content: # model settings
  evidence_loss = dict(type='EvidenceLoss',
                        num_classes=101,
                        evidence='exp',
                        loss_type='log',
                        with_kldiv=False,
                        with_avuloss=False,
                        annealing_method='exp')
  
  # mae huge
  model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='VisionTransformer3D',
          patch_size=16,
          embed_dim=1280,
          depth=32,
          num_heads=16,
          mlp_ratio=4,
          qkv_bias=True,
          num_classes=0,
          # pretrained='work_dirs/mae/finetune_ucf101_mae_dnn/huangbingkun/model/vit_h_hybridv2_pt_1200e_k700_ft_rep_2.pth'
      ),
      cls_head=dict(
          type='BaseClsHead',
          loss_cls=evidence_loss,
          in_channels=1280,
          num_classes=101,
          dropout_ratio=0.5,
      )) 
  
  # model training and testing settings
  evidence='exp'  # only used for EDL
  test_cfg = dict(average_clips='score')
  # 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'),
      dict(
          type='SampleFrames',
          clip_len=32,
          frame_interval=2,
          num_clips=1,
          test_mode=True),
      # dict(
      #     type='SampleFrames',
      #     clip_len=1,
      #     frame_interval=1,
      #     num_clips=32,
      #     test_mode=True),
      # dict(type='OpenCVDecode'),
      dict(type='DecordDecode'),
      dict(type='Resize', scale=(-1, 224)),
      dict(type='ThreeCrop', crop_size=224),
      # dict(type='CenterCrop', crop_size=224),
      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=4,
      workers_per_gpu=4,
      test=dict(
          type=dataset_type,
          ann_file=None,
          data_prefix=None,
          pipeline=test_pipeline))
  dist_params = dict(backend='nccl')