Add cls G1-G2 model lite tipc

deploy/lite/Makefile

 ARM_ABI = arm8
 export ARM_ABI
 
-include ../Makefile.def
+LITE_ROOT=./inference_lite_lib.android.armv8
 
-LITE_ROOT=../../../
+include ${LITE_ROOT}/demo/cxx/Makefile.def
 
 THIRD_PARTY_DIR=${LITE_ROOT}/third_party
 
@@ -29,7 +29,7 @@ OPENCV_LIBS = ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PATH}/libs/libopencv_im
               ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PATH}/3rdparty/libs/libtbb.a \
               ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PATH}/3rdparty/libs/libcpufeatures.a
 
-OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/${ARM_PATH}/include
+OPENCV_INCLUDE = -I${LITE_ROOT}/third_party/${OPENCV_VERSION}/${ARM_PATH}/include
 
 CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
 

deploy/lite/config.txt

-clas_model_file ./MobileNetV3_large_x1_0.nb
-label_path ./imagenet1k_label_list.txt
+clas_model_file /data/local/tmp/arm_cpu/MobileNetV3_large_x1_0.nb
+label_path /data/local/tmp/arm_cpu/imagenet1k_label_list.txt
 resize_short_size 256
 crop_size 224
 visualize 0
+num_threads 1
+batch_size 1
+precision FP32
+runtime_device arm_cpu
 enable_benchmark 0
+tipc_benchmark 0

deploy/lite/image_classfication.cpp

@@ -21,6 +21,7 @@
 #include <opencv2/opencv.hpp>
 #include <sys/time.h>
 #include <vector>
+#include "AutoLog/auto_log/lite_autolog.h"
 
 using namespace paddle::lite_api; // NOLINT
 using namespace std;
@@ -149,8 +150,10 @@ cv::Mat CenterCropImg(const cv::Mat &img, const int &crop_size) {
 std::vector<RESULT>
 RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
              const std::map<std::string, std::string> &config,
-             const std::vector<std::string> &word_labels, double &cost_time) {
+             const std::vector<std::string> &word_labels, double &cost_time,
+             std::vector<double> *time_info) {
   // Read img
+  auto preprocess_start = std::chrono::steady_clock::now();
   int resize_short_size = stoi(config.at("resize_short_size"));
   int crop_size = stoi(config.at("crop_size"));
   int visualize = stoi(config.at("visualize"));
@@ -172,8 +175,8 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
   std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
   const float *dimg = reinterpret_cast<const float *>(img_fp.data);
   NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
-
-  auto start = std::chrono::system_clock::now();
+  auto preprocess_end = std::chrono::steady_clock::now();
+  auto inference_start = std::chrono::system_clock::now();
   // Run predictor
   predictor->Run();
 
@@ -181,9 +184,10 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
   std::unique_ptr<const Tensor> output_tensor(
       std::move(predictor->GetOutput(0)));
   auto *output_data = output_tensor->data<float>();
-  auto end = std::chrono::system_clock::now();
+  auto inference_end = std::chrono::system_clock::now();
+  auto postprocess_start = std::chrono::system_clock::now();
   auto duration =
-      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+      std::chrono::duration_cast<std::chrono::microseconds>(inference_end - inference_start);
   cost_time = double(duration.count()) *
               std::chrono::microseconds::period::num /
               std::chrono::microseconds::period::den;
@@ -196,6 +200,13 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
   cv::Mat output_image;
   auto results =
       PostProcess(output_data, output_size, word_labels, output_image);
+  auto postprocess_end = std::chrono::system_clock::now();
+  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+  time_info->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  time_info->push_back(double(inference_diff.count() * 1000));
+  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+  time_info->push_back(double(postprocess_diff.count() * 1000));
 
   if (visualize) {
     std::string output_image_path = "./clas_result.png";
@@ -309,6 +320,12 @@ int main(int argc, char **argv) {
 
   std::string clas_model_file = config.at("clas_model_file");
   std::string label_path = config.at("label_path");
+  std::string crop_size = config.at("crop_size");
+  int num_threads = stoi(config.at("num_threads"));
+  int batch_size = stoi(config.at("batch_size"));
+  std::string precision = config.at("precision");
+  std::string runtime_device = config.at("runtime_device");
+  bool tipc_benchmark = bool(stoi(config.at("tipc_benchmark")));
 
