diff --git a/deploy/lite/ocr_db_crnn.cc b/deploy/lite/ocr_db_crnn.cc index 9a7d6548654bdd21110f0fe343efd92a13dcb4c0..011d4adbeb65732f12c263ddfec94afb84bf5969 100644 --- a/deploy/lite/ocr_db_crnn.cc +++ b/deploy/lite/ocr_db_crnn.cc @@ -307,21 +307,10 @@ RunDetModel(std::shared_ptr predictor, cv::Mat img, return filter_boxes; } -std::shared_ptr loadModel(std::string model_file, std::string power_mode, int num_threads) { +std::shared_ptr 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 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 num_threads = argv[6]; - std::string batchsize = argv[7]; - std::string power_mode = argv[8]; + std::string runtime_device = argv[5]; + std::string precision = argv[6]; + std::string num_threads = argv[7]; + 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 rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads)); - auto cls_predictor = loadModel(cls_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, 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 num_threads = argv[4]; - std::string batchsize = argv[5]; - std::string power_mode = argv[6]; + std::string runtime_device = argv[3]; + std::string precision = argv[4]; + std::string num_threads = argv[5]; + 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 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, - 0, - 0, + runtime_device, 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 num_threads = argv[4]; - std::string batchsize = argv[5]; - std::string power_mode = argv[6]; + std::string runtime_device = argv[3]; + std::string precision = argv[4]; + std::string num_threads = argv[5]; + 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 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, - 0, - 0, + runtime_device, std::stoi(num_threads), std::stoi(batchsize), "dynamic", precision, - power_mode, time_info, cv_all_img_names.size()); autolog.report(); diff --git a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..af71ce3b870e27f9dc046f00a53f266950f6f112 --- /dev/null +++ b/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 diff --git a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_java_metal_arm_gpu.txt b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_java_metal_arm_gpu.txt deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_java_opencl_arm_gpu.txt b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_java_opencl_arm_gpu.txt deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/test_tipc/docs/lite_auto_log.png b/test_tipc/docs/lite_auto_log.png index cd9256db40232d689ea67a1bbef2b768c5f98753..d5b6b450d445d0047fb3836bfd9c726adebb7b9f 100644 Binary files a/test_tipc/docs/lite_auto_log.png and b/test_tipc/docs/lite_auto_log.png differ diff --git a/test_tipc/docs/lite_log.png b/test_tipc/docs/lite_log.png index 24ae5abc7167049ac879428e5e105a6e67d3c36d..2b3e40b3fa2700a4c715269229cdfe582d29f90a 100644 Binary files a/test_tipc/docs/lite_log.png and b/test_tipc/docs/lite_log.png differ diff --git a/test_tipc/docs/test_lite.md b/test_tipc/docs/test_lite.md deleted file mode 100644 index 01ae0cb4b471f1219f88ffa9e2c11d50765233d3..0000000000000000000000000000000000000000 --- a/test_tipc/docs/test_lite.md +++ /dev/null @@ -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： - - - -在每一个log中，都会调用autolog打印如下信息： - - - - - -## 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)。 diff --git a/test_tipc/docs/test_lite_arm_cpu_cpp.md b/test_tipc/docs/test_lite_arm_cpu_cpp.md new file mode 100644 index 0000000000000000000000000000000000000000..6f58026a315dabb8810e56b6d694733c1c72019c --- /dev/null +++ b/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： + + + +在每一个log中，都会调用autolog打印如下信息： + + + + + +## 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)。 diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 9b63bf5b20cd08b4ab08c17d7fd84f53feb93967..61ac2f3450c39617832913f2ad1b0bd2ec815e35 100644 --- a/test_tipc/prepare.sh +++ b/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]}") diff --git a/test_tipc/prepare_lite.sh b/test_tipc/prepare_lite.sh new file mode 100644 index 0000000000000000000000000000000000000000..6a08d96298592c829547df9fa30ef4149ddc5b00 --- /dev/null +++ b/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 ${model_path} diff --git a/test_tipc/readme.md b/test_tipc/readme.md index 1d8df7da6cf6d1319cedd329e4202fa674e8538b..2cc5ab407093d527e180aaf9470759d3be279d5c 100644 --- a/test_tipc/readme.md +++ b/test_tipc/readme.md @@ -80,7 +80,7 @@ test_tipc/ ├── test_train_inference_python.sh # 测试python训练预测的主程序 ├── test_inference_cpp.sh # 测试c++预测的主程序 ├── test_serving.sh # 测试serving部署预测的主程序 -├── test_lite.sh # 测试lite部署预测的主程序 +├── test_lite_arm_cpu_cpp.sh # 测试lite在arm_cpu上部署的C++预测的主程序 ├── compare_results.py # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内 └── readme.md # 使用文档 ``` @@ -107,4 +107,4 @@ test_tipc/ [test_train_inference_python 使用](docs/test_train_inference_python.md) [test_inference_cpp 使用](docs/test_inference_cpp.md) [test_serving 使用](docs/test_serving.md) -[test_lite 使用](docs/test_lite.md) +[test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md) diff --git a/test_tipc/test_lite.sh b/test_tipc/test_lite.sh deleted file mode 100644 index 1fd9d3c7186207922c436e7981622c707a56596f..0000000000000000000000000000000000000000 --- a/test_tipc/test_lite.sh +++ /dev/null @@ -1,69 +0,0 @@ -#!/bin/bash -source ./common_func.sh -export LD_LIBRARY_PATH=${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 diff --git a/test_tipc/test_lite_arm_cpu_cpp.sh b/test_tipc/test_lite_arm_cpu_cpp.sh new file mode 100644 index 0000000000000000000000000000000000000000..04eebbd28a334f7ac7819f8ff55d7b3192f4b490 --- /dev/null +++ b/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