From d2152f49d495f50182b83d3ca93bf06e149c581d Mon Sep 17 00:00:00 2001 From: cuicheng01 <45199522+cuicheng01@users.noreply.github.com> Date: Fri, 19 Nov 2021 17:13:34 +0800 Subject: [PATCH] =?UTF-8?q?=E6=96=B0=E5=A2=9Etipc=20lite=5Farm=5Fcpp?= =?UTF-8?q?=E6=96=87=E6=A1=A3=20(#5378)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 新增lite_arm_cpp测试文档 * add Lite_arm_cpp测测试开发规范.md * update Lite_arm docs --- ...00\345\217\221\350\247\204\350\214\203.md" | 311 +++++++++++++++++ ...00\345\217\221\350\247\204\350\214\203.md" | 315 ++++++++++++++++++ .../images/lite_arm_cpu_params.png" | Bin 0 -> 193843 bytes .../images/lite_arm_gpu_opencl_params.png" | Bin 0 -> 208053 bytes .../images/lite_auto_log.png" | Bin 0 -> 214784 bytes .../images/tipc_lite_infer.png" | Bin 0 -> 983343 bytes ...07\346\241\243\350\247\204\350\214\203.md" | 109 ++++++ .../images/lite_auto_log.png" | Bin 0 -> 214784 bytes .../images/lite_log.png" | Bin 0 -> 172714 bytes 9 files 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"docs/tipc_test/\346\265\213\350\257\225\346\226\207\346\241\243/Lite_arm_cpp\351\242\204\346\265\213\346\265\213\350\257\225\346\226\207\346\241\243\350\247\204\350\214\203.md" create mode 100644 "docs/tipc_test/\346\265\213\350\257\225\346\226\207\346\241\243/images/lite_auto_log.png" create mode 100644 "docs/tipc_test/\346\265\213\350\257\225\346\226\207\346\241\243/images/lite_log.png" diff --git "a/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/Lite_arm_cpu_cpp\351\242\204\346\265\213\346\265\213\350\257\225\345\274\200\345\217\221\350\247\204\350\214\203.md" "b/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/Lite_arm_cpu_cpp\351\242\204\346\265\213\346\265\213\350\257\225\345\274\200\345\217\221\350\247\204\350\214\203.md" new file mode 100644 index 00000000..ddc70124 --- /dev/null +++ "b/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/Lite_arm_cpu_cpp\351\242\204\346\265\213\346\265\213\350\257\225\345\274\200\345\217\221\350\247\204\350\214\203.md" @@ -0,0 +1,311 @@ +# 目录 + +- [1.概述](#概述) +- [2.总览](#总览) + - [2.1 训推一体自动化测试](#训推一体自动化测试) + - [2.2 文本检测样板间概览](#文本检测样板间概览) +- [3.Lite端ARM_CPU预测接入TIPC流程](#Lite端ARM_CPU预测接入TIPC流程) + - [3.1 准备数据和环境](#准备数据和环境) + - [3.2 规范化输出预测日志](#规范化输出预测日志) + - [3.2.1 日志规范](#日志规范) + - [3.2.2 接入步骤](#接入步骤) + - [3.3 编写自动化测试代码](#编写自动化测试代码) +- [4.附录](#附录) + - [4.1 自动化测试脚本test_arm_cpp.sh函数介绍](#自动化测试脚本test_arm_cpp.sh函数介绍) + - [4.2 其他说明](#其他说明) + + +# 1、概述 + +训推一体认证(TIPC)旨在监控框架代码更新可能导致的模型训练、预测报错、性能下降等问题。本文主要介绍TIPC中基于ARM_CPU设备的Lite预测cpp测试的接入规范和监测点,是在基础测试上针对Lite测试的补充说明。 + +主要监控的内容有: + +- 飞桨框架更新后,代码仓库模型基于ARM_CPU的Lite预测cpp测试是否能正常走通;(比如API的不兼容升级) + +- 飞桨框架更新后,代码仓库模型基于ARM_CPU的的Lite预测cpp测试速度是否合理; + +为了能监控上述问题,希望把代码仓库模型的Lite预测测试加到飞桨框架的CI和CE中,提升PR合入的质量。因此,需要在代码仓库中加入运行脚本(不影响套件正常运行),完成模型的自动化测试。 + +可以建立的CI/CE机制包括: + +**全量数据走通开源模型Lite预测,并验证模型预测速度和精度是否符合设定预期;(单模型30分钟内)** + + a. 保证预测结果正确,预测速度符合预期(QA添加中) + +**注:** 由于CI有时间限制,所以在测试的时候需要限制运行时间,所以需要构建一个很小的数据集完成测试。 + + +# 2、总览 + + +## 2.1 训推一体自动化测试 + +本规范最终的测试的链条如下,可以根据模型开发规范和代码仓库需要,适当删减链条。 +![](images/tipc_lite_infer.png) +上图所示为Lite端的链条,共288条。其中,本文档主要介绍其中基于ARM_CPU的cpp测试链条。 + + +## 2.2 文本检测样板间概览 + +在PaddleOCR中,以文本检测为例,提供了本规范的样板间,可以完成概述部分提到的1种CI/CE机制。 + +Lite预测测试工具位于PaddleOCR dygraph分支下的test_tipc目录,与Lite预测ARM_CPU样板间相关的主要文件如下: + +``` +test_tipc/ +├── common_func.sh +├── configs # 配置文件目录 +│   ├── ppocr_det_mobile +│   │   ├── model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt +│   │   ├── ... +│ ├── ... +│   ├── ppocr_system_mobile +│   │   ├── model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt +│   │   └── ... +├── prepare_lite_cpp.sh # 完成test_cpu_cpp.sh运行所需要的数据和模型下载 +├── test_lite_arm_cpp.sh # lite测试主程序 +``` + +不同代码仓库的`configs`目录下的内容可根据实际情况进行调整, 配置文件`model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`需满足[TIPC配置文件命名规范](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/test_tipc#%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E5%91%BD%E5%90%8D%E8%A7%84%E8%8C%83)。 + + +# 3、Lite端ARM_CPU预测接入TIPC流程 + +Lite端ARM_CPU预测接入TIPC包含如下三个步骤,接下来将依次介绍这三个部分。 + +- 准备数据和环境 +- 规范化输出预测日志 +- 编写自动化测试代码 + + +## 3.1 准备数据和环境 + +同标准TIPC测试流程一样,在`prepare_lite_arm_cpp.sh`中准备好所需数据和环境,包括: + +- 少量预测数据 +- inference模型 +- 运行Lite所需要的可执行文件 + +以PaddleOCR文本检测模型为例,使用方式: + +``` +bash test_tipc/prepare_lite_arm_cpp.sh test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt +``` +`prepare_lite_arm_cpp.sh`具体内容: + +1.解析`model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`部分用于预测的参数字段,方便后续预测。 + +``` +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]}") +``` + +2.转换`infernce model`到Lite预测的`.nb`模型 + +``` +# prepare lite .nb model +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 + +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 +``` + +3.准备测试数据 + +``` +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 ${data_file} && cd ../ +``` + +4.准备Lite预测环境,此处需要下载或者编译Paddle-Lite预测库。 + +``` +# prepare lite env +paddlelite_zipfile=$(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 +``` + +5.交叉编译获得在手机上的可以运行的可执行文件 + +``` +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} +``` + +运行结果会在当前目录上生成test_lite.tar,里边的内容大致如下: +``` +├── common_func.sh # 通用函数,如解析参数等 +├── config.txt # 文本检测、识别的配置文件 +├── libc++_shared.so +├── libpaddle_light_api_shared.so +├── models # 模型 +│ ├── ch_ppocr_mobile_v2.0_det_opt.nb +│ └── ch_ppocr_mobile_v2.0_det_slim_opt.nb +├── ocr_db_crnn //可执行文件 +├── model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt # 参数配置 +├── ppocr_keys_v1.txt # 文本识别对应的字典文件 +├── test_data # 测试数据 +│ └── icdar2015_lite +│ └── text_localization +│ ├── ch4_test_images +│ │ ├── img_233.jpg +│ │ ├── img_603.jpg +│ │ ├── img_612.jpg +│ │ └── img_61.jpg +│ ├── icdar_c4_train_imgs +│ │ ├── img_233.jpg +│ │ ├── img_603.jpg +│ │ ├── img_612.jpg +│ │ └── img_61.jpg +│ ├── test_icdar2015_label.txt +│ └── train_icdar2015_label.txt +└── test_lite_arm_cpp.sh # 测试脚本 +``` + +## 3.2 规范化输出预测日志 + + +### 3.2.1 日志规范 + +类似于python、C++预测等基础测试链条,Lite预测链条也需要规范不同套件中预测输出的格式,方便QA统一自动化测试。