content: # model settings
  model = dict(
      type='Recognizer3D',
      backbone=dict(type='X3D', gamma_w=1, gamma_b=2.25, gamma_d=2.2),
      cls_head=dict(
          type='X3DHead',
          in_channels=432,
          num_classes=400,
          spatial_type='avg',
          dropout_ratio=0.5,
          fc1_bias=False))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips='prob')
  # dataset settings
  dataset_type = 'RawframeDataset'
  data_root_val = 'data/kinetics400/rawframes_val'
  ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
  img_norm_cfg = dict(
      mean=[114.75, 114.75, 114.75], std=[57.38, 57.38, 57.38], to_bgr=False)
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=13,
          frame_interval=6,
          num_clips=10,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 192)),
      dict(type='CenterCrop', crop_size=192),
      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=2,
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  
  dist_params = dict(backend='nccl')
  