fix some problem in lite deploy (#5488)

configs/keypoint/tiny_pose/README.md

@@ -35,16 +35,19 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时关键点检测
 
 ## 模型库
 ### 关键点检测模型
-| 模型  | 输入尺寸 | AP (COCO Val) | 单人推理耗时 (FP32)| 单人推理耗时（FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16)|
-| :------------------------ | :-------:  | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: |
-| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
-| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+| 模型        | 输入尺寸 | AP (COCO Val) | 单人推理耗时 (FP32) | 单人推理耗时（FP16) |             配置文件             |                           模型权重                           |                         预测部署模型                         |                  Paddle-Lite部署模型（FP32)                  |                  Paddle-Lite部署模型（FP16)                  |
+| :---------- | :------: | :-----------: | :-----------------: | :-----------------: | :------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
+| PP-TinyPose |  128*96  |     58.1      |       4.57ms        |       3.27ms        | [Config](./tinypose_128x96.yml)  | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.tar) |
+| PP-TinyPose | 256*192  |     68.8      |       14.07ms       |       8.33ms        | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.tar) |
+
+
 
 ### 行人检测模型
-| 模型  | 输入尺寸 | mAP (COCO Val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16)|
-| :------------------------ | :-------:  | :------: | :------: | :---: | :---: | :---: | :---: | :---: | :---: |
-| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb) |
-| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb) |
+| 模型                 | 输入尺寸 | mAP (COCO Val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) |                           配置文件                           |                           模型权重                           |                         预测部署模型                         |                  Paddle-Lite部署模型（FP32)                  |                  Paddle-Lite部署模型（FP16)                  |
+| :------------------- | :------: | :------------: | :-----------------: | :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
+| PicoDet-S-Pedestrian | 192*192  |      29.0      |       4.30ms        |       2.37ms        | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.tar) |
+| PicoDet-S-Pedestrian | 320*320  |      38.5      |       10.26ms       |       6.30ms        | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.tar) |
+
 
 
 **说明**

configs/keypoint/tiny_pose/README_en.md

@@ -37,14 +37,14 @@ If you want to deploy it on the mobile devives, you also need:
 ### Keypoint Detection Model
 | Model  | Input Size | AP (COCO Val) | Inference Time for Single Person (FP32)| Inference Time for Single Person（FP16) | Config | Model Weights | Deployment Model | Paddle-Lite Model（FP32) | Paddle-Lite Model（FP16)|
 | :------------------------ | :-------:  | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: |
-| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.nb) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
-| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.tar) |
+| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.tar) |
 
 ### Pedestrian Detection Model
 | Model  | Input Size | mAP (COCO Val) | Average Inference Time (FP32)| Average Inference Time (FP16) | Config | Model Weights | Deployment Model | Paddle-Lite Model（FP32) | Paddle-Lite Model（FP16)|
 | :------------------------ | :-------:  | :------: | :------: | :---: | :---: | :---: | :---: | :---: | :---: |
-| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.nb) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb) |
-| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.nb) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb) |
+| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.tar) |
+| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [Deployment Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite Model(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.tar) |
 
 
 **Tips**

deploy/lite/README.md

@@ -12,7 +12,12 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 
 ### 1.1 准备交叉编译环境
 交叉编译环境用于编译 Paddle Lite 和 PaddleDetection 的C++ demo。
-支持多种开发环境，不同开发环境的编译流程请参考对应文档，请确保安装完成Java jdk、Android NDK(R17以上)。
+支持多种开发环境，不同开发环境的编译流程请参考对应文档，请确保安装完成Java jdk、Android NDK(R17 < version < R21，其他版本以上未做测试)。
+设置NDK_ROOT命令：
+```shell
+export NDK_ROOT=[YOUR_NDK_PATH]/android-ndk-r17c
+```
+
 
 1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
 2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
@@ -21,7 +26,7 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 ### 1.2 准备预测库
 
 预测库有两种获取方式：
-1. [**建议**]直接下载，预测库下载链接如下：
+1. [**建议**]直接下载，预测库下载链接如下：(请注意使用模型FP32/16版本需要与库相对应)
       |平台| 架构 | 预测库下载链接|
       |-|-|-|
       |Android| arm7 | [inference_lite_lib](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) |
@@ -31,7 +36,7 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 **注意**：1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 目前只提供Android端demo，IOS端demo可以参考[Paddle-Lite IOS demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/master/PaddleLite-ios-demo)
 
