content: # model settings
  model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='ResNet3dSlowOnly',
          depth=50,
          pretrained=None,
          lateral=False,
          conv1_kernel=(1, 7, 7),
          conv1_stride_t=1,
          pool1_stride_t=1,
          inflate=(0, 0, 1, 1),
          norm_eval=False),
      cls_head=dict(
          type='I3DHead',
          in_channels=2048,
          num_classes=200,
          spatial_type='avg',
          dropout_ratio=0.5))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips='prob')
  # dataset settings
  dataset_type = 'VideoDataset'
  # The flag indicates using joint training
  omnisource = True
  
  data_root = 'data/OmniSource/kinetics_200_train'
  data_root_val = 'data/OmniSource/kinetics_200_val'
  gg_root = 'data/OmniSource/googleimage_200'
  
  ann_file_train = 'data/OmniSource/annotations/kinetics_200/k200_train.txt'
  ann_file_gg = ('data/OmniSource/annotations/googleimage_200/'
                 'tsn_8seg_googleimage_200_wodup.txt')
  
  ann_file_val = 'data/OmniSource/annotations/kinetics_200/k200_val.txt'
  ann_file_test = 'data/OmniSource/annotations/kinetics_200/k200_val.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='DecordInit'),
      dict(type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1),
      dict(type='DecordDecode'),
      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'])
  ]
  train_gg_pipeline = [
      dict(type='ImageDecode'),
      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='BuildPseudoClip', clip_len=8),
      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='DecordInit'),
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=1,
          test_mode=True),
      dict(type='DecordDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=256),
      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='DecordInit'),
      dict(
          type='SampleFrames',
          clip_len=8,
          frame_interval=8,
          num_clips=10,
          test_mode=True),
      dict(type='DecordDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='ThreeCrop', crop_size=256),
      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=2,
      train=[
          dict(
              type=dataset_type,
              ann_file=ann_file_train,
              data_prefix=data_root,
              pipeline=train_pipeline),
          dict(
              type='ImageDataset',
              ann_file=ann_file_gg,
              data_prefix=gg_root,
              pipeline=train_gg_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=0.15, 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)
  total_epochs = 256
  checkpoint_config = dict(interval=8)
  workflow = [('train', 1)]
  evaluation = dict(
      interval=8, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  log_config = dict(
      interval=20, hooks=[
          dict(type='TextLoggerHook'),
      ])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = ('./work_dirs/omnisource/'
              'slowonly_r50_8x8x1_256e_minikinetics_googleimage_rgb')
  load_from = None
  resume_from = None
  find_unused_parameters = False
  