text: import os
  
  # model settings
  input_size = 448
  width_mult = 1.0
  model = dict(
      type='SingleStageDetector',
      backbone=dict(
          type='mobilenetv2_w1',
          out_indices=(4, 5),
          frozen_stages=-1,
          norm_eval=False,
          pretrained=True,
      ),
      neck=None,
      bbox_head=dict(
          type='SSDHead',
          input_size=input_size,
          in_channels=(int(width_mult * 96), int(width_mult * 320)),
          num_classes=2,
          anchor_strides=(16, 32),
          anchor_widths=(
              [14.0373, 37.4827, 21.952, 51.8187],
              [213.5467, 115.584, 192.3413, 76.3093, 112.896]),
          anchor_heights=(
              [22.4, 59.136, 38.08, 94.3787],
              [339.7333, 243.264, 185.92, 156.9493, 108.416]),
          target_means=(.0, .0, .0, .0),
          target_stds=(0.1, 0.1, 0.2, 0.2),
          depthwise_heads=True,
          depthwise_heads_activations='relu',
          loss_balancing=True))
  cudnn_benchmark = True
  train_cfg = dict(
      assigner=dict(
          type='MaxIoUAssigner',
          pos_iou_thr=0.4,
          neg_iou_thr=0.4,
          min_pos_iou=0.,
          ignore_iof_thr=-1,
          gt_max_assign_all=False),
      smoothl1_beta=1.,
      use_giou=False,
      use_focal=False,
      allowed_border=-1,
      pos_weight=-1,
      neg_pos_ratio=3,
      debug=False)
  test_cfg = dict(
      nms=dict(type='nms', iou_thr=0.45),
      min_bbox_size=0,
      score_thr=0.02,
      max_per_img=200)
  # model training and testing settings
  # dataset settings
  dataset_type = 'CustomCocoDataset'
  data_root = 'data/WIDERFace/'
  img_norm_cfg = dict(mean=[0, 0, 0], std=[255, 255, 255], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='PhotoMetricDistortion',
          brightness_delta=32,
          contrast_range=(0.5, 1.5),
          saturation_range=(0.5, 1.5),
          hue_delta=18),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
          min_crop_size=0.1),
      dict(type='Resize', img_scale=(input_size, input_size), keep_ratio=False),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(input_size, input_size),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      imgs_per_gpu=32,
      workers_per_gpu=4,
      train=dict(
          type='RepeatDataset',
          times=2,
          dataset=dict(
              type=dataset_type,
              classes=('face',),
              ann_file=data_root + '/train.json',
              min_size=17,
              img_prefix=data_root,
              pipeline=train_pipeline
          )
      ),
      val=dict(
          type=dataset_type,
          classes=('face',),
          ann_file=data_root + '/val.json',
          img_prefix=data_root,
          test_mode=True,
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          classes=('face',),
          ann_file=data_root + '/val.json',
          img_prefix=data_root,
          test_mode=True,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict()
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=1200,
      warmup_ratio=1.0 / 3,
      step=[40, 55, 65])
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=100,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 70
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = os.path.join('outputs', os.path.basename(__file__)[:-3])
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  