text: # model settings
  model = dict(
      type='FasterRCNN',
      pretrained='https://github.com/matej-ulicny/harmonic-networks/releases/download/0.1.0/harm_resnet101-62e185b1.pth',
      backbone=dict(
          type='HarmResNet',
          depth=101,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          num_outs=5),
      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, 64],
          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)),
      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=21,
          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)))
  # 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 = 'VOCDataset'
  data_root = 'data/VOCdevkit/'
  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=6,
      workers_per_gpu=2,
      train=dict(
          type='RepeatDataset',  # to avoid reloading datasets frequently
          times=1,
          dataset=dict(
              type=dataset_type,
              ann_file=[
                  data_root + 'VOC2007/ImageSets/Main/trainval.txt',
                  data_root + 'VOC2012/ImageSets/Main/trainval.txt'
              ],
              img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
              img_scale=(1000, 600),
              img_norm_cfg=img_norm_cfg,
              size_divisor=32,
              flip_ratio=0.5,
              with_mask=False,
              with_crowd=True,
              with_label=True)),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
          img_prefix=data_root + 'VOC2007/',
          img_scale=(1000, 600),
          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 + 'VOC2007/ImageSets/Main/test.txt',
          img_prefix=data_root + 'VOC2007/',
          img_scale=(1000, 600),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0,
          with_mask=False,
          with_label=False,
          test_mode=True))
  # optimizer
  optimizer = dict(type='SGD', lr=0.0075, 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', step=[9])
  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/faster_rcnn_hr101_fpn_voc0712'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  