text: # model settings
  model = dict(
      type='FasterRCNN',
      pretrained='open-mmlab://resnet50_caffe',
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type='BN', requires_grad=False),
          norm_eval=True,
          style='caffe'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          num_outs=5),
      rpn_head=dict(
          type='GARPNHead',
          in_channels=256,
          feat_channels=256,
          octave_base_scale=8,
          scales_per_octave=3,
          octave_ratios=[0.5, 1.0, 2.0],
          anchor_strides=[4, 8, 16, 32, 64],
          anchor_base_sizes=None,
          anchoring_means=[.0, .0, .0, .0],
          anchoring_stds=[0.07, 0.07, 0.14, 0.14],
          target_means=(.0, .0, .0, .0),
          target_stds=[0.07, 0.07, 0.11, 0.11],
          loc_filter_thr=0.01,
          loss_loc=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
      bbox_roi_extractor=dict(
          type='SingleRoIExtractor',
          roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
          out_channels=256,
          featmap_strides=[4, 8, 16, 32]),
      bbox_head=dict(
          type='SharedFCBBoxHead',
          num_fcs=2,
          in_channels=256,
          fc_out_channels=1024,
          roi_feat_size=7,
          num_classes=81,
          target_means=[0., 0., 0., 0.],
          target_stds=[0.05, 0.05, 0.1, 0.1],
          reg_class_agnostic=False,
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))
  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          ga_assigner=dict(
              type='ApproxMaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          ga_sampler=dict(
              type='RandomSampler',
              num=256,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=256,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          allowed_border=-1,
          pos_weight=-1,
          center_ratio=0.2,
          ignore_ratio=0.5,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=2000,
          max_num=300,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.6,
              neg_iou_thr=0.6,
              min_pos_iou=0.6,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=256,
              pos_fraction=0.25,
              neg_pos_ub=-1,
              add_gt_as_proposals=True),
          pos_weight=-1,
          debug=False))
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=1000,
          nms_post=1000,
          max_num=300,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=1e-3, 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=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
  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=2,
      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
  optimizer = dict(type='SGD', lr=0.02, 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
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/ga_faster_rcnn_r50_caffe_fpn_1x'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  