Merge pull request #1984 from HydrogenSulfate/tipc_pyserv

Add python serving infer chain

deploy/paddleserving/config.yml

@@ -30,4 +30,4 @@ op:
             client_type: local_predictor
 
             #Fetch结果列表，以client_config中fetch_var的alias_name为准
-            fetch_list: ["prediction"] 
+            fetch_list: ["prediction"]

deploy/paddleserving/recognition/config.yml

@@ -31,7 +31,7 @@ op:
 
             #Fetch结果列表，以client_config中fetch_var的alias_name为准
             fetch_list: ["features"]
-            
+
     det:
         concurrency: 1
         local_service_conf:

test_tipc/README.md

@@ -103,10 +103,11 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
 
 ## 4 开始测试
 
-各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：  
+各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
 
 - [test_train_inference_python 使用](docs/test_train_inference_python.md)：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
 - [test_inference_cpp 使用](docs/test_inference_cpp.md) ：测试基于C++的模型推理。
 - [test_serving 使用](docs/test_serving.md) ：测试基于Paddle Serving的服务化部署功能。
 - [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md): 测试基于Paddle-Lite的ARM CPU端c++预测部署功能.
 - [test_paddle2onnx 使用](docs/test_paddle2onnx.md)：测试Paddle2ONNX的模型转化功能，并验证正确性。
+- [test_serving_infer_python 使用](docs/test_serving_infer_python.md)：测试python serving功能。

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:MobileNetV3_large_x1_0
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_serving/
+--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PP-ShiTu/PPShiTu_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPShiTu
+python:python3.7
+cls_inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
+det_inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./models/general_PPLCNet_x2_5_lite_v1.0_infer/
+--dirname:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./models/general_PPLCNet_x2_5_lite_v1.0_serving/
+--serving_client:./models/general_PPLCNet_x2_5_lite_v1.0_client/
+--serving_server:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/
+--serving_client:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
+serving_dir:./paddleserving/recognition
+web_service:recognition_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPHGNet_small
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPHGNet_small_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPHGNet_small_serving/
+--serving_client:./deploy/paddleserving/PPHGNet_small_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPHGNet_tiny
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPHGNet_tiny_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPHGNet_tiny_serving/
+--serving_client:./deploy/paddleserving/PPHGNet_tiny_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x0_25
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x0_25_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x0_25_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x0_25_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x0_35
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x0_35_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x0_35_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x0_35_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x0_5
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x0_5_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x0_5_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x0_5_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py
\ No newline at end of file

test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x0_75
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x0_75_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x0_75_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x0_75_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x1_0
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x1_0_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x1_0_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x1_0_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x1_5
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x1_5_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x1_5_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x1_5_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x2_0
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x2_0_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x2_0_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x2_0_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNet_x2_5
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNet_x2_5_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNet_x2_5_serving/
+--serving_client:./deploy/paddleserving/PPLCNet_x2_5_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:PPLCNetV2_base
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/PPLCNetV2_base_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/PPLCNetV2_base_serving/
+--serving_client:./deploy/paddleserving/PPLCNetV2_base_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ResNet50
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/ResNet50_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/ResNet50_serving/
+--serving_client:./deploy/paddleserving/ResNet50_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ResNet50_vd
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/ResNet50_vd_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/ResNet50_vd_serving/
+--serving_client:./deploy/paddleserving/ResNet50_vd_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py

test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:SwinTransformer_tiny_patch4_window7_224
+python:python3.7
+inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
+trans_model:-m paddle_serving_client.convert
+--dirname:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_serving/
+--serving_client:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_client/
+serving_dir:./deploy/paddleserving
+web_service:classification_web_service.py
+--use_gpu:0|null
+pipline:pipeline_http_client.py
\ No newline at end of file

