text: _base_ = '../_base_/default_runtime.py'
  
  # model settings
  img_size = 400
  model = dict(
      type='YOLACT',
      pretrained='torchvision://resnet50',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=-1,  # do not freeze stem
          norm_cfg=dict(type='BN', requires_grad=True),
          norm_eval=False,  # update the statistics of bn
          zero_init_residual=False,
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs='on_input',
          num_outs=5,
          upsample_cfg=dict(mode='bilinear')),
      bbox_head=dict(
          type='YOLACTHead',
          num_classes=80,
          in_channels=256,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              octave_base_scale=3,
              scales_per_octave=1,
              base_sizes=[8, 16, 32, 64, 128],
              ratios=[0.5, 1.0, 2.0],
              strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
              centers=[(550 * 0.5 / x, 550 * 0.5 / x)
                       for x in [69, 35, 18, 9, 5]]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[0.1, 0.1, 0.2, 0.2]),
          loss_cls=dict(
              type='CrossEntropyLoss',
              use_sigmoid=False,
              reduction='none',
              loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
          num_head_convs=1,
          num_protos=32,
          use_ohem=True),
      mask_head=dict(
          type='YOLACTProtonet',
          in_channels=256,
          num_protos=32,
          num_classes=80,
          max_masks_to_train=100,
          loss_mask_weight=6.125),
      segm_head=dict(
          type='YOLACTSegmHead',
          num_classes=80,
          in_channels=256,
          loss_segm=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
  # training and testing settings
  train_cfg = dict(
      assigner=dict(
          type='MaxIoUAssigner',
          pos_iou_thr=0.5,
          neg_iou_thr=0.4,
          min_pos_iou=0.,
          ignore_iof_thr=-1,
          gt_max_assign_all=False),
      # smoothl1_beta=1.,
      allowed_border=-1,
      pos_weight=-1,
      neg_pos_ratio=3,
      debug=False)
  test_cfg = dict(
      nms_pre=1000,
      min_bbox_size=0,
      score_thr=0.05,
      iou_thr=0.5,
      top_k=200,
      max_per_img=100)
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  img_norm_cfg = dict(
      mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)),
      dict(
          type='PhotoMetricDistortion',
          brightness_delta=32,
          contrast_range=(0.5, 1.5),
          saturation_range=(0.5, 1.5),
          hue_delta=18),
      dict(
          type='Expand',
          mean=img_norm_cfg['mean'],
          to_rgb=img_norm_cfg['to_rgb'],
          ratio_range=(1, 4)),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
          min_crop_size=0.3),
      dict(type='Resize', img_scale=(img_size, img_size), keep_ratio=False),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(img_size, img_size),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'train2017/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=4e-3, momentum=0.9, weight_decay=5e-4)
  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=0.1,
      step=[20, 42, 49, 52])
  total_epochs = 55
  cudnn_benchmark = True
  evaluation = dict(interval=5, metric=['bbox', 'segm'])
  