text: log_level = 'INFO'
  load_from = None
  # load_from = '/home/yl/working/opensource/mmpose/work_dirs/vipnas_res50_coco_384x288_udp/latest.pth'
  resume_from = None
  # resume_from = '/home/yl/working/opensource/mmpose/work_dirs/mobilenetv2_coco_384x288_pre_transform_udp/latest.pth'
  dist_params = dict(backend='nccl')
  workflow = [('train', 1)]
  checkpoint_config = dict(interval=10)
  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=[300, 350])
  total_epochs = 410
  log_config = dict(
      interval=5,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook')
      ])
  
  channel_cfg = dict(
      num_output_channels=2,
      dataset_joints=2,
      dataset_channel=[
          [0, 1],
      ],
      inference_channel=[
          0, 1
      ])
  target_type = 'GaussianHeatmap'
  # model settings
  model = dict(
      # type='TopDown',
      pretrained='https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_coco_256x192-cc43b466_20210624.pth',
      # backbone=dict(type='MobileNetV2', widen_factor=1., out_indices=(7, )),
      # keypoint_head=dict(
      #     type='TopdownHeatmapSimpleHead',
      #     in_channels=1280,
      #     out_channels=channel_cfg['num_output_channels'],
      #     loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
  
      type='TopDown',
  
      backbone=dict(type='ViPNAS_ResNet', depth=50),
      keypoint_head=dict(
          type='ViPNASHeatmapSimpleHead',
          in_channels=608,
          out_channels=channel_cfg['num_output_channels'],
          loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
  
      train_cfg=dict(),
      test_cfg=dict(
          flip_test=False,
          post_process='default',
          shift_heatmap=True,
          target_type=target_type,
          modulate_kernel=11,
          use_udp=True))
  
  data_cfg = dict(
      # image_size=[288, 384],
      # heatmap_size=[72, 96],
      image_size=[512, 288],
      heatmap_size=[128, 72],
      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=True,
      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=10, scale_factor=0.2),
      dict(type='TopDownAffine', use_udp=True),
      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='TopDownGenerateTarget',
          sigma=2,
          encoding='UDP',
          target_type=target_type),
      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', use_udp=True),
      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 = '/home/yl/working/data/recordedSamples/ultraviolet/color'
  data = dict(
      samples_per_gpu=16,
      workers_per_gpu=2,
      val_dataloader=dict(samples_per_gpu=4),
      test_dataloader=dict(samples_per_gpu=4),
      train=dict(
          type='TopDownCocoWeijingDataset',
          ann_file=f'{data_root}/4_no_uv.json',
          img_prefix=f'{data_root}/4_no_uv/',
          data_cfg=data_cfg,
          pipeline=train_pipeline),
      val=dict(
          type='TopDownCocoWeijingDataset',
          ann_file=f'{data_root}/4_no_uv_val.json',
          img_prefix=f'{data_root}/4_no_uv_val/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
      test=dict(
          type='TopDownCocoWeijingDataset',
          ann_file=f'{data_root}/4_no_uv_val.json',
          img_prefix=f'{data_root}/4_no_uv_val/',
          data_cfg=data_cfg,
          pipeline=val_pipeline),
  )
  