Merge pull request #3958 from MissPenguin/dygraph

support rec for cpp cice
2026-09-24 23:33:08 +08:00 · 2021-09-08 11:45:16 +08:00
parent f09a28c624 7be68dbd9d
commit d41586fc4f
7 changed files with 119 additions and 40 deletions
@@ -63,6 +63,7 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
 DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
 DEFINE_int32(rec_batch_num, 1, "rec_batch_num.");
 DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
+// DEFINE_string(char_list_file, "./ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");


 using namespace std;
@@ -148,12 +149,28 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
      time_info[1] += rec_times[1];
      time_info[2] += rec_times[2];
    }
-    
+        
+    if (FLAGS_benchmark) {
+        AutoLogger autolog("ocr_rec", 
+                           FLAGS_use_gpu,
+                           FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn,
+                           FLAGS_cpu_threads,
+                           1, 
+                           "dynamic", 
+                           FLAGS_precision, 
+                           time_info, 
+                           cv_all_img_names.size());
+        autolog.report();
+    }
    return 0;
 }


 int main_system(std::vector<cv::String> cv_all_img_names) {
+    std::vector<double> time_info_det = {0, 0, 0};
+    std::vector<double> time_info_rec = {0, 0, 0};
+
    DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                   FLAGS_gpu_mem, FLAGS_cpu_threads, 
                   FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
@@ -174,12 +191,10 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
                       FLAGS_enable_mkldnn, FLAGS_char_list_file,
                       FLAGS_use_tensorrt, FLAGS_precision);

-    auto start = std::chrono::system_clock::now();
-
    for (int i = 0; i < cv_all_img_names.size(); ++i) {
      LOG(INFO) << "The predict img: " << cv_all_img_names[i];

-      cv::Mat srcimg = cv::imread(FLAGS_image_dir, cv::IMREAD_COLOR);
+      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] << endl;
        exit(1);
@@ -189,7 +204,10 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
      std::vector<double> rec_times;
        
      det.Run(srcimg, boxes, &det_times);
-    
+      time_info_det[0] += det_times[0];
+      time_info_det[1] += det_times[1];
+      time_info_det[2] += det_times[2];
+        
      cv::Mat crop_img;
      for (int j = 0; j < boxes.size(); j++) {
        crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
@@ -198,18 +216,36 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
          crop_img = cls->Run(crop_img);
        }
        rec.Run(crop_img, &rec_times);
+        time_info_rec[0] += rec_times[0];
+        time_info_rec[1] += rec_times[1];
+        time_info_rec[2] += rec_times[2];
      }
-        
-      auto end = std::chrono::system_clock::now();
-      auto duration =
-          std::chrono::duration_cast<std::chrono::microseconds>(end - start);
-      std::cout << "Cost  "
-                << double(duration.count()) *
-                       std::chrono::microseconds::period::num /
-                       std::chrono::microseconds::period::den
-                << "s" << std::endl;
    }
-      
+    if (FLAGS_benchmark) {
+        AutoLogger autolog_det("ocr_det", 
+                            FLAGS_use_gpu,
+                            FLAGS_use_tensorrt,
+                            FLAGS_enable_mkldnn,
+                            FLAGS_cpu_threads,
+                            1, 
+                            "dynamic", 
+                            FLAGS_precision, 
+                            time_info_det, 
+                            cv_all_img_names.size());
+        AutoLogger autolog_rec("ocr_rec", 
+                            FLAGS_use_gpu,
+                            FLAGS_use_tensorrt,
+                            FLAGS_enable_mkldnn,
+                            FLAGS_cpu_threads,
+                            1, 
+                            "dynamic", 
+                            FLAGS_precision, 
+                            time_info_rec, 
+                            cv_all_img_names.size());
+        autolog_det.report();
+        std::cout << endl;
+        autolog_rec.report();
+    }  
    return 0;
 }

@@ -62,7 +62,7 @@ inference:./deploy/cpp_infer/build/ppocr det
 --precision:fp32|fp16
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
+null:null
 --benchmark:True
 ===========================serving_params===========================
 trans_model:-m paddle_serving_client.convert
@@ -53,7 +53,7 @@ inference:tools/infer/predict_system.py
 use_opencv:True
 infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
 infer_quant:False
-inference:./deploy/cpp_infer/build/ppocr det
+inference:./deploy/cpp_infer/build/ppocr system
 --use_gpu:True|False
 --enable_mkldnn:True|False
 --cpu_threads:1|6
@@ -62,6 +62,6 @@ inference:./deploy/cpp_infer/build/ppocr det
 --precision:fp32|fp16
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
+--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
 --benchmark:True

