text: dataset_type = 'CocoDataset'
  data_root = '/data1/qilei_chen/DATA/erosive_ulcer_mix/'
  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=(512, 512), keep_ratio=False),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      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=(512, 512),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(type='RandomFlip'),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type='CocoDataset',
          ann_file=data_root+'annotations/train_mix.json',
          img_prefix=data_root+'images/',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(type='LoadAnnotations', with_bbox=True),
              dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
              dict(type='RandomFlip', flip_ratio=0.5),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='DefaultFormatBundle'),
              dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
          ],classes=('erosive', 'ulcer')),
      val=dict(
          type='CocoDataset',
          ann_file=data_root+'annotations/test_mix.json',
          img_prefix=data_root+'images/',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(512, 512),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=False),
                      dict(type='RandomFlip'),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ],classes=('erosive', 'ulcer')),
      test=dict(
          type='CocoDataset',
          ann_file=data_root+'annotations/test_mix.json',
          img_prefix=data_root+'images/',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(512, 512),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=False),
                      dict(type='RandomFlip'),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]),classes=('erosive', 'ulcer'))
  evaluation = dict(interval=1, metric='bbox')
  optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=None)
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[8, 11])
  runner = dict(type='EpochBasedRunner', max_epochs=24)
  checkpoint_config = dict(interval=1)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  custom_hooks = [dict(type='NumClassCheckHook')]
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = data_root+"work_dirs/gfl_r50_fpn_1x_coco_512/latest.model"
  workflow = [('train', 1)]
  model = dict(
      type='GFL',
      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,
          start_level=1,
          add_extra_convs='on_output',
          num_outs=5),
      bbox_head=dict(
          type='GFLHead',
          num_classes=2,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              ratios=[1.0],
              octave_base_scale=8,
              scales_per_octave=1,
              strides=[8, 16, 32, 64, 128]),
          loss_cls=dict(
              type='QualityFocalLoss',
              use_sigmoid=True,
              beta=2.0,
              loss_weight=1.0),
          loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
          reg_max=16,
          loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
      train_cfg=dict(
          assigner=dict(type='ATSSAssigner', topk=9),
          allowed_border=-1,
          pos_weight=-1,
          debug=False),
      test_cfg=dict(
          nms_pre=1000,
          min_bbox_size=0,
          score_thr=0.05,
          nms=dict(type='nms', iou_threshold=0.1),
          max_per_img=100))