text: # model settings
  model = dict(
      type='RetinaNet',
      pretrained='open-mmlab://resnext101_64x4d',
      backbone=dict(
          type='KiteX',
          depth=101,
          groups=64,
          base_width=4,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          style='pytorch',with_cp=True),
      neck=dict(
          type='kiteFPN',
          in_channels=[256, 256, 256, 256, 512, 512, 512, 1024, 1024, 2048],
          start_level=0,
          out_channels=256,
          add_extra_convs=True,
          num_outs=11),
      bbox_head=dict(
          type='RetinaHead',
          num_classes=81,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          octave_base_scale=4,
          scales_per_octave=3,
          anchor_ratios=[0.5, 1.0, 2.0],
          anchor_strides=[4,4/0.8333333,4/0.66666666,4/0.5,8/0.8333333,8/0.6666666,8/0.5,16/0.666666666,16/0.5,32/0.5,128],
          target_means=[.0, .0, .0, .0],
          target_stds=[1.0, 1.0, 1.0, 1.0],
          loss_cls=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=0.11, 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),
      allowed_border=-1,
      pos_weight=-1,
      debug=False)
  test_cfg = dict(
      nms_pre=1000,
      min_bbox_size=0,
      score_thr=0.05,
      nms=dict(type='nms', iou_thr=0.5),
      max_per_img=100)
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  data = dict(
      imgs_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2017.json',
          img_prefix=data_root + 'train2017/',
          img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0.5,
          with_mask=False,
          with_crowd=False,
          with_label=True),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0,
          with_mask=False,
          with_crowd=False,
          with_label=True),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'val2017/',
          img_scale=(1333, 800),
          img_norm_cfg=img_norm_cfg,
          size_divisor=32,
          flip_ratio=0,
          with_mask=False,
          with_crowd=False,
          with_label=False,
          test_mode=True))
  # 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=1.0 / 3,
      step=[8, 11])
  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 = 12
  device_ids = range(8)
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/retinanet_x101_64x4d_fpn_1x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  