content: # model settings
  model = dict(
      type='Recognizer3D',
      backbone=dict(
          type='ResNet3d',
          pretrained2d=True,
          pretrained='torchvision://resnet50',
          depth=50,
          conv_cfg=dict(type='Conv3d'),
          norm_eval=False,
          inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)),
          zero_init_residual=False),
      cls_head=dict(
          type='I3DHead',
          num_classes=101,
          in_channels=2048,
          spatial_type='avg',
          dropout_ratio=0.5,
          init_std=0.01))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips='prob')
  # dataset settings
  dataset_type = 'VideoDataset'
  data_root = 'data/ucf101/videos'
  data_root_val = 'data/ucf101/videos'
  ann_file_train = 'data/ucf101/ucf101_train_split_1_videos.txt'
  ann_file_val = 'data/ucf101/ucf101_val_split_1_videos.txt'
  ann_file_test = 'data/ucf101/ucf101_val_split_1_videos.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='OpenCVInit', num_threads=1),
      dict(type='DenseSampleFrames', clip_len=32, frame_interval=2, num_clips=1),
      dict(type='OpenCVDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(
          type='MultiScaleCrop',
          input_size=224,
          scales=(1, 0.8),
          random_crop=False,
          max_wh_scale_gap=0),
      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'])
  ]
  val_pipeline = [
      dict(type='OpenCVInit', num_threads=1),
      dict(
          type='DenseSampleFrames',
          clip_len=32,
          frame_interval=2,
          num_clips=1,
          test_mode=True),
      dict(type='OpenCVDecode'),
      dict(type='Resize', scale=(-1, 256)),
      dict(type='CenterCrop', crop_size=224),
      dict(type='Flip', flip_ratio=0),
      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='OpenCVInit', num_threads=1),
      dict(
          type='DenseSampleFrames',
          clip_len=32,
          frame_interval=2,
          num_clips=1,
          test_mode=True),
      dict(type='OpenCVDecode'),
      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='NCTHW'),
      dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['imgs'])
  ]
  data = dict(
      videos_per_gpu=8,  # set to 8 for training
      workers_per_gpu=4,  # set to 4 for training
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          start_index=0,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          start_index=0,
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          start_index=0,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.001, momentum=0.9,   # change from 0.01 to 0.001
      weight_decay=0.0001, nesterov=True)  # 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=[20, 40])
  total_epochs = 50 # change from 100 to 50
  checkpoint_config = dict(interval=10)
  evaluation = dict(
      interval=5, 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/finetune_ucf101_i3d_dnn/'
  load_from = 'https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_256p_32x2x1_100e_kinetics400_rgb/i3d_r50_dense_256p_32x2x1_100e_kinetics400_rgb_20200725-24eb54cc.pth'  # model path can be found in model zoo
  resume_from = None
  workflow = [('train', 1)]
  