text: model = dict(
      type='MaskRCNN',
      pretrained='open-mmlab://detectron2/resnet50_caffe',
      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=False),
          norm_eval=True,
          style='caffe'),
      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, 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='L1Loss', loss_weight=1.0)),
      roi_head=dict(
          type='StandardRoIHead',
          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, 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='L1Loss', 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='FCNMaskHead',
              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))),
      train_cfg=dict(
          rpn=dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.3,
                  min_pos_iou=0.3,
                  match_low_quality=True,
                  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,
              debug=False),
          rpn_proposal=dict(
              nms_across_levels=False,
              nms_pre=2000,
              nms_post=1000,
              max_num=1000,
              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,
                  match_low_quality=True,
                  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_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_threshold=0.5),
              max_per_img=100,
              mask_thr_binary=0.5)))
  dataset_type = 'CocoDataset'
  data_root = '../../../data/coco/'
  img_norm_cfg = dict(
      mean=(103.53, 116.28, 123.675), std=(1.0, 1.0, 1.0), to_rgb=False)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='LoadAnnotations',
          with_bbox=True,
          with_mask=True,
          poly2mask=False),
      dict(
          type='Resize',
          img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
                     (1333, 768), (1333, 800)],
          multiscale_mode='value',
          keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(
          type='Normalize',
          mean=[103.53, 116.28, 123.675],
          std=[1.0, 1.0, 1.0],
          to_rgb=False),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1024, 768),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(
                  type='Normalize',
                  mean=[103.53, 116.28, 123.675],
                  std=[1.0, 1.0, 1.0],
                  to_rgb=False),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type='RepeatDataset',
          times=1,
          dataset=dict(
              type='CocoDataset',
              ann_file=data_root + 'annotations/instances_train2017.json',
              img_prefix=data_root + 'train2017/',
              pipeline=train_pipeline)),
      val=dict(
          type='CocoDataset',
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline),
      test=dict(
          type='CocoDataset',
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline))
  evaluation = dict(interval=1, metric=['bbox', 'segm'])
  optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=None)
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[28, 34])
  total_epochs = 36
  checkpoint_config = dict(interval=1)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  