content: # model settings
  model = dict(
      type='Recognizer3DBNN',
      backbone=dict(
          type='ResNet3dSlowOnly',
          depth=50,
          pretrained='torchvision://resnet50',
          lateral=False,
          out_indices=(2, 3),
          conv1_kernel=(1, 7, 7),
          conv1_stride_t=1,
          pool1_stride_t=1,
          inflate=(0, 0, 1, 1),
          norm_eval=False),
      neck=dict(
          type='TPN',
          in_channels=(1024, 2048),
          out_channels=1024,
          spatial_modulation_cfg=dict(
              in_channels=(1024, 2048), out_channels=2048),
          temporal_modulation_cfg=dict(downsample_scales=(8, 8)),
          upsample_cfg=dict(scale_factor=(1, 1, 1)),
          downsample_cfg=dict(downsample_scale=(1, 1, 1)),
          level_fusion_cfg=dict(
              in_channels=(1024, 1024),
              mid_channels=(1024, 1024),
              out_channels=2048,
              downsample_scales=((1, 1, 1), (1, 1, 1)))),
      cls_head=dict(
          type='TPNBNNHead',
          num_classes=101,
          in_channels=2048,
          spatial_type='avg',
          consensus=dict(type='AvgConsensus', dim=1),
          dropout_ratio=0,  # for bnn, dropout is not necessary
          init_std=0.01))
  test_cfg = dict(average_clips='prob', npass=10)
  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='OpenCVInit', num_threads=1),
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=10,
          test_mode=True),
      dict(type='OpenCVDecode'),
      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', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=2,
      workers_per_gpu=2,
      test=dict(
          type=dataset_type,
          ann_file=None,
          data_prefix=None,
          pipeline=test_pipeline))
  