text: _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
  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='InstaBoost',
          action_candidate=('normal', 'horizontal', 'skip'),
          action_prob=(1, 0, 0),
          scale=(0.8, 1.2),
          dx=15,
          dy=15,
          theta=(-1, 1),
          color_prob=0.5,
          hflag=False,
          aug_ratio=0.5),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      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']),
  ]
  data = dict(train=dict(pipeline=train_pipeline))
  # learning policy
  lr_config = dict(step=[32, 44])
  runner = dict(type='EpochBasedRunner', max_epochs=48)  
  