text: # dataset settings
  dataset_type = 'CityscapesDataset'
  data_root = '/home/ihakki/h3dr/data/cityscapes/'
  img_norm_cfg = dict(
      mean=[51.57563733, 56.56139993, 51.88684484],
      std=[31.34092065, 32.66129945, 31.04255947],
      to_rgb=False)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.3, 0.5, 0.7, 0.9),
          min_crop_size=0.5),
      dict(type='Resize', img_scale=(1024, 512), keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size=(512, 1024)),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1024, 512),
          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='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=4,
      workers_per_gpu=2,
      train=dict(
          type='RepeatDataset',
          times=8,
          dataset=dict(
              type=dataset_type,
              ann_file=data_root +
              'annotations_70/instancesonly_filtered_gtFine_train.json',
              img_prefix=data_root + 'leftImgOptExp/train/',
              pipeline=train_pipeline)),
      val=dict(
          type=dataset_type,
          ann_file=data_root +
          'annotations_70/instancesonly_filtered_gtFine_val.json',
          img_prefix=data_root + 'leftImgOptExp/val/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root +
          'annotations_70/instancesonly_filtered_gtFine_test.json',
          img_prefix=data_root + 'leftImgOptExp/test/',
          pipeline=test_pipeline))
  evaluation = dict(interval=1, metric='bbox')
  