text: #!/usr/bin/env python3.6
  # -*- coding:utf-8 -*-
  __author__ = 'Lu ShaoAn'
  __version__ = '1.0'
  __date__ = '2021.05.26'
  __copyright__ = 'Copyright 2021, PI'
  
  
  _base_ = '../_base_/default_runtime.py'
  # model settings
  model = dict(
      type='YOLOV3',
      pretrained='open-mmlab://darknet53',
      backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)),
      neck=dict(
          type='YOLOV3Neck',
          num_scales=3,
          in_channels=[1024, 512, 256],
          out_channels=[512, 256, 128]),
      bbox_head=dict(
          type='YOLOV3Head',
          num_classes=1,
          in_channels=[512, 256, 128],
          out_channels=[1024, 512, 256],
          anchor_generator=dict(
              type='YOLOAnchorGenerator',
              base_sizes=[[(350, 350), (350, 340), (370, 350)],
                          [(30, 61), (40, 70), (25, 54)],
                          [(10, 10), (15, 15), (15, 15)]],
              strides=[32, 16, 8]),
          bbox_coder=dict(type='YOLOBBoxCoder'),
          featmap_strides=[32, 16, 8],
          loss_cls=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=1.0,
              reduction='sum'),
          loss_conf=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=1.0,
              reduction='sum'),
          loss_xy=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=2.0,
              reduction='sum'),
          loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
      # training and testing settings
      train_cfg=dict(
          assigner=dict(
              type='GridAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.5,
              min_pos_iou=0)),
      test_cfg=dict(
          nms_pre=1000,
          min_bbox_size=0,
          score_thr=0.05,
          conf_thr=0.005,
          nms=dict(type='nms', iou_threshold=0.45),
          max_per_img=100))
  # dataset settings
  dataset_type = 'PlugDataset'
  # data_root = '/media/lsa/MobileDisk3/dataset/PieProject/line3'
  data_root = '/media/lsa/MobileDisk3/dataset/PieProject/line/alldata'
  img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='PhotoMetricDistortion'),
      dict(
          type='Expand',
          mean=img_norm_cfg['mean'],
          to_rgb=img_norm_cfg['to_rgb'],
          ratio_range=(1, 2)),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
          min_crop_size=0.3),
      dict(type='Resize', img_scale=[(320, 320), (608, 608)], 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=(608, 608),
          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'])
          ])
  ]
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=1,
      train=dict(
          type=dataset_type,
          ann_file=data_root,
          img_prefix=data_root,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root,
          img_prefix=data_root,
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root,
          img_prefix=data_root,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.0005, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=100,  # same as burn-in in darknet
      warmup_ratio=0.01,
      step=[10, 50, 80])
  # runtime settings
  checkpoint_config = dict(interval=5)
  log_config = dict(
      interval=10,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook')
      ])
  runner = dict(type='EpochBasedRunner', max_epochs=100)
  evaluation = dict(interval=1, metric=['bbox'])
  