text: _base_ = [
      '../_base_/models/retinanet_r50_fpn.py',
      '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
  ]
  cudnn_benchmark = True
  # model settings
  norm_cfg = dict(type='BN', requires_grad=True)
  model = dict(
      type='RetinaNet',
      pretrained='torchvision://resnet50',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=norm_cfg,
          norm_eval=False,
          style='pytorch'),
      neck=dict(type='NASFPN', stack_times=7, norm_cfg=norm_cfg),
      bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
      # training and testing settings
      train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
  # dataset settings
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='Resize',
          img_scale=(640, 640),
          ratio_range=(0.8, 1.2),
          keep_ratio=True),
      dict(type='RandomCrop', crop_size=(640, 640)),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size=(640, 640)),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(640, 640),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=128),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=4,
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD',
      lr=0.08,
      momentum=0.9,
      weight_decay=0.0001,
      paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=1000,
      warmup_ratio=0.1,
      step=[30, 40])
  # runtime settings
  runner = dict(max_epochs=50)
  