针对Lite的预测log规范输出工具也已集成到AutoLog工具包。 + +Lite测试要求规范输出预测结果及以下信息: + +- 运行的硬件:如ARM_CPU、ARM_GPU_OPENCL +- 运行的模型名称: 如ch_PP-OCRv2_det_infer +- 进程数量:如1或者4 +- batch_size: 如1或者4 +- 性能信息,基于Lite预测的各阶段平均预测时间(包括前处理时间、inference时间、后处理时间) +- 模型类型:FP32或者INT8 + + +### 3.2.2 接入步骤 + +代码修改主要有以下两步:预测耗时打点和打印输出信息。下面分别介绍: + +1.添加预测耗时打点 +(1)在模型预测中,统计前处理,预测,后处理时间,可参考[代码](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/lite/ocr_db_crnn.cc#L567)。 +(2)使用autolog工具打印日志,参考[代码](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/lite/ocr_db_crnn.cc#L572)。主要包括: + +- 在样板间克隆AutoLog代码库:git clone https://github.com/cuicheng01/AutoLog.git +- 引入头文件: +``` +#include "AutoLog/auto_log/autolog.h" +``` +- 调用AutoLogger类打印日志: +``` +if (strcmp(argv[9], "True") == 0) { + AutoLogger autolog(det_model_file, + runtime_device, + std::stoi(num_threads), + std::stoi(batchsize), + "dynamic", + precision, + time_info, + cv_all_img_names.size()); + autolog.report(); +} +``` + +2.输出日志格式如下: +![](images/lite_auto_log.png) + + +## 3.3 编写自动化测试代码 + +自动化测试脚本包括三个部分,分别是运行脚本`test_lite_arm_cpp.sh`,参数文件`params.txt`,在OCR文本检测的Lite测试ARM_CPU样板间中,该参数文件为`model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`,数据模型准备脚本`prepare_lite_cpp.sh`。 +按如下方式在参数文件`model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`中添加Lite预测部分参数: +![](images/lite_arm_cpu_params.png) + +参数说明: + +| 参数 | 参数介绍 | +| :------------------------------------------ | :------------------------------------------------------- | +| inference: ./ocr_db_crnn det | Lite预测命令 | +| det_infer_model:ch_PP-OCRv2_det_infer\|ch_PP-OCRv2_det_slim_quant_infer | 模型名称 | +| runtime_device:ARM_CPU | 运行设备名称 | +| –cpu_threads:1|4 | 设置ARM线程数,如果要测试ARM上不同线程下的预测速度和精度,可以设置多个值,不同值用|隔开 | +| –det_batch_size: 1 | 设置batch_size 的参数,暂时只支持1 | +| –image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/ | 设置预测的数据路径 | +| –config_dir:./config.txt | 设置预测的数据路径 | +| –benchmark:True | 设置是否开启AutoLog的参数 | + + +# 4、附录 + + +## 4.1 自动化测试脚本test_lite_arm_cpp.sh函数介绍 + +Lite预测核心函数: + +func_lite_rec() :执行文本识别模型的Lite预测函数,根据不同的输入配置完成相应配置的预测 + +func_lite_det() :执行文本检测模型的Lite预测函数,根据不同的输入配置完成相应配置的预测 + +func_lite_system() :执行端到端文本识别的Lite预测函数,根据不同的输入配置完成相应配置的预测 + +func_parser_value() :解析`params.txt`中`:`后的部分 + +status_check() :状态检查函数,获取上条指令运行的状态,如果是0,则运行成功,如果是其他则运行失败,失败和成功的指令都会存放在`results.log`文件中 + + +## 4.2 注意事项 + +所有的Lite环境和模型数据等都是在docker中生成,后将相关的可执行文件、测试图片、模型等上传手机,通过`test_arm_cpp.sh`来完成多种链条的测试。 + + diff --git "a/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/Lite_arm_gpu_opencl_cpp\351\242\204\346\265\213\346\265\213\350\257\225\345\274\200\345\217\221\350\247\204\350\214\203.md" "b/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/Lite_arm_gpu_opencl_cpp\351\242\204\346\265\213\346\265\213\350\257\225\345\274\200\345\217\221\350\247\204\350\214\203.md" new file mode 100644 index 00000000..031fb8b9 --- /dev/null +++ "b/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/Lite_arm_gpu_opencl_cpp\351\242\204\346\265\213\346\265\213\350\257\225\345\274\200\345\217\221\350\247\204\350\214\203.md" @@ -0,0 +1,315 @@ +# 目录 + +- [1.