@@ -49,3 +49,18 @@ inference:tools/infer/predict_rec.py
 --save_log_path:./test/output/
 --benchmark:True
 null:null
+===========================cpp_infer_params===========================
+use_opencv:True
+infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr rec
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32|fp16
+--rec_model_dir:
+--image_dir:./inference/rec_inference/
+null:null
+--benchmark:True
@@ -64,7 +64,7 @@ elif [ ${MODE} = "whole_infer" ];then
    cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
    ln -s ./icdar2015_infer ./icdar2015
    cd ../
-elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
+elif [ ${MODE} = "infer" ];then
    if [ ${model_name} = "ocr_det" ]; then
        eval_model_name="ch_ppocr_mobile_v2.0_det_train"
        rm -rf ./train_data/icdar2015
@@ -87,6 +87,21 @@ elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
        cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../
    fi 
+elif [ ${MODE} = "cpp_infer" ];then
+    if [ ${model_name} = "ocr_det" ]; then
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
+        cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
+    elif [ ${model_name} = "ocr_rec" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
+        cd ./inference && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf rec_inference.tar && cd ../
+    elif  [ ${model_name} = "ocr_system" ]; then
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_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_det_data_50.tar && cd ../
+    fi 
 fi

 if [ ${MODE} = "serving_infer" ];then
@@ -23,36 +23,46 @@ test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从

 ```bash
 tests/
-├── ocr_det_params.txt   # 测试OCR检测模型的参数配置文件
-├── ocr_rec_params.txt   # 测试OCR识别模型的参数配置文件
-└── prepare.sh           # 完成test.sh运行所需要的数据和模型下载
-└── test.sh              # 根据
+├── ocr_det_params.txt            # 测试OCR检测模型的参数配置文件
+├── ocr_rec_params.txt            # 测试OCR识别模型的参数配置文件
+├── ocr_ppocr_mobile_params.txt   # 测试OCR检测+识别模型串联的参数配置文件
+└── prepare.sh                    # 完成test.sh运行所需要的数据和模型下载
+└── test.sh                       # 测试主程序
 ```

 # 使用方法
+
 test.sh包含四种运行模式，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；
-```
+
+- 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 test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
 bash tests/test.sh ./tests/ocr_det_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；
-```
+```  
+
+- 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 tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
 bash tests/test.sh ./tests/ocr_det_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;
-```
+- 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 tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
-用法1:
+# 用法1:
 bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
-用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
+# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
 bash tests/test.sh ./tests/ocr_det_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
-```
+- 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 tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
 bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-```
+```  
+
+- 模式5：cpp_infer , CE： 验证inference model的c++预测是否走通；
+```shell
+bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer'
+bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer'
+```  
@@ -192,7 +192,8 @@ if [ ${MODE} = "cpp_infer" ]; then
    cpp_infer_model_key=$(func_parser_key "${lines[62]}")
    cpp_image_dir_key=$(func_parser_key "${lines[63]}")
    cpp_infer_img_dir=$(func_parser_value "${lines[63]}")
-    cpp_save_log_key=$(func_parser_key "${lines[64]}")
+    cpp_infer_key1=$(func_parser_key "${lines[64]}")
+    cpp_infer_value1=$(func_parser_value "${lines[64]}")
    cpp_benchmark_key=$(func_parser_key "${lines[65]}")
    cpp_benchmark_value=$(func_parser_value "${lines[65]}")
 fi
@@ -368,7 +369,8 @@ function func_cpp_inference(){
                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
                        set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
                        set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
-                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
+                        set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
+                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
                        eval $command
                        last_status=${PIPESTATUS[0]}
                        eval "cat ${_save_log_path}"
@@ -396,7 +398,8 @@ function func_cpp_inference(){
                        set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
                        set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
                        set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
-                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
+                        set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
+                        command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
                        eval $command
                        last_status=${PIPESTATUS[0]}
                        eval "cat ${_save_log_path}"