text: # model settings
  model = dict(
      type='FCOS',
      pretrained='open-mmlab://resnet50_caffe',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type='BN', requires_grad=False),
          style='caffe'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs=True,
          extra_convs_on_inputs=False,  # use P5
          num_outs=5,
          relu_before_extra_convs=True),
      bbox_head=dict(
          type='DDBMultiBDRHead',
          num_classes=81,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          strides=[8, 16, 32, 64, 128],
          multi_bd_kernel_size=[9, 7, 5, 3, 3],
          bd_threshold=0.9,
          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=1.0),
          loss_sorted_bbox=dict(type='GIoULoss', loss_weight=1.0),
          bd_detach=True,
          bd_rank_num=10,
          loss_dist_scores=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          # loss_dist_scores=dict(type='MSELoss', loss_weight=1),
          loss_centerness=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
  # training and testing settings
  train_cfg = 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 = '/coco/data/2017/'
  img_norm_cfg = dict(
      mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      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=4,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'images/train2017/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'images/val2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'images/val2017/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD',
      lr=0.01,
      momentum=0.9,
      weight_decay=0.0001,
      paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.))
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='constant',
      warmup_iters=500,
      warmup_ratio=1.0 / 3,
      step=[8, 11])
  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 = 12
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/fcos_r50_caffe_fpn_gn_1x_4gpu'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  