text: model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='ResNet3dSlowOnly',
          depth=50,
          pretrained='torchvision://resnet50',
          lateral=False,
          in_channels=2,
          conv1_kernel=(1, 7, 7),
          conv1_stride_t=1,
          pool1_stride_t=1,
          inflate=(0, 0, 1, 1),
          with_pool2=False,
          norm_eval=False),
      cls_head=dict(
          type='I3DHead',
          in_channels=2048,
          num_classes=400,
          spatial_type='avg',
          dropout_ratio=0.5))
  train_cfg = None
  test_cfg = dict(average_clips=None)
  dataset_type = 'RawframeDataset'
  data_root = 'data/kinetics400/rawframes_train'
  data_root_val = 'data/kinetics400/rawframes_val'
  ann_file_train = 'data/kinetics400/kinetics_flow_train_list.txt'
  ann_file_val = 'data/kinetics400/kinetics_flow_val_list.txt'
  ann_file_test = 'data/kinetics400/kinetics_flow_val_list.txt'
  img_norm_cfg = dict(mean=[128, 128], std=[128, 128])
  train_pipeline = [
      dict(type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1),
      dict(type='FrameSelector'),
      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=8,
          frame_interval=8,
          num_clips=1,
          test_mode=True),
      dict(type='FrameSelector'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', 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'])
  ]
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=10,
          test_mode=True),
      dict(type='FrameSelector'),
      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=12,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          modality='Flow',
          filename_tmpl='{}_{:05d}.jpg',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          modality='Flow',
          filename_tmpl='{}_{:05d}.jpg',
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          modality='Flow',
          filename_tmpl='{}_{:05d}.jpg',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.06, 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 = 196
  checkpoint_config = dict(interval=4)
  workflow = [('train', 1)]
  evaluation = dict(
      interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          #   dict(type='TensorboardLoggerHook'),
      ])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/slowonly_r50_8x8x1_256e_kinetics400_flow'
  load_from = None
  resume_from = None
  find_unused_parameters = False
  