test_tipc/docs/test_serving_infer_python.md

+# Linux GPU/CPU PYTHON 服务化部署测试
+
+Linux GPU/CPU  PYTHON 服务化部署测试的主程序为`test_serving_infer.sh`，可以测试基于Python的模型服务化部署功能。
+
+
+## 1. 测试结论汇总
+
+- 推理相关：
+
+| 算法名称   | 模型名称  | device_CPU          | device_GPU |
+|  :----:   |  :----: |   :----:            |  :----:  |
+|  MobileNetV3   |  MobileNetV3_large_x1_0 |  支持 | 支持 |
+|  PP-ShiTu   |  PPShiTu_general_rec、PPShiTu_mainbody_det |  支持 | 支持 |
+|  PPHGNet   |  PPHGNet_small |  支持 | 支持 |
+|  PPHGNet   |  PPHGNet_tiny |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_25 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_35 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_5 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_75 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x1_0 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x1_5 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x2_0 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x2_5 |  支持 | 支持 |
+|  PPLCNetV2   |  PPLCNetV2_base |  支持 | 支持 |
+|  ResNet   |  ResNet50 |  支持 | 支持 |
+|  ResNet   |  ResNet50_vd |  支持 | 支持 |
+|  SwinTransformer   |  SwinTransformer_tiny_patch4_window7_224 |  支持 | 支持 |
+
+
+## 2. 测试流程
+
+### 2.1 准备数据
+
+分类模型默认使用`./deploy/paddleserving/daisy.jpg`作为测试输入图片，无需下载
+识别模型默认使用`drink_dataset_v1.0/test_images/001.jpeg`作为测试输入图片，在**2.2 准备环境**中会下载好。
+
+### 2.2 准备环境
+
+
+- 安装PaddlePaddle：如果您已经安装了2.2或者以上版本的paddlepaddle，那么无需运行下面的命令安装paddlepaddle。
+    ```shell
+    # 需要安装2.2及以上版本的Paddle
+    # 安装GPU版本的Paddle
+    python3.7 -m pip install paddlepaddle-gpu==2.2.0
+    # 安装CPU版本的Paddle
+    python3.7 -m pip install paddlepaddle==2.2.0
+    ```
+
+- 安装依赖
+    ```shell
+    python3.7 -m pip install  -r requirements.txt
+    ```
+- 安装 PaddleServing 相关组件，包括serving-server、serving_client、serving-app，自动下载并解压推理模型
+  ```bash
+  bash test_tipc/prepare.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt serving_infer
+  ```
+
+### 2.3 功能测试
+
+测试方法如下所示，希望测试不同的模型文件，只需更换为自己的参数配置文件，即可完成对应模型的测试。
+
+```bash
+bash test_tipc/test_serving_infer_python.sh ${your_params_file} lite_train_lite_infer
+```
+
+以`ResNet50`的`Linux GPU/CPU PYTHON 服务化部署测试`为例，命令如下所示。
+
+
+```bash
+bash test_tipc/test_serving_infer_python.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt serving_infer
+```
+
+输出结果如下，表示命令运行成功。
+
+```
+Run successfully with command - python3.7 pipeline_http_client.py > ../../test_tipc/output/ResNet50/server_infer_gpu_pipeline_http_batchsize_1.log 2>&1!
+Run successfully with command - python3.7 pipeline_http_client.py > ../../test_tipc/output/ResNet50/server_infer_cpu_pipeline_http_batchsize_1.log 2>&1 !
+```
+
+预测结果会自动保存在 `./test_tipc/output/ResNet50/server_infer_gpu_pipeline_http_batchsize_1.log` ，可以看到 PaddleServing 的运行结果：
+
+```
+{'err_no': 0, 'err_msg': '', 'key': ['label', 'prob'], 'value': ["['daisy']", '[0.998314619064331]']}
+```
+
+
+如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。

test_tipc/prepare.sh

@@ -162,13 +162,29 @@ fi
 if [ ${MODE} = "serving_infer" ];then
     # prepare serving env
     python_name=$(func_parser_value "${lines[2]}")
-    ${python_name} -m pip install install paddle-serving-server-gpu==0.6.1.post101
-    ${python_name} -m pip install paddle_serving_client==0.6.1
-    ${python_name} -m pip install paddle-serving-app==0.6.1
+    ${python_name} -m pip install install paddle-serving-server-gpu==0.7.0.post102
+    ${python_name} -m pip install paddle_serving_client==0.7.0
+    ${python_name} -m pip install paddle-serving-app==0.7.0
+    if [[ ${model_name} =~ "ShiTu" ]]; then
+        cls_inference_model_url=$(func_parser_value "${lines[3]}")
+        cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}")
+        det_inference_model_url=$(func_parser_value "${lines[4]}")
+        det_tar_name=$(func_get_url_file_name "${det_inference_model_url}")
+        cd ./deploy
+        mkdir models
+        cd models
+        wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name}
+        wget -nc ${det_inference_model_url} && tar xf ${det_tar_name}
+        cd ..
+    else
+        cls_inference_model_url=$(func_parser_value "${lines[3]}")
+        cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}")
+        cd ./deploy/paddleserving
+        wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name}
+        cd ../../
+    fi
     unset http_proxy
     unset https_proxy
-    cd ./deploy/paddleserving
-    wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
 fi
 
