text: _base_ = [
      '../_base_/models/cgnet.py', '../_base_/datasets/cityscapes.py',
      '../_base_/default_runtime.py'
  ]
  
  # optimizer
  optimizer = dict(type='Adam', lr=0.001, eps=1e-08, weight_decay=0.0005)
  optimizer_config = dict()
  # learning policy
  lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
  # runtime settings
  total_iters = 60000
  checkpoint_config = dict(by_epoch=False, interval=4000)
  evaluation = dict(interval=4000, metric='mIoU')
  
  img_norm_cfg = dict(
      mean=[72.39239876, 82.90891754, 73.15835921], std=[1, 1, 1], to_rgb=True)
  crop_size = (680, 680)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations'),
      dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
      dict(type='RandomCrop', crop_size=crop_size),
      dict(type='RandomFlip', flip_ratio=0.5),
      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=(2048, 1024),
          # 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=8,
      workers_per_gpu=8,
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  