diff --git a/deploy/lite/ocr_db_crnn.cc b/deploy/lite/ocr_db_crnn.cc index 26891c8566a10d26a23beeee87ec7275088c6961..9a7d6548654bdd21110f0fe343efd92a13dcb4c0 100644 --- a/deploy/lite/ocr_db_crnn.cc +++ b/deploy/lite/ocr_db_crnn.cc @@ -12,12 +12,14 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "paddle_api.h" // NOLINT #include +#include "paddle_api.h" // NOLINT +#include "paddle_place.h" #include "cls_process.h" #include "crnn_process.h" #include "db_post_process.h" +#include "AutoLog/auto_log/lite_autolog.h" using namespace paddle::lite_api; // NOLINT using namespace std; @@ -27,7 +29,7 @@ void NeonMeanScale(const float *din, float *dout, int size, const std::vector mean, const std::vector scale) { if (mean.size() != 3 || scale.size() != 3) { - std::cerr << "[ERROR] mean or scale size must equal to 3\n"; + std::cerr << "[ERROR] mean or scale size must equal to 3" << std::endl; exit(1); } float32x4_t vmean0 = vdupq_n_f32(mean[0]); @@ -159,7 +161,8 @@ void RunRecModel(std::vector>> boxes, cv::Mat img, std::vector &rec_text_score, std::vector charactor_dict, std::shared_ptr predictor_cls, - int use_direction_classify) { + int use_direction_classify, + std::vector *times) { std::vector mean = {0.5f, 0.5f, 0.5f}; std::vector scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f}; @@ -226,14 +229,15 @@ void RunRecModel(std::vector>> boxes, cv::Mat img, std::vector>> RunDetModel(std::shared_ptr predictor, cv::Mat img, - std::map Config) { + std::map Config, std::vector *times) { // Read img int max_side_len = int(Config["max_side_len"]); int det_db_use_dilate = int(Config["det_db_use_dilate"]); cv::Mat srcimg; img.copyTo(srcimg); - + + auto preprocess_start = std::chrono::steady_clock::now(); std::vector ratio_hw; img = DetResizeImg(img, max_side_len, ratio_hw); cv::Mat img_fp; @@ -248,8 +252,10 @@ RunDetModel(std::shared_ptr predictor, cv::Mat img, std::vector scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f}; const float *dimg = reinterpret_cast(img_fp.data); NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale); + auto preprocess_end = std::chrono::steady_clock::now(); // Run predictor + auto inference_start = std::chrono::steady_clock::now(); predictor->Run(); // Get output and post process @@ -257,8 +263,10 @@ RunDetModel(std::shared_ptr predictor, cv::Mat img, std::move(predictor->GetOutput(0))); auto *outptr = output_tensor->data(); auto shape_out = output_tensor->shape(); + auto inference_end = std::chrono::steady_clock::now(); // Save output + auto postprocess_start = std::chrono::steady_clock::now(); float pred[shape_out[2] * shape_out[3]]; unsigned char cbuf[shape_out[2] * shape_out[3]]; @@ -287,14 +295,35 @@ RunDetModel(std::shared_ptr predictor, cv::Mat img, std::vector>> filter_boxes = FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg); + auto postprocess_end = std::chrono::steady_clock::now(); + + std::chrono::duration preprocess_diff = preprocess_end - preprocess_start; + times->push_back(double(preprocess_diff.count() * 1000)); + std::chrono::duration inference_diff = inference_end - inference_start; + times->push_back(double(inference_diff.count() * 1000)); + std::chrono::duration postprocess_diff = postprocess_end - postprocess_start; + times->push_back(double(postprocess_diff.count() * 1000)); return filter_boxes; } -std::shared_ptr loadModel(std::string model_file) { +std::shared_ptr loadModel(std::string model_file, std::string power_mode, 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 = CreatePaddlePredictor(config); return predictor; @@ -354,60 +383,255 @@ std::map LoadConfigTxt(std::string config_path) { return dict; } -int main(int argc, char **argv) { - if (argc < 5) { - std::cerr << "[ERROR] usage: " << argv[0] - << " det_model_file cls_model_file rec_model_file image_path " - "charactor_dict\n"; +void check_params(int argc, char **argv) { + if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) { + std::cerr << "Please choose one mode of [det, rec, system] !" << std::endl; exit(1); } - std::string det_model_file = argv[1]; - std::string rec_model_file = argv[2]; - std::string cls_model_file = argv[3]; - std::string img_path = argv[4]; - std::string dict_path = argv[5]; + 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; + exit(1); + } + } + + 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; + exit(1); + } + } + + 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; + exit(1); + } + } +} + +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 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) { + std::cerr << "Only support FP32 or INT8." << std::endl; + exit(1); + } + + std::vector cv_all_img_names; + cv::glob(img_dir, cv_all_img_names); //// load config from txt file - auto Config = LoadConfigTxt("./config.txt"); + auto Config = LoadConfigTxt(det_config_path); int use_direction_classify = int(Config["use_direction_classify"]); - auto start = std::chrono::system_clock::now(); + auto charactor_dict = ReadDict(dict_path); + 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); - auto rec_predictor = loadModel(rec_model_file); - auto cls_predictor = loadModel(cls_model_file); + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl; + exit(1); + } + + std::vector det_times; + auto boxes = RunDetModel(det_predictor, srcimg, Config, &det_times); + + std::vector rec_text; + std::vector rec_text_score; + + std::vector rec_times; + RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, + charactor_dict, cls_predictor, use_direction_classify, &rec_times); + + //// visualization + auto img_vis = Visualization(srcimg, boxes); + + //// print recognized text + for (int i = 0; i < rec_text.size(); i++) { + std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i] + << std::endl; + } + } +} + +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 img_dir = argv[7]; + std::string det_config_path = argv[8]; + + if (strcmp(argv[3], "FP32") != 0 && strcmp(argv[3], "INT8") != 0) { + std::cerr << "Only support FP32 or INT8." << std::endl; + exit(1); + } + + std::vector cv_all_img_names; + cv::glob(img_dir, cv_all_img_names); + + //// load config from txt file + auto Config = LoadConfigTxt(det_config_path); + + auto det_predictor = loadModel(det_model_file, power_mode, std::stoi(num_threads)); + + std::vector time_info = {0, 0, 0}; + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl; + exit(1); + } + + std::vector times; + auto boxes = RunDetModel(det_predictor, srcimg, Config, ×); + + //// visualization + auto img_vis = Visualization(srcimg, boxes); + std::cout << boxes.size() << " bboxes have detected:" << std::endl; + + // for (int i=0; i cv_all_img_names; + cv::glob(img_dir, cv_all_img_names); auto charactor_dict = ReadDict(dict_path); charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc charactor_dict.push_back(" "); - cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR); - auto boxes = RunDetModel(det_predictor, srcimg, Config); + auto rec_predictor = loadModel(rec_model_file, power_mode, std::stoi(num_threads)); - std::vector rec_text; - std::vector rec_text_score; + std::shared_ptr cls_predictor; - RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, - charactor_dict, cls_predictor, use_direction_classify); + std::vector time_info = {0, 0, 0}; + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::cout << "The predict