text: # model settings
  model = dict(
      type='RetinaNet',
      pretrained='torchvision://resnet50',
      backbone=dict(type='ResNet',
                    depth=50,
                    num_stages=4,
                    out_indices=(0, 1, 2, 3),
                    frozen_stages=1,
                    style='pytorch'),
      neck=[
          dict(type='FPN',
               in_channels=[256, 512, 1024, 2048],
               out_channels=256,
               start_level=1,
               add_extra_convs=True,
               num_outs=5),
          dict(
              type='SEPC',
              out_channels=256,
              Pconv_num=4,
              pconv_deform=False,
              lcconv_deform=False,
              iBN=True,  # please set imgs/gpu >= 4
          )
      ],
      bbox_head=dict(type='FSAFHead',
                     num_classes=81,
                     in_channels=256,
                     stacked_convs=0,
                     feat_channels=256,
                     octave_base_scale=1,
                     scales_per_octave=1,
                     anchor_ratios=[1.0],
                     anchor_strides=[8, 16, 32, 64, 128],
                     coder=dict(type='TBLRCoder', ),
                     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='IoULossTBLR',
                                    eps=1e-6,
                                    loss_weight=1.0)))
  # training and testing settings
  train_cfg = dict(assigner=dict(type='EffectiveAreaAssigner',
                                 pos_area_thr=0.2,
                                 neg_area_thr=0.2,
                                 min_pos_iof=0.01),
                   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)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=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']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='MultiScaleFlipAug',
           img_scale=(1333, 800),
           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=4,
      workers_per_gpu=2,
      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 = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=dict(max_norm=10, 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', port=20256)
  log_level = 'INFO'
  work_dir = 'work_dirs/fsaf_r50_fpn_1x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  