text: _base_ = [
      '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/cityscapes.py',
      '../_base_/default_runtime.py', '../_base_/schedules/schedule_320k.py'
  ]
  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'),
      dict(type='Resize', img_scale=(2048, 1024), 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=(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(
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  
  model = dict(
      decode_head=dict(num_classes=19), auxiliary_head=dict(num_classes=19))
  