text: _base_ = [
      '../_base_/models/faster_rcnn_r50_fpn.py',
      '../_base_/datasets/coco_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  checkpoint_config = dict(interval=18)
  log_config = dict(
      interval=10000,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  model = dict(
      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='DensityFPN'),
      rpn_head=dict(
          type='RPNHead',
          # adaptive=False,
          in_channels=256,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              scales=[4],
              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_density=dict(type='MSELoss', reduction='mean', loss_weight=0.01),
          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)),
  
      roi_head=dict(
          type='TailRoIHead',
          labels_tail=[1, 2],
          labels=[0],
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', out_size=7, sample_num=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          bbox_head_tail=dict(
              type='TailBBoxHead',
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=3,
              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=2.0),
              loss_bbox=dict(type='L1Loss', loss_weight=2.0)),
          bbox_head=dict(
              type='TailBBoxHead',
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=3,
              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))))
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=2000,
          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=500))
  # dataset settings
  dataset_type = 'CocoDataset'
  classes = ('car', 'truck', 'bus')
  data_root = '/home/omnisky/ywp/data/uavdt/'
  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=[(1000, 600)], 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=[(1000, 600)],
          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']),
  
          ])
  ]
  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_tail=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_tail=dict(
              type='ClassBalancedPosSampler',
              num=512,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=True,
              labels=[1, 2]),
  
          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='ClassBalancedPosSampler',
              num=512,
              pos_fraction=0.25,
              neg_pos_ub=-1,
              add_gt_as_proposals=True,
              labels=[0]),
          pos_weight=-1,
          debug=False))
  data = dict(
      samples_per_gpu=1,
      workers_per_gpu=1,
      train=dict(
          type=dataset_type,
          classes=classes,
          ann_file=data_root + 'annotations/train.json',
          img_prefix=data_root + 'images/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          classes=classes,
          ann_file=data_root + 'annotations/val.json',
          img_prefix=data_root + 'images/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          classes=classes,
          ann_file=data_root + 'annotations/val.json',
          img_prefix=data_root + 'images/',
          pipeline=test_pipeline))
  evaluation = dict(interval=1, metric='bbox')
  optimizer = dict(type='SGD', lr=0.004, momentum=0.95, weight_decay=5 * 1e-4)
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=1.0 / 3,
      step=[8, 12, 16])
  total_epochs = 18
  # load_from = '/data/yuweiping/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth'
  