text: # model settings
  model = dict(
      type='FasterRCNNMulPreFPNACat',
      pretrained='torchvision://resnet50',
      backbone=dict(
          type='MulResnet',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          style='pytorch'
      ),
      neck=dict(
          type='MLFPN',
          backbone_choice="ResNet",  # "SSD" or "ResNet"
          in_channels=[256, 512, 1024, 2048],
          out_indices=[0, 1, 2, 3],  # kai add the param, correspond to 'scale_outs_num'(not used!)
          planes=256,  # out_channels of each scale feature
          scale_outs_num=4,  # the num of scale obtained by each TUM
          tum_num=4,  # the num of TUM module: 2 -> 4
          smooth=True,  # the param is used in TUM
          base_feature_size=4,  # ?
          base_choice=2,  # the param is used to choose 'ResNet' or others
          base_list=[2, 3],  # the param is used to choose the elements in 'dim_conv'
          norm=True,
          ssd_style_tum=False,  # ?
          # the size of the smallest tum ouput =>( '-2' or '/2')
      ),  # backbone + MFLPN -> [4, 2048, H, W]
      rpn_head=dict(
          type='RPNHead',
          in_channels=1024,  # kai: 256 -> 512 -> 1024
          feat_channels=256,
          anchor_ratios=[1/0.5, 1],
          anchor_scales=[8, 10, 12, 14],
          anchor_strides=[4, 8, 16, 32],
          anchor_base_sizes=[4, 8, 16, 32],
          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=1024,           # kai: 256 -> 512 -> 1024
          featmap_strides=[4, 8, 16, 32]),
      bbox_head=dict(
          type='SharedFCBBoxHead',
          num_fcs=2,
          in_channels=1024,            # kai: 256 -> 512 -> 1024
          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=0.25,
              neg_pos_ub=-1,
              add_gt_as_proposals=False,
              pos_balance_sampling=False,
              neg_balance_thr=0),
          allowed_border=0,
          pos_weight=-1,
          smoothl1_beta=1.0,
          debug=False,
          nms=dict(
              nms_across_levels=False,
              nms_pre=20000,
              nms_post=2000,
              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 = 'KaistDataset'
  data_root = '/media/ser248/3rd/WangCK/Data/datasets/kaist_mlfpn-rgbt/'
  # data_root = '/home/wangck/WangCK/Data/datasets/kaist_mlfpn-rgbt/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  img_norm_cfg_t = dict(
      mean=[123.675, 123.675, 123.675], std=[58.395, 58.395, 58.395], to_rgb=False)
  data = dict(
      imgs_per_gpu=4,     # diff
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations-pkl/train-all.pkl',
          img_prefix=data_root + 'images/',
          img_scale=1.5,
          img_norm_cfg=img_norm_cfg,
          img_norm_cfg_t=img_norm_cfg_t,
          size_divisor=None,
          flip_ratio=0.5,
          with_mask=False,
          with_crowd=True,
          with_label=True),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations-pkl/test-all.pkl',
          img_prefix=data_root + 'images/',
          img_scale=1.5,
          img_norm_cfg=img_norm_cfg,
          img_norm_cfg_t=img_norm_cfg_t,
          size_divisor=None,
          flip_ratio=0,
          with_mask=False,
          with_crowd=True,
          with_label=True),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations-pkl/test-all.pkl',
          img_prefix=data_root + 'images/',
          img_scale=1.5,
          img_norm_cfg=img_norm_cfg,
          size_divisor=None,
          flip_ratio=0,
          with_mask=False,
          with_label=False,
          test_mode=True))
  
  # 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 = 25
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  # work_dir = '/media/ser248/3rd/WangCK/Data/work_dirs/mul_faster_rcnn_r50_mlfpn_add_kaist'
  work_dir = '/home/wangck/WangCK/Data/work_dirs/mul_faster_rcnn_r50_mlfpn_add_kaist'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  