content: model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='ResNet3dSlowFast',
          pretrained=None,
          resample_rate=8,  # tau
          speed_ratio=8,  # alpha
          channel_ratio=8,  # beta_inv
          slow_pathway=dict(
              type='resnet3d',
              depth=50,
              pretrained=None,
              lateral=True,
              conv1_kernel=(1, 7, 7),
              dilations=(1, 1, 1, 1),
              conv1_stride_t=1,
              pool1_stride_t=1,
              inflate=(0, 0, 1, 1),
              norm_eval=False),
          fast_pathway=dict(
              type='resnet3d',
              depth=50,
              pretrained=None,
              lateral=False,
              base_channels=8,
              conv1_kernel=(5, 7, 7),
              conv1_stride_t=1,
              pool1_stride_t=1,
              norm_eval=False)),
      cls_head=dict(
          type='SlowFastHead',
          in_channels=2304,  # 2048+256
          num_classes=400,
          spatial_type='avg',
          dropout_ratio=0.5))
  train_cfg = None
  test_cfg = dict(average_clips='prob')
  dataset_type = 'RawframeDataset'
  data_root = 'data/kinetics400/rawframes_train'
  data_root_val = 'data/kinetics400/rawframes_val'
  ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
  ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
  ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.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='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='RandomResizedCrop'),
      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='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs', 'label'])
  ]
  val_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=32,
          frame_interval=2,
          num_clips=1,
          test_mode=True),
      dict(type='RawFrameDecode'),
      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='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=32,
          frame_interval=2,
          num_clips=10,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='ThreeCrop', crop_size=256),
      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'])
  ]
  data = dict(
      videos_per_gpu=8,
      workers_per_gpu=4,
      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=0.1, momentum=0.9,
      weight_decay=0.0001)  # 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='CosineAnnealing',
      min_lr=0,
      warmup='linear',
      warmup_by_epoch=True,
      warmup_iters=34)
  total_epochs = 256
  checkpoint_config = dict(interval=4)
  workflow = [('train', 1)]
  evaluation = dict(
      interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          #    dict(type='TensorboardLoggerHook'),
      ])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/slowfast_r50_3d_4x16x1_256e_kinetics400_rgb'
  load_from = None
  resume_from = None
  find_unused_parameters = False
  