text: _base_ = ['../../../../_base_/datasets/posetrack18.py']
  log_level = 'INFO'
  load_from = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_384x288-314c8528_20200708.pth'  # noqa: E501
  resume_from = None
  dist_params = dict(backend='nccl')
  workflow = [('train', 1)]
  checkpoint_config = dict(interval=1)
  evaluation = dict(interval=1, metric='mAP', save_best='Total 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=[10, 15])
  total_epochs = 20
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  
  channel_cfg = dict(
      num_output_channels=17,
      dataset_joints=17,
      dataset_channel=[
          [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
      ],
      inference_channel=[
          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
      ])
  
  # model settings
  model = dict(
      type='TopDown',
      pretrained=None,
      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=[288, 384],
      heatmap_size=[72, 96],
      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=0.4,
      bbox_file='data/posetrack18/annotations/'
      'posetrack18_val_human_detections.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=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 = val_pipeline
  
  data_root = 'data/posetrack18'
  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='TopDownPoseTrack18Dataset',
          ann_file=f'{data_root}/annotations/posetrack18_train.json',
          img_prefix=f'{data_root}/',
          data_cfg=data_cfg,
          pipeline=train_pipeline,
          dataset_info={{_base_.dataset_info}}),
      val=dict(
          type='TopDownPoseTrack18Dataset',
          ann_file=f'{data_root}/annotations/posetrack18_val.json',
          img_prefix=f'{data_root}/',
          data_cfg=data_cfg,
          pipeline=val_pipeline,
          dataset_info={{_base_.dataset_info}}),
      test=dict(
          type='TopDownPoseTrack18Dataset',
          ann_file=f'{data_root}/annotations/posetrack18_val.json',
          img_prefix=f'{data_root}/',
          data_cfg=data_cfg,
          pipeline=test_pipeline,
          dataset_info={{_base_.dataset_info}}),
  )
  