text: _base_ = [
      '../../_base_/models/tsm_r50.py', '../../_base_/schedules/sgd_tsm_50e.py',
      '../../_base_/default_runtime.py'
  ]
  
  # model settings
  model = dict(backbone=dict(num_segments=16), cls_head=dict(num_segments=16))
  
  # dataset settings
  dataset_type = 'RawframeDataset'
  data_root = 'data/kinetics400/rawframes_train'
  data_root_val = 'data/kinetics400/rawframes_val'
  ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
  ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
  ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.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=1, frame_interval=1, num_clips=16),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(
          type='MultiScaleCrop',
          input_size=224,
          scales=(1, 0.875, 0.75, 0.66),
          random_crop=False,
          max_wh_scale_gap=1,
          num_fixed_crops=13),
      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='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs', 'label'])
  ]
  val_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=16,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=224),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=16,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='TenCrop', crop_size=224),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=6,
      workers_per_gpu=2,
      test_dataloader=dict(videos_per_gpu=1),
      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=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  
  # optimizer
  optimizer = dict(
      lr=0.0075,  # this lr is used for 8 gpus
  )
  
  # runtime settings
  checkpoint_config = dict(interval=5)
  work_dir = './work_dirs/tsm_r50_1x1x16_50e_kinetics400_rgb/'
  