text: _base_ = [
      '../swin/cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py'
  ]
  
  model = dict(
      backbone=dict(
          type='CBSwinTransformer',
      ),
      neck=dict(
          type='CBFPN',
      ),
      test_cfg = dict(
          rcnn=dict(
              score_thr=0.001,
              nms=dict(type='soft_nms'),
          )
      )
  )
  
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  
  albu_train_transforms = [
      dict(
          type='ShiftScaleRotate',
          shift_limit=0.0,
          scale_limit=0.0,
          rotate_limit=20,
          interpolation=1,
          p=0.2),
  ]
  
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='AutoAugment',
           policies=[
               [
                   dict(type='Resize',
                        img_scale=[(320, 320), (384, 384), (448, 448),
                                   (512, 512), (576, 576)],
                        multiscale_mode='value',
                        keep_ratio=True)
               ],
               [
                   dict(type='Resize',
                        img_scale=[(320, 320), (576, 576)],
                        multiscale_mode='value',
                        keep_ratio=True),
                   dict(type='RandomCrop',
                        crop_type='absolute_range',
                        crop_size=(320, 320),
                        allow_negative_crop=True),
                   dict(type='Resize',
                        img_scale=[(320, 320), (384, 384), (448, 448),
                                   (512, 512), (576, 576)],
                        multiscale_mode='value',
                        override=True,
                        keep_ratio=True)
               ]
           ]),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=[(320, 320), (576, 576)],
          flip=True,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  samples_per_gpu=2
  data = dict(samples_per_gpu=samples_per_gpu,
              train=dict(pipeline=train_pipeline),
              val=dict(pipeline=test_pipeline),
              test=dict(pipeline=test_pipeline))
  optimizer = dict(lr=0.0001*(samples_per_gpu/2))
  