text: _base_ = '../_base_/default_runtime.py'
  
  
  # model settings
  model = dict(
      type='YOLOV3',
      backbone=dict(
          type='Darknet',
          depth=53,
          out_indices=(3, 4, 5),
          init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
      neck=dict(
          type='YOLOV3Neck',
          num_scales=3,
          in_channels=[1024, 512, 256],
          out_channels=[512, 256, 128]),
      bbox_head=dict(
          type='YOLOV3Head',
          num_classes=2,
          in_channels=[512, 256, 128],
          out_channels=[1024, 512, 256],
          anchor_generator=dict(
              type='YOLOAnchorGenerator',
              base_sizes=[[(116, 90), (156, 198), (373, 326)],
                          [(30, 61), (62, 45), (59, 119)],
                          [(10, 13), (16, 30), (33, 23)]],
              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 = 'CustomDataset'
  data_root = '/home/deepo/sanofius/DATA/OBJECTS/images/'
  ann_file_train = '/home/deepo/sanofius/TRAINING/mouse_detection/train_annot_file.pickle'
  ann_file_val = '/home/deepo/sanofius/TRAINING/mouse_detection/val_annot_file.pickle'
  classes=('rien','mouse')
  # # use the pre-trained model
  # load_from = 'http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth'  # model path can be found in model zoo
  
  
  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', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=[(320, 320), (320, 320)], keep_ratio=False),
      dict(type='RandomFlip', flip_ratio=0.0),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  val_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=[(320, 320), (320, 320)], keep_ratio=False),
      dict(type='RandomFlip', flip_ratio=0.0),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img']),
  ]
  data = dict(
      samples_per_gpu=16,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          img_prefix=data_root,
          pipeline=train_pipeline,
          classes=classes),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          img_prefix=data_root,
          pipeline=val_pipeline,
          classes=classes,
          test_mode = False)
  )
  
  # # runtime settings
  checkpoint_config = dict(interval=1)
  work_dir = '/home/deepo/sanofius/TRAINING/mouse_detection/workdir/try_1'
  log_level = 'DEBUG'
  
  
  optimizer = dict(type='SGD', lr=0.001, 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=2000,  # same as burn-in in darknet
      warmup_ratio=0.1,
      step=[218, 246])
  # runtime settings
  runner = dict(type='EpochBasedRunner', max_epochs=20)
  evaluation = dict(interval=1, metric=['mAP'])
  