text: # dataset settings
  dataset_type = 'MGSDataset'
  data_root = 'data/mgs'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  crop_size = (768, 768)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', reduce_zero_label=False),
      dict(type='Resize', img_scale=(1120, 768), ratio_range=(0.5, 2.0)),
      dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
      dict(type='RandomFlip', prob=0.5),
      dict(type='PhotoMetricDistortion'),
      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='MultiScaleFlipAug',
          img_scale=(1120, 768),
          # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=1,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          data_root=data_root,
          img_dir='images/train',
          ann_dir='groundtruth/train',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          data_root=data_root,
          img_dir='images/val',
          ann_dir='groundtruth/val',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          data_root=data_root,
          img_dir='images/val',
          ann_dir='groundtruth/val',
          pipeline=test_pipeline))
  