text: """
  ##################################################################################################
  # Copyright Info :    Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.
  # Filename       :    gaspin_res32_attn.py
  # Abstract       :    GASPIN transformation recognition Model
  
  # Current Version:    1.0.0
  # Date           :    2021-06-11
  ##################################################################################################
  """
  # encoding=utf-8
  
  _base_ = [
      './baseline.py'
  ]
  
  
  """
  1. Model Settings
  include model-related setting, such as model type, user-selected modules and parameters.
  """
  # model parameters for changing the GA-SPIN transformation
  model = dict(
      transformation=dict(
           type='GA_SPIN_Transformer',
           input_channel=1,
           offsets=True,
           default_type=6,
           _delete_=True,
      ),
  )
  
  
  data = dict(
      samples_per_gpu=64)
  
  
  # checkpoint setting
  checkpoint_config = dict(type="DavarCheckpointHook",
                           interval=1,
                           iter_interval=5000,
                           by_epoch=True,
                           by_iter=True,
                           filename_tmpl='ckpt/res32_ace_e{}.pth',
                           metric="accuracy",
                           rule="greater",
                           save_mode="lightweight",
                           init_metric=-1,
                           model_milestone=0.5
                           )
  
  # logger setting
  log_config = dict(
      interval=50,
      hooks=[dict(type='TextLoggerHook'), ])
  
  
  # evaluation setting
  evaluation = dict(start=3,
                    start_iter=0.5,
                    save_best="accuracy",
                    interval=1,
                    iter_interval=5000,
                    model_type="RECOGNIZOR",
                    eval_mode="lightweight",
                    by_epoch=True,
                    by_iter=True,
                    rule="greater",
                    metric=['accuracy', 'NED'],
                    )
  
  
  # runner setting
  runner = dict(type='EpochBasedRunner', max_epochs=6)
  
  # work directory
  work_dir = '/data1/workdir/davar_opensource/gaspin/'
  
  # distributed training setting
  dist_params = dict(backend='nccl')
  