Merge pull request #4615 from cuicheng01/dygraph

update tipc lite demo

deploy/lite/ocr_db_crnn.cc

@@ -307,21 +307,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
   return filter_boxes;
 }
 
-std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, std::string power_mode, int num_threads) {
+std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, int num_threads) {
   MobileConfig config;
   config.set_model_from_file(model_file);
 
-  if (power_mode == "LITE_POWER_HIGH"){
-      config.set_power_mode(LITE_POWER_HIGH);
-  } else {
-      if (power_mode == "LITE_POWER_LOW") {
-          config.set_power_mode(LITE_POWER_HIGH);
-      } else {
-          std::cerr << "Only support LITE_POWER_HIGH or LITE_POWER_HIGH." << std::endl;
-          exit(1);
-      }
-  }
-
   config.set_threads(num_threads);
 
   std::shared_ptr<PaddlePredictor> predictor =
@@ -391,7 +380,7 @@ void check_params(int argc, char **argv) {
   if (strcmp(argv[1], "det") == 0) {
       if (argc < 9){
         std::cerr << "[ERROR] usage:" << argv[0]
-                  << " det det_model num_threads batchsize power_mode img_dir det_config lite_benchmark_value" << std::endl;
+                  << " det det_model runtime_device num_threads batchsize img_dir det_config lite_benchmark_value" << std::endl;
         exit(1);
       }
   }
@@ -399,7 +388,7 @@ void check_params(int argc, char **argv) {
   if (strcmp(argv[1], "rec") == 0) {
       if (argc < 9){
         std::cerr << "[ERROR] usage:" << argv[0]
-                  << " rec rec_model num_threads batchsize power_mode img_dir key_txt lite_benchmark_value" << std::endl;
+                  << " rec rec_model runtime_device num_threads batchsize img_dir key_txt lite_benchmark_value" << std::endl;
         exit(1);
       }
   }
@@ -407,7 +396,7 @@ void check_params(int argc, char **argv) {
   if (strcmp(argv[1], "system") == 0) {
       if (argc < 12){
         std::cerr << "[ERROR] usage:" << argv[0]
-                  << " system det_model rec_model clas_model num_threads batchsize power_mode img_dir det_config key_txt lite_benchmark_value" << std::endl;
+                  << " system det_model rec_model clas_model runtime_device num_threads batchsize img_dir det_config key_txt lite_benchmark_value" << std::endl;
         exit(1);
       }
   }
@@ -417,15 +406,15 @@ void system(char **argv){
   std::string det_model_file = argv[2];
   std::string rec_model_file = argv[3];
   std::string cls_model_file = argv[4];
-  std::string precision = argv[5];
+  std::string runtime_device = argv[5];
-  std::string num_threads = argv[6];
+  std::string precision = argv[6];
-  std::string batchsize = argv[7];
+  std::string num_threads = argv[7];
-  std::string power_mode = argv[8];
+  std::string batchsize = argv[8];
   std::string img_dir = argv[9];
   std::string det_config_path = argv[10];
   std::string dict_path = argv[11];
 
-  if (strcmp(argv[5], "FP32") != 0 && strcmp(argv[5], "INT8") != 0) {
+  if (strcmp(argv[6], "FP32") != 0 && strcmp(argv[6], "INT8") != 0) {
       std::cerr << "Only support FP32 or INT8." << std::endl;
       exit(1);
   }
@@ -441,9 +430,9 @@ void system(char **argv){
   charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
   charactor_dict.push_back(" ");
 
-  auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads));
+  auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
-  auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads));
+  auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
-  auto cls_predictor = loadModel(cls_model_file, power_mode, std::stoi(num_threads));
+  auto cls_predictor = loadModel(cls_model_file, std::stoi(num_threads));
 
   for (int i = 0; i < cv_all_img_names.size(); ++i) {
     std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
@@ -477,14 +466,14 @@ void system(char **argv){
 
 void det(int argc, char **argv) {
   std::string det_model_file = argv[2];
-  std::string precision = argv[3];
+  std::string runtime_device = argv[3];
-  std::string num_threads = argv[4];
+  std::string precision = argv[4];
-  std::string batchsize = argv[5];
+  std::string num_threads = argv[5];
-  std::string power_mode = argv[6];
+  std::string batchsize = argv[6];
   std::string img_dir = argv[7];
   std::string det_config_path = argv[8];
 
