text: # model settings
  norm_cfg = dict(type="GN", num_groups=32, requires_grad=True)
  
  model = dict(
      type="RepPointsDetector",
      pretrained="open-mmlab://resnext101_32x4d",
      backbone=dict(
          type="ResNeXt",
          depth=101,
          groups=32,
          base_width=4,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type="BN", requires_grad=True),
          style="pytorch",
          dcn=dict(type="DCN", deformable_groups=1, fallback_on_stride=False),
          stage_with_dcn=(False, True, True, True),
      ),
      neck=dict(
          type="FPN",
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs=True,
          num_outs=5,
          norm_cfg=norm_cfg,
      ),
      bbox_head=dict(
          type="RepPointsHead",
          num_classes=81,
          in_channels=256,
          feat_channels=256,
          point_feat_channels=256,
          stacked_convs=3,
          num_points=9,
          gradient_mul=0.1,
          point_strides=[8, 16, 32, 64, 128],
          point_base_scale=4,
          norm_cfg=norm_cfg,
          loss_cls=dict(
              type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
          ),
          loss_bbox_init=dict(type="SmoothL1Loss", beta=0.11, loss_weight=0.5),
          loss_bbox_refine=dict(type="SmoothL1Loss", beta=0.11, loss_weight=1.0),
          transform_method="moment",
      ),
  )
  # training and testing settings
  train_cfg = dict(
      init=dict(
          assigner=dict(type="PointAssigner", scale=4, pos_num=1),
          allowed_border=-1,
          pos_weight=-1,
          debug=False,
      ),
      refine=dict(
          assigner=dict(
              type="MaxIoUAssigner",
              pos_iou_thr=0.5,
              neg_iou_thr=0.4,
              min_pos_iou=0,
              ignore_iof_thr=-1,
          ),
          allowed_border=-1,
          pos_weight=-1,
          debug=False,
      ),
  )
  test_cfg = dict(
      nms_pre=1000,
      min_bbox_size=0,
      score_thr=0.05,
      nms=dict(type="nms", iou_thr=0.5),
      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
  )
  train_pipeline = [
      dict(type="LoadImageFromFile"),
      dict(type="LoadAnnotations", with_bbox=True),
      dict(
          type="Resize",
          img_scale=[(1333, 480), (1333, 960)],
          keep_ratio=True,
          multiscale_mode="range",
      ),
      dict(type="RandomFlip", flip_ratio=0.5),
      dict(type="Normalize", **img_norm_cfg),
      dict(type="Pad", size_divisor=32),
      dict(type="DefaultFormatBundle"),
      dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
  ]
  test_pipeline = [
      dict(type="LoadImageFromFile"),
      dict(
          type="MultiScaleFlipAug",
          img_scale=(1333, 800),
          flip=False,
          transforms=[
              dict(type="Resize", keep_ratio=True),
              dict(type="RandomFlip"),
              dict(type="Normalize", **img_norm_cfg),
              dict(type="Pad", size_divisor=32),
              dict(type="ImageToTensor", keys=["img"]),
              dict(type="Collect", keys=["img"]),
          ],
      ),
  ]
  data = dict(
      imgs_per_gpu=2,
      workers_per_gpu=2,
      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,
      ),
  )
  evaluation = dict(interval=1, metric="bbox")
  # optimizer
  optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
  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 / 3,
      step=[16, 22],
  )
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type="TextLoggerHook"),
          # dict(type='TensorboardLoggerHook')
      ],
  )
  # yapf:enable
  # runtime settings
  total_epochs = 24
  dist_params = dict(backend="nccl")
  log_level = "INFO"
  work_dir = "./work_dirs/reppoints_moment_x101_dcn_fpn_2x_mt"
  load_from = None
  resume_from = None
  workflow = [("train", 1)]
  