add cpp serving infer

deploy/paddleserving/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt

+feed_var {
+  name: "x"
+  alias_name: "x"
+  is_lod_tensor: false
+  feed_type: 1
+  shape: 3
+  shape: 224
+  shape: 224
+}
+feed_var {
+  name: "boxes"
+  alias_name: "boxes"
+  is_lod_tensor: false
+  feed_type: 1
+  shape: 6
+}
+fetch_var {
+  name: "save_infer_model/scale_0.tmp_1"
+  alias_name: "features"
+  is_lod_tensor: false
+  fetch_type: 1
+  shape: 512
+}
+fetch_var {
+  name: "boxes"
+  alias_name: "boxes"
+  is_lod_tensor: false
+  fetch_type: 1
+  shape: 6
+}
+
+

deploy/paddleserving/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/serving_server_conf.prototxt

+feed_var {
+  name: "x"
+  alias_name: "x"
+  is_lod_tensor: false
+  feed_type: 1
+  shape: 3
+  shape: 224
+  shape: 224
+}
+feed_var {
+  name: "boxes"
+  alias_name: "boxes"
+  is_lod_tensor: false
+  feed_type: 1
+  shape: 6
+}
+fetch_var {
+  name: "save_infer_model/scale_0.tmp_1"
+  alias_name: "features"
+  is_lod_tensor: false
+  fetch_type: 1
+  shape: 512
+}
+fetch_var {
+  name: "boxes"
+  alias_name: "boxes"
+  is_lod_tensor: false
+  fetch_type: 1
+  shape: 6
+}

deploy/paddleserving/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt

+feed_var {
+  name: "im_shape"
+  alias_name: "im_shape"
+  is_lod_tensor: false
+  feed_type: 1
+  shape: 2
+}
+feed_var {
+  name: "image"
+  alias_name: "image"
+  is_lod_tensor: false
+  feed_type: 7
+  shape: -1
+  shape: -1
+  shape: 3
+}
+fetch_var {
+  name: "save_infer_model/scale_0.tmp_1"
+  alias_name: "save_infer_model/scale_0.tmp_1"
+  is_lod_tensor: true
+  fetch_type: 1
+  shape: -1
+}
+fetch_var {
+  name: "save_infer_model/scale_1.tmp_1"
+  alias_name: "save_infer_model/scale_1.tmp_1"
+  is_lod_tensor: false
+  fetch_type: 2
+}

deploy/paddleserving/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/serving_server_conf.prototxt

+feed_var {
+  name: "im_shape"
+  alias_name: "im_shape"
+  is_lod_tensor: false
+  feed_type: 1
+  shape: 2
+}
+feed_var {
+  name: "image"
+  alias_name: "image"
+  is_lod_tensor: false
+  feed_type: 7
+  shape: -1
+  shape: -1
+  shape: 3
+}
+fetch_var {
+  name: "save_infer_model/scale_0.tmp_1"
+  alias_name: "save_infer_model/scale_0.tmp_1"
+  is_lod_tensor: true
+  fetch_type: 1
+  shape: -1
+}
+fetch_var {
+  name: "save_infer_model/scale_1.tmp_1"
+  alias_name: "save_infer_model/scale_1.tmp_1"
+  is_lod_tensor: false
+  fetch_type: 2
+}

deploy/paddleserving/preprocess/serving_client_conf.prototxt

-feed_var {
-  name: "inputs"
-  alias_name: "inputs"
-  is_lod_tensor: false
-  feed_type: 20
-  shape: 1
-}
-fetch_var {
-  name: "save_infer_model/scale_0.tmp_1"
-  alias_name: "prediction"
-  is_lod_tensor: false
-  fetch_type: 1
-  shape: 1000
-}
\ No newline at end of file

test_tipc/README.md

@@ -112,4 +112,5 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
 - [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_serving_infer_cpp 使用](docs/test_serving_infer_python.md)：测试python serving功能。
 - [test_train_fleet_inference_python 使用](./docs/test_train_fleet_inference_python.md)：测试基于Python的多机多卡训练与推理等基本功能。

test_tipc/docs/test_serving_infer_cpp.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_client、serving-app，自动编译带自定义OP的serving_server包（测试PP-ShiTu时），以及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_cpp_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.sh ${your_params_file}
+```
+
+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.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+```
+
+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/docs/test_serving_infer_python.md

@@ -60,14 +60,14 @@ Linux GPU/CPU  PYTHON 服务化部署测试的主程序为`test_serving_infer.sh
 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
+bash test_tipc/test_serving_infer.sh ${your_params_file}
 ```
 
 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
+bash test_tipc/test_serving_infer.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
 ```
 
