yolov5m.pt triton部署

1.创建triton docker镜像

docker pull nvcr.io/nvidia/tritonserver:21.10-py3

2.将yolov5m转为onnx

A.下载yolov5官方代码
B.在运行export.py文件之前，因为onnx必须转成动态的，需要修改dynamic参数，并且根据自己需求修改精度（一般为FP16）。

python path/to/export.py --weights yolov5m.pt --img 640 --include onnx --dynamic --half

3.配置triton模型仓库，

A.模型仓库的目录如下：

<model-repository-path>/ 
<model-name>/   # model-name必须与配置文件config.pbtxt中的name一致
config.pbtxt    # 新建一个文件，用vim打开，并按下述步骤配置
1/                # 新建文件夹1，并在里面放入已转成的yolov5.onnx
model.onnx 

修改配置文件：
格式如下：

```python
name: "yolov5m_half"       # 命名与一致
platform: "onnxruntime_onnx" 
max_batch_size : 16 
input [ 
{ 
name: "images" 
data_type: TYPE_FP32 
format: FORMAT_NONE 
dims: [ 3, 640, 640 ] 
} 
]
output [ 
{ 
name: "output" 
data_type: TYPE_FP32 
dims: [8400,7 ] 
} 
] 

关于onnx模型的输入输出维度，可以使用下面几行代码查看

import onnx
model_onnx = onnx.load('model.onnx')
print(model_onnx.graph.input)
print(model_onnx.graph.output)

若显示的维度不是具体的数字或输入输出有多个维度，如：

[name: "images"
type {
  tensor_type {
    elem_type: 10
    shape {
      dim {
        dim_param: "batch"
      }
      dim {
        dim_value: 3
      }
      dim {
        dim_param: "height"
      }
      dim {
        dim_param: "width"
      }
    }
  }
}
]
[name: "output"
type {
  tensor_type {
    elem_type: 10
    shape {
      dim {
        dim_param: "batch"
      }
      dim {
        dim_param: "anchors"
      }
      dim {
        dim_value: 6
      }
    }
  }
}
, name: "524"
type {
  tensor_type {
    elem_type: 10
    shape {
      dim {
        dim_value: 1
      }
      dim {
        dim_value: 3
      }
      dim {
        dim_value: 48
      }
      dim {
        dim_value: 80
      }
      dim {
        dim_value: 6
      }
    }
  }
}
, name: "622"
type {
  tensor_type {
    elem_type: 10
    shape {
      dim {
        dim_value: 1
      }
      dim {
        dim_value: 3
      }
      dim {
        dim_value: 24
      }
      dim {
        dim_value: 40
      }
      dim {
        dim_value: 6
      }
    }
  }
}
, name: "720"
type {
  tensor_type {
    elem_type: 10
    shape {
      dim {
        dim_value: 1
      }
      dim {
        dim_value: 3
      }
      dim {
        dim_value: 12
      }
      dim {
        dim_value: 20
      }
      dim {
        dim_value: 6
      }
    }
  }
}
]

则在配置文件中直接将其对应维度的位置设置为-1.

4.启动triton

systemctl start docker   
systemctl enable docker
docker run --gpus=1 --rm -p8000:8000 -p8001:8001 --ipc=host -p8002:8002 -v /home/xxy/triton/server-main/docs/examples/model_repository:/models 5c99e9b6586e tritonserver --model-repository=/models --model-control-mode=None --strict-model-config=false --allow-metrics=false  
# 这行命令用以查看上述步骤部署的yolov5 triton版本是否无误

若成功，就可以看到如下画面：

5.测试triton调用是否成功

A.在python环境中安装triton库

pip install tritonclient[all]

B.同步调用

import cv2 
import numpy as np 
import tritonclient.grpc as grpcclient 
if __name__ == '__main__': 
imgpath = '/home/xxy/Pictures/2_00000.jpg'    # 数据 
img0 = cv2.imread(imgpath)   # 预处理
img = cv2.resize(img0,(640,640)) 
img = img[:, :, ::-1].transpose(2, 0, 1) 
img = np.ascontiguousarray(img) 
img = img.astype(np.float32) 
img /= 255.0 
img = np.stack([img],0) #可多张图⽚，这个list⾥弄成多张图就⾏ 
# 初始化triton客⼾端对象 
triton_client = grpcclient.InferenceServerClient( 
url='0.0.0.0:8001', verbose=False) #ip地址为启动docker之后输出的：Started GRPCInferenceService的地址，端口直接 8001  
outputs = [ 
grpcclient.InferRequestedOutput('output'), 
]
inputs = [grpcclient.InferInput('images', img.shape, 'FP32')] 
inputs[0].set_data_from_numpy(img) 
result = triton_client.infer( 
'yolov5m_half', 
inputs, 
request_id=str('1'),   # request_id 值随便填
model_version='', 
outputs=outputs) 
#拿到结果 
output_array = result.as_numpy('output')  # 将其转为numpy类型数据输出查看

C.异步调用

import cv2 
import numpy as np 
import tritonclient.grpc as grpcclient 
import queue 
from functools import partial 
class UserData: 
def __init__(self): 
self._completed_requests = queue.Queue() 

def completion_callback(user_data, result, error): 
user_data._completed_requests.put((result, error))

if __name__ == '__main__': 
imgpath = '/home/xxy/Pictures/2_00000.jpg'   # 数据
img0 = cv2.imread(imgpath) 
img = cv2.resize(img0,(640,640)) 
img = img[:, :, ::-1].transpose(2, 0, 1) # h*w*c convert to c*h*w 
img = np.ascontiguousarray(img) 
img = img.astype(np.float32) 
img /= 255.0 # 0 - 255 to 0.0 - 1.0 
img = img.reshape(1,img.shape[0],img.shape[1],img.shape[2]) 
user_data = UserData() 
triton_client = grpcclient.InferenceServerClient( 
url='0.0.0.0:8001', verbose=False) 
outputs = [ 
grpcclient.InferRequestedOutput('output'), 
]
inputs = [grpcclient.InferInput('images', img.shape, 'FP32')] 
inputs[0].set_data_from_numpy(img) 
triton_client.start_stream(partial(completion_callback, user_data)) 
for i in range(100): 
result = triton_client.async_stream_infer( 
'yolov5m_half', 
inputs, 
request_id=str(i), 
model_version='', 
outputs=outputs) 
while True: 
(results, error) = user_data._completed_requests.get() 
result = results.get_response(True) 
print(result['id']) 
print(results.as_numpy('output'))