text: # model setting
  model = dict(
      type='FastRCNN',
      backbone=dict(
          type='ResNet3dSlowOnly',
          depth=101,
          pretrained=None,
          pretrained2d=False,
          lateral=False,
          num_stages=4,
          conv1_kernel=(1, 7, 7),
          conv1_stride_t=1,
          pool1_stride_t=1,
          spatial_strides=(1, 2, 2, 1)),
      roi_head=dict(
          type='AVARoIHead',
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor3D',
              roi_layer_type='RoIAlign',
              output_size=8,
              with_temporal_pool=True),
          bbox_head=dict(
              type='BBoxHeadAVA',
              in_channels=2048,
              num_classes=81,
              multilabel=True,
              dropout_ratio=0.5)),
      train_cfg=dict(
          rcnn=dict(
              assigner=dict(
                  type='MaxIoUAssignerAVA',
                  pos_iou_thr=0.9,
                  neg_iou_thr=0.9,
                  min_pos_iou=0.9),
              sampler=dict(
                  type='RandomSampler',
                  num=32,
                  pos_fraction=1,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=1.0,
              debug=False)),
      test_cfg=dict(rcnn=dict(action_thr=0.002)))
  
  dataset_type = 'AVADataset'
  data_root = 'data/ava/rawframes'
  anno_root = 'data/ava/annotations'
  
  ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
  ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
  
  exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
  exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
  
  label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
  
  proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
                         'recall_93.9.pkl')
  proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
  
  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='SampleAVAFrames', clip_len=8, frame_interval=8),
      dict(type='RawFrameDecode'),
      dict(type='RandomRescale', scale_range=(256, 320)),
      dict(type='RandomCrop', size=256),
      dict(type='Flip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCTHW', collapse=True),
      # Rename is needed to use mmdet detectors
      dict(type='Rename', mapping=dict(imgs='img')),
      dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
      dict(
          type='ToDataContainer',
          fields=[
              dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
          ]),
      dict(
          type='Collect',
          keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
          meta_keys=['scores', 'entity_ids'])
  ]
  # The testing is w/o. any cropping / flipping
  val_pipeline = [
      dict(type='SampleAVAFrames', clip_len=8, frame_interval=8),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='FormatShape', input_format='NCTHW', collapse=True),
      # Rename is needed to use mmdet detectors
      dict(type='Rename', mapping=dict(imgs='img')),
      dict(type='ToTensor', keys=['img', 'proposals']),
      dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
      dict(
          type='Collect',
          keys=['img', 'proposals'],
          meta_keys=['scores', 'img_shape'],
          nested=True)
  ]
  
  data = dict(
      videos_per_gpu=6,
      workers_per_gpu=4,
      # During testing, each video may have different shape
      val_dataloader=dict(videos_per_gpu=1),
      test_dataloader=dict(videos_per_gpu=1),
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          exclude_file=exclude_file_train,
          pipeline=train_pipeline,
          label_file=label_file,
          proposal_file=proposal_file_train,
          person_det_score_thr=0.9,
          data_prefix=data_root),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          exclude_file=exclude_file_val,
          pipeline=val_pipeline,
          label_file=label_file,
          proposal_file=proposal_file_val,
          person_det_score_thr=0.9,
          data_prefix=data_root))
  data['test'] = data['val']
  
  optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
  # 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=[10, 15],
      warmup='linear',
      warmup_by_epoch=True,
      warmup_iters=5,
      warmup_ratio=0.1)
  total_epochs = 20
  checkpoint_config = dict(interval=1)
  workflow = [('train', 1)]
  evaluation = dict(interval=1, save_best='mAP@0.5IOU')
  log_config = dict(
      interval=20, hooks=[
          dict(type='TextLoggerHook'),
      ])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = ('./work_dirs/ava/'
              'slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb')
  load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'
               'omni/slowonly_r101_without_omni_8x8x1_'
               'kinetics400_rgb_20200926-0c730aef.pth')
  resume_from = None
  find_unused_parameters = False
  