text: import copy
  _base_ = '../../base.py'
  # model settings
  model = dict(
      type='BYOL',
      pretrained=None,
      base_momentum=0.996,
      backbone=dict(
          type='ResNet',
          depth=50,
          in_channels=3,
          out_indices=[4],  # 0: conv-1, x: stage-x
          norm_cfg=dict(type='BN')),
      neck=dict(
          type='NonLinearNeckV2',
          in_channels=2048,
          hid_channels=4096,
          out_channels=256,
          with_avg_pool=True),
      head=dict(type='LatentPredictHead',
                size_average=True,
                predictor=dict(type='NonLinearNeckV2',
                               in_channels=256, hid_channels=4096,
                               out_channels=256, with_avg_pool=False)))
  # dataset settings
  data_source_cfg = dict(
      type='ImageNet',
      memcached=True,
      mclient_path='/mnt/lustre/share/memcached_client')
  data_train_list = 'data/imagenet/meta/train.txt'
  data_train_root = 'data/imagenet/train'
  dataset_type = 'BYOLDataset'
  img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  train_pipeline = [
      dict(type='RandomResizedCrop', size=224, interpolation=3), # bicubic
      dict(type='RandomHorizontalFlip'),
      dict(
          type='RandomAppliedTrans',
          transforms=[
              dict(
                  type='ColorJitter',
                  brightness=0.4,
                  contrast=0.4,
                  saturation=0.2,
                  hue=0.1)
          ],
          p=0.8),
      dict(type='RandomGrayscale', p=0.2),
      dict(
          type='RandomAppliedTrans',
          transforms=[
              dict(
                  type='GaussianBlur',
                  sigma_min=0.1,
                  sigma_max=2.0,
                  kernel_size=23)
          ],
          p=1.),
      dict(type='RandomAppliedTrans',
           transforms=[dict(type='Solarization')], p=0.),
      dict(type='ToTensor'),
      dict(type='Normalize', **img_norm_cfg),
  ]
  train_pipeline1 = copy.deepcopy(train_pipeline)
  train_pipeline2 = copy.deepcopy(train_pipeline)
  train_pipeline2[4]['p'] = 0.1 # gaussian blur
  train_pipeline2[5]['p'] = 0.2 # solarization
  
  data = dict(
      imgs_per_gpu=32,  # total 32*8=256
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          data_source=dict(
              list_file=data_train_list, root=data_train_root,
              **data_source_cfg),
          pipeline1=train_pipeline1,
          pipeline2=train_pipeline2))
  # additional hooks
  custom_hooks = [
      dict(type='BYOLHook', end_momentum=1.)
  ]
  # optimizer
  optimizer = dict(type='LARS', lr=0.2, weight_decay=0.0000015, momentum=0.9,
                   paramwise_options={
                      '(bn|gn)(\d+)?.(weight|bias)': dict(weight_decay=0., lars_exclude=True),
                      'bias': dict(weight_decay=0., lars_exclude=True)})
  # learning policy
  lr_config = dict(
      policy='CosineAnnealing',
      min_lr=0.,
      warmup='linear',
      warmup_iters=2,
      warmup_ratio=0.0001, # cannot be 0
      warmup_by_epoch=True)
  checkpoint_config = dict(interval=10)
  # runtime settings
  total_epochs = 200
  