content: # model settings
  model = dict(
      type='Recognizer2D',
      backbone=dict(
          type='ResNet',
          pretrained='torchvision://resnet50',
          depth=50,
          norm_eval=False),
      cls_head=dict(
          type='TSNHead',
          num_classes=400,
          in_channels=2048,
          spatial_type='avg',
          consensus=dict(type='AvgConsensus', dim=1),
          dropout_ratio=0.4,
          init_std=0.01))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips=None)
  # 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=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=25,
          test_mode=True),
      dict(type='RawFrameDecode'),
      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='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      workers_per_gpu=4,
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  dist_params = dict(backend='nccl')
  