-  if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) {
+  if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) {
       std::cerr << "Only support FP32 or INT8." << std::endl;
       exit(1);
   }
@@ -495,7 +484,7 @@ void det(int argc, char **argv) {
   //// load config from txt file
   auto Config = LoadConfigTxt(det_config_path);
 
-  auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads));
+  auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
 
   std::vector<double> time_info = {0, 0, 0};
   for (int i = 0; i < cv_all_img_names.size(); ++i) {
@@ -530,14 +519,11 @@ void det(int argc, char **argv) {
 
   if (strcmp(argv[9], "True") == 0) {
     AutoLogger autolog(det_model_file, 
-                       0,
+                       runtime_device,
-                       0,
-                       0,
                        std::stoi(num_threads),
                        std::stoi(batchsize), 
                        "dynamic", 
                        precision, 
-                       power_mode,
                        time_info, 
                        cv_all_img_names.size());
     autolog.report();
@@ -546,14 +532,14 @@ void det(int argc, char **argv) {
 
 void rec(int argc, char **argv) {
   std::string rec_model_file = argv[2];
-  std::string precision = argv[3];
+  std::string runtime_device = argv[3];
-  std::string num_threads = argv[4];
+  std::string precision = argv[4];
-  std::string batchsize = argv[5];
+  std::string num_threads = argv[5];
-  std::string power_mode = argv[6];
+  std::string batchsize = argv[6];
   std::string img_dir = argv[7];
   std::string dict_path = argv[8];
 
-  if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) {
+  if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) {
       std::cerr << "Only support FP32 or INT8." << std::endl;
       exit(1);
   }
@@ -565,7 +551,7 @@ void rec(int argc, char **argv) {
   charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
   charactor_dict.push_back(" ");
 
-  auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads));
+  auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
 
   std::shared_ptr<PaddlePredictor> cls_predictor;
 
@@ -603,14 +589,11 @@ void rec(int argc, char **argv) {
   // TODO: support autolog
   if (strcmp(argv[9], "True") == 0) {
     AutoLogger autolog(rec_model_file, 
-                       0,
+                       runtime_device,
-                       0,
-                       0,
                        std::stoi(num_threads),
                        std::stoi(batchsize), 
                        "dynamic", 
                        precision, 
-                       power_mode,
                        time_info, 
                        cv_all_img_names.size());
     autolog.report();

test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt

@@ -0,0 +1,12 @@
+===========================lite_params===========================
+inference:./ocr_db_crnn det
+infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
+runtime_device:ARM_CPU
+--cpu_threads:1|4
+--det_batch_size:1
+--rec_batch_size:1
+--system_batch_size:1
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+--rec_dict_dir:./ppocr_keys_v1.txt
+--benchmark:True

