content: # model settings
  model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='C3D',
          pretrained=  # noqa: E251
          'https://download.openmmlab.com/mmaction/recognition/c3d/c3d_sports1m_pretrain_20201016-dcc47ddc.pth',  # noqa: E501
          style='pytorch',
          conv_cfg=dict(type='Conv3d'),
          norm_cfg=None,
          act_cfg=dict(type='ReLU'),
          dropout_ratio=0.5,
          init_std=0.005),
      cls_head=dict(
          type='I3DHead',
          num_classes=101,
          in_channels=4096,
          spatial_type=None,
          dropout_ratio=0.5,
          init_std=0.01))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips='score')
  # dataset settings
  dataset_type = 'RawframeDataset'
  data_root = 'data/ucf101/rawframes'
  data_root_val = 'data/ucf101/rawframes'
  split = 1  # official train/test splits. valid numbers: 1, 2, 3
  ann_file_train = f'data/ucf101/ucf101_train_split_{split}_rawframes.txt'
  ann_file_val = f'data/ucf101/ucf101_val_split_{split}_rawframes.txt'
  ann_file_test = f'data/ucf101/ucf101_val_split_{split}_rawframes.txt'
  img_norm_cfg = dict(mean=[104, 117, 128], std=[1, 1, 1], to_bgr=False)
  train_pipeline = [
      dict(type='SampleFrames', clip_len=16, frame_interval=1, num_clips=1),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(128, 171)),
      dict(type='RandomCrop', size=112),
      dict(type='Flip', flip_ratio=0.5),
      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='SampleFrames',
          clip_len=16,
          frame_interval=1,
          num_clips=1,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(128, 171)),
      dict(type='CenterCrop', crop_size=112),
      dict(type='Flip', flip_ratio=0),
      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'])
  ]
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=16,
          frame_interval=1,
          num_clips=10,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(128, 171)),
      dict(type='CenterCrop', crop_size=112),
      dict(type='Flip', flip_ratio=0),
      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'])
  ]
  data = dict(
      videos_per_gpu=30,
      workers_per_gpu=2,
      test_dataloader=dict(videos_per_gpu=1),
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.001, momentum=0.9,
      weight_decay=0.0005)  # 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 = 45
  checkpoint_config = dict(interval=5)
  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 = f'./work_dirs/c3d_sports1m_16x1x1_45e_ucf101_split_{split}_rgb/'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  