text: _base_ = [
      '../../../../_base_/default_runtime.py',
      '../../../../_base_/datasets/mpii.py'
  ]
  evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
  
  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=50, hooks=[
          dict(type='TextLoggerHook'),
      ])
  
  channel_cfg = dict(
      num_output_channels=16,
      dataset_joints=16,
      dataset_channel=list(range(16)),
      inference_channel=list(range(16)))
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained='https://download.openmmlab.com/mmpose/'
      'pretrain_models/hrnet_w48-8ef0771d.pth',
      backbone=dict(
          type='HRNet',
          in_channels=3,
          extra=dict(
              stage1=dict(
                  num_modules=1,
                  num_branches=1,
                  block='BOTTLENECK',
                  num_blocks=(4, ),
                  num_channels=(64, )),
              stage2=dict(
                  num_modules=1,
                  num_branches=2,
                  block='BASIC',
                  num_blocks=(4, 4),
                  num_channels=(48, 96)),
              stage3=dict(
                  num_modules=4,
                  num_branches=3,
                  block='BASIC',
                  num_blocks=(4, 4, 4),
                  num_channels=(48, 96, 192)),
              stage4=dict(
                  num_modules=3,
                  num_branches=4,
                  block='BASIC',
                  num_blocks=(4, 4, 4, 4),
                  num_channels=(48, 96, 192, 384))),
      ),
      keypoint_head=dict(
          type='TopdownHeatmapSimpleHead',
          in_channels=48,
          out_channels=channel_cfg['num_output_channels'],
          num_deconv_layers=0,
          extra=dict(final_conv_kernel=1, ),
          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=[256, 256],
      heatmap_size=[64, 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'],
      use_gt_bbox=True,
      bbox_file=None,
  )
  
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='TopDownRandomFlip', flip_prob=0.5),
      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', 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='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/mpii'
  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='TopDownMpiiDataset',
          ann_file=f'{data_root}/annotations/mpii_train.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=train_pipeline,
          dataset_info={{_base_.dataset_info}}),
      val=dict(
          type='TopDownMpiiDataset',
          ann_file=f'{data_root}/annotations/mpii_val.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=val_pipeline,
          dataset_info={{_base_.dataset_info}}),
      test=dict(
          type='TopDownMpiiDataset',
          ann_file=f'{data_root}/annotations/mpii_val.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=test_pipeline,
          dataset_info={{_base_.dataset_info}}),
  )
  