text: _base_ = "finetune-eval-base.py"
  
  # dataset settings
  data_source_cfg = dict(
      type="ImageNet",
      memcached=False,
      mclient_path='/no/matter',
      # this will be ignored if type != ImageListMultihead
      
  )
  
  
  data_train_list = "data/ucmerced/meta/train_labeled_repeat-1000.txt"
  data_train_root = 'data/ucmerced'
  
  data_val_list = "data/ucmerced/meta/val_labeled.txt"
  data_val_root = 'data/ucmerced'
  
  data_test_list = "data/ucmerced/meta/test_labeled.txt"
  data_test_root = 'data/ucmerced'
  
  dataset_type = "ClassificationDataset"
  img_norm_cfg = dict(mean=[0.4842,0.4901,0.4505], std=[0.2180,0.2021,0.1958])
  
  train_pipeline = [
      dict(type='RandomResizedCrop', size=224),
      dict(type='RandomHorizontalFlip'),
      dict(type='ToTensor'),
      dict(type='Normalize', **img_norm_cfg),
  ]
  test_pipeline = [
      dict(type='Resize', size=256),
      dict(type='CenterCrop', size=224),
      dict(type='ToTensor'),
      dict(type='Normalize', **img_norm_cfg),
  ]
  data = dict(
      batch_size=64,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          data_source=dict(
              list_file=data_train_list, root=data_train_root,
              **data_source_cfg),
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          data_source=dict(
              list_file=data_val_list, root=data_val_root, **data_source_cfg),
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          data_source=dict(
              list_file=data_test_list, root=data_test_root, **data_source_cfg),
          pipeline=test_pipeline))
  
  
  custom_hooks = [
      dict(
          name="val",
          type='ValidateHook',
          dataset=data['val'],
          by_epoch=False,
          initial=False,
          interval=100,
          imgs_per_gpu=32,
          workers_per_gpu=4,
          eval_param=dict(topk=(1,5))),
      dict(
          name="test",
          type='ValidateHook',
          dataset=data['test'],
          by_epoch=False,
          initial=False,
          interval=100,
          imgs_per_gpu=32,
          workers_per_gpu=4,
          eval_param=dict(topk=(1,5))),
  ]
  
  by_iter =True
  
  # learning policy
  lr_config = dict(
      by_epoch=False,
      policy='step',
      step=[833,1667],
      gamma=0.1  # multiply LR by this number at each step
  )
  
  # momentum and weight decay from VTAB and IDRL
  optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.,
                   paramwise_options={'\Ahead.': dict(lr_mult=100)})
  
  
  # runtime settings
  # total iters or total epochs
  total_iters=2500
  checkpoint_config = dict(interval=10000)
  
  log_config = dict(
      interval=1,
      by_epoch=False,
      hooks=[
          dict(type='TextLoggerHook', by_epoch=False),
          dict(type='TensorboardLoggerHook', by_epoch=False)
      ])
  optimizer_config = dict(update_interval=4)
  