 if [ ${MODE} = "paddle2onnx_infer" ];then

test_tipc/test_serving.sh

-#!/bin/bash
-source test_tipc/common_func.sh
-
-FILENAME=$1
-dataline=$(awk 'NR==1, NR==18{print}'  $FILENAME)
-
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-
-# parser serving
-model_name=$(func_parser_value "${lines[1]}")
-python=$(func_parser_value "${lines[2]}")
-trans_model_py=$(func_parser_value "${lines[3]}")
-infer_model_dir_key=$(func_parser_key "${lines[4]}")
-infer_model_dir_value=$(func_parser_value "${lines[4]}")
-model_filename_key=$(func_parser_key "${lines[5]}")
-model_filename_value=$(func_parser_value "${lines[5]}")
-params_filename_key=$(func_parser_key "${lines[6]}")
-params_filename_value=$(func_parser_value "${lines[6]}")
-serving_server_key=$(func_parser_key "${lines[7]}")
-serving_server_value=$(func_parser_value "${lines[7]}")
-serving_client_key=$(func_parser_key "${lines[8]}")
-serving_client_value=$(func_parser_value "${lines[8]}")
-serving_dir_value=$(func_parser_value "${lines[9]}")
-web_service_py=$(func_parser_value "${lines[10]}")
-web_use_gpu_key=$(func_parser_key "${lines[11]}")
-web_use_gpu_list=$(func_parser_value "${lines[11]}")
-web_use_mkldnn_key=$(func_parser_key "${lines[12]}")
-web_use_mkldnn_list=$(func_parser_value "${lines[12]}")
-web_cpu_threads_key=$(func_parser_key "${lines[13]}")
-web_cpu_threads_list=$(func_parser_value "${lines[13]}")
-web_use_trt_key=$(func_parser_key "${lines[14]}")
-web_use_trt_list=$(func_parser_value "${lines[14]}")
-web_precision_key=$(func_parser_key "${lines[15]}")
-web_precision_list=$(func_parser_value "${lines[15]}")
-pipeline_py=$(func_parser_value "${lines[16]}")
-image_dir_key=$(func_parser_key "${lines[17]}")
-image_dir_value=$(func_parser_value "${lines[17]}")
-
-LOG_PATH="../../test_tipc/output"
-mkdir -p ./test_tipc/output
-status_log="${LOG_PATH}/results_serving.log"
-
-function func_serving(){
-    IFS='|'
-    _python=$1
-    _script=$2
-    _model_dir=$3
-    # pdserving
-    set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
-    set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
-    set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
-    set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
-    set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
-    set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
-    trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
-    eval $trans_model_cmd
-    cd ${serving_dir_value}
-    echo $PWD
-    unset https_proxy
-    unset http_proxy
-    for python in ${python[*]}; do
-        if [ ${python} = "cpp"]; then
-            for use_gpu in ${web_use_gpu_list[*]}; do
-                if [ ${use_gpu} = "null" ]; then
-                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
-                    eval $web_service_cmd
-                    sleep 2s
-                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
-                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
-                    eval $pipeline_cmd
-                    status_check $last_status "${pipeline_cmd}" "${status_log}"
-                    sleep 2s
-                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
-                else
-                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
-                    eval $web_service_cmd
-                    sleep 2s
-                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
-                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
-                    eval $pipeline_cmd
-                    status_check $last_status "${pipeline_cmd}" "${status_log}"
-                    sleep 2s
-                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9                
-                fi
-            done
-        else
-            # python serving
-            for use_gpu in ${web_use_gpu_list[*]}; do
-                echo ${ues_gpu}
-                if [ ${use_gpu} = "null" ]; then
-                    for use_mkldnn in ${web_use_mkldnn_list[*]}; do
-                        if [ ${use_mkldnn} = "False" ]; then
-                            continue
-                        fi
-                        for threads in ${web_cpu_threads_list[*]}; do
-                            set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
-                            web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &"
-                            eval $web_service_cmd
-                            sleep 2s
-                            for pipeline in ${pipeline_py[*]}; do
-                                _save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
-                                pipeline_cmd="${python} ${pipeline} ${set_image_dir} > ${_save_log_path} 2>&1 "
-                                eval $pipeline_cmd
-                                last_status=${PIPESTATUS[0]}
-                                eval "cat ${_save_log_path}"
-                                status_check $last_status "${pipeline_cmd}" "${status_log}"
-                                sleep 2s
-                            done
-                            ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
-                        done
-                    done
-                elif [ ${use_gpu} = "0" ]; then
-                    for use_trt in ${web_use_trt_list[*]}; do
-                        for precision in ${web_precision_list[*]}; do
-                            if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
-                                continue
-                            fi
-                            if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
-                                continue
-                            fi
-                            if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
-                                continue
-                            fi
-                            set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
-                            set_precision=$(func_set_params "${web_precision_key}" "${precision}")
-                            web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} & "
-                            eval $web_service_cmd
-                        
-                            sleep 2s
-                            for pipeline in ${pipeline_py[*]}; do
-                                _save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
-                                pipeline_cmd="${python} ${pipeline} ${set_image_dir}> ${_save_log_path} 2>&1"
-                                eval $pipeline_cmd
-                                last_status=${PIPESTATUS[0]}
-                                eval "cat ${_save_log_path}"
-                                status_check $last_status "${pipeline_cmd}" "${status_log}"
-                                sleep 2s
-                            done
-                            ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
-                        done
-                    done
-                else
-                    echo "Does not support hardware other than CPU and GPU Currently!"
-                fi
-            done
-        fi
-    done
-}
-
-
-# set cuda device
-GPUID=$2
-if [ ${#GPUID} -le 0 ];then
-    env=" "
-else
-    env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-fi
-set CUDA_VISIBLE_DEVICES
-eval $env
-
-
-echo "################### run test ###################"
-
-export Count=0
-IFS="|"
-func_serving "${web_service_cmd}"

