_outs=5))
  
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='RResize', img_scale=(1024, 1024)),
      dict(
          type='RRandomFlip',
          flip_ratio=[0.25, 0.25, 0.25],
          direction=['horizontal', 'vertical', 'diagonal']),
      dict(
          type='PolyRandomRotate',
          rotate_ratio=0.5,
          angles_range=180,
          auto_bound=False,
          rect_classes=[9, 11],
          version=angle_version),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
  ]
  data = dict(
      train=dict(
          pipeline=train_pipeline,
          ann_file=data_root + 'trainval/annfiles/',
          img_prefix=data_root + 'trainval/images/'),
      val=dict(
          ann_file=data_root + 'trainval/annfiles/',
          img_prefix=data_root + 'trainval/images/'),
      test=dict(
          ann_file=data_root + 'test/images/',
          img_prefix=data_root + 'test/images/'))
  
  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.)
          }))
  
  find_unused_parameters = True
  