text: # dataset settings
  _base_ = '../dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
  dataset_type = 'MyDataset'
  data_root = './datasets/'
  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='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='LoadAnnotations', with_bbox=True),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(640, 480),
          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']),
          ]),
      
  ]
  
  anno_file = 'groundtruth.csv'
  #anno_file = 'part.cvv'
  
  _dataset=dict(
          type='MyDataset',
          ann_file=[
                  data_root + 'groundtruth.csv'
          ],
          img_prefix=[data_root + 'images'],
          pipeline=train_pipeline)
  
  test_dataset =dict(
          type='MyDataset',
          ann_file=[
                  data_root + 'test.csv'
          ],
          img_prefix=[data_root + 'images'],
          pipeline=test_pipeline)
  
  
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=_dataset,val=test_dataset,test=test_dataset)
  evaluation = dict(interval=1, metric='mAP')
  
  
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='WandbLoggerHook',init_kwargs=dict(project='mmdetection-tools', name='deform_iou'))
      ])
  
  train_cfg = dict(rcnn=dict(sampler=dict(type='OHEMSampler')))
  test_cfg = dict(
      rcnn=dict(
          score_thr=0.05,
          nms=dict(type='soft_nms', iou_thr=0.5),
          max_per_img=100))
  model = dict(
      roi_head=dict(
          bbox_head=dict(
              num_classes=1,
              reg_decoded_bbox=True,
              loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
  load_from = 'https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth'
  total_epochs=30
  