test_tipc/test_serving_infer.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+FILENAME=$1
+dataline=$(awk 'NR==1, NR==19{print}'  $FILENAME)
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+function func_get_url_file_name(){
+    strs=$1
+    IFS="/"
+    array=(${strs})
+    tmp=${array[${#array[@]}-1]}
+    echo ${tmp}
+}
+
+# parser serving
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+trans_model_py=$(func_parser_value "${lines[4]}")
+infer_model_dir_key=$(func_parser_key "${lines[5]}")
+infer_model_dir_value=$(func_parser_value "${lines[5]}")
+model_filename_key=$(func_parser_key "${lines[6]}")
+model_filename_value=$(func_parser_value "${lines[6]}")
+params_filename_key=$(func_parser_key "${lines[7]}")
+params_filename_value=$(func_parser_value "${lines[7]}")
+serving_server_key=$(func_parser_key "${lines[8]}")
+serving_server_value=$(func_parser_value "${lines[8]}")
+serving_client_key=$(func_parser_key "${lines[9]}")
+serving_client_value=$(func_parser_value "${lines[9]}")
+serving_dir_value=$(func_parser_value "${lines[10]}")
+web_service_py=$(func_parser_value "${lines[11]}")
+web_use_gpu_key=$(func_parser_key "${lines[12]}")
+web_use_gpu_list=$(func_parser_value "${lines[12]}")
+pipeline_py=$(func_parser_value "${lines[13]}")
+
+
+function func_serving_cls(){
+    LOG_PATH="../../test_tipc/output/${model_name}"
+    mkdir -p ${LOG_PATH}
+    status_log="${LOG_PATH}/results_serving.log"
+    IFS='|'
+
+    # pdserving
+    set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
+    set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
+    set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
+    set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
+    set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
+
+    trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
+    eval $trans_model_cmd
+
+    # modify the alias_name of fetch_var to "outputs"
+    server_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_server_value}/serving_server_conf.prototxt"
+    eval ${server_fetch_var_line_cmd}
+    client_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_client_value}/serving_client_conf.prototxt"
+    eval ${client_fetch_var_line_cmd}
+
+    prototxt_dataline=$(awk 'NR==1, NR==3{print}'  ${serving_server_value}/serving_server_conf.prototxt)
+    IFS=$'\n'
+    prototxt_lines=(${prototxt_dataline})
+    feed_var_name=$(func_parser_value "${prototxt_lines[2]}")
+    IFS='|'
+
+    cd ${serving_dir_value}
+    unset https_proxy
+    unset http_proxy
+
+    # modify the input_name in "classification_web_service.py" to be consistent with feed_var.name in prototxt
+    set_web_service_feet_var_cmd="sed -i '/preprocess/,/input_imgs}/s/{.*: input_imgs}/{${feed_var_name}: input_imgs}/' ${web_service_py}"
+    eval ${set_web_service_feet_var_cmd}
+
+    model_config=21
+    serving_server_dir_name=$(func_get_url_file_name "$serving_server_value")
+    set_model_config_cmd="sed -i '${model_config}s/model_config: .*/model_config: ${serving_server_dir_name}/' config.yml"
+    eval ${set_model_config_cmd}
+
+    for python in ${python[*]}; do
+        if [[ ${python} = "cpp" ]]; then
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                if [ ${use_gpu} = "null" ]; then
+                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
+                    eval $web_service_cmd
+                    sleep 5s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 5s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                else
+                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
+                    eval $web_service_cmd
+                    sleep 5s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 5s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                fi
+            done
+        else
+            # python serving
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                if [[ ${use_gpu} = "null" ]]; then
+                    device_type_line=24
+                    set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 0/' config.yml"
+                    eval $set_device_type_cmd
+
+                    devices_line=27
+                    set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"\"/' config.yml"
+                    eval $set_devices_cmd
+
+                    web_service_cmd="${python} ${web_service_py} &"
+                    eval $web_service_cmd
+                    sleep 5s
+                    for pipeline in ${pipeline_py[*]}; do
+                        _save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
+                        pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1 "