   // Load Labels
   std::vector<std::string> word_labels = LoadLabels(label_path);
@@ -319,8 +336,9 @@ int main(int argc, char **argv) {
     cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
 
     double run_time = 0;
+    std::vector<double> time_info;
     std::vector<RESULT> results =
-        RunClasModel(clas_predictor, srcimg, config, word_labels, run_time);
+        RunClasModel(clas_predictor, srcimg, config, word_labels, run_time, &time_info);
 
     std::cout << "===clas result for image: " << img_path << "===" << std::endl;
     for (int i = 0; i < results.size(); i++) {
@@ -338,6 +356,19 @@ int main(int argc, char **argv) {
     } else {
       std::cout << "Current time cost: " << run_time << " s." << std::endl;
     }
+    if (tipc_benchmark) {
+      AutoLogger autolog(clas_model_file,
+                         runtime_device,
+                         num_threads,
+                         batch_size,
+                         crop_size,
+                         precision,
+                         time_info,
+                         1);
+    std::cout << "=======================TIPC Lite Information=======================" << std::endl;
+    autolog.report();
+    }
+
   }
 
   return 0;

deploy/lite/readme.md

@@ -25,8 +25,8 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 1. [建议]直接下载，预测库下载链接如下：
       |平台|预测库下载链接|
       |-|-|
-      |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)|
-      |iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz)|
+      |iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
 
       **注**：
       1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，
@@ -44,11 +44,11 @@ git checkout develop
 
 **注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2)。
 
-直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
+直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
 预测库的文件目录如下：
 
 ```
-inference_lite_lib.android.armv8/
+inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/
 |-- cxx                                        C++ 预测库和头文件
 |   |-- include                                C++ 头文件
 |   |   |-- paddle_api.h
@@ -86,7 +86,7 @@ Python下安装 `paddlelite`，目前最高支持`Python3.7`。
 **注意**：`paddlelite`whl包版本必须和预测库版本对应。
 
 ```shell
-pip install paddlelite==2.8
+pip install paddlelite==2.10
 ```
 
 之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下
@@ -146,6 +146,24 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --
 
 **注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
 
+<a name="2.1.4"></a>
+
+#### 2.1.4 执行编译，得到可执行文件clas_system
+
+```shell
+# 克隆 Autolog 代码库，以便获取自动化日志
+cd PaddleClas_root_path
+cd deploy/lite/
+git clone https://github.com/LDOUBLEV/AutoLog.git
+```
+
+```shell
+# 克隆 Autolog 代码库，以便获取自动化日志
+make -j
+```
+
+执行 `make` 命令后，会在当前目录生成 `clas_system` 可执行文件，该文件用于 Lite 预测。
+
 <a name="2.2与手机联调"></a>
 ### 2.2 与手机联调
 
@@ -167,7 +185,7 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --
 
     win上安装需要去谷歌的安卓平台下载ADB软件包进行安装：[链接](https://developer.android.com/studio)
 
-4. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入：
+3. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入：
 
