content: # model settings
  model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='ResNet3d',
          pretrained2d=True,
          pretrained='torchvision://resnet50',
          depth=50,
          conv_cfg=dict(type='Conv3d'),
          norm_eval=False,
          inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)),
          zero_init_residual=False),
      cls_head=dict(
          type='I3DHead',
          num_classes=400,
          in_channels=2048,
          spatial_type='avg',
          dropout_ratio=0.5,
          init_std=0.01))
  # model training and testing settings
  test_cfg = dict(average_clips='prob')
  # dataset settings
  dataset_type = 'VideoDataset'
  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='DecordInit', num_threads=1),
      dict(
          type='SampleFrames',
          clip_len=32,
          frame_interval=2,
          num_clips=1,
          test_mode=True),
      dict(type='DecordDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='ThreeCrop', crop_size=256),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=1,
      workers_per_gpu=2,
      test=dict(
          type=dataset_type,
          ann_file=None,
          data_prefix=None,
          pipeline=test_pipeline))
  