content: batching_timeout = 10 #默认的凑batch的超时时间
  precision = "fp16" 
  instance_num=3
  #### main graph ####
  [jpg_decoder]
  backend = "Sequential[DecodeTensor,(result2other)cvtColorTensor,ResizePadTensor,SyncTensor]"
  color = "bgr"
  instance_num = 10
  data_format="hwc"
  max_h = 640
  max_w = 640
  
  next = "detect"
  
  [detect]
  backend = "S[TensorrtTensor,SyncTensor]" 
  batching_timeout = 5
  instance_num = 1
  max = 8
  
  std="255,255,255"
  
  model = "./yolov8n.onnx" 
  "model::cache" = "./yolov8n.trt" 
  
  
  next="post_det"
  [post_det]
  net_h=640
  net_w=640 # for PostProcYolox
  backend="SyncTensor[PostProcYoloV8]"
  next = "sub_graph"
  instance_num = 8
  
  [sub_graph] 
  map="jpg_decoder[other:data,color:color],post_det[TASK_BOX_KEY:TASK_BOX_KEY]"
  
  backend = "MapReduce"
  jump="cls_preprocess"
  
  split="TASK_BOX_KEY"
  merge="score_1,result"
  
  next="final"
  instance_num=20
  
  [final]
  
  
  
  #### sub graph ####
  [cls_preprocess]
  backend="Sequential[PPLCropTensor,PPLResizeTensor,cvtColorTensor,SyncTensor]"
  resize_h=224
  resize_w=224
  color="rgb"
  instance_num=8
  # save_dir="./"
  
  next="cls_1"
  
  #### branch 1 ####
  [cls_1]
  mean = "123.675, 116.28, 103.53" # 255*"0.485, 0.456, 0.406"
  std = "58.395, 57.120, 57.375" # 255*"0.229, 0.224, 0.225"
  backend="S[TensorrtTensor,SoftmaxArgMaxTensor,SyncTensor]"
  max=4
  model="./resnet18.onnx"
  "model::cache"="./resnet18.trt"
  
  # postprocessor="SoftmaxArgMax"
  
  next="post_cls_1"
  
  [post_cls_1]
  filter="filter_score"
  
  mean = "123.675, 116.28, 103.53" # 255*"0.485, 0.456, 0.406"
  std = "58.395, 57.120, 57.375" # 255*"0.229, 0.224, 0.225"
  backend="S[TensorrtTensor,SoftmaxArgMaxTensor,SyncTensor]"
  max=4
  model="././resnet18.onnx"
  "model::cache"="./resnet18.trt"
  
  # postprocessor="SoftmaxArgMax"
  
  
  next="subgraph_final"
  
  #### branch 2 ####
  
  
  [subgraph_final]
  map="post_cls_1[score:score_1,result:data]"
   
  
  