test_tipc/docs/test_lite.md

@@ -1,72 +0,0 @@
-# Lite预测功能测试
-
-Lite预测功能测试的主程序为`test_lite.sh`，可以测试基于Lite预测库的模型推理功能。
-
-## 1. 测试结论汇总
-
-目前Lite端的样本间支持以方式的组合：
-
-**字段说明：**
-- 输入设置：包括C++预测、python预测、java预测
-- 模型类型：包括正常模型（FP32）和量化模型（FP16）
-- batch-size：包括1和4
-- predictor数量：包括多predictor预测和单predictor预测
-- 功耗模式：包括高性能模式（LITE_POWER_HIGH）和省电模式（LITE_POWER_LOW）
-- 预测库来源：包括下载方式和编译方式，其中编译方式分为以下目标硬件：(1)ARM CPU;(2)Linux XPU;(3)OpenCL GPU;(4)Metal GPU
-
-| 模型类型 | batch-size | predictor数量 | 功耗模式 | 预测库来源 | 支持语言 |
-|  :----:   |  :----:   |  :----:  |  :----:  |  :----:  |  :----:  |
-| 正常模型/量化模型 | 1 | 1 | 高性能模式/省电模式 | 下载方式 | C++预测 |
-
-
-## 2. 测试流程
-运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
-
-### 2.1 功能测试
-
-先运行`prepare.sh`准备数据和模型，模型和数据会打包到test_lite.tar中，将test_lite.tar上传到手机上，解压后进`入test_lite`目录中，然后运行`test_lite.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
-
-```shell
-
-# 数据和模型准备
-bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "lite_infer"
-
-# 手机端测试:
-bash test_lite.sh ppocr_det_mobile_params.txt
-
-```  
-
-**注意**：由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
-
-#### 运行结果
-
-各测试的运行情况会打印在 `./output/` 中：
-运行成功时会输出：
-
-```
-Run successfully with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1!
-Run successfully with command xxx
-...
-```
-
-运行失败时会输出：
-
-```
-Run failed with command - ./ocr_db_crnn det ./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb INT8 4 1 LITE_POWER_LOW ./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg ./config.txt True > ./output/lite_ch_ppocr_mobile_v2.0_det_slim_opt.nb_precision_INT8_batchsize_1_threads_4_powermode_LITE_POWER_LOW_singleimg_True.log 2>&1!
-Run failed with command xxx
-...
-```
-
-在./output/文件夹下，会存在如下日志，每一个日志都是不同配置下的log结果：
-
-<img src="lite_log.png" width="1000">
-
-在每一个log中，都会调用autolog打印如下信息：
-
-<img src="lite_auto_log.png" width="1000">
-
-
-
-## 3. 更多教程
-
-本文档为功能测试用，更详细的Lite端预测使用教程请参考：[Lite端部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)。

test_tipc/docs/test_lite_arm_cpu_cpp.md

@@ -0,0 +1,71 @@
+# Lite\_arm\_cpu\_cpp预测功能测试
+
+Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以在ARM CPU上基于Lite预测库测试模型的C++推理功能。
+
+## 1. 测试结论汇总
+
+目前Lite端的样本间支持以方式的组合：
+
+**字段说明：**
+- 模型类型：包括正常模型（FP32）和量化模型（INT8）
+- batch-size：包括1和4
+- threads：包括1和4
+- predictor数量：包括多predictor预测和单predictor预测
+- 预测库来源：包括下载方式和编译方式
+
+| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 |
+|  :----:   |  :----:  | :----:  |  :----:  |  :----:  |
+| 正常模型/量化模型 | 1 | 1/4 |  1 | 下载方式 |
+
+
+## 2. 测试流程
+运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+
+### 2.1 功能测试
+
+先运行`prepare_lite.sh`，运行后会在当前路径下生成`test_lite.tar`，其中包含了测试数据、测试模型和用于预测的可执行文件。将`test_lite.tar`上传到被测试的手机上，在手机的终端解压该文件，进入`test_lite`目录中，然后运行`test_lite_arm_cpu_cpp.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
+
+```shell
+
+# 数据和模型准备
+bash test_tipc/prepare_lite.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+
+# 手机端测试:
+bash test_lite_arm_cpu_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+
+```  
+
+**注意**：由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
+
+#### 运行结果
+
+各测试的运行情况会打印在 `./output/` 中：
+运行成功时会输出：
+
+```
+Run successfully with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1  ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
+Run successfully with command xxx
+...
+```
+
+运行失败时会输出：
+
+```
+Run failed with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1  ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
+Run failed with command xxx
+...
+```
+
+在./output/文件夹下，会存在如下日志，每一个日志都是不同配置下的log结果：
+
+<img src="lite_log.png" width="1000">
+
+在每一个log中，都会调用autolog打印如下信息：
+
+<img src="lite_auto_log.png" width="1000">
+
+
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的Lite端预测使用教程请参考：[Lite端部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)。

test_tipc/prepare.sh

@@ -3,7 +3,7 @@ FILENAME=$1
 
 # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
 #                 'whole_infer', 'klquant_whole_infer',
-#                 'cpp_infer', 'serving_infer',  'lite_infer']
+#                 'cpp_infer', 'serving_infer']
 
