text: # model settings
  model = dict(
      type='DARDet',
      pretrained='torchvision://resnet50',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
          stage_with_dcn=(False, True, True, True),
          frozen_stages=1,
          norm_cfg=dict(type='BN', requires_grad=True),
          norm_eval=True,
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs=True,
          extra_convs_on_inputs=False,  # use P5
          num_outs=5,
          relu_before_extra_convs=True),
      bbox_head=dict(
          type='DARDetHead',
          num_classes=15,
          in_channels=256,
          stacked_convs=3,
          feat_channels=256,
          strides=[8, 16, 32, 64, 128],
          center_sampling=False,
          dcn_on_last_conv=True,#False
          use_atss=True,
          use_vfl=True,
          loss_cls=dict(
              type='VarifocalLoss',
              use_sigmoid=True,
              alpha=0.75,
              gamma=2.0,
              iou_weighted=True,
              loss_weight=1.0),
              loss_rbox=dict( type='RotatedIoULoss', loss_weight=1.5),
              loss_rbox_refine=dict( type='RotatedIoULoss', loss_weight=2.0)),
      # training and testing settings
      train_cfg=dict(
          assigner=dict(type='ATSSAssigner', topk=9),
          allowed_border=-1,
          pos_weight=-1,
          debug=False),
      test_cfg=dict(
          nms_pre=2000,
          min_bbox_size=0,
          score_thr=0.05,
          nms=dict(type='nms', iou_threshold=0.1),
          max_per_img=1500))
  
  # data setting
  dataset_type = 'DotaKDataset'
  data_root = '/media/zf/E/Dataset/dota1-split-1024/'
  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, with_mask=True,poly2mask=False),
      dict(type='Resize', img_scale=(1024, 1024), keep_ratio=True),
      dict(type='RandomFlip', direction=['horizontal','vertical', 'diagonal'], flip_ratio=0.5),
      dict(type='Rotate', prob=0.7),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1024, 1024),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='DefaultFormatBundle'),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=6,
      workers_per_gpu=2,
      train=dict(
          _delete_=True,
          type='ClassBalancedDataset',
          oversample_thr=6e-2,
          dataset=dict(
              type=dataset_type,
              ann_file=data_root + 'trainval1024/reppoint_keypoints_train2017.json',
              img_prefix=data_root +  'trainval1024/images/',
              pipeline=train_pipeline)),
      val=dict(
          type=dataset_type,
          samples_per_gpu=6,
          ann_file=data_root + 'annotations/reppoint_keypoints_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'test1024/DOTA_test1024.json',
          img_prefix=data_root + 'test1024/images/',
          pipeline=test_pipeline))
  
  # optimizer
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[16, 22])
  runner = dict(type='EpochBasedRunner', max_epochs=24)
  work_dir = '/media/zf/E/Dataset/dota1-split-1024/workdir/DARDet_r50_DCN_rotate'
  load_from =None#'/media/zf/E/mmdetection213_2080/checkpoint/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth'
  resume_from = '/media/zf/E/Dataset/dota1-split-1024/workdir/DARDet_r50_DCN_rotate/latest.pth'
  evaluation = dict(interval=3, metric='bbox',eval_dir= work_dir,
          gt_dir='/media/zf/E/Dataset/dota_1024_s2anet2/valGTtxt/')
  checkpoint_config = dict(interval=1)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  custom_hooks = [dict(type='NumClassCheckHook')]
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  workflow = [('train', 1)]
  