 ```shell
 adb devices
@@ -178,40 +196,18 @@ List of devices attached
 744be294    device
 ```
 
-5. 准备优化后的模型、预测库文件、测试图像和类别映射文件。
-
-```shell
-cd PaddleClas_root_path
-cd deploy/lite/
 
-# 运行prepare.sh
-# prepare.sh 会将预测库文件、测试图像和使用的字典文件放置在预测库中的demo/cxx/clas文件夹下
-sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
+4. 将优化后的模型、预测库文件、测试图像和类别映射文件push到手机上。
 
-# 进入lite demo的工作目录
-cd /{lite prediction library path}/inference_lite_lib.android.armv8/
-cd demo/cxx/clas/
-
-# 将C++预测动态库so文件复制到debug文件夹中
-cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
-```
-
-`prepare.sh` 以 `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` 作为测试图像，将测试图像复制到`demo/cxx/clas/debug/` 文件夹下。
-将 `paddle_lite_opt` 工具优化后的模型文件放置到 `/{lite prediction library path}/inference_lite_lib.android.armv8/demo/cxx/clas/debug/` 文件夹下。本例中，使用[2.1.3](#2.1.3)生成的 `MobileNetV3_large_x1_0.nb` 模型文件。
-
-执行完成后，clas文件夹下将有如下文件格式：
-
-```
-demo/cxx/clas/
-|-- debug/
-|   |--MobileNetV3_large_x1_0.nb                优化后的分类器模型文件
-|   |--tabby_cat.jpg                           	待测试图像
-|   |--imagenet1k_label_list.txt                类别映射文件
-|   |--libpaddle_light_api_shared.so    C++预测库文件
-|   |--config.txt                       分类预测超参数配置
-|-- config.txt                  				分类预测超参数配置
-|-- image_classfication.cpp            	图像分类代码文件
-|-- Makefile                    				编译文件
+```shell
+adb shell mkdir -p /data/local/tmp/arm_cpu/
+adb push clas_system /data/local/tmp/arm_cpu/
+adb shell chmod +x /data/local/tmp/arm_cpu//clas_system
+adb push inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/arm_cpu/
+adb push MobileNetV3_large_x1_0.nb /data/local/tmp/arm_cpu/
+adb push config.txt /data/local/tmp/arm_cpu/
+adb push ../../ppcls/utils/imagenet1k_label_list.txt /data/local/tmp/arm_cpu/
+adb push imgs/tabby_cat.jpg /data/local/tmp/arm_cpu/
 ```
 
 #### 注意：
@@ -224,32 +220,22 @@ clas_model_file ./MobileNetV3_large_x1_0.nb # 模型文件地址
 label_path ./imagenet1k_label_list.txt 			# 类别映射文本文件
 resize_short_size 256 # resize之后的短边边长
 crop_size 224 				# 裁剪后用于预测的边长
-visualize 0 # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为clas_result.png的图像文件。
+visualize 0 # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为clas_result.png的图像文件
+num_threads 1 # 线程数，默认是1。
+precision FP32 # 精度类型，可以选择 FP32 或者 INT8，默认是 FP32。
+runtime_device arm_cpu # 设备类型，默认是 arm_cpu
+enable_benchmark 0 # 是否开启benchmark， 默认是 0
+tipc_benchmark 0 # 是否开启tipc_benchmark，默认是 0
 ```
 
-5. 启动调试，上述步骤完成后就可以使用ADB将文件夹 `debug/` push到手机上运行，步骤如下：
+5. 执行预测命令
 
-```shell
-# 执行编译，得到可执行文件clas_system
-make -j
-
-# 将编译得到的可执行文件移动到debug文件夹中
-mv clas_system ./debug/
-
-# 将上述debug文件夹push到手机上
-adb push debug /data/local/tmp/
-
-adb shell
-cd /data/local/tmp/debug
-export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
+执行以下命令，可完成在手机上的预测。
 
-# clas_system可执行文件的使用方式为:
-# ./clas_system 配置文件路径  测试图像路径
-./clas_system ./config.txt ./tabby_cat.jpg
+```shell
+adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/clas_system /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/tabby_cat.jpg'
 ```
 
-如果对代码做了修改，则需要重新编译并push到手机上。
-
 运行效果如下：
 
 <div align="center">
@@ -263,3 +249,4 @@ A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模
 
 Q2：换一个图测试怎么做？  
 A2：替换 debug 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。
+

test_tipc/common_func.sh

@@ -16,6 +16,14 @@ function func_parser_value(){
     echo ${tmp}
 }
 
+function func_parser_value_lite(){
+    strs=$1
+    IFS=$2
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+
 function func_set_params(){
     key=$1
     value=$2