img: " << cv_all_img_names[i] << std::endl; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - auto end = std::chrono::system_clock::now(); - auto duration = - std::chrono::duration_cast(end - start); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl; + exit(1); + } - //// visualization - auto img_vis = Visualization(srcimg, boxes); + int width = srcimg.cols; + int height = srcimg.rows; + std::vector upper_left = {0, 0}; + std::vector upper_right = {width, 0}; + std::vector lower_right = {width, height}; + std::vector lower_left = {0, height}; + std::vector> box = {upper_left, upper_right, lower_right, lower_left}; + std::vector>> boxes = {box}; + + std::vector rec_text; + std::vector rec_text_score; + std::vector times; + RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, + charactor_dict, cls_predictor, 0, ×); + + //// print recognized text + for (int i = 0; i < rec_text.size(); i++) { + std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i] + << std::endl; + } + } + // TODO: support autolog + if (strcmp(argv[9], "True") == 0) { + AutoLogger autolog(rec_model_file, + 0, + 0, + 0, + std::stoi(num_threads), + std::stoi(batchsize), + "dynamic", + precision, + power_mode, + time_info, + cv_all_img_names.size()); + autolog.report(); + } +} + +int main(int argc, char **argv) { + check_params(argc, argv); + std::cout << "mode: " << argv[1] << endl; - //// print recognized text - for (int i = 0; i < rec_text.size(); i++) { - std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i] - << std::endl; + if (strcmp(argv[1], "system") == 0) { + system(argv); } - std::cout << "花费了" - << double(duration.count()) * - std::chrono::microseconds::period::num / - std::chrono::microseconds::period::den - << "秒" << std::endl; + if (strcmp(argv[1], "det") == 0) { + det(argc, argv); + } + + if (strcmp(argv[1], "rec") == 0) { + rec(argc, argv); + } return 0; -} \ No newline at end of file +} diff --git a/test_tipc/configs/ppocr_det_mobile_params.txt b/test_tipc/configs/ppocr_det_mobile_params.txt index 3d2117d7ca9b444f55b9c9f343647026af7e97c6..0e8edb62a8aa881993a95fe9a550de17aceaa435 100644 --- a/test_tipc/configs/ppocr_det_mobile_params.txt +++ b/test_tipc/configs/ppocr_det_mobile_params.txt @@ -98,3 +98,13 @@ null:null --benchmark:True null:null null:null +===========================lite_params=========================== +inference:./ocr_db_crnn det +infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb +--cpu_threads:1|4 +--batch_size:1 +--power_mode:LITE_POWER_HIGH|LITE_POWER_LOW +--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg +--config_dir:./config.txt +--rec_dict_dir:./ppocr_keys_v1.txt +--benchmark:True diff --git a/test_tipc/docs/install.md b/test_tipc/docs/install.md index 40614f178c0d6607f6fc8aaf7b7c3516e093994f..28b92426fa04da79ce63381fffa9f52a0f42813f 100644 --- a/test_tipc/docs/install.md +++ b/test_tipc/docs/install.md @@ -1,16 +1,13 @@ -## 1. 环境准备 +## 环境配置 本教程适用于PTDN目录下基础功能测试的运行环境搭建。 推荐环境： -- CUDA 10.1/10.2 -- CUDNN 7.6/cudnn8.1 -- TensorRT 6.1.0.5 / 7.1 / 7.2 +- CUDA 10.1 +- CUDNN 7.6 +- TensorRT 6.1.0.5 / 7.1 -环境配置可以选择docker镜像安装，或者在本地环境Python搭建环境。推荐使用docker镜像安装，避免不必要的环境配置。 - -## 2. Docker 镜像安装 推荐docker镜像安装，按照如下命令创建镜像，当前目录映射到镜像中的`/paddle`目录下 ``` @@ -19,80 +16,7 @@ cd /paddle # 安装带TRT的paddle pip3.7 install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl -``` - -## 3 Python 环境构建 - -非docker环境下，环境配置比较灵活，推荐环境组合配置： -- CUDA10.1 + CUDNN7.6 + TensorRT 6 -- CUDA10.2 + CUDNN8.1 + TensorRT 7 -- CUDA11.1 + CUDNN8.1 + TensorRT 7 - -下面以 CUDA10.2 + CUDNN8.1 + TensorRT 7 配置为例，介绍环境配置的流程。 - -### 3.1 安装CUDNN - -如果当前环境满足CUDNN版本的要求，可以跳过此步骤。 - -以CUDNN8.1 安装安装为例，安装步骤如下，首先下载CUDNN，从[Nvidia官网](https://developer.nvidia.com/rdp/cudnn-archive)下载CUDNN8.1版本，下载符合当前系统版本的三个deb文件，分别是： -- cuDNN Runtime Library ，如：libcudnn8_8.1.0.77-1+cuda10.2_amd64.deb -- cuDNN Developer Library ，如：libcudnn8-dev_8.1.0.77-1+cuda10.2_amd64.deb -- cuDNN Code Samples，如：libcudnn8-samples_8.1.0.77-1+cuda10.2_amd64.deb - -deb安装可以参考[官方文档](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb)，安装方式如下 -``` -# x.x.x表示下载的版本号 -# $HOME为工作目录 -sudo dpkg -i libcudnn8_x.x.x-1+cudax.x_arm64.deb -sudo dpkg -i libcudnn8-dev_8.x.x.x-1+cudax.x_arm64.deb -sudo dpkg -i libcudnn8-samples_8.x.x.x-1+cudax.x_arm64.deb - -# 验证是否正确安装 -cp -r /usr/src/cudnn_samples_v8/ $HOME -cd $HOME/cudnn_samples_v8/mnistCUDNN - -# 编译 -make clean && make -./mnistCUDNN -``` -如果运行mnistCUDNN完后提示运行成功，则表示安装成功。如果运行后出现freeimage相关的报错，需要按照提示安装freeimage库: -``` -sudo apt-get install libfreeimage-dev -sudo apt-get install libfreeimage -``` -### 3.2 安装TensorRT - -首先，从[Nvidia官网TensorRT板块](https://developer.nvidia.com/tensorrt-getting-started)下载TensorRT，这里选择7.1.3.4版本的TensorRT，注意选择适合自己系统版本和CUDA版本的TensorRT，另外建议下载TAR package的安装包。 - -以Ubuntu16.04+CUDA10.2为例，下载并解压后可以参考[官方文档](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-713/install-guide/index.html#installing-tar)的安装步骤，按照如下步骤安装: -``` -# 以下安装命令中 '${version}' 为下载的TensorRT版本，如7.1.3.4 -# 设置环境变量， 为解压后的TensorRT的lib目录 -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: - -# 安装TensorRT -cd TensorRT-${version}/python -pip3.7 install tensorrt-*-cp3x-none-linux_x86_64.whl - -# 安装graphsurgeon -cd TensorRT-${version}/graphsurgeon -``` - - -### 3.3 安装PaddlePaddle - - -安装的TensorRT版本需要和Paddle安装包中编译进去的TensorRT版本进行对应，带TensorRT的Paddle安装包可以从[链接](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#python)下载带TRT的paddle： -这里选择下载 linux-cuda10.2-trt7-gcc8.2 Python3.7版本的Paddle： -``` -# 从下载链接中可以看到是paddle2.1.1-cuda10.2-cudnn8.1版本 -wget https://paddle-wheel.bj.bcebos.com/with-trt/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddlepaddle_gpu-2.1.1-cp37-cp37m-linux_x86_64.whl -pip3.7 install -U paddlepaddle_gpu-2.1.1-cp37-cp37m-linux_x86_64.whl -``` - -## 4. 安装PaddleOCR依赖 -``` # 安装AutoLog git clone https://github.com/LDOUBLEV/AutoLog cd AutoLog @@ -100,6 +24,7 @@ pip3.7 install -r requirements.txt python3.7 setup.py bdist_wheel pip3.7 install ./dist/auto_log-1.0.0-py3-none-any.whl + # 下载OCR代码 cd ../ git clone https://github.com/PaddlePaddle/PaddleOCR @@ -120,4 +45,4 @@ A. 问题一般是当前安装paddle版本带TRT，但是本地环境找不到Te ``` export LD_LIBRARY_PATH=/usr/local/python3.7.0/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/paddle/package/TensorRT-6.0.1.5/lib ``` -或者问题是下载的TensorRT版本和当前paddle中编译的TRT版本不匹配，需要下载版本相符的TensorRT重新安装。 +或者问题是下载的TensorRT版本和当前paddle中编译的TRT版本不匹配，需要下载版本相符的TRT。 diff --git a/test_tipc/docs/test_inference_cpp.md b/test_tipc/docs/test_inference_cpp.md index 25db1b5b6b1aa101a8f8969cfae3efc02e542971..24655d96ba1acaadd489019ec260999c981107de 100644 --- a/test_tipc/docs/test_inference_cpp.md +++ b/test_tipc/docs/test_inference_cpp.md @@ -15,15 +15,15 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于 ## 2. 测试流程 ### 2.1 功能测试 -先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```PTDN/output```目录下生成`cpp_infer_*.log`后缀的日志文件。 +先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。 ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt "cpp_infer" +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "cpp_infer" # 用法1: -bash PTDN/test_inference_cpp.sh ./PTDN/configs/ppocr_det_mobile_params.txt +bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 -bash PTDN/test_inference_cpp.sh ./PTDN/configs/ppocr_det_mobile_params.txt '1' +bash test_tipc/test_inference_cpp.sh ./test_tipc/configs/ppocr_det_mobile_params.txt '1' ``` @@ -37,12 +37,12 @@ bash PTDN/test_inference_cpp.sh ./PTDN/configs/ppocr_det_mobile_params.txt '1' #### 使用方式 运行命令： ```shell -python3.7 PTDN/compare_results.py --gt_file=./PTDN/results/cpp_*.txt --log_file=./PTDN/output/cpp_*.log --atol=1e-3 --rtol=1e-3 +python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/cpp_*.txt --log_file=./test_tipc/output/cpp_*.log --atol=1e-3 --rtol=1e-3 ``` 参数介绍： -- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在PTDN/result/ 文件夹下 -- log_file: 指向运行PTDN/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入 +- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下 +- log_file: 指向运行test_tipc/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入 - atol: 设置的绝对误差 - rtol: 设置的相对误差 diff --git a/test_tipc/docs/test_serving.md b/test_tipc/docs/test_serving.md index c6b35630392249ea969585c69a9e4c3d35f1cf52..fb0848bfb5e37e4b0af39fa9bb2b13b4046c9a50 100644 --- a/test_tipc/docs/test_serving.md +++ b/test_tipc/docs/test_serving.md @@ -15,18 +15,18 @@ PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试 ## 2. 测试流程 ### 2.1 功能测试 -先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```PTDN/output```目录下生成`serving_infer_*.log`后缀的日志文件。 +先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。 ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt "serving_infer" +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt "serving_infer" # 用法: -bash PTND/test_serving.sh ./PTDN/configs/ppocr_det_mobile_params.txt +bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile_params.txt ``` #### 运行结果 -各测试的运行情况会打印在 `PTDN/output/results_serving.log` 中： +各测试的运行情况会打印在 `test_tipc/output/results_serving.log` 中： 运行成功时会输出： ``` @@ -44,7 +44,7 @@ Run failed with command - xxxxx ... ``` -详细的预测结果会存在 PTDN/output/ 文件夹下，例如`server_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log`中会返回检测框的坐标: +详细的预测结果会存在 test_tipc/output/ 文件夹下，例如`server_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log`中会返回检测框的坐标: ``` {'err_no': 0, 'err_msg': '', 'key': ['dt_boxes'], 'value': ['[[[ 78. 642.]\n [409. 640.]\n [409. 657.]\n diff --git a/test_tipc/docs/test_train_inference_python.md b/test_tipc/docs/test_train_inference_python.md index 89885ddfa3c1f36a120d713e39689767f8fc6342..fa14863fdad02dcb9b69f45494cc18b24ceaf36f 100644 --- a/test_tipc/docs/test_train_inference_python.md +++ b/test_tipc/docs/test_train_inference_python.md @@ -46,42 +46,42 @@ ### 2.2 功能测试 -先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```PTDN/output```目录下生成`python_infer_*.log`格式的日志文件。 +先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。 `test_train_inference_python.sh`包含5种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是： - 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度； ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'lite_train_infer' -bash PTDN/test_train_inference_python.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'lite_train_infer' +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_infer' +bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'lite_train_infer' ``` - 模式2：whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理； ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'whole_infer' -bash PTDN/test_train_inference_python.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'whole_infer' +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' +bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_infer' ``` - 模式3：infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度; ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'infer' +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'infer' # 用法1: -bash PTDN/test_train_inference_python.