text: _base_ = [
      '../../_base_/models/slowonly_r50.py', '../../_base_/default_runtime.py'
  ]
  
  # model settings
  
  model = dict(cls_head=dict(num_classes=200, dropout_ratio=0.8))
  
  # dataset settings
  dataset_type = 'RawframeDataset'
  data_root = '/data2/zyl/workspace/video_datasets/anet1.3_train_frames'
  data_root_val = '/data2/zyl/workspace/video_datasets/anet1.3_val_frames'
  ann_file_train = '/home/zhiyuan/mmaction2-master/anet_train_video.txt'
  ann_file_val = '/home/zhiyuan/mmaction2-master/anet_val_video.txt'
  ann_file_test = '/home/zhiyuan/mmaction2-master/anet_val_video.txt'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
  train_pipeline = [
      dict(type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1),
      dict(type='FrameSelector'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='RandomResizedCrop'),
      dict(type='Resize', scale=(224, 224), keep_ratio=False),
      dict(type='Flip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs', 'label'])
  ]
  val_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=1,
          test_mode=True),
      dict(type='FrameSelector'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=256),
      dict(type='Flip', flip_ratio=0),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=10,
          test_mode=True),
      dict(type='FrameSelector'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='ThreeCrop', crop_size=256),
      dict(type='Flip', flip_ratio=0),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=1,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  evaluation = dict(
      interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  
  # optimizer
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
  lr_config = dict(
      policy='CosineAnnealing',
      min_lr = 0)
  total_epochs = 40
  
  # runtime settings
  work_dir = './work_dirs/slowonly_r50_8x8x1_40e_activitynet_video_rgb_mg_sampler/'
  load_from = ('/home/zhiyuan/mmaction2-master/slowonly_r50_8x8x1_256e_kinetics400_rgb_20200703-a79c555a.pth')
  