text: net = dict(
      type='Detector',
  )
  
  backbone = dict(
      type='ResNetWrapper',
      resnet='resnet50',
      pretrained=True,
      replace_stride_with_dilation=[False, True, True],
      out_conv=True,
      in_channels=[64, 128, 256, -1]
  )
  featuremap_out_channel = 128
  featuremap_out_stride = 8
  sample_y = range(589, 230, -20)
  
  aggregator = dict(
      type='RESA',
      direction=['d', 'u', 'r', 'l'],
      alpha=2.0,
      iter=4,
      conv_stride=9,
  )
  
  heads = dict( 
      type='LaneSeg',
      decoder=dict(type='PlainDecoder'),
      exist=dict(type='ExistHead'),
      thr=0.3,
      sample_y=sample_y,
  )
  
  trainer = dict(
      type='RESA'
  )
  
  evaluator = dict(
      type='CULane',        
  )
  
  optimizer = dict(
    type = 'SGD',
    lr = 0.030,
    weight_decay = 1e-4,
    momentum = 0.9
  )
  
  epochs = 12
  batch_size = 8
  total_iter = (88880 // batch_size) * epochs
  import math
  scheduler = dict(
      type = 'LambdaLR',
      lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9)
  )
  
  seg_loss_weight = 1.0
  eval_ep = 6
  save_ep = epochs
  
  bg_weight = 0.4
  
  img_norm = dict(
      mean=[103.939, 116.779, 123.68],
      std=[1., 1., 1.]
  )
  
  img_height = 288
  img_width = 800
  cut_height = 240 
  ori_img_h = 590
  ori_img_w = 1640
  
  train_process = [
      dict(type='RandomRotation', degree=(-2, 2)),
      dict(type='RandomHorizontalFlip'),
      dict(type='Resize', size=(img_width, img_height)),
      dict(type='Normalize', img_norm=img_norm),
      dict(type='ToTensor', keys=['img', 'mask', 'lane_exist']),
  ]
  
  val_process = [
      dict(type='Resize', size=(img_width, img_height)),
      dict(type='Normalize', img_norm=img_norm),
      dict(type='ToTensor', keys=['img']),
  ]
  
  dataset_path = './data/CULane'
  dataset = dict(
      train=dict(
          type='CULane',
          data_root=dataset_path,
          split='train',
          processes=train_process,
      ),
      val=dict(
          type='CULane',
          data_root=dataset_path,
          split='test',
          processes=val_process,
      ),
      test=dict(
          type='CULane',
          data_root=dataset_path,
          split='test',
          processes=val_process,
      )
  )
  
  
  workers = 12
  num_classes = 4 + 1
  ignore_label = 255
  log_interval = 1000
  
  