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:MobileNetV3_large_x1_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:MobileNetV3_large_x1_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_25_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x0_25
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_5_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x0_5
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_75_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x0_75
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x1_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x1_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x1_5_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x1_5
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x2_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x2_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x2_5_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x2_5
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/ResNet/ResNet50_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:ResNet50
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/ResNet/ResNet50_vd_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:ResNet50_vd
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:SwinTransformer_tiny_patch4_window7_224
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/docs/test_lite_arm_cpu_cpp.md

+# Lite_arm_cpp_cpu 预测功能测试
+
+Lite_arm_cpp_cpu 预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以测试基于 Paddle-Lite 预测库的模型推理功能。
+
+## 1. 测试结论汇总
+
+| 模型类型 |device | batchsize | 精度类型| 线程数 |
+|  :----:   |  :----: |   :----:   |  :----:  | :----: |
+| 正常模型 | arm_cpu | 1 | FP32 | 1 |
+
+## 2. 测试流程
+运行环境配置请参考[文档](https://github.com/PaddlePaddle/models/blob/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/lite_infer_cpp_arm_cpu/README.md) 的内容配置 TIPC Lite 的运行环境。
+
+### 2.1 功能测试
+先运行 `prepare_lite_arm_cpu_cpp.sh` 准备数据和模型，然后运行 `test_lite_arm_cpu_cpp.sh` 进行测试，最终在 `./output` 目录下生成 `lite_*.log` 后缀的日志文件。
+
+```shell
+bash test_tipc/prepare_lite_arm_cpu_cpp.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt
+```
+
+运行预测指令后，在`./output`文件夹下自动会保存运行日志，包括以下文件：
+
+```shell
+test_tipc/output/
+|- results.log    # 运行指令状态的日志
+|- lite_MobileNetV3_large_x1_0_runtime_device_arm_cpu_precision_FP32_batchsize_1_threads_1.log  # ARM_CPU 上 FP32 状态下，线程数设置为1，测试batch_size=1条件下的预测运行日志
+......
+```
+其中results.log中包含了每条指令的运行状态，如果运行成功会输出：
+
+```
+Run successfully with command - adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/mobilenet_v3 /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/demo.jpg'  > ./output/lite_MobileNetV3_large_x1_0_runtime_device_arm_cpu_precision_FP32_batchsize_1_threads_1.log 2>&1!
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/mobilenet_v3 /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/demo.jpg'  > ./output/lite_MobileNetV3_large_x1_0_runtime_device_arm_cpu_precision_FP32_batchsize_1_threads_1.log 2>&1!
+......
+```
+可以很方便的根据results.log中的内容判定哪一个指令运行错误。
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的 Lite 预测使用教程请参考：[PaddleLite 推理部署](../../docs/zh_CN/inference_deployment/paddle_lite_deploy.md)  。

