text: # model settings
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      type='CascadeEncoderDecoder',
      num_stages=2,
      pretrained='open-mmlab://msra/hrnetv2_w18',
      backbone=dict(
          type='HRNet',
          norm_cfg=norm_cfg,
          norm_eval=False,
          extra=dict(
              stage1=dict(
                  num_modules=1,
                  num_branches=1,
                  block='BOTTLENECK',
                  num_blocks=(4, ),
                  num_channels=(64, )),
              stage2=dict(
                  num_modules=1,
                  num_branches=2,
                  block='BASIC',
                  num_blocks=(4, 4),
                  num_channels=(18, 36)),
              stage3=dict(
                  num_modules=4,
                  num_branches=3,
                  block='BASIC',
                  num_blocks=(4, 4, 4),
                  num_channels=(18, 36, 72)),
              stage4=dict(
                  num_modules=3,
                  num_branches=4,
                  block='BASIC',
                  num_blocks=(4, 4, 4, 4),
                  num_channels=(18, 36, 72, 144)))),
      decode_head=[
          dict(
              type='FCNHead',
              in_channels=[18, 36, 72, 144],
              channels=sum([18, 36, 72, 144]),
              in_index=(0, 1, 2, 3),
              input_transform='resize_concat',
              kernel_size=1,
              num_convs=1,
              concat_input=False,
              dropout_ratio=-1,
              num_classes=19,
              norm_cfg=norm_cfg,
              align_corners=False,
              loss_decode=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
          dict(
              type='OCRHead',
              in_channels=[18, 36, 72, 144],
              in_index=(0, 1, 2, 3),
              input_transform='resize_concat',
              channels=512,
              ocr_channels=256,
              dropout_ratio=-1,
              num_classes=19,
              norm_cfg=norm_cfg,
              align_corners=False,
              loss_decode=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
      ])
  # model training and testing settings
  train_cfg = dict()
  test_cfg = dict(mode='whole')
  