text: _base_ = [
      '../../../../_base_/default_runtime.py',
      '../../../../_base_/datasets/rhd2d.py'
  ]
  evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
  
  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
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  
  channel_cfg = dict(
      num_output_channels=21,
      dataset_joints=21,
      dataset_channel=[
          [
              0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
              19, 20
          ],
      ],
      inference_channel=[
          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
          20
      ])
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained='torchvision://resnet50',
      backbone=dict(type='ResNet', depth=50),
      keypoint_head=dict(
          type='TopdownHeatmapSimpleHead',
          in_channels=2048,
          out_channels=channel_cfg['num_output_channels'],
          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=[224, 224],
      heatmap_size=[56, 56],
      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'])
  
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='TopDownGetBboxCenterScale', padding=1.25),
      dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
      dict(type='TopDownRandomFlip', flip_prob=0.5),
      dict(
          type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3),
      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', 'flip_pairs'
          ]),
  ]
  
  val_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='TopDownGetBboxCenterScale', padding=1.25),
      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='Collect',
          keys=['img'],
          meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
  ]
  
  test_pipeline = val_pipeline
  
  data_root = 'data/rhd'
  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='Rhd2DDataset',
          ann_file=f'{data_root}/annotations/rhd_train.json',
          img_prefix=f'{data_root}/',
          data_cfg=data_cfg,
          pipeline=train_pipeline,
          dataset_info={{_base_.dataset_info}}),
      val=dict(
          type='Rhd2DDataset',
          ann_file=f'{data_root}/annotations/rhd_test.json',
          img_prefix=f'{data_root}/',
          data_cfg=data_cfg,
          pipeline=val_pipeline,
          dataset_info={{_base_.dataset_info}}),
      test=dict(
          type='Rhd2DDataset',
          ann_file=f'{data_root}/annotations/rhd_test.json',
          img_prefix=f'{data_root}/',
          data_cfg=data_cfg,
          pipeline=test_pipeline,
          dataset_info={{_base_.dataset_info}}),
  )
  