text: _base_ = [
      '../../../../_base_/default_runtime.py',
      '../../../../_base_/datasets/coco_wholebody.py'
  ]
  evaluation = dict(interval=10, metric='mAP', save_best='AP')
  
  optimizer = dict(
      type='Adam',
      lr=5e-4,
  )
  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
  channel_cfg = dict(
      num_output_channels=133,
      dataset_joints=133,
      dataset_channel=[
          list(range(133)),
      ],
      inference_channel=list(range(133)))
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained=None,
      backbone=dict(type='ViPNAS_MobileNetV3'),
      keypoint_head=dict(
          type='ViPNASHeatmapSimpleHead',
          in_channels=160,
          out_channels=channel_cfg['num_output_channels'],
          num_deconv_filters=(160, 160, 160),
          num_deconv_groups=(160, 160, 160),
          loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
      train_cfg=dict(),
      test_cfg=dict(
          flip_test=True,
          post_process='default',
          shift_heatmap=True,
          modulate_kernel=11))
  
  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,
      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=30,
          scale_factor=0.25),
      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', sigma=2),
      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=64,
      workers_per_gpu=2,
      val_dataloader=dict(samples_per_gpu=32),
      test_dataloader=dict(samples_per_gpu=32),
      train=dict(
          type='TopDownCocoWholeBodyDataset',
          ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json',
          img_prefix=f'{data_root}/train2017/',
          data_cfg=data_cfg,
          pipeline=train_pipeline,
          dataset_info={{_base_.dataset_info}}),
      val=dict(
          type='TopDownCocoWholeBodyDataset',
          ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
          img_prefix=f'{data_root}/val2017/',
          data_cfg=data_cfg,
          pipeline=val_pipeline,
          dataset_info={{_base_.dataset_info}}),
      test=dict(
          type='TopDownCocoWholeBodyDataset',
          ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
          img_prefix=f'{data_root}/val2017/',
          data_cfg=data_cfg,
          pipeline=test_pipeline,
          dataset_info={{_base_.dataset_info}}),
  )
  