+                        eval $pipeline_cmd
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${pipeline_cmd}" "${status_log}"
+                        sleep 5s
+                    done
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                elif [ ${use_gpu} -eq 0 ]; then
+                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
+                        continue
+                    fi
+                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
+                        continue
+                    fi
+                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
+                        continue
+                    fi
+
+                    device_type_line=24
+                    set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 1/' config.yml"
+                    eval $set_device_type_cmd
+
+                    devices_line=27
+                    set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"${use_gpu}\"/' config.yml"
+                    eval $set_devices_cmd
+
+                    web_service_cmd="${python} ${web_service_py} & "
+                    eval $web_service_cmd
+                    sleep 5s
+                    for pipeline in ${pipeline_py[*]}; do
+                        _save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
+                        pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1"
+                        eval $pipeline_cmd
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${pipeline_cmd}" "${status_log}"
+                        sleep 5s
+                    done
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                else
+                    echo "Does not support hardware [${use_gpu}] other than CPU and GPU Currently!"
+                fi
+            done
+        fi
+    done
+}
+
+
+function func_serving_rec(){
+    LOG_PATH="../../../test_tipc/output/${model_name}"
+    mkdir -p ${LOG_PATH}
+    status_log="${LOG_PATH}/results_serving.log"
+    trans_model_py=$(func_parser_value "${lines[5]}")
+    cls_infer_model_dir_key=$(func_parser_key "${lines[6]}")
+    cls_infer_model_dir_value=$(func_parser_value "${lines[6]}")
+    det_infer_model_dir_key=$(func_parser_key "${lines[7]}")
+    det_infer_model_dir_value=$(func_parser_value "${lines[7]}")
+    model_filename_key=$(func_parser_key "${lines[8]}")
+    model_filename_value=$(func_parser_value "${lines[8]}")
+    params_filename_key=$(func_parser_key "${lines[9]}")
+    params_filename_value=$(func_parser_value "${lines[9]}")
+
+    cls_serving_server_key=$(func_parser_key "${lines[10]}")
+    cls_serving_server_value=$(func_parser_value "${lines[10]}")
+    cls_serving_client_key=$(func_parser_key "${lines[11]}")
+    cls_serving_client_value=$(func_parser_value "${lines[11]}")
+
+    det_serving_server_key=$(func_parser_key "${lines[12]}")
+    det_serving_server_value=$(func_parser_value "${lines[12]}")
+    det_serving_client_key=$(func_parser_key "${lines[13]}")
+    det_serving_client_value=$(func_parser_value "${lines[13]}")
+
+    serving_dir_value=$(func_parser_value "${lines[14]}")
+    web_service_py=$(func_parser_value "${lines[15]}")
+    web_use_gpu_key=$(func_parser_key "${lines[16]}")
+    web_use_gpu_list=$(func_parser_value "${lines[16]}")
+    pipeline_py=$(func_parser_value "${lines[17]}")
+
+    IFS='|'
+
+    # pdserving
+    cd ./deploy
+    set_dirname=$(func_set_params "${cls_infer_model_dir_key}" "${cls_infer_model_dir_value}")
+    set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
+    set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
+    set_serving_server=$(func_set_params "${cls_serving_server_key}" "${cls_serving_server_value}")
+    set_serving_client=$(func_set_params "${cls_serving_client_key}" "${cls_serving_client_value}")
+    cls_trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
+    eval $cls_trans_model_cmd
+
+    set_dirname=$(func_set_params "${det_infer_model_dir_key}" "${det_infer_model_dir_value}")
+    set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
+    set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
+    set_serving_server=$(func_set_params "${det_serving_server_key}" "${det_serving_server_value}")
+    set_serving_client=$(func_set_params "${det_serving_client_key}" "${det_serving_client_value}")
+    det_trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
+    eval $det_trans_model_cmd
+
+    # modify the alias_name of fetch_var to "outputs"
+    server_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"features\"/' $cls_serving_server_value/serving_server_conf.prototxt"
