text: # dataset settings
  dataset_type = 'MyCustomDataset'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  crop_size = (512, 512)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotationsOne'),
      dict(type='Resize', img_scale=(512, 512), ratio_range=None),
      dict(type='RandomFlip', flip_ratio=0.0),
      dict(type='RandomUpDownFlip', flip_ratio=0),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_semantic_seg']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='SingleScaleFourFlipAug',
          img_scale=(512, 512),
          flip_lr=False,
          flip_ud=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='RandomUpDownFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=4,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file='data/train_sub.json',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file='data/eval_5000.json',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file='data/test_B.json',
          pipeline=test_pipeline))
  