text: _base_ = [
      '../_base_/datasets/coco_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  work_dir='./work_dirs/iou-aware_rcnn_final/'
  # model settings
  model = dict(
      type='FasterRCNN',
      pretrained='torchvision://resnet50',
      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=True),
          norm_eval=True,
          style='pytorch'),
      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_generator=dict(
              type='AnchorGenerator',
              scales=[8],
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              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='L1Loss', loss_weight=1.0)),
      roi_head=dict(
          type='IoUAwareRoIHead',
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          bbox_head=[
              dict(
                  type='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=80,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.1, 0.1, 0.2, 0.2]),
                  reg_class_agnostic=False,
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
              dict(
                  type='IoUAwareBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=80,
                  reg_class_agnostic=True,
                  loss_iou=dict(type='L1Loss', 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,
              match_low_quality=True,
              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,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=1000,
          max_num=1000,
          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,
                  match_low_quality=False,
                  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=0.5,# the initial proposals without gt bboxes
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  match_low_quality=False,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='IoUSampler',
                  num=32),
              pos_weight=-1,
              debug=False
          ),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,# the regressed proposals from the initial proposals
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  match_low_quality=False,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='IoUSampler',
                  num=32),
              pos_weight=-1,
              debug=False
          )
      ])
  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,
          #score_thr=0.05,
          nms=dict(type='nms', iou_threshold=0.5),
          #ms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.001),
          max_per_img=100)
      # soft-nms is also supported for rcnn testing
      # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
  )
  