test_tipc/prepare_lite_arm_cpu_cpp.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+BASIC_CONFIG="./config.txt"
+CONFIG=$1
+
+# parser tipc config
+IFS=$'\n'
+TIPC_CONFIG=$1
+tipc_dataline=$(cat $TIPC_CONFIG)
+tipc_lines=(${tipc_dataline})
+
+runtime_device=$(func_parser_value_lite "${tipc_lines[0]}" ":")
+lite_arm_work_path=$(func_parser_value_lite "${tipc_lines[1]}" ":")
+lite_arm_so_path=$(func_parser_value_lite "${tipc_lines[2]}" ":")
+clas_model_name=$(func_parser_value_lite "${tipc_lines[3]}" ":")
+inference_cmd=$(func_parser_value_lite "${tipc_lines[4]}" ":")
+num_threads_list=$(func_parser_value_lite "${tipc_lines[5]}" ":")
+batch_size_list=$(func_parser_value_lite "${tipc_lines[6]}" ":")
+precision_list=$(func_parser_value_lite "${tipc_lines[7]}" ":")
+
+
+# Prepare config and test.sh
+work_path="./deploy/lite"
+cp ${CONFIG} ${work_path}
+cp test_tipc/test_lite_arm_cpu_cpp.sh ${work_path}
+
+# Prepare model
+cd ${work_path}
+pip3 install paddlelite==2.10
+model_url="https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/${clas_model_name}_infer.tar"
+wget --no-proxy ${model_url}
+model_tar=$(echo ${model_url} | awk -F "/" '{print $NF}')
+tar -xf ${model_tar}
+paddle_lite_opt --model_dir=${clas_model_name}_infer --model_file=${clas_model_name}_infer/inference.pdmodel --param_file=${clas_model_name}_infer/inference.pdiparams --valid_targets=arm --optimize_out=${clas_model_name}
+rm -rf ${clas_model_name}_infer*
+
+# Prepare paddlelite library
+paddlelite_lib_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz"
+wget ${paddlelite_lib_url}
+paddlelite_lib_file=$(echo ${paddlelite_lib_url} | awk -F "/" '{print $NF}')
+tar -xzf ${paddlelite_lib_file}
+mv ${paddlelite_lib_file%*.tar.gz} inference_lite_lib.android.armv8
+rm -rf ${paddlelite_lib_file%*.tar.gz}*
+
+# Compile and obtain executable binary file
+git clone https://github.com/LDOUBLEV/AutoLog.git
+make
+
+# push executable binary, library, lite model, data, etc. to arm device
+adb shell mkdir -p ${lite_arm_work_path}
+adb push $(echo ${inference_cmd} | awk '{print $1}') ${lite_arm_work_path}
+adb shell chmod +x ${lite_arm_work_path}/$(echo ${inference_cmd} | awk '{print $1}')
+adb push ${lite_arm_so_path} ${lite_arm_work_path}
+adb push ${clas_model_name}.nb ${lite_arm_work_path}
+adb push ${BASIC_CONFIG} ${lite_arm_work_path}
+adb push ../../ppcls/utils/imagenet1k_label_list.txt ${lite_arm_work_path}
+adb push imgs/$(echo ${inference_cmd} | awk '{print $3}') ${lite_arm_work_path}

test_tipc/prepare_lite_cpp.sh

-#!/bin/bash
-source ./test_tipc/common_func.sh
-FILENAME=$1
-dataline=$(cat ${FILENAME})
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-IFS=$'\n'
-
-inference_cmd=$(func_parser_value "${lines[1]}")
-DEVICE=$(func_parser_value "${lines[2]}")
-det_lite_model_list=$(func_parser_value "${lines[3]}")
-rec_lite_model_list=$(func_parser_value "${lines[4]}")
