text: model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='ResNet3dSlowOnly',
          depth=50,
          pretrained=None,
          in_channels=28,
          base_channels=32,
          num_stages=3,
          out_indices=(2, ),
          stage_blocks=(4, 6, 3),
          conv1_stride_s=1,
          pool1_stride_s=1,
          inflate=(0, 1, 1),
          spatial_strides=(2, 2, 2),
          temporal_strides=(1, 1, 2),
          dilations=(1, 1, 1)),
      cls_head=dict(
          type='I3DHead',
          in_channels=512,
          num_classes=11,
          spatial_type='avg',
          dropout_ratio=0.5),
      train_cfg=dict(),
      test_cfg=dict(average_clips='prob'))
  
  dataset_type = 'PoseDataset'
  ann_file_train = 'data/user_data/annotations/result-train.pkl'
  ann_file_val = 'data/user_data/annotations/result-valid.pkl'
  ann_file_test = 'data/user_data/annotations/result-test.pkl'
  
  left_kp = [0, 1, 4, 10, 11, 12, 13, 19, 20, 21]
  right_kp = [2, 3, 5, 13, 14, 15, 22, 23, 24]
  
  train_pipeline = [
      dict(type='UniformSampleFrames', clip_len=10),
      dict(type='PoseDecode'),
      dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),
      dict(type='Resize', scale=(-1, 64)),
      dict(type='RandomResizedCrop', area_range=(0.56, 1.0)),
      dict(type='Resize', scale=(512, 512), keep_ratio=False),
      dict(type='Flip', flip_ratio=0.5),
      dict(
          type='GeneratePoseTarget',
          sigma=0.6,
          use_score=True,
          with_kp=True,
          with_limb=False),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs', 'label'])
  ]
  val_pipeline = [
      dict(type='UniformSampleFrames', clip_len=10, num_clips=1, test_mode=True),
      dict(type='PoseDecode'),
      dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),
      dict(type='Resize', scale=(-1, 64)),
      dict(type='CenterCrop', crop_size=64),
      dict(
          type='GeneratePoseTarget',
          sigma=0.6,
          use_score=True,
          with_kp=True,
          with_limb=False),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  test_pipeline = [
      dict(
          type='UniformSampleFrames', clip_len=10, num_clips=10, test_mode=True),
      dict(type='PoseDecode'),
      dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),
      dict(type='Resize', scale=(-1, 64)),
      dict(type='CenterCrop', crop_size=64),
      dict(
          type='GeneratePoseTarget',
          sigma=0.6,
          use_score=True,
          with_kp=True,
          with_limb=False,
          double=True),
      dict(type='FormatShape', input_format='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=2,
      workers_per_gpu=2,
      test_dataloader=dict(videos_per_gpu=1),
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix='',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix='',
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix='',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.2, momentum=0.9,
      weight_decay=0.0003)  # 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', by_epoch=False, min_lr=0)
  total_epochs = 500
  checkpoint_config = dict(interval=10)
  workflow = [('train', 10)]
  evaluation = dict(
      interval=10,
      metrics=['top_k_accuracy', 'mean_class_accuracy'],
      topk=(1, 5))
  log_config = dict(
      interval=20, hooks=[
          dict(type='TextLoggerHook'),
      ])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/posec3d/slowonly_r50_u48_500e_user_keypoint_rgb'
  load_from = None
  resume_from = None
  find_unused_parameters = False
  