text: # model settings
  model = dict(
      type='SOLOv2',
      pretrained='torchvision://resnet50',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3), # C2, C3, C4, C5
          frozen_stages=1,
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=0,
          num_outs=5),
      bbox_head=dict(
          type='SOLOv2Head',
          num_classes=9,
          in_channels=256,
          stacked_convs=4,
          seg_feat_channels=512,
          strides=[8, 8, 16, 32, 32],
          scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),
          sigma=0.2,
          num_grids=[80, 72, 64, 48, 32],
          ins_out_channels=256,
          loss_ins=dict(
              type='DiceLoss',
              use_sigmoid=True,
              loss_weight=3.0),
          loss_cate=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0)),
      mask_feat_head=dict(
              type='MaskFeatHead',
              in_channels=256,
              out_channels=128,
              start_level=0,
              end_level=3,
              num_classes=256,
              # Replace with syncbn
              norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
      )
  # training and testing settings
  train_cfg = dict()
  test_cfg = dict(
      nms_pre=500,
      score_thr=0.1,
      mask_thr=0.5,
      update_thr=0.05,
      kernel='gaussian',  # gaussian/linear
      sigma=2.0,
      max_per_img=100)
  # dataset settings
  dataset_type = 'CityscapesDataset'
  data_root = 'data/cityscapes/'
  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), (1333, 768), (1333, 736),
                      (1333, 704), (1333, 672), (1333, 640)],
           multiscale_mode='value',
           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']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1333, 800),
          # img_scale=(1980, 1200),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      imgs_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_train.json',
          img_prefix=data_root + 'leftImg8bit/train/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
          img_prefix=data_root + 'leftImg8bit/val/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
          img_prefix=data_root + 'leftImg8bit/val/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  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=0.01,
      step=[27, 33])
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 38
  device_ids = range(8)
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/cityscapes/solov2_release_r50_fpn_8gpu_3x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  