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))),
          aux_head_cfg=dict(out_channels=101, loss_weight=0.5)),
      cls_head=dict(
          type='TPNBNNHead',
          loss_cls=dict(type='BayesianNNLoss'),
          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))
  train_cfg = dict(loss_weight=1e-6, npass=2)
  test_cfg = dict(average_clips='prob', npass=10)
  dataset_type = 'VideoDataset'
  data_root = 'data/ucf101/videos'
  data_root_val = 'data/ucf101/videos'
  ann_file_train = 'data/ucf101/ucf101_train_split_1_videos.txt'
  ann_file_val = 'data/ucf101/ucf101_val_split_1_videos.txt'
  ann_file_test = 'data/ucf101/ucf101_val_split_1_videos.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='OpenCVInit', num_threads=1),
      dict(type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1),
      dict(type='OpenCVDecode'),
      dict(type='RandomResizedCrop'),
      dict(type='Resize', scale=(224, 224), keep_ratio=False),
      dict(type='Flip', flip_ratio=0.5),
      dict(type='ColorJitter', color_space_aug=True),
      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', 'label'])
  ]
  val_pipeline = [
      dict(type='OpenCVInit', num_threads=1),
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=1,
          test_mode=True),
      dict(type='OpenCVDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=224),
      dict(type='ColorJitter', color_space_aug=True),
      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'])
  ]
  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=8,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          start_index=0,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          start_index=0,
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          start_index=0,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.002, momentum=0.9, weight_decay=0.0001,
      nesterov=True)  # this lr is used for 8 gpus
  optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
  # learning policy
  lr_config = dict(policy='step', step=[20, 40])
  total_epochs = 50
  checkpoint_config = dict(interval=10)
  evaluation = dict(
      interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook'),
      ])
  # runtime settings
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/tpn_slowonly/finetune_ucf101_tpn_slowonly_bnn'  # noqa: E501
  load_from = 'https://download.openmmlab.com/mmaction/recognition/tpn/tpn_imagenet_pretrained_slowonly_r50_8x8x1_150e_kinetics_rgb/tpn_imagenet_pretrained_slowonly_r50_8x8x1_150e_kinetics_rgb_20200923-52629684.pth'
  resume_from = None
  workflow = [('train', 1)]
  