text: _base_ = [
      '../_base_/datasets/coco_instance.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  # model settings
  model = dict(
      type='HybridTaskCascade',
      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=True),
          norm_eval=True,
          style='pytorch',
          init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
      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_generator=dict(
              type='AnchorGenerator',
              scales=[8],
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              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)),
      roi_head=dict(
          type='HybridTaskCascadeRoIHead',
          interleaved=True,
          mask_info_flow=True,
          num_stages=3,
          stage_loss_weights=[1, 0.5, 0.25],
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          bbox_head=[
              dict(
                  type='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=80,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[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='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=80,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      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='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=80,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      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))
          ],
          mask_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          mask_head=[
              dict(
                  type='HTCMaskHead',
                  with_conv_res=False,
                  num_convs=4,
                  in_channels=256,
                  conv_out_channels=256,
                  num_classes=80,
                  loss_mask=dict(
                      type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
              dict(
                  type='HTCMaskHead',
                  num_convs=4,
                  in_channels=256,
                  conv_out_channels=256,
                  num_classes=80,
                  loss_mask=dict(
                      type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
              dict(
                  type='HTCMaskHead',
                  num_convs=4,
                  in_channels=256,
                  conv_out_channels=256,
                  num_classes=80,
                  loss_mask=dict(
                      type='CrossEntropyLoss', use_mask=True, 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_pre=2000,
              max_per_img=2000,
              nms=dict(type='nms', iou_threshold=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)
          ]),
      test_cfg=dict(
          rpn=dict(
              nms_pre=1000,
              max_per_img=1000,
              nms=dict(type='nms', iou_threshold=0.7),
              min_bbox_size=0),
          rcnn=dict(
              score_thr=0.001,
              nms=dict(type='nms', iou_threshold=0.5),
              max_per_img=100,
              mask_thr_binary=0.5)))
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1333, 800),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip', flip_ratio=0.5),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))
  