 MODE=$2
 
@@ -34,7 +34,7 @@ trainer_list=$(func_parser_value "${lines[14]}")
 
 # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
 #                 'whole_infer', 'klquant_whole_infer',
-#                 'cpp_infer', 'serving_infer',  'lite_infer']
+#                 'cpp_infer', 'serving_infer']
 MODE=$2
 
 if [ ${MODE} = "lite_train_lite_infer" ];then
@@ -169,40 +169,6 @@ if [ ${MODE} = "serving_infer" ];then
     cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar && cd ../
 fi
 
-
-if [ ${MODE} = "lite_infer" ];then    
-    # prepare lite nb model and test data
-    current_dir=${PWD}
-    wget -nc  -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb
-    wget -nc  -P ./models https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb
-    wget -nc  -P ./test_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-    cd ./test_data && tar -xf icdar2015_lite.tar && rm icdar2015_lite.tar && cd ../
-    # prepare lite env
-    export http_proxy=http://172.19.57.45:3128
-    export https_proxy=http://172.19.57.45:3128
-    paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
-    paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
-    paddlelite_file=${paddlelite_zipfile:0:66}
-    wget ${paddlelite_url}
-    tar -xf ${paddlelite_zipfile}
-    mkdir -p  ${paddlelite_file}/demo/cxx/ocr/test_lite
-    mv models 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 ./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 test_tipc/configs/ppocr_det_mobile_params.txt test_tipc/test_lite.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
-    cd ${paddlelite_file}/demo/cxx/ocr/
-    git clone https://github.com/LDOUBLEV/AutoLog.git
-    unset http_proxy
-    unset https_proxy
-    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}
-fi
-
-
 if [ ${MODE} = "paddle2onnx_infer" ];then
     # prepare serving env
     python_name=$(func_parser_value "${lines[2]}")

test_tipc/prepare_lite.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+source ./test_tipc/common_func.sh
+FILENAME=$1
+dataline=$(cat ${FILENAME})
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+IFS=$'\n'
+lite_model_list=$(func_parser_value "${lines[2]}")
+
+# prepare lite .nb model
+pip install paddlelite==2.9
+current_dir=${PWD}
+IFS="|"
+model_path=./inference_models
+for model in ${lite_model_list[*]}; do
+    inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
+    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=arm --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 ${data_file} && cd ../
+
+# prepare lite env
+paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
+paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
+paddlelite_file=${paddlelite_zipfile:0:66}
+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_cpu_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 -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/readme.md