概述](#概述) +- [2.总览](#总览) + - [2.1 训推一体自动化测试](#训推一体自动化测试) + - [2.2 文本检测样板间概览](#文本检测样板间概览) +- [3.Lite端ARM_GPU_OPENCL预测接入TIPC流程](#Lite端ARM_GPU_OPENCL预测接入TIPC流程) + - [3.1 准备数据和环境](#准备数据和环境) + - [3.2 规范化输出预测日志](#规范化输出预测日志) + - [3.2.1 日志规范](#日志规范) + - [3.2.2 接入步骤](#接入步骤) + - [3.3 编写自动化测试代码](#编写自动化测试代码) +- [4.附录](#附录) + - [4.1 自动化测试脚本test_arm_cpp.sh函数介绍](#自动化测试脚本test_arm_cpp.sh函数介绍) + - [4.2 其他说明](#其他说明) + + +# 1、概述 + +训推一体认证(TIPC)旨在监控框架代码更新可能导致的模型训练、预测报错、性能下降等问题。本文主要介绍TIPC中基于ARM_GPU_OPENCL设备的Lite预测cpp测试的接入规范和监测点,是在基础测试上针对Lite测试的补充说明。 + +主要监控的内容有: + +- 飞桨框架更新后,代码仓库模型基于ARM_GPU_OPENCL的Lite预测cpp测试是否能正常走通;(比如API的不兼容升级) + +- 飞桨框架更新后,代码仓库模型基于ARM_GPU_OPENCL的的Lite预测cpp测试速度是否合理; + +为了能监控上述问题,希望把代码仓库模型的Lite预测测试加到飞桨框架的CI和CE中,提升PR合入的质量。因此,需要在代码仓库中加入运行脚本(不影响套件正常运行),完成模型的自动化测试。 + +可以建立的CI/CE机制包括: + +**全量数据走通开源模型Lite预测,并验证模型预测速度和精度是否符合设定预期;(单模型30分钟内)** + + a. 保证预测结果正确,预测速度符合预期(QA添加中) + +**注:** 由于CI有时间限制,所以在测试的时候需要限制运行时间,所以需要构建一个很小的数据集完成测试。 + + +# 2、总览 + + +## 2.1 训推一体自动化测试 + +本规范最终的测试的链条如下,可以根据模型开发规范和代码仓库需要,适当删减链条。 +![](images/tipc_lite_infer.png) +上图所示为Lite端的链条,共288条。其中,本文档主要介绍其中基于ARM_GPU_OPENCL的cpp测试链条。 + + +## 2.2 文本检测样板间概览 + +在PaddleOCR中,以文本检测为例,提供了本规范的样板间,可以完成概述部分提到的1种CI/CE机制。 + +Lite预测测试工具位于PaddleOCR dygraph分支下的test_tipc目录,与Lite预测ARM_GPU_OPENCL样板间相关的主要文件如下: + +``` +test_tipc/ +├── common_func.sh +├── configs # 配置文件目录 +│   ├── ppocr_det_mobile +│   │   ├── model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt +│   │   ├── ... +│ ├── ... +│   ├── ppocr_system_mobile +│   │   ├── model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt +│   │   └── ... +├── prepare_lite_cpp.sh # 完成test_arm_cpp.sh运行所需要的数据和模型下载 +├── test_lite_arm_cpp.sh # lite测试主程序 +``` + +不同代码仓库的`configs`目录下的内容可根据实际情况进行调整, 配置文件`model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt`需满足[TIPC配置文件命名规范](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/test_tipc#%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E5%91%BD%E5%90%8D%E8%A7%84%E8%8C%83)。 + + +# 3、Lite端ARM_GPU_OPENCL预测接入TIPC流程 + +Lite端ARM_GPU_OPENCL预测接入TIPC包含如下三个步骤,接下来将依次介绍这三个部分。 + +- 准备数据和环境 +- 规范化输出预测日志 +- 编写自动化测试代码 + + +## 3.1 准备数据和环境 + +同标准TIPC测试流程一样,在`prepare_lite_arm_cpp.sh`中准备好所需数据和环境,包括: + +- 少量预测数据 +- inference模型 +- 运行Lite所需要的可执行文件 + +以PaddleOCR文本检测模型为例,使用方式: + +``` +bash test_tipc/prepare_lite_arm_cpp.sh test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt +``` +`prepare_lite_arm_cpp.sh`具体内容: + +1.解析`model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt`部分用于预测的参数字段,方便后续预测。 + +``` +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]}") +``` + +2.转换`infernce model`到Lite预测的`.nb`模型 + +``` +# prepare lite .nb model +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 + +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 +``` + +3.准备测试数据 + +``` +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 ${data_file} && cd ../ +``` + +4.