-cls_lite_model_list=$(func_parser_value "${lines[5]}")
-
-if [[ $inference_cmd =~ "det" ]];then
-    lite_model_list=${det_lite_model_list}
-elif [[ $inference_cmd =~ "rec" ]];then
-    lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-elif [[ $inference_cmd =~ "system" ]];then
-    lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-else
-    echo "inference_cmd is wrong, please check."
-    exit 1
-fi
-
-if [ ${DEVICE} = "ARM_CPU" ];then
-    valid_targets="arm"
-    paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz"
-    end_index="66"
-elif [ ${DEVICE} = "ARM_GPU_OPENCL" ];then
-    valid_targets="opencl"
-    paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.armv8.clang.with_exception.with_extra.with_cv.opencl.tar.gz"
-    end_index="71"
-else
-    echo "DEVICE only suport ARM_CPU, ARM_GPU_OPENCL."
-    exit 2    
-fi
-
-# prepare lite .nb model
-pip install paddlelite==2.10-rc
-current_dir=${PWD}
-IFS="|"
-model_path=./inference_models
-
-for model in ${lite_model_list[*]}; do
-    if [[ $model =~ "PP-OCRv2" ]];then
-        inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
-    elif [[ $model =~ "v2.0" ]];then
-        inference_model_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/${model}.tar
-    else 
-        echo "Model is wrong, please check."
-        exit 3
-    fi
-    inference_model=${inference_model_url##*/}
-    wget -nc  -P ${model_path} ${inference_model_url}
-    cd ${model_path} && tar -xf ${inference_model} && cd ../
-    model_dir=${model_path}/${inference_model%.*}
-    model_file=${model_dir}/inference.pdmodel
-    param_file=${model_dir}/inference.pdiparams
-    paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=${valid_targets} --optimize_out=${model_dir}_opt
-done
-
-# prepare test data
-data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-model_path=./inference_models
-inference_model=${inference_model_url##*/}
-data_file=${data_url##*/}
-wget -nc  -P ./inference_models ${inference_model_url}
-wget -nc  -P ./test_data ${data_url}
-cd ./inference_models && tar -xf ${inference_model} && cd ../
-cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
-
-# prepare lite env
-paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
-paddlelite_file=${paddlelite_zipfile:0:${end_index}}
-wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
-mkdir -p  ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
-cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp ${FILENAME} test_tipc/test_lite_arm_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
-cd ${paddlelite_file}/demo/cxx/ocr/
-git clone https://github.com/cuicheng01/AutoLog.git
-
-# make
-make -j
-sleep 1
-make -j
-cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
-tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
-rm -rf ${paddlelite_file}* && rm -rf ${model_path}