@@ -80,7 +80,7 @@ test_tipc/
 ├── test_train_inference_python.sh    # 测试python训练预测的主程序
 ├── test_inference_cpp.sh             # 测试c++预测的主程序
 ├── test_serving.sh                   # 测试serving部署预测的主程序
-├── test_lite.sh                      # 测试lite部署预测的主程序
+├── test_lite_arm_cpu_cpp.sh          # 测试lite在arm_cpu上部署的C++预测的主程序
 ├── compare_results.py                # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内
 └── readme.md                         # 使用文档
 ```
@@ -107,4 +107,4 @@ test_tipc/
 [test_train_inference_python 使用](docs/test_train_inference_python.md)  
 [test_inference_cpp 使用](docs/test_inference_cpp.md)  
 [test_serving 使用](docs/test_serving.md)  
-[test_lite 使用](docs/test_lite.md)  
+[test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md)  

test_tipc/test_lite.sh

@@ -1,69 +0,0 @@
-#!/bin/bash
-source ./common_func.sh
-export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
-
-FILENAME=$1
-dataline=$(awk 'NR==102, NR==111{print}'  $FILENAME)
-echo $dataline
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-
-# parser lite inference
-lite_inference_cmd=$(func_parser_value "${lines[1]}")
-lite_model_dir_list=$(func_parser_value "${lines[2]}")
-lite_cpu_threads_list=$(func_parser_value "${lines[3]}")
-lite_batch_size_list=$(func_parser_value "${lines[4]}")
-lite_power_mode_list=$(func_parser_value "${lines[5]}")
-lite_infer_img_dir_list=$(func_parser_value "${lines[6]}")
-lite_config_dir=$(func_parser_value "${lines[7]}")
-lite_rec_dict_dir=$(func_parser_value "${lines[8]}")
-lite_benchmark_value=$(func_parser_value "${lines[9]}")
-
-LOG_PATH="./output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
-
-
-function func_lite(){
-    IFS='|'
-    _script=$1
-    _lite_model=$2
-    _log_path=$3
-    _img_dir=$4
-    _config=$5
-    if [[ $lite_model =~ "slim" ]]; then
-        precision="INT8"
-    else
-        precision="FP32"
-    fi
-    is_single_img=$(echo $_img_dir | grep -E ".jpg|.jpeg|.png|.JPEG|.JPG")
-    if [[ "$is_single_img" != "" ]]; then
-        single_img="True"
-    else
-        single_img="False"
-    fi
-
-    # lite inference
-    for num_threads in ${lite_cpu_threads_list[*]}; do
-        for power_mode in ${lite_power_mode_list[*]}; do
-            for batchsize in ${lite_batch_size_list[*]}; do
-                model_name=$(echo $lite_model | awk -F "/" '{print $NF}')
-                _save_log_path="${_log_path}/lite_${model_name}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}_powermode_${power_mode}_singleimg_${single_img}.log"
-                command="${_script} ${lite_model} ${precision} ${num_threads} ${batchsize} ${power_mode} ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
-                eval ${command}
-                status_check $? "${command}" "${status_log}"
-            done
-        done
-    done
-}
-
-
-echo "################### run test ###################"
-IFS="|"
-for lite_model in ${lite_model_dir_list[*]}; do
-    #run lite inference
-    for img_dir in ${lite_infer_img_dir_list[*]}; do
-        func_lite "${lite_inference_cmd}" "${lite_model}" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
-    done
-done

test_tipc/test_lite_arm_cpu_cpp.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+source ./common_func.sh
+export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
+
+FILENAME=$1
+dataline=$(cat $FILENAME)
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+# parser lite inference
+lite_inference_cmd=$(func_parser_value "${lines[1]}")
+lite_model_dir_list=$(func_parser_value "${lines[2]}")
+runtime_device=$(func_parser_value "${lines[3]}")
+lite_cpu_threads_list=$(func_parser_value "${lines[4]}")
+lite_batch_size_list=$(func_parser_value "${lines[5]}")
+lite_infer_img_dir_list=$(func_parser_value "${lines[8]}")
+lite_config_dir=$(func_parser_value "${lines[9]}")
+lite_rec_dict_dir=$(func_parser_value "${lines[10]}")
+lite_benchmark_value=$(func_parser_value "${lines[11]}")
+
+
+LOG_PATH="./output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results.log"
+
+
+function func_lite(){
+    IFS='|'
+    _script=$1
+    _lite_model=$2
+    _log_path=$3
+    _img_dir=$4
+    _config=$5
+    if [[ $lite_model =~ "slim" ]]; then
+        precision="INT8"
+    else
+        precision="FP32"
+    fi
+
+    # lite inference
+    for num_threads in ${lite_cpu_threads_list[*]}; do
+	for batchsize in ${lite_batch_size_list[*]}; do
+            _save_log_path="${_log_path}/lite_${_lite_model}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}.log"
+            command="${_script} ${_lite_model} ${runtime_device} ${precision} ${num_threads} ${batchsize}  ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
+            eval ${command}
+            status_check $? "${command}" "${status_log}"
+        done
+    done
+}
+
+
+echo "################### run test ###################"
+IFS="|"
+for lite_model in ${lite_model_dir_list[*]}; do
+    #run lite inference
+    for img_dir in ${lite_infer_img_dir_list[*]}; do
+        func_lite "${lite_inference_cmd}" "${lite_model}_opt.nb" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
+    done
+done