text: # This config is used to generate long-term feature bank.
  _base_ = ['../_base_/models/slowonly_r50.py']
  
  # model settings
  lfb_prefix_path = 'data/ava/lfb_half'
  dataset_mode = 'train'  # ['train', 'val', 'test']
  
  model = dict(
      roi_head=dict(
          shared_head=dict(
              type='LFBInferHead',
              lfb_prefix_path=lfb_prefix_path,
              dataset_mode=dataset_mode,
              use_half_precision=True)))
  
  # dataset settings
  dataset_type = 'AVADataset'
  data_root = 'data/ava/rawframes'
  anno_root = 'data/ava/annotations'
  
  ann_file_infer = f'{anno_root}/ava_{dataset_mode}_v2.1.csv'
  
  exclude_file_infer = (
      f'{anno_root}/ava_{dataset_mode}_excluded_timestamps_v2.1.csv')
  
  label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
  
  proposal_file_infer = (
      f'{anno_root}/ava_dense_proposals_{dataset_mode}.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)
  
  infer_pipeline = [
      dict(
          type='SampleAVAFrames', clip_len=4, frame_interval=16, test_mode=True),
      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', 'img_key'],
          nested=True)
  ]
  
  data = dict(
      videos_per_gpu=1,
      workers_per_gpu=2,
      test=dict(
          type=dataset_type,
          ann_file=ann_file_infer,
          exclude_file=exclude_file_infer,
          pipeline=infer_pipeline,
          label_file=label_file,
          proposal_file=proposal_file_infer,
          person_det_score_thr=0.9,
          data_prefix=data_root))
  
  dist_params = dict(backend='nccl')
  