content: # model settings
  model = dict(
      type='Recognizer2D',
      backbone=dict(
          type='ResNetTIN',
          pretrained='torchvision://resnet50',
          depth=50,
          norm_eval=False,
          shift_div=4),
      cls_head=dict(
          type='TSMHead',
          num_classes=174,
          in_channels=2048,
          spatial_type='avg',
          consensus=dict(type='AvgConsensus', dim=1),
          dropout_ratio=0.8,
          init_std=0.001,
          is_shift=False))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips=None)
  # dataset settings
  dataset_type = 'RawframeDataset'
  data_root = 'data/sth-v2/rawframes_train/'
  data_root_val = 'data/sth-v2/rawframes_val/'
  ann_file_train = 'data/sth-v2/sth-v2_train_list.txt'
  ann_file_val = 'data/sth-v2/sth-v2_val_list.txt'
  ann_file_test = 'data/sth-v2/sth-v2_val_list.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='SampleFrames', clip_len=1, frame_interval=1, num_clips=8),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(
          type='MultiScaleCrop',
          input_size=224,
          scales=(1, 0.875, 0.75, 0.66),
          random_crop=False,
          max_wh_scale_gap=1),
      dict(type='Resize', scale=(224, 224), keep_ratio=False),
      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=224),
      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=8,
          test_mode=True),
      dict(type='RawFrameDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=224),
      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=6,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          filename_tmpl='{:05}.jpg',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          filename_tmpl='{:05}.jpg',
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          filename_tmpl='{:05}.jpg',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD',
      constructor='TSMOptimizerConstructor',
      paramwise_cfg=dict(fc_lr5=True),
      lr=0.02,  # this lr is used for 8 gpus
      momentum=0.9,
      weight_decay=0.0005)
  optimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='CosineAnnealing',
      by_epoch=False,
      warmup='linear',
      warmup_iters=1,
      warmup_by_epoch=True,
      min_lr=0)
  total_epochs = 40
  checkpoint_config = dict(interval=1)
  evaluation = dict(
      interval=2, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook'),
      ])
  # runtime settings
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/tin_r50_1x1x8_40e_sthv2_rgb/'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  