准备Lite预测环境,此处需要下载或者编译Paddle-Lite预测库。 + +``` +# prepare lite env +paddlelite_zipfile=$(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 + +``` +5.交叉编译获得在手机上的可以运行的可执行文件 + +``` + +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} +``` + +运行结果会在当前目录上生成test_lite.tar,里边的内容大致如下: + +``` +├── common_func.sh # 通用函数,如解析参数等 +├── config.txt # 文本检测、识别的配置文件 +├── libc++_shared.so +├── libpaddle_light_api_shared.so +├── models # 模型 +│ ├── ch_ppocr_mobile_v2.0_det_opt.nb +│ └── ch_ppocr_mobile_v2.0_det_slim_opt.nb +├── ocr_db_crnn //可执行文件 +├── model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt # 参数配置 +├── ppocr_keys_v1.txt # 文本识别对应的字典文件 +├── test_data # 测试数据 +│ └── icdar2015_lite +│ └── text_localization +│ ├── ch4_test_images +│ │ ├── img_233.jpg +│ │ ├── img_603.jpg +│ │ ├── img_612.jpg +│ │ └── img_61.jpg +│ ├── icdar_c4_train_imgs +│ │ ├── img_233.jpg +│ │ ├── img_603.jpg +│ │ ├── img_612.jpg +│ │ └── img_61.jpg +│ ├── test_icdar2015_label.txt +│ └── train_icdar2015_label.txt +└── test_lite_arm_cpp.sh # 测试脚本 +``` + +## 3.2 规范化输出预测日志 + + +### 3.2.1 日志规范 + +类似于python、C++预测等基础测试链条,Lite预测链条也需要规范不同套件中预测输出的格式,方便QA统一自动化测试。针对Lite的预测log规范输出工具也已集成到AutoLog工具包。 + +Lite测试要求规范输出预测结果及以下信息: + +- 运行的硬件:如ARM_CPU、ARM_GPU_OPENCL +- 运行的模型名称: 如ch_PP-OCRv2_det_infer +- 进程数量:如1或者4 +- batch_size: 如1或者4 +- 性能信息,基于Lite预测的各阶段平均预测时间(包括前处理时间、inference时间、后处理时间) +- 模型类型:FP32或者INT8 + + +### 3.2.2 接入步骤 + +代码修改主要有以下两步:预测耗时打点和打印输出信息。下面分别介绍: + +1.添加预测耗时打点 +(1)在模型预测中,统计前处理,预测,后处理时间,可参考[代码](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/lite/ocr_db_crnn.cc#L567)。 +(2)使用autolog工具打印日志,参考[代码](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/lite/ocr_db_crnn.cc#L572)。主要包括: + +- 在样板间克隆AutoLog代码库:git clone https://github.com/cuicheng01/AutoLog.git +- 引入头文件: +``` +#include "AutoLog/auto_log/autolog.h" +``` +- 调用AutoLogger类打印日志: +``` +if (strcmp(argv[9], "True") == 0) { + AutoLogger autolog(det_model_file, + runtime_device, + std::stoi(num_threads), + std::stoi(batchsize), + "dynamic", + precision, + time_info, + cv_all_img_names.size()); + autolog.report(); +} +``` + +2.输出日志格式如下: +![](images/lite_auto_log.png) + + +## 3.3 编写自动化测试代码 + +自动化测试脚本包括三个部分,分别是运行脚本`test_lite_arm_cpp.sh`,参数文件`params.txt`,在OCR文本检测的Lite测试ARM_GPU_OPENCL样板间中,该参数文件为`model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt`,数据模型准备脚本`prepare_lite_cpp.sh`。 + +按如下方式在参数文件`model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt`中添加Lite预测部分参数: +![](images/lite_arm_gpu_opencl_params.png) + +参数说明: + +| 参数 | 参数介绍 | +| :------------------------------------------ | :------------------------------------------------------- | +| inference: ./ocr_db_crnn det | Lite预测命令 | +| det_infer_model:ch_PP-OCRv2_det_infer\|ch_PP-OCRv2_det_slim_quant_infer | 模型名称 | +| runtime_device:ARM_GPU_OPENCL | 运行设备名称 | +| –cpu_threads:1|4 | 设置ARM线程数,如果要测试ARM上不同线程下的预测速度和精度,可以设置多个值,不同值用|隔开 | +| –det_batch_size: 1 | 设置batch_size 的参数,暂时只支持1 | +| –image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/ | 设置预测的数据路径 | +| –config_dir:./config.txt | 设置预测的数据路径 | +| –benchmark:True | 设置是否开启AutoLog的参数 | + + +# 4、附录 + + +## 4.1 自动化测试脚本test_lite_arm_cpp.sh函数介绍 + +Lite预测核心函数: + +func_lite_rec() :执行文本识别模型的Lite预测函数,根据不同的输入配置完成相应配置的预测 + +func_lite_det() :执行文本检测模型的Lite预测函数,根据不同的输入配置完成相应配置的预测 + +func_lite_system() :执行端到端文本识别的Lite预测函数,根据不同的输入配置完成相应配置的预测 + +func_parser_value() :解析`params.txt`中`:`后的部分 + +status_check() :状态检查函数,获取上条指令运行的状态,如果是0,则运行成功,如果是其他则运行失败,失败和成功的指令都会存放在`results.log`文件中 + + + +## 4.2 注意事项 + +所有的Lite环境和模型数据等都是在docker中生成,后将相关的可执行文件、测试图片、模型等上传手机,通过`test_lite_arm_cpp.sh`来完成多种链条的测试。 + + diff --git "a/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/images/lite_arm_cpu_params.png" "b/docs/tipc_test/\345\274\200\345\217\221\346\226\207\346\241\243/images/lite_arm_cpu_params.png" new file mode 100644 index 0000000000000000000000000000000000000000..0a12003d464caaf190965b0dcdad77ec4b9bd0ed GIT binary patch literal 193843 zcmeFYWl$VZ*Y67i3GNQT-Ce^3PjJ@|+$}f+88kQqch>}h1`9H{!yv%|K{B|z4l`%+ zJm<7h5Exx3L+97)H8dWQC@wU%t$synKt<%~XJ- zBj93Dq&ruXrLF^<%XRNIA%-uHOCbot3jR?(Q;r?KRcmAUw+=d$}M 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+本文档和[基础训练预测文档]()大体结构类似,主要去掉了训练相关的部分,文档目录结构如下: + +# 目录 +- [1.测试结论汇总](#测试结论汇总) +- [2.测试流程](#测试流程) + - [2.1 功能测试](#功能测试) + - [2.1.1 基于ARM_CPU测试](#基于ARM_CPU测试) + - [2.1.1 基于ARM_GPU_OPENCL测试](#基于ARM_GPU_OPENCL测试) + - [2.2 运行结果](#运行结果) +- [3.更多教程](#更多教程) + + +## 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:包括单predictor预测和多predictor预测 +- 预测库来源:包括下载方式和编译方式 +- 测试硬件:ARM\_CPU/ARM\_GPU_OPENCL + +| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 | 测试硬件 | +| :----: | :----: | :----: | :----: | :----: | :----: | +| 正常模型/量化模型 | 1 | 1/4 | 单/多 | 下载方式 | ARM\_CPU/ARM\_GPU_OPENCL | + + +## 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_cpp.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_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。 + + +#### 2.1.1 基于ARM\_CPU测试 + +```shell + +# 数据和模型准备 +bash test_tipc/prepare_lite_cpp.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_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt + +``` + + +#### 2.1.2 基于ARM\_GPU\_OPENCL测试 + +```shell + +# 数据和模型准备 +bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt + +# 手机端测试: +bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt + +``` + + +**注意**: + +1.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[安卓手机通过termux连接电脑](.https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/test_tipc/docs/termux_for_android.md)。 + +2.如果测试文本检测和识别完整的pipeline,在执行`prepare_lite_cpp.sh`时,配置文件需替换为`test_tipc/configs/ppocr_system_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt`。在手机端测试阶段,配置文件同样修改为该文件。 + + +### 2.2 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/docs/tipc_test/\346\265\213\350\257\225\346\226\207\346\241\243/images/lite_auto_log.png" 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