text: # model settings
  model = dict(
      type='CascadeRCNN',
      num_stages=3,
      pretrained='torchvision://resnet50',
      # defect finding network parameters
      dfn_balance=dict(type='inverse', period=1, scale_factor=1.0, init_weight=1.0, background_id=1),
      # category_ids for not training, start from 1
      ignore_ids=[1],
      backbone=dict(
          type='ResNet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          style='pytorch',
          num_classes=2,
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          num_outs=5),
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_scales=[8],
          anchor_ratios=[0.04, 0.13, 0.28, 0.48, 0.73, 0.88, 2.91, 5.91, 8.17],
          anchor_strides=[4, 8, 16, 32, 64],
          target_means=[.0, .0, .0, .0],
          target_stds=[1.0, 1.0, 1.0, 1.0],
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, 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=15,
              target_means=[0., 0., 0., 0.],
              target_stds=[0.1, 0.1, 0.2, 0.2],
              reg_class_agnostic=True,
              loss_cls=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
              loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
          dict(
              type='SharedFCBBoxHead',
              num_fcs=2,
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=15,
              target_means=[0., 0., 0., 0.],
              target_stds=[0.05, 0.05, 0.1, 0.1],
              reg_class_agnostic=True,
              loss_cls=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
              loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
          dict(
              type='SharedFCBBoxHead',
              num_fcs=2,
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=15,
              target_means=[0., 0., 0., 0.],
              target_stds=[0.033, 0.033, 0.067, 0.067],
              reg_class_agnostic=True,
              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(
          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=0,
          pos_weight=-1,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=2000,
          max_num=2000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=[
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=-1,
              debug=False),
          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=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=-1,
              debug=False),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.7,
                  min_pos_iou=0.7,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              pos_weight=-1,
              debug=False)
      ],
      stage_loss_weights=[1, 0.5, 0.25])
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=1000,
          nms_post=1000,
          max_num=1000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100))
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/fabric'
  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=[(1223, 2446), (500, 1000)], keep_ratio=True, multiscale_mode='range'),
      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/instance_train.json',
          img_prefix=data_root + '/trainval/',
          pipeline=train_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + '/annotations/instance_test.json',
          img_prefix=data_root + '/trainval/',
          # category_ids for not coco_eval, start from 0
          ignore_ids=[0],
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.02 / 8, 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
  dataset_name = 'bag_tricks'
  first_model_cfg = None
  total_epochs = 12
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = '../work_dirs/' + dataset_name + '/defectnet/defectnet_inverse_mst_range_1223x2446_500x1000_k9'
  resume_from = None
  load_from = '../work_dirs/bag_tricks/defectnet/defectnet_inverse_mst_range_1223x2446_500x1000/epoch_12.pth'
  workflow = [('train', 1)]
  