content: # model settings
  evidence_loss = dict(type='EvidenceLoss',
                        num_classes=101,
                        evidence='exp',
                        loss_type='log',
                        with_kldiv=False,
                        with_avuloss=True,
                        annealing_method='exp')
  model = dict(
      type='Recognizer2D',
      backbone=dict(
          type='ResNetTSM',
          pretrained='torchvision://resnet50',
          depth=50,
          norm_eval=False,
          shift_div=8),
      cls_head=dict(
          type='TSMHead',
          loss_cls=evidence_loss,
          num_classes=101,
          in_channels=2048,
          spatial_type='avg',
          consensus=dict(type='AvgConsensus', dim=1),
          dropout_ratio=0.5,
          init_std=0.001,
          is_shift=True))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips='evidence', evidence_type='exp')
  # dataset settings
  dataset_type = 'VideoDataset'
  data_root = 'data/kinetics10/videos_train'
  data_root_val = 'data/kinetics10/videos_val'
  ann_file_train = 'data/kinetics10/kinetics10_train_list_videos.txt'
  ann_file_val = 'data/kinetics10/kinetics10_val_list_videos.txt'
  ann_file_test = 'data/kinetics10/kinetics10_val_list_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='DenseSampleFrames', clip_len=1, frame_interval=1, num_clips=8),
      dict(type='OpenCVDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(
          type='MultiScaleCrop',
          input_size=224,
          scales=(1, 0.875, 0.75, 0.66),
          random_crop=False,
          max_wh_scale_gap=1,
          num_fixed_crops=13),
      dict(type='Resize', scale=(224, 224), keep_ratio=False),
      dict(type='Flip', flip_ratio=0.5),
      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', 'label'])
  ]
  val_pipeline = [
      dict(type='OpenCVInit', num_threads=1),
      dict(
          type='DenseSampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=8,
          test_mode=True),
      dict(type='OpenCVDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=224),
      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'])
  ]
  test_pipeline = [
      dict(type='OpenCVInit', num_threads=1),
      dict(
          type='DenseSampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=8,
          test_mode=True),
      dict(type='OpenCVDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=224),
      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(
      videos_per_gpu=4,
      workers_per_gpu=4,
      val_dataloader=dict(videos_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',
      constructor='TSMOptimizerConstructor',
      paramwise_cfg=dict(fc_lr5=True),
      lr=0.001,  # this lr is used for 8 gpus
      momentum=0.9,
      weight_decay=0.0001)
  optimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))
  # learning policy
  lr_config = dict(policy='step', step=[20, 40])
  total_epochs = 50
  checkpoint_config = dict(interval=10)
  evaluation = dict(
      interval=2, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook'),
      ])
  annealing_runner = True
  # runtime settings
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/tsm/train_kinetics10_tsm_DEAR_noDebias'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  