text: # fp16 settings
  fp16 = dict(loss_scale=512.)
  
  # model settings
  model = dict(
      type='CascadeRCNN',
      num_stages=3,
      pretrained='modelzoo://resnet18',
      backbone=dict(
         type='ResNet',
          depth=18,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_eval=False,
          style='pytorch'),
      neck=dict(
          type='FPN',
          #in_channels=[256, 512, 1024, 2048],
          in_channels=[64, 128,256,512],
          out_channels=256,
          num_outs=5),
      rpn_head=dict(
          type='GARPNHead',
          in_channels=256,
          feat_channels=256,
          octave_base_scale=8,
          scales_per_octave=3,
          #octave_ratios=[0.5, 1.0, 2.0],
          octave_ratios=[1,1.25],
          anchor_strides=[4, 8, 16, 32, 64],
          anchor_base_sizes=None,
          anchoring_means=[.0, .0, .0, .0],
          anchoring_stds=[0.07, 0.07, 0.14, 0.14],
          target_means=(.0, .0, .0, .0),
          target_stds=[0.07, 0.07, 0.11, 0.11],
          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='BoundedIoULoss', beta=0.2,loss_weight=1.0),
  	#loss_shape=dict(
          #    type='IoULoss',
          #    use_bounded=True,
          #    beta=0.2,
          #    loss_weight=1.0),
          loss_cls=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=1.0),
          loss_bbox=dict(
              type='SmoothL1Loss',
              beta=1.0,
              loss_weight=1.0)),
      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=2,
              target_means=[0., 0., 0., 0.],
              target_stds=[0.05, 0.05, 0.1, 0.1],
              reg_class_agnostic=True,
              loss_cls=dict(
                  type='CrossEntropyLoss',
                  use_sigmoid=False,
                  loss_weight=1.0),
              loss_bbox=dict(
                  type='SmoothL1Loss',
                  beta=1.0,
                  loss_weight=1.0)),
          dict(
              type='SharedFCBBoxHead',
              num_fcs=2,
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=2,
              target_means=[0., 0., 0., 0.],
              target_stds=[0.033, 0.033, 0.067, 0.067],
              reg_class_agnostic=True,
              loss_cls=dict(
                  type='CrossEntropyLoss',
                  use_sigmoid=False,
                  loss_weight=1.0),
              loss_bbox=dict(
                  type='SmoothL1Loss',
                  beta=1.0,
                  loss_weight=1.0)),
          dict(
              type='SharedFCBBoxHead',
              num_fcs=2,
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=2,
              target_means=[0., 0., 0., 0.],
              target_stds=[0.025, 0.025, 0.05, 0.05],
              reg_class_agnostic=True,
              loss_cls=dict(
                  type='CrossEntropyLoss',
                  use_sigmoid=False,
                  loss_weight=1.0),
              loss_bbox=dict(
                  type='SmoothL1Loss',
                  beta=1.0,
                  loss_weight=1.0))
      ])
  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          ga_assigner=dict(
              type='ApproxMaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              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=0.7,
              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=-1,
          pos_weight=-1,
          center_ratio=0.2,
          ignore_ratio=0.5,
          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=0.6,
                  neg_iou_thr=0.6,
                  min_pos_iou=0.6,
                  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),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.7,
                  min_pos_iou=0.7,
                  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),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.8,
                  neg_iou_thr=0.8,
                  min_pos_iou=0.8,
                  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)
      ],
      stage_loss_weights=[1, 0.5, 0.25])
  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=1e-3, nms=dict(type='nms', iou_thr=0.5), max_per_img=750),
      keep_all_stages=False)
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'WIDER/'
  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=1,
      workers_per_gpu=1,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'WIDER_train/instances_train2019.json',
          img_prefix=data_root + 'WIDER_train/images/',
          img_scale=(640, 640),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0.5,
          with_mask=False,
          with_crowd=True,
          with_label=True,
          extra_aug=dict(
              photo_metric_distortion=dict(
                  brightness_delta=32,
                  contrast_range=(0.5, 1.5),
                  saturation_range=(0.5, 1.5),
                  hue_delta=18),
              random_crop_face=dict(min_crop_size=0.3)),
          resize_keep_ratio=True),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'WIDER_val/instances_val2019.json',
          img_prefix=data_root + 'WIDER_val/images/',
          img_scale=(1300, 800),
          #img_scale=[(750, 500),(1300, 800),(2560, 1700),(1656, 1100),(2108, 1400)],
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=1,
          with_mask=False,
          with_crowd=True,
          with_label=True),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'WIDER_val/instances_val2019.json',
          img_prefix=data_root + 'WIDER_val/images/',
          img_scale=(1300, 800),
          #img_scale=[(999, 800),(1374, 1100),(1748, 1400),(2123, 1700)],
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0.0,
          with_mask=False,
          with_label=False,
          test_mode=True))
  # optimizer
  optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001,
                   auto_adjust=True, base_batch_size=16)
  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=[17, 20])
      # by_epoch=True)
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 21
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/widerface_resnet50_bnnofix_ratio1.25'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  