text: log_level = 'INFO'
  load_from = None
  resume_from = None
  dist_params = dict(backend='nccl')
  workflow = [('train', 1)]
  checkpoint_config = dict(interval=1)
  evaluation = dict(interval=1, metric=['NME'], key_indicator='NME')
  
  optimizer = dict(
      type='Adam',
      lr=2e-3,
  )
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[40, 55])
  total_epochs = 60
  log_config = dict(
      interval=5,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  
  channel_cfg = dict(
      num_output_channels=19,
      dataset_joints=19,
      dataset_channel=[
          list(range(19)),
      ],
      inference_channel=list(range(19)))
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained='open-mmlab://msra/hrnetv2_w18',
      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=(18, 36)),
              stage3=dict(
                  num_modules=4,
                  num_branches=3,
                  block='BASIC',
                  num_blocks=(4, 4, 4),
                  num_channels=(18, 36, 72)),
              stage4=dict(
                  num_modules=3,
                  num_branches=4,
                  block='BASIC',
                  num_blocks=(4, 4, 4, 4),
                  num_channels=(18, 36, 72, 144),
                  multiscale_output=True),
              upsample=dict(mode='bilinear', align_corners=False))),
      keypoint_head=dict(
          type='TopDownSimpleHead',
          in_channels=[18, 36, 72, 144],
          in_index=(0, 1, 2, 3),
          input_transform='resize_concat',
          out_channels=channel_cfg['num_output_channels'],
          num_deconv_layers=0,
          extra=dict(
              final_conv_kernel=1, num_conv_layers=1, num_conv_kernels=(1, ))),
      train_cfg=dict(),
      test_cfg=dict(
          flip_test=True,
          post_process='unbiased',
          shift_heatmap=True,
          modulate_kernel=11),
      loss_pose=dict(type='JointsMSELoss', use_target_weight=True))
  
  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=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, unbiased_encoding=True),
      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/aflw'
  data = dict(
      samples_per_gpu=64,
      workers_per_gpu=2,
      train=dict(
          type='FaceAFLWDataset',
          ann_file=f'{data_root}/annotations/face_landmarks_aflw_train.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=train_pipeline),
      val=dict(
          type='FaceAFLWDataset',
          ann_file=f'{data_root}/annotations/face_landmarks_aflw_test.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
      test=dict(
          type='FaceAFLWDataset',
          ann_file=f'{data_root}/annotations/face_landmarks_aflw_test.json',
          img_prefix=f'{data_root}/images/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
  )
  