text: _base_ = [
      '../_base_/datasets/coco_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  model = dict(
      type='ATSS',
      backbone=dict(type="LapFusion", encoder="resnet", lv6=True, act="RELU", norm="BN", rank=0, max_depth=1, depth=50,
                    plot=False),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs='on_output',
          num_outs=5),
      bbox_head=dict(
          type='ATSSHead',
          num_classes=10,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              ratios=[1.0],
              octave_base_scale=8,
              scales_per_octave=1,
              strides=[8, 16, 32, 64, 128]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[0.1, 0.1, 0.2, 0.2]),
          loss_cls=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
          loss_depth=dict(type="DepthLoss", loss_weight=1.0),
          loss_centerness=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
      # training and testing settings
      train_cfg=dict(
          assigner=dict(type='ATSSAssigner', topk=9),
          allowed_border=-1,
          pos_weight=-1,
          debug=False),
      test_cfg=dict(
          nms_pre=1000,
          min_bbox_size=0,
          score_thr=0.3,
          nms=dict(type='nms', iou_threshold=0.6),
          max_per_img=100))
  # optimizer
  optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
  