text: # model settings
  model = dict(
      type='FasterRCNN',
      pretrained='modelzoo://resnet50',
      backbone=dict(
          type='DSNetcenter',
          depth=('s3', 's6'),
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          style='pytorch', with_cp=True),
      neck=None,
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_scales=[8],
          anchor_ratios=[0.5, 1.0, 2.0],
          anchor_strides=[4, 8, 16, 32],
          target_means=[.0, .0, .0, .0],
          target_stds=[1.0, 1.0, 1.0, 1.0],
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
          loss_adv = dict(type='AdversarialLoss',),with_adv=True,),
      bbox_roi_extractor=dict(
          type='SingleRoIExtractor',
          roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
          out_channels=256,
          featmap_strides=[4, 8, 16, 32]),
      bbox_head=dict(
          type='SharedFCBBoxHead',
          num_fcs=2,
          in_channels=256,
          fc_out_channels=1024,
          roi_feat_size=7,
          num_classes=81,
          target_means=[0., 0., 0., 0.],
          target_stds=[0.1, 0.1, 0.2, 0.2],
          reg_class_agnostic=False,
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0),
          loss_adv = dict(type='AdversarialLoss',),with_adv=True,
      ))
  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=256,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          allowed_border=0,
          pos_weight=-1,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=2000,
          max_num=2000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.5,
              min_pos_iou=0.5,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=512,
              pos_fraction=0.25,
              neg_pos_ub=-1,
              add_gt_as_proposals=True),
          pos_weight=-1,
          debug=False))
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=1000,
          nms_post=1000,
          max_num=1000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100)
      # soft-nms is also supported for rcnn testing
      # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
  )
  # 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)
  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/',
          img_scale=(1333*1.2, 800*1.2),
          img_norm_cfg=img_norm_cfg,
          size_divisor=64,
          flip_ratio=0.5,
          with_mask=False,
          with_crowd=True,
          with_label=True),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(1333*1.2, 800*1.2),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0,
          with_mask=False,
          with_crowd=True,
          with_label=True),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=64,
          flip_ratio=0,
          with_mask=False,
          with_label=False,
          test_mode=True))
  # optimizer
  optimizer = dict(type='SGD', lr=0.02, 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=5,
      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/faster_rcnn_r50_fpn_1x_deshcenter_2advloss'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  