 输出结果如下，表示命令运行成功。

test_tipc/prepare.sh

@@ -203,19 +203,19 @@ if [[ ${MODE} = "serving_infer" ]]; then
     ${python_name} -m pip install paddle_serving_client==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
     ${python_name} -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
     python_name=$(func_parser_value "${lines[2]}")
-    if [[ ${FILENAME} =~ "cpp" ]]; then
-        pushd ./deploy/paddleserving
-        # bash build_server.sh ${python_name}
-        popd
+    if [[ ${FILENAME} =~ "cpp" ] && [ ${model_name} =~ "ShiTu" ]]; then
+        bash build_server.sh ${python_name}
     else
         ${python_name} -m pip install install paddle-serving-server-gpu==0.9.0.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
     fi
     if [[ ${model_name} =~ "ShiTu" ]]; then
+        ${python_name} -m pip install faiss-cpu==1.7.1post2 -i https://pypi.tuna.tsinghua.edu.cn/simple
         cls_inference_model_url=$(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
+        wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar --no-check-certificate && tar -xf drink_dataset_v1.0.tar
         mkdir models
         cd models
         wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name}

test_tipc/test_serving_infer.sh

@@ -206,8 +206,9 @@ function func_serving_cls(){
 
 
 function func_serving_rec(){
-    LOG_PATH="../../../test_tipc/output/${model_name}"
+    LOG_PATH="test_tipc/output/${model_name}"
     mkdir -p ${LOG_PATH}
+    LOG_PATH="../../../${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]}")
@@ -244,6 +245,7 @@ function func_serving_rec(){
     done
 
     # pdserving
+    export SERVING_BIN=$PWD/Serving/server-build-gpu-opencv/core/general-server/serving
     cd ./deploy
     set_dirname=$(func_set_params "${cls_infer_model_dir_key}" "${cls_infer_model_dir_value}")
     set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
@@ -261,11 +263,14 @@ function func_serving_rec(){
     det_trans_model_cmd="${python_interp} ${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}
+    cp_prototxt_cmd="cp ./paddleserving/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/*.prototxt ${cls_serving_server_value}"
+    eval ${cp_prototxt_cmd}
+    cp_prototxt_cmd="cp ./paddleserving/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/*.prototxt ${cls_serving_client_value}"
+    eval ${cp_prototxt_cmd}
+    cp_prototxt_cmd="cp ./paddleserving/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/*.prototxt ${det_serving_client_value}"
+    eval ${cp_prototxt_cmd}
+    cp_prototxt_cmd="cp ./paddleserving/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/*.prototxt ${det_serving_server_value}"
+    eval ${cp_prototxt_cmd}
 
     prototxt_dataline=$(awk 'NR==1, NR==3{print}'  ${cls_serving_server_value}/serving_server_conf.prototxt)
     IFS=$'\n'
@@ -278,27 +283,11 @@ function func_serving_rec(){
     unset http_proxy
 
     if [[ ${FILENAME} =~ "cpp" ]]; then
-        det_serving_client_dir_name=$(func_get_url_file_name "$det_serving_client_value")
-        set_det_client_config_line_cmd="sed -i '/MainbodyDetect/,/serving_client_conf.prototxt/s/models\/.*\/serving_client_conf.prototxt/models\/${det_serving_client_dir_name}\/serving_client_conf.prototxt/' ${pipeline_py}"
-        eval ${set_det_client_config_line_cmd}
-
-        cls_serving_client_dir_name=$(func_get_url_file_name "$cls_serving_client_value")
-        set_cls_client_config_line_cmd="sed -i '/ObjectRecognition/,/serving_client_conf.prototxt/s/models\/.*\/serving_client_conf.prototxt/models\/${cls_serving_client_dir_name}\/serving_client_conf.prototxt/' ${pipeline_py}"
-        eval ${set_cls_client_config_line_cmd}
-
-        set_pipeline_py_feed_var_cmd="sed -i '/ObjectRecognition/,/feed={\"x\": batch_imgs}/s/{.*: batch_imgs}/{${feed_var_name}: batch_imgs}/' ${pipeline_py}"
-        eval ${set_pipeline_py_feed_var_cmd}
-
         for use_gpu in ${web_use_gpu_list[*]}; do
             if [ ${use_gpu} = "null" ]; then
-
                 det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
-                web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../models/${det_serving_server_dir_name} --port 9293 >>log_mainbody_detection.txt &"
-
-                cls_serving_server_dir_name=$(func_get_url_file_name "$cls_serving_server_value")
-                web_service_cpp_cmd2="${python_interp} -m paddle_serving_server.serve --model ../../models/${cls_serving_server_dir_name} --port 9294 >>log_feature_extraction.txt &"
+                web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 &"
                 eval $web_service_cpp_cmd
-                eval $web_service_cpp_cmd2
                 sleep 5s
                 _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_batchsize_1.log"
                 pipeline_cmd="${python_interp} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "
@@ -310,12 +299,8 @@ function func_serving_rec(){
                 sleep 5s
             else
                 det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
-                web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../models/${det_serving_server_dir_name} --port 9293 --gpu_id=${use_gpu} >>log_mainbody_detection.txt &"
-
-                cls_serving_server_dir_name=$(func_get_url_file_name "$cls_serving_server_value")
-                web_service_cpp_cmd2="${python_interp} -m paddle_serving_server.serve --model ../../models/${cls_serving_server_dir_name} --port 9294 --gpu_id=${use_gpu} >>log_feature_extraction.txt &"
+                web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 --gpu_id=${use_gpu} &"
                 eval $web_service_cpp_cmd
-                eval $web_service_cpp_cmd2
                 sleep 5s
                 _save_log_path="${LOG_PATH}/server_infer_cpp_gpu_batchsize_1.log"
                 pipeline_cmd="${python_interp} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "