text: # model settings
  model = dict(
      type='FasterRCNN',
      pretrained='torchvision://vgg16',
      backbone=dict(
          type='VGG',
          depth=16,
          num_stages=5,
          out_indices=(4,),       # This is a tuple containing an element 4
          frozen_stages=1,
          with_last_pool=False
      ),
      neck=None,
      rpn_head=dict(
          type='RPNHead',
          in_channels=512,
          feat_channels=256,
          anchor_scales=[4, 6, 8, 10, 12, 14],
          anchor_ratios=[1.0 / 0.5, 1.0],
          anchor_strides=[16],
          target_means=[.0, .0, .0, .0],
          target_stds=[1.0, 1.0, 1.0, 1.0],
          # use_sigmoid_cls=True
          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=-1),
          out_channels=512,
          featmap_strides=[16]),
      bbox_head=dict(
          type='SharedFCBBoxHead',
          num_fcs=2,
          in_channels=512,
          fc_out_channels=256,
          roi_feat_size=7,
          num_classes=2,  # background and pederstrian
          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)))
  
  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=120,
              pos_fraction=1.0 / 4,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          allowed_border=0,
          pos_weight=-1,
          debug=False,
          nms=dict(
              nms_across_levels=False,
              nms_pre=20000,
              nms_post=20000,
              max_num=5000,
              nms_thr=0.9,
              min_bbox_size=0)),
      rcnn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=64,
              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=10000,
          nms_post=10000,
          max_num=300,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=0.1, nms=dict(type='nms', iou_thr=0.5), max_per_img=40))
  
  # dataset settings
  # dataset_type = 'ExtendedCvcDataset'
  dataset_type = 'CvcDataset'
  data_root = '/home/server-248/WangCK/Data/datasets/CVC/'
  img_norm_cfg = dict(
      mean=[123.675, 123.675, 123.675], std=[58.395, 58.395, 58.395], to_rgb=False)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=(640, 480), keep_ratio=True),    # diff
      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=(640, 480),      # diff
          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-pkl/train-all.pkl',
          img_prefix=data_root + 'images/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations-pkl/test-all.pkl',
          img_prefix=data_root + 'images/',
          pipeline=test_pipeline),
      test=dict(
          type='CocoDataset',
          ann_file=data_root + 'annotations-json/test-all.json',
          img_prefix=data_root + 'images/',
          ipipeline=test_pipeline,
          test_mode=True))        # It is added by kai
  
  # optimizer
  optimizer = dict(type='SGD', lr=0.001, 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=2000,
      # warmup_ratio=1.0 / 3,
      step=[4, 8])
  checkpoint_config = dict(interval=1)
  
  # yapf:disable
  log_config = dict(
      interval=1000,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  
  # runtime settings
  total_epochs = 20
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = '../../work_dirs/faster_rcnn_v16_c5_cvc'     # log文件和模型保存的路径
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  