text: model = dict(
      type='basenet',
      pretrained='',
      backbone=dict(
          type='ResNet',
          depth=101,
          num_stages=4,
          block_num=[3, 4, 23, 3],
  
      ),
      att=dict(
          with_att=False,
          type='glore',
          att_stage=[False,False,True,False],
          att_pos='after_add',
          att_location=[[],[],[5,11,17],[]],
      ),
      module=dict(
          type='mask_nl',
          downsample=True,
          whiten_type=['channel'],
          temp=0.1,
          with_gc=True,
          use_out=True,
          out_bn=True,
          mask_type='common',
          use_key_mask=True,
          use_query_mask=False,
          mask_pos='after',
          use_softmax=True
      )
  )
  train_cfg = dict(
      batch_size=8,
      learning_rate=1e-2,
      momentum=0.9,
      num_steps=60000,
      power=0.9,
      random_seed=1234,
      restore_from='./dataset/resnet101-imagenet.pth',
      save_num_images=2,
      start_iters=0,
      save_from=59000,
      save_pred_every=100,
      snapshot_dir='snapshots/',
      weight_decay=0.0005
  )
  data_cfg = dict(
      data_dir='cityscapes',
      data_list='./dataset/list/cityscapes/train.lst',
      ignore_label=255,
      input_size='769,769',
      num_classes=19,
  )
  