text: # model settings
  model = dict(
      type="HybridTaskCascade",
      num_stages=3,
      pretrained="open-mmlab://resnext101_32x4d",
      interleaved=True,
      mask_info_flow=True,
      backbone=dict(
          type="ResNeXt",
          depth=101,
          groups=32,
          base_width=4,
          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, 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=81,
              target_means=[0.0, 0.0, 0.0, 0.0],
              target_stds=[0.1, 0.1, 0.2, 0.2],
              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=81,
              target_means=[0.0, 0.0, 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=81,
              target_means=[0.0, 0.0, 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),
          ),
      ],
      mask_roi_extractor=dict(
          type="SingleRoIExtractor",
          roi_layer=dict(type="RoIAlign", out_size=14, sample_num=2),
          out_channels=256,
          featmap_strides=[4, 8, 16, 32],
      ),
      mask_head=dict(
          type="HTCMaskHead",
          num_convs=4,
          in_channels=256,
          conv_out_channels=256,
          num_classes=81,
          loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0),
      ),
      semantic_roi_extractor=dict(
          type="SingleRoIExtractor",
          roi_layer=dict(type="RoIAlign", out_size=14, sample_num=2),
          out_channels=256,
          featmap_strides=[8],
      ),
      semantic_head=dict(
          type="FusedSemanticHead",
          num_ins=5,
          fusion_level=1,
          num_convs=4,
          in_channels=256,
          conv_out_channels=256,
          num_classes=183,
          ignore_label=255,
          loss_weight=0.2,
      ),
  )
  # 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,
              ),
              mask_size=28,
              pos_weight=-1,
              debug=False,
          ),
          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,
              ),
              mask_size=28,
              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,
              ),
              mask_size=28,
              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=0.001,
          nms=dict(type="nms", iou_thr=0.5),
          max_per_img=100,
          mask_thr_binary=0.5,
      ),
      keep_all_stages=False,
  )
  # 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=1,
      workers_per_gpu=1,
      train=dict(
          type=dataset_type,
          ann_file=data_root + "annotations/instances_train2017.json",
          img_prefix=data_root + "train2017/",
          img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0.5,
          seg_prefix=data_root + "stuffthingmaps/train2017/",
          seg_scale_factor=1 / 8,
          with_mask=True,
          with_crowd=True,
          with_label=True,
          with_semantic_seg=True,
      ),
      val=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=32,
          flip_ratio=0,
          with_mask=True,
          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=32,
          flip_ratio=0,
          with_mask=True,
          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, 19],
  )
  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 = 20
  dist_params = dict(backend="nccl")
  log_level = "INFO"
  work_dir = "./work_dirs/htc_x101_32x4d_fpn_20e"
  load_from = None
  resume_from = None
  workflow = [("train", 1)]
  