text: # Data pipeline
  from configs.lane_detection.common.datasets.tusimple_seg import dataset
  from configs.lane_detection.common.datasets.train_level1a_360 import train_augmentation
  from configs.lane_detection.common.datasets.test_360 import test_augmentation
  
  # Optimization pipeline
  from configs.lane_detection.common.optims.segloss_7class import loss
  from configs.lane_detection.common.optims.sgd02 import optimizer
  from configs.lane_detection.common.optims.ep50_poly_warmup200 import lr_scheduler
  
  
  train = dict(
      exp_name='resnet34_scnn_tusimple-aug',
      workers=8,
      batch_size=10,
      checkpoint=None,
      # Device args
      world_size=2,
      dist_url='tcp://localhost:12345',
      device='cuda',
  
      val_num_steps=0,  # Seg IoU validation (mostly useless)
      save_dir='./checkpoints',
  
      input_size=(360, 640),
      original_size=(720, 1280),
      num_classes=7,
      num_epochs=50,
      collate_fn=None,  # 'dict_collate_fn' for LSTR
      seg=True  # Seg-based method or not
  )
  
  test = dict(
      exp_name='resnet34_scnn_tusimple-aug',
      workers=10,
      batch_size=40,
      checkpoint='./checkpoints/resnet34_scnn_tusimple-aug/model.pt',
      # Device args
      device='cuda',
  
      save_dir='./checkpoints',
  
      seg=True,
      gap=10,
      ppl=56,
      thresh=0.3,
      collate_fn=None,  # 'dict_collate_fn' for LSTR
      input_size=(360, 640),
      original_size=(720, 1280),
      max_lane=5,
      dataset_name='tusimple'
  )
  
  model = dict(
      name='standard_segmentation_model',
      backbone_cfg=dict(
          name='predefined_resnet_backbone',
          backbone_name='resnet34',
          return_layer='layer4',
          pretrained=True,
          replace_stride_with_dilation=[False, True, True]
      ),
      reducer_cfg=dict(
          name='RESAReducer',
          in_channels=512,
          reduce=128
      ),
      spatial_conv_cfg=dict(
          name='SpatialConv',
          num_channels=128
      ),
      classifier_cfg=dict(
          name='DeepLabV1Head',
          in_channels=128,
          num_classes=7,
          dilation=1
      ),
      lane_classifier_cfg=dict(
          name='SimpleLaneExist',
          num_output=7 - 1,
          flattened_size=6160
      )
  )
  