text: # model settings
  model = dict(
      type='RetinaNet',
      pretrained='open-mmlab://resnet18_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),
          norm_eval=True,
          style='caffe'),
      neck=dict(
          type='FPN',
          in_channels=[64, 128, 256, 512],
          out_channels=256,
          start_level=1,
          add_extra_convs=True,
          num_outs=5),
      bbox_head=dict(
          type='GARetinaHead',
          num_classes=81,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          octave_base_scale=4,
          scales_per_octave=3,
          octave_ratios=[0.5, 1.0, 2.0],
          anchor_strides=[8, 16, 32, 64, 128],
          anchor_base_sizes=None,
          anchoring_means=[.0, .0, .0, .0],
          anchoring_stds=[1.0, 1.0, 1.0, 1.0],
          target_means=(.0, .0, .0, .0),
          target_stds=[1.0, 1.0, 1.0, 1.0],
          loc_filter_thr=0.01,
          loss_loc=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_shape=dict(
              type='IoULoss', style='bounded', beta=0.2, loss_weight=1.0),
          loss_cls=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)))
  # training and testing settings
  train_cfg = dict(
      ga_assigner=dict(
          type='ApproxMaxIoUAssigner',
          pos_iou_thr=0.5,
          neg_iou_thr=0.4,
          min_pos_iou=0.4,
          ignore_iof_thr=-1),
      ga_sampler=dict(
          type='RandomSampler',
          num=256,
          pos_fraction=0.5,
          neg_pos_ub=-1,
          add_gt_as_proposals=False),
      assigner=dict(
          type='MaxIoUAssigner',
          pos_iou_thr=0.5,
          neg_iou_thr=0.5,
          min_pos_iou=0.0,
          ignore_iof_thr=-1),
      allowed_border=-1,
      pos_weight=-1,
      center_ratio=0.2,
      ignore_ratio=0.5,
      debug=False,
      # added by WSK
      # If use_diff_thr is set to be True, the type of loss_cls must be 'IOUbalancedSigmoidFocalLoss'.
      #
      use_diff_thr=False,
      assginer_cls=dict(
          type='MaxIoUAssigner',
          pos_iou_thr=0.4,
          neg_iou_thr=0.4,
          min_pos_iou=0,
          ignore_iof_thr=-1)
  )
  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=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
  data = dict(
      # imgs_per_gpu=2,
      imgs_per_gpu=4,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'train2017/',
          img_scale=(1000, 600),
          # img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0.5,
          with_mask=False,
          with_crowd=False,
          with_label=True),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(1000, 600),
          # img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0,
          with_mask=False,
          with_crowd=False,
          with_label=True),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(1000, 600),
          # img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0,
          with_mask=False,
          with_crowd=False,
          with_label=False,
          test_mode=True))
  # 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=[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
  device_ids = range(4)
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/ga_retinanet_r18_caffe_fpn_1x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  