text: checkpoint_config = dict(interval=10)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  model = dict(
      type='YOLOV3',
      pretrained = '/data/zhaozhiyuan/mmdetection_checkpoints/yololite/pretrained_on_imagenet.pkl',
      backbone=dict(type='YoloLite'),
      neck=dict(
          type='YOLOV3Neck',
          num_scales=3,
          in_channels=[256, 128, 64],
          out_channels=[128, 64, 32]),
      bbox_head=dict(
          type='YOLOV3Head',
          num_classes=41,
          in_channels=[128, 64, 32],
          out_channels=[256, 128, 64],
          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')))
  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_type = 'VOCDataset'
  data_root = '/data/zhaozhiyuan/VOCdevkit_0104'
  img_norm_cfg = dict(mean=[0, 0, 0], std=[255.0, 255.0, 255.0], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='PhotoMetricDistortion'),
      dict(type='Expand', mean=[0, 0, 0], to_rgb=True, 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), keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(
          type='Normalize',
          mean=[0, 0, 0],
          std=[255.0, 255.0, 255.0],
          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=(320, 320),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(
                  type='Normalize',
                  mean=[0, 0, 0],
                  std=[255.0, 255.0, 255.0],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=128,
      workers_per_gpu=4,
      train=dict(
          type='VOCDataset',
          ann_file='/data/zhaozhiyuan/VOCdevkit_0104/VOC2007/ImageSets/Main/trainval.txt',
          img_prefix='/data/zhaozhiyuan/VOCdevkit_0104/VOC2007',
          pipeline=[
              dict(type='LoadImageFromFile', to_float32=True),
              dict(type='LoadAnnotations', with_bbox=True),
              dict(type='PhotoMetricDistortion'),
              dict(
                  type='Expand', mean=[0, 0, 0], to_rgb=True,
                  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), keep_ratio=True),
              dict(type='RandomFlip', flip_ratio=0.5),
              dict(
                  type='Normalize',
                  mean=[0, 0, 0],
                  std=[255.0, 255.0, 255.0],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='DefaultFormatBundle'),
              dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
          ]),
      val=dict(
          type='VOCDataset',
          ann_file='/data/zhaozhiyuan/VOCdevkit_0104/VOC2007/ImageSets/Main/test.txt',
          img_prefix='/data/zhaozhiyuan/VOCdevkit_0104/VOC2007',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(320, 320),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=True),
                      dict(type='RandomFlip'),
                      dict(
                          type='Normalize',
                          mean=[0, 0, 0],
                          std=[255.0, 255.0, 255.0],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]),
      test=dict(
          type='VOCDataset',
          ann_file='/data/zhaozhiyuan/VOCdevkit_0104/VOC2007/ImageSets/Main/test.txt',
          img_prefix='/data/zhaozhiyuan/VOCdevkit_0104/VOC2007',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(320, 320),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=True),
                      dict(type='RandomFlip'),
                      dict(
                          type='Normalize',
                          mean=[0, 0, 0],
                          std=[255.0, 255.0, 255.0],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]))
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.1,
      step=[100, 218, 246])
  total_epochs = 273
  evaluation = dict(interval=10, metric=['mAP'])
  work_dir = '/data/zhaozhiyuan/mmdetection_checkpoints/yolov3_lite_d53_320_273e_coco'
  gpu_ids = range(0, 1)
  