text: log_level = 'INFO'
  load_from = None
  resume_from = None
  dist_params = dict(backend='nccl')
  workflow = [('train', 1)]
  checkpoint_config = dict(interval=220)
  evaluation = dict(interval=5, metric='PCK', key_indicator='PCK')
  COLOR = 'black'
  EXTEND = '10'
  
  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=1,
      hooks=[
          dict(type='TextLoggerHook'),
      ])
  
  channel_cfg = dict(
      num_output_channels=9,
      dataset_joints=9,
      dataset_channel=[
          [
              0, 1, 2, 3, 4, 5, 6, 7, 8
          ],
      ],
      inference_channel=[
          0, 1, 2, 3, 4, 5, 6, 7, 8
      ])
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained='torchvision://resnet50',
      backbone=dict(type='ResNet', depth=50),
      keypoint_head=dict(
          type='TopDownSimpleHead',
          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=[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'])
  
  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', '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 = [
      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'
          ]),
  ]
  
  
  dataset_type = 'AnimalHorse10Dataset'
  data_root = 'data/horse/training_data/horse_leg_back_256'+'_'+COLOR+'_'+EXTEND
  
  data = dict(
      samples_per_gpu=32,
      workers_per_gpu=2,
      val_dataloader=dict(samples_per_gpu=32),
      test_dataloader=dict(samples_per_gpu=32),
      train=dict(
          type=dataset_type,
          ann_file=f'{data_root}/annotations/train.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=f'{data_root}/annotations/val.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=f'{data_root}/annotations/test.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=test_pipeline),
  )