content: # model settings
  category_nums = dict(
      action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
  target_cate = 'action'
  
  model = dict(
      type='Recognizer2D',
      backbone=dict(
          type='ResNet',
          pretrained='torchvision://resnet18',
          depth=18,
          norm_eval=False),
      cls_head=dict(
          type='TSNHead',
          num_classes=category_nums[target_cate],
          in_channels=512,
          spatial_type='avg',
          multi_class=True,
          consensus=dict(type='AvgConsensus', dim=1),
          loss_cls=dict(type='BCELossWithLogits', loss_weight=333.),
          dropout_ratio=0.4,
          init_std=0.01))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips=None)
  # dataset settings
  dataset_type = 'RawframeDataset'
  data_root = 'data/hvu/rawframes_train'
  data_root_val = 'data/hvu/rawframes_val'
  ann_file_train = f'data/hvu/hvu_{target_cate}_train.json'
  ann_file_val = f'data/hvu/hvu_{target_cate}_val.json'
  ann_file_test = f'data/hvu/hvu_{target_cate}_val.json'
  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=1, frame_interval=1, num_clips=8),
      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='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs', 'label'])
  ]
  val_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=8,
          test_mode=True),
      dict(type='RawFrameDecode'),
      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='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  test_pipeline = [
      dict(
          type='SampleFrames',
          clip_len=1,
          frame_interval=1,
          num_clips=25,
          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='NCHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=32,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          pipeline=train_pipeline,
          multi_class=True,
          num_classes=category_nums[target_cate],
          filename_tmpl='img_{:05d}.jpg'),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          pipeline=val_pipeline,
          multi_class=True,
          num_classes=category_nums[target_cate],
          filename_tmpl='img_{:05d}.jpg'),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline,
          multi_class=True,
          num_classes=category_nums[target_cate],
          filename_tmpl='img_{:05d}.jpg'))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.01, 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='step', step=[40, 80])
  total_epochs = 100
  checkpoint_config = dict(interval=1)
  evaluation = dict(interval=2, metrics=['mean_average_precision'])
  log_config = dict(
      interval=20, hooks=[
          dict(type='TextLoggerHook'),
      ])
  # runtime settings
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = f'./work_dirs/tsn_r18_1x1x8_100e_hvu_{target_cate}_rgb/'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  