text: # model settings
  model = dict(
      type='CenterNet',
      pretrained='./pretrain/darknet53.pth',
      backbone=dict(
          type='DarknetV3',
          layers=[1, 2, 8, 8, 4],
          inplanes=[3, 32, 64, 128, 256, 512],
          planes=[32, 64, 128, 256, 512, 1024],
          norm_cfg=dict(type='BN'),
          out_indices=(1, 2, 3, 4),
          frozen_stages=1,
          norm_eval=False),
      neck=dict(type='None'),
      bbox_head=dict(
          type='CXTHead',
          inplanes=(128, 256, 512, 1024),
          head_conv=128,
          wh_conv=64,
          use_deconv=False,
          norm_after_upsample=False,
          hm_head_conv_num=2,
          wh_head_conv_num=2,
          ct_head_conv_num=1,
          fovea_hm=False,
          num_classes=81,
          use_exp_wh=False,
          wh_offset_base=16,
          wh_agnostic=True,
          shortcut_cfg=(1, 2, 3),
          shortcut_attention=(False, False, False),
          norm_cfg=dict(type='BN'),
          norm_wh=False,
          avg_wh_weightv3=False,
          hm_center_ratio=0.27,
          center_ratio=0.01,
          hm_init_value=None,
          giou_weight=5.,
          merge_weight=1.,
          hm_weight=1.,
          ct_weight=1.))
  cudnn_benchmark = True
  # training and testing settings
  train_cfg = dict(
      vis_every_n_iters=100,
      debug=False)
  test_cfg = dict(
      score_thr=0.01,
      max_per_img=100)
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  data = dict(
      imgs_per_gpu=12,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'train2017/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0003,
                   paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.))
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=1.0 / 5,
      step=[18, 22])
  checkpoint_config = dict(save_every_n_steps=200, max_to_keep=1, keep_every_n_epochs=18)
  bbox_head_hist_config = dict(
      model_type=['ConvModule', 'DeformConvPack'],
      sub_modules=['bbox_head'],
      save_every_n_steps=200)
  # yapf:disable
  log_config = dict(interval=20)
  # yapf:enable
  # runtime settings
  total_epochs = 24
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = 'eft53_htct027_whratio001_v1l_3lr_wd3e4_s123_nos_2x'
  load_from = None
  resume_from = 'work_dirs/1908/0802_T2_eft53_V1L_HMCR027_RATIO001_AGN_1X/eft53_htct027_whratio001_v1l_3lr_wd3e4_s123_nos_1x_0802_1434/epoch_9.pth'
  workflow = [('train', 1)]
  