text: _base_ = [
      '../_base_/models/mask_rcnn_r50_fpn.py',
      '../_base_/datasets/coco_instance.py',
      '../_base_/default_runtime.py'
  ]
  model = dict(
      pretrained='pretrained_model/resnet50-19c8e357.pth',
      roi_head=dict(
          bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)))
  test_cfg = dict(
      rcnn=dict(
          score_thr=0.0001,
          # LVIS allows up to 300
          max_per_img=300))
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
  ]
  dataset_type = 'LVISDataset'
  data_root = 'data/coco/'
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/lvis_v0.5_train.json',
          img_prefix=data_root + 'train2017/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/lvis_v0.5_val.json',
          img_prefix=data_root + 'val2017/'),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/lvis_v0.5_val.json',
          img_prefix=data_root + 'val2017/'))
  evaluation = dict(metric=['bbox', 'segm'])
  # schedule
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[16, 22])
  total_epochs = 24
  