+    eval ${server_fetch_var_line_cmd}
+    client_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"features\"/' $cls_serving_client_value/serving_client_conf.prototxt"
+    eval ${client_fetch_var_line_cmd}
+
+    prototxt_dataline=$(awk 'NR==1, NR==3{print}'  ${cls_serving_server_value}/serving_server_conf.prototxt)
+    IFS=$'\n'
+    prototxt_lines=(${prototxt_dataline})
+    feed_var_name=$(func_parser_value "${prototxt_lines[2]}")
+    IFS='|'
+
+    cd ${serving_dir_value}
+    unset https_proxy
+    unset http_proxy
+
+    # modify the input_name in "recognition_web_service.py" to be consistent with feed_var.name in prototxt
+    set_web_service_feet_var_cmd="sed -i '/preprocess/,/input_imgs}/s/{.*: input_imgs}/{${feed_var_name}: input_imgs}/' ${web_service_py}"
+    eval ${set_web_service_feet_var_cmd}
+
+    for python in ${python[*]}; do
+        if [[ ${python} = "cpp" ]]; then
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                if [ ${use_gpu} = "null" ]; then
+                    web_service_cpp_cmd="${python} web_service_py"
+
+                    eval $web_service_cmd
+                    sleep 5s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 5s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                else
+                    web_service_cpp_cmd="${python} web_service_py"
+                    eval $web_service_cmd
+                    sleep 5s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 5s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                fi
+            done
+        else
+            # python serving
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                if [[ ${use_gpu} = "null" ]]; then
+                    device_type_line=24
+                    set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 0/' config.yml"
+                    eval $set_device_type_cmd
+
+                    devices_line=27
+                    set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"\"/' config.yml"
+                    eval $set_devices_cmd
+
+                    web_service_cmd="${python} ${web_service_py} &"
+                    eval $web_service_cmd
+                    sleep 5s
+                    for pipeline in ${pipeline_py[*]}; do
+                        _save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
+                        pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1 "
+                        eval $pipeline_cmd
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${pipeline_cmd}" "${status_log}"
+                        sleep 5s
+                    done
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                elif [ ${use_gpu} -eq 0 ]; then
+                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
+                        continue
+                    fi
+                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
+                        continue
+                    fi
+                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
+                        continue
+                    fi
+
+                    device_type_line=24
+                    set_device_type_cmd="sed -i '${device_type_line}s/device_type: .*/device_type: 1/' config.yml"
+                    eval $set_device_type_cmd
+
+                    devices_line=27
+                    set_devices_cmd="sed -i '${devices_line}s/devices: .*/devices: \"${use_gpu}\"/' config.yml"
+                    eval $set_devices_cmd
+
+                    web_service_cmd="${python} ${web_service_py} & "
+                    eval $web_service_cmd
+                    sleep 10s
+                    for pipeline in ${pipeline_py[*]}; do
+                        _save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
+                        pipeline_cmd="${python} ${pipeline} > ${_save_log_path} 2>&1"
+                        eval $pipeline_cmd
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${pipeline_cmd}" "${status_log}"
+                        sleep 10s
+                    done
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                else
+                    echo "Does not support hardware [${use_gpu}] other than CPU and GPU Currently!"
+                fi
+            done
+        fi
+    done
+}
+
+
+# set cuda device
+GPUID=$2
+if [ ${#GPUID} -le 0 ];then
+    env=" "
+else
+    env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+fi
+set CUDA_VISIBLE_DEVICES
+eval $env
+
+
+echo "################### run test ###################"
+
+export Count=0
+IFS="|"
+if [[ ${model_name} =~ "ShiTu" ]]; then
+    func_serving_rec
+else
+    func_serving_cls
+fi