text: log_level = 'INFO'
  load_from = None
  resume_from = None
  dist_params = dict(backend='nccl')
  workflow = [('train', 1)]
  checkpoint_config = dict(interval=10)
  evaluation = dict(interval=10, metric='mAP', key_indicator='AP')
  
  optimizer = dict(
      type='Adam',
      lr=5e-3,
  )
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[170, 200])
  total_epochs = 210
  log_config = dict(
      interval=50, hooks=[
          dict(type='TextLoggerHook'),
      ])
  
  channel_cfg = dict(
      num_output_channels=17,
      dataset_joints=17,
      dataset_channel=[
          [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
      ],
      inference_channel=[
          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
      ])
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained=None,
      backbone=dict(
          type='RSN',
          unit_channels=256,
          num_stages=1,
          num_units=4,
          num_blocks=[3, 4, 6, 3],
          num_steps=4,
          norm_cfg=dict(type='BN')),
      keypoint_head=dict(
          type='TopDownMSMUHead',
          out_shape=(64, 48),
          unit_channels=256,
          out_channels=channel_cfg['num_output_channels'],
          num_stages=1,
          num_units=4,
          use_prm=False,
          norm_cfg=dict(type='BN'),
          loss_keypoint=[
              dict(
                  type='JointsMSELoss', use_target_weight=True, loss_weight=0.25)
          ] * 3 + [
              dict(
                  type='JointsOHKMMSELoss',
                  use_target_weight=True,
                  loss_weight=1.)
          ]),
      train_cfg=dict(),
      test_cfg=dict(
          flip_test=True,
          post_process='megvii',
          shift_heatmap=False,
          modulate_kernel=5))
  
  data_cfg = dict(
      image_size=[192, 256],
      heatmap_size=[48, 64],
      num_output_channels=channel_cfg['num_output_channels'],
      num_joints=channel_cfg['dataset_joints'],
      dataset_channel=channel_cfg['dataset_channel'],
      inference_channel=channel_cfg['inference_channel'],
      soft_nms=False,
      use_nms=False,
      nms_thr=1.0,
      oks_thr=0.9,
      vis_thr=0.2,
      use_gt_bbox=False,
      det_bbox_thr=0.0,
      bbox_file='data/coco/person_detection_results/'
      'COCO_val2017_detections_AP_H_56_person.json',
  )
  
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='TopDownRandomFlip', flip_prob=0.5),
      dict(
          type='TopDownHalfBodyTransform',
          num_joints_half_body=8,
          prob_half_body=0.3),
      dict(
          type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
      dict(type='TopDownAffine'),
      dict(type='ToTensor'),
      dict(
          type='NormalizeTensor',
          mean=[0.485, 0.456, 0.406],
          std=[0.229, 0.224, 0.225]),
      dict(
          type='TopDownGenerateTarget',
          kernel=[(11, 11), (9, 9), (7, 7), (5, 5)],
          encoding='Megvii'),
      dict(
          type='Collect',
          keys=['img', 'target', 'target_weight'],
          meta_keys=[
              'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
              'rotation', 'bbox_score', 'flip_pairs'
          ]),
  ]
  
  val_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='TopDownAffine'),
      dict(type='ToTensor'),
      dict(
          type='NormalizeTensor',
          mean=[0.485, 0.456, 0.406],
          std=[0.229, 0.224, 0.225]),
      dict(
          type='Collect',
          keys=[
              'img',
          ],
          meta_keys=[
              'image_file', 'center', 'scale', 'rotation', 'bbox_score',
              'flip_pairs'
          ]),
  ]
  
  test_pipeline = val_pipeline
  
  data_root = 'data/coco'
  data = dict(
      samples_per_gpu=32,
      workers_per_gpu=4,
      train=dict(
          type='TopDownCocoDataset',
          ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
          img_prefix=f'{data_root}/train2017/',
          data_cfg=data_cfg,
          pipeline=train_pipeline),
      val=dict(
          type='TopDownCocoDataset',
          ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
          img_prefix=f'{data_root}/val2017/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
      test=dict(
          type='TopDownCocoDataset',
          ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
          img_prefix=f'{data_root}/val2017/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
  )
  