text: _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
  model = dict(
      backbone=dict(
          _delete_=True,
          type='HRNet',
          extra=dict(
              stage1=dict(
                  num_modules=1,
                  num_branches=1,
                  block='BOTTLENECK',
                  num_blocks=(4, ),
                  num_channels=(64, )),
              stage2=dict(
                  num_modules=1,
                  num_branches=2,
                  block='BASIC',
                  num_blocks=(4, 4),
                  num_channels=(32, 64)),
              stage3=dict(
                  num_modules=4,
                  num_branches=3,
                  block='BASIC',
                  num_blocks=(4, 4, 4),
                  num_channels=(32, 64, 128)),
              stage4=dict(
                  num_modules=3,
                  num_branches=4,
                  block='BASIC',
                  num_blocks=(4, 4, 4, 4),
                  num_channels=(32, 64, 128, 256))),
          init_cfg=dict(
              type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
      neck=dict(
          _delete_=True,
          type='HRFPN',
          in_channels=[32, 64, 128, 256],
          out_channels=256))
  # learning policy
  lr_config = dict(step=[16, 19])
  runner = dict(type='EpochBasedRunner', max_epochs=20)
  