test_tipc/test_lite_arm_cpp.sh

-#!/bin/bash
-source ./common_func.sh
-export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
-
-FILENAME=$1
-dataline=$(cat $FILENAME)
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-
-# parser lite inference
-inference_cmd=$(func_parser_value "${lines[1]}")
-runtime_device=$(func_parser_value "${lines[2]}")
-det_model_list=$(func_parser_value "${lines[3]}")
-rec_model_list=$(func_parser_value "${lines[4]}")
-cls_model_list=$(func_parser_value "${lines[5]}")
-cpu_threads_list=$(func_parser_value "${lines[6]}")
-det_batch_size_list=$(func_parser_value "${lines[7]}")
-rec_batch_size_list=$(func_parser_value "${lines[8]}")
-infer_img_dir_list=$(func_parser_value "${lines[9]}")
-config_dir=$(func_parser_value "${lines[10]}")
-rec_dict_dir=$(func_parser_value "${lines[11]}")
-benchmark_value=$(func_parser_value "${lines[12]}")
-
-if [[ $inference_cmd =~ "det" ]]; then
-    lite_model_list=${det_lite_model_list}
-elif [[ $inference_cmd =~ "rec" ]]; then
-    lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-elif [[ $inference_cmd =~ "system" ]]; then
-    lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-else
-    echo "inference_cmd is wrong, please check."
-    exit 1
-fi
-
-LOG_PATH="./output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
-
-
-function func_test_det(){
-    IFS='|'
-    _script=$1
-    _det_model=$2
-    _log_path=$3
-    _img_dir=$4
-    _config=$5
-    if [[ $_det_model =~ "slim" ]]; then
-        precision="INT8"
-    else
-        precision="FP32"
-    fi
-
-    # lite inference
-    for num_threads in ${cpu_threads_list[*]}; do
-	for det_batchsize in ${det_batch_size_list[*]}; do
-            _save_log_path="${_log_path}/lite_${_det_model}_runtime_device_${runtime_device}_precision_${precision}_det_batchsize_${det_batchsize}_threads_${num_threads}.log"
-            command="${_script} ${_det_model} ${runtime_device} ${precision} ${num_threads} ${det_batchsize}  ${_img_dir} ${_config} ${benchmark_value} > ${_save_log_path} 2>&1"
-            eval ${command}
-            status_check $? "${command}" "${status_log}"
-        done
-    done
-}
-
-function func_test_rec(){
-    IFS='|'
-    _script=$1
-    _rec_model=$2
-    _cls_model=$3
-    _log_path=$4
-    _img_dir=$5
-    _config=$6
-    _rec_dict_dir=$7
-
-    if [[ $_det_model =~ "slim" ]]; then
-        _precision="INT8"
-    else
-        _precision="FP32"
-    fi
-
-    # lite inference
-    for num_threads in ${cpu_threads_list[*]}; do
-	for rec_batchsize in ${rec_batch_size_list[*]}; do
-            _save_log_path="${_log_path}/lite_${_rec_model}_${cls_model}_runtime_device_${runtime_device}_precision_${_precision}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
-            command="${_script} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${rec_batchsize}  ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
-            eval ${command}
-            status_check $? "${command}" "${status_log}"
-        done
-    done
-}
-
-function func_test_system(){
-    IFS='|'
-    _script=$1
-    _det_model=$2
-    _rec_model=$3
-    _cls_model=$4
-    _log_path=$5
-    _img_dir=$6
-    _config=$7
-    _rec_dict_dir=$8
-    if [[ $_det_model =~ "slim" ]]; then
-        _precision="INT8"
-    else
-        _precision="FP32"
-    fi
-
-    # lite inference
-    for num_threads in ${cpu_threads_list[*]}; do
-	for det_batchsize in ${det_batch_size_list[*]}; do
-	   for rec_batchsize in ${rec_batch_size_list[*]}; do
-                _save_log_path="${_log_path}/lite_${_det_model}_${_rec_model}_${_cls_model}_runtime_device_${runtime_device}_precision_${_precision}_det_batchsize_${det_batchsize}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
-                command="${_script} ${_det_model} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${det_batchsize}  ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
-               eval ${command}
-               status_check $? "${command}" "${status_log}"
-	    done
-        done
-    done
-}
-
-
-echo "################### run test ###################"
-
-if [[ $inference_cmd =~ "det" ]]; then
-    IFS="|"
-    det_model_list=(${det_model_list[*]})
-
-    for i in {0..1}; do
-        #run lite inference
-        for img_dir in ${infer_img_dir_list[*]}; do
-            func_test_det "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}"
-        done
-    done
-
-elif [[ $inference_cmd =~ "rec" ]]; then
-    IFS="|"
-    rec_model_list=(${rec_model_list[*]})
-    cls_model_list=(${cls_model_list[*]})
-
-    for i in {0..1}; do
-        #run lite inference
-        for img_dir in ${infer_img_dir_list[*]}; do
-            func_test_rec "${inference_cmd}" "${rec_model}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${rec_dict_dir}" "${config_dir}"
-        done
-    done
-
-elif [[ $inference_cmd =~ "system" ]]; then
-    IFS="|"
-    det_model_list=(${det_model_list[*]})
-    rec_model_list=(${rec_model_list[*]})
-    cls_model_list=(${cls_model_list[*]})
-
-    for i in {0..1}; do
-	#run lite inference
-        for img_dir in ${infer_img_dir_list[*]}; do
-            func_test_system "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${rec_model_list[i]}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}" "${rec_dict_dir}"
-        done
-    done
-fi