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'infer' +bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'infer' # 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号 -bash PTDN/test_train_inference_python.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'infer' '1' +bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'infer' '1' ``` - 模式4：whole_train_infer，CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度； ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'whole_train_infer' -bash PTDN/test_train_inference_python.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'whole_train_infer' +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_infer' +bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'whole_train_infer' ``` - 模式5：klquant_infer，测试离线量化； ```shell -bash PTDN/prepare.sh ./PTDN/configs/ppocr_det_mobile_params.txt 'klquant_infer' -bash PTDN/test_train_inference_python.sh PTDN/configs/ppocr_det_mobile_params.txt 'klquant_infer' +bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_infer' +bash test_tipc/test_train_inference_python.sh test_tipc/configs/ppocr_det_mobile_params.txt 'klquant_infer' ``` @@ -95,12 +95,12 @@ bash PTDN/test_train_inference_python.sh PTDN/configs/ppocr_det_mobile_params.tx #### 使用方式 运行命令： ```shell -python3.7 PTDN/compare_results.py --gt_file=./PTDN/results/python_*.txt --log_file=./PTDN/output/python_*.log --atol=1e-3 --rtol=1e-3 +python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3 ``` 参数介绍： -- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在PTDN/result/ 文件夹下 -- log_file: 指向运行PTDN/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入 +- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下 +- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入 - atol: 设置的绝对误差 - rtol: 设置的相对误差 diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index d842f4f573d0b1bd697bdad9b67a765ebcf6da6c..737256be5d39156a68189e72de5ebd413fabc3ff 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -2,7 +2,7 @@ FILENAME=$1 # MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', -# 'cpp_infer', 'serving_infer', 'klquant_infer'] +# 'cpp_infer', 'serving_infer', 'klquant_infer', 'lite_infer'] MODE=$2 @@ -136,3 +136,37 @@ if [ ${MODE} = "serving_infer" ];then wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar 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=inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv + 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 PTDN/configs/ppocr_det_mobile_params.txt PTDN/test_lite.sh PTDN/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 ${current_dir} +fi + diff --git a/test_tipc/readme.md b/test_tipc/readme.md index ccc0c8af4808328fbee17493fe95cc0847e10fbf..1d8df7da6cf6d1319cedd329e4202fa674e8538b 100644 --- a/test_tipc/readme.md +++ b/test_tipc/readme.md @@ -1,9 +1,9 @@ -# 推理部署导航 +# 飞桨训推一体认证 ## 1. 简介 -飞桨除了基本的模型训练和预测，还提供了支持多端多平台的高性能推理部署工具。本文档提供了PaddleOCR中所有模型的推理部署导航PTDN（Paddle Train Deploy Navigation），方便用户查阅每种模型的推理部署打通情况，并可以进行一键测试。 +飞桨除了基本的模型训练和预测，还提供了支持多端多平台的高性能推理部署工具。本文档提供了PaddleOCR中所有模型的飞桨训推一体认证 (Training and Inference Pipeline Certification(TIPC)) 信息和测试工具，方便用户查阅每种模型的训练推理部署打通情况，并可以进行一键测试。
@@ -58,7 +58,7 @@ ### 目录介绍 ```shell -PTDN/ +test_tipc/ ├── configs/ # 配置文件目录 ├── det_mv3_db.yml # 测试mobile版ppocr检测模型训练的yml文件 ├── det_r50_vd_db.yml # 测试server版ppocr检测模型训练的yml文件 diff --git a/test_tipc/test_lite.sh b/test_tipc/test_lite.sh new file mode 100644 index 0000000000000000000000000000000000000000..832003ba302fe86995e20029cdb019e72d9ce162 --- /dev/null +++ b/test_tipc/test_lite.sh @@ -0,0 +1,69 @@ +#!/bin/bash +source ./common_func.sh +export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH + +FILENAME=$1 +dataline=$(awk 'NR==101, NR==110{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