test_tipc/test_lite_arm_cpu_cpp.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+current_path=$PWD
+
+IFS=$'\n'
+
+TIPC_CONFIG=$1
+tipc_dataline=$(cat $TIPC_CONFIG)
+tipc_lines=(${tipc_dataline})
+
+work_path="./deploy/lite"
+cd ${work_path}
+
+BASIC_CONFIG="config.txt"
+basic_dataline=$(cat $BASIC_CONFIG)
+basic_lines=(${basic_dataline})
+
+# parser basic config
+label_path=$(func_parser_value_lite "${basic_lines[1]}" " ")
+resize_short_size=$(func_parser_value_lite "${basic_lines[2]}" " ")
+crop_size=$(func_parser_value_lite "${basic_lines[3]}" " ")
+visualize=$(func_parser_value_lite "${basic_lines[4]}" " ")
+enable_benchmark=$(func_parser_value_lite "${basic_lines[9]}" " ")
+tipc_benchmark=$(func_parser_value_lite "${basic_lines[10]}" " ")
+
+# parser tipc config
+runtime_device=$(func_parser_value_lite "${tipc_lines[0]}" ":")
+lite_arm_work_path=$(func_parser_value_lite "${tipc_lines[1]}" ":")
+lite_arm_so_path=$(func_parser_value_lite "${tipc_lines[2]}" ":")
+clas_model_name=$(func_parser_value_lite "${tipc_lines[3]}" ":")
+inference_cmd=$(func_parser_value_lite "${tipc_lines[4]}" ":")
+num_threads_list=$(func_parser_value_lite "${tipc_lines[5]}" ":")
+batch_size_list=$(func_parser_value_lite "${tipc_lines[6]}" ":")
+precision_list=$(func_parser_value_lite "${tipc_lines[7]}" ":")
+
+LOG_PATH=${current_path}"/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results.log"
+
+#run Lite TIPC
+function func_test_tipc(){
+    IFS="|"
+    _basic_config=$1
+    _model_name=$2
+    _log_path=$3
+    for num_threads in ${num_threads_list[*]}; do
+        if [ $(uname) = "Darwin" ]; then
+            sed -i " " "s/num_threads.*/num_threads ${num_threads}/" ${_basic_config}
+        elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+            sed -i "s/num_threads.*/num_threads ${num_threads}/" ${_basic_config}
+        fi
+        for batch_size in ${batch_size_list[*]}; do
+            if [ $(uname) = "Darwin" ]; then
+                sed -i " " "s/batch_size.*/batch_size ${batch_size}/" ${_basic_config}
+            elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+                sed -i "s/batch_size.*/batch_size ${batch_size}/" ${_basic_config}
+            fi
+            for precision in ${precision_list[*]}; do
+                if [ $(uname) = "Darwin" ]; then
+                    sed -i " " "s/precision.*/precision ${precision}/" ${_basic_config}
+                elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+                    sed -i "s/precision.*/precision ${precision}/" ${_basic_config}
+                fi
+                _save_log_path="${_log_path}/lite_${_model_name}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batch_size}_threads_${num_threads}.log"
+                real_inference_cmd=$(echo ${inference_cmd} | awk -F " " '{print path $1" "path $2" "path $3}' path="$lite_arm_work_path")
+                command1="adb push ${_basic_config} ${lite_arm_work_path}"
+                eval ${command1}
+                command2="adb shell 'export LD_LIBRARY_PATH=${lite_arm_work_path}; ${real_inference_cmd}'  > ${_save_log_path} 2>&1"
+                eval ${command2}
+                status_check $? "${command2}" "${status_log}"
+            done
+        done
+    done
+}
+
+
+echo "################### run test tipc ###################"
+label_map=$(echo ${label_path} | awk -F "/" '{print $NF}')
+
+if [ $(uname) = "Darwin" ]; then
+    # for Mac
+    sed -i " " "s/runtime_device.*/runtime_device arm_cpu/" ${BASIC_CONFIG}
+    escape_lite_arm_work_path=$(echo ${lite_arm_work_path//\//\\\/})
+    sed -i " " "s/clas_model_file.*/clas_model_file ${escape_lite_arm_work_path}${clas_model_name}.nb/" ${BASIC_CONFIG}
+    sed -i " " "s/label_path.*/label_path ${escape_lite_arm_work_path}${label_map}/" ${BASIC_CONFIG}
+    sed -i " " "s/tipc_benchmark.*/tipc_benchmark 1/" ${BASIC_CONFIG}
+elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+    # for Linux
+    sed -i "s/runtime_device.*/runtime_device arm_cpu/" ${BASIC_CONFIG}
+    escape_lite_arm_work_path=$(echo ${lite_arm_work_path//\//\\\/})
+    sed -i "s/clas_model_file.*/clas_model_file ${escape_lite_arm_work_path}${clas_model_name}/" ${BASIC_CONFIG}
+    sed -i "s/label_path.*/label_path ${escape_lite_arm_work_path}${label_path}/" ${BASIC_CONFIG}
+    sed -i "s/tipc_benchmark.*/tipc_benchmark 1/" ${BASIC_CONFIG}
+fi
+func_test_tipc ${BASIC_CONFIG} ${clas_model_name} ${LOG_PATH}