 
-2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
+2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下(Lite库在不断更新，如若下列命令无效，请以Lite官方repo为主)：
 ```shell
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite

deploy/lite/include/config_parser.h

@@ -29,7 +29,7 @@
 
 namespace PaddleDetection {
 
-void load_jsonf(std::string jsonfile, Json::Value& jsondata);
+void load_jsonf(std::string jsonfile, const Json::Value& jsondata);
 
 // Inference model configuration parser
 class ConfigPaser {
@@ -43,13 +43,14 @@ class ConfigPaser {
     Json::Value config;
     load_jsonf(model_dir + OS_PATH_SEP + cfg + ".json", config);
 
-    // Get model arch : YOLO, SSD, RetinaNet, RCNN, Face
+    // Get model arch : YOLO, SSD, RetinaNet, RCNN, Face, PicoDet, HRNet
     if (config.isMember("arch")) {
       arch_ = config["arch"].as<std::string>();
     } else {
-      std::cerr << "Please set model arch,"
-                << "support value : YOLO, SSD, RetinaNet, RCNN, Face."
-                << std::endl;
+      std::cerr
+          << "Please set model arch,"
+          << "support value : YOLO, SSD, RetinaNet, RCNN, Face, PicoDet, HRNet."
+          << std::endl;
       return false;
     }
 

deploy/lite/src/config_parser.cc

@@ -16,7 +16,7 @@
 
 namespace PaddleDetection {
 
-void load_jsonf(std::string jsonfile, Json::Value &jsondata) {
+void load_jsonf(std::string jsonfile, const Json::Value &jsondata) {
   std::ifstream ifs;
   ifs.open(jsonfile);
 

deploy/lite/src/main.cc

@@ -43,10 +43,8 @@ void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
             << std::endl;
   RT_Config["model_dir_det"].as<std::string>().erase(
       RT_Config["model_dir_det"].as<std::string>().find_last_not_of("/") + 1);
-  std::cout
-      << "detection model_name: "
-      << RT_Config["model_dir_det"].as<std::string>()
-      << std::endl;
+  std::cout << "detection model_name: "
+            << RT_Config["model_dir_det"].as<std::string>() << std::endl;
   std::cout << "----------------------- Perf info ------------------------"
             << std::endl;
   std::cout << "Total number of predicted data: " << img_num
@@ -59,7 +57,7 @@ void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
             << ", postprocess_time(ms): " << det_time[2] / img_num << std::endl;
 }
 
-void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num){
+void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num) {
   std::cout << "----------------------- Data info -----------------------"
             << std::endl;
   std::cout << "batch_size_keypoint: "
@@ -69,16 +67,16 @@ void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num){
   RT_Config["model_dir_keypoint"].as<std::string>().erase(
       RT_Config["model_dir_keypoint"].as<std::string>().find_last_not_of("/") +
       1);
-  std::cout
-      << "keypoint model_name: "
-      << RT_Config["model_dir_keypoint"].as<std::string>() << std::endl;
+  std::cout << "keypoint model_name: "
+            << RT_Config["model_dir_keypoint"].as<std::string>() << std::endl;
   std::cout << "----------------------- Perf info ------------------------"
             << std::endl;
   std::cout << "Total number of predicted data: " << img_num
             << " and total time spent(ms): "
-            << std::accumulate(det_time.begin(), det_time.end(), 0.) << std::endl;
+            << std::accumulate(det_time.begin(), det_time.end(), 0.)
+            << std::endl;
   img_num = std::max(1, img_num);
-  std::cout << "Average time cost per person:" << std::endl 
+  std::cout << "Average time cost per person:" << std::endl
             << "preproce_time(ms): " << det_time[0] / img_num
             << ", inference_time(ms): " << det_time[1] / img_num
             << ", postprocess_time(ms): " << det_time[2] / img_num << std::endl;
@@ -136,7 +134,7 @@ void PredictImage(const std::vector<std::string> all_img_paths,
                   PaddleDetection::KeyPointDetector* keypoint,
                   const std::string& output_dir = "output") {
   std::vector<double> det_t = {0, 0, 0};
-  int steps = ceil(float(all_img_paths.size()) / batch_size_det);
+  int steps = ceil(static_cast<float>(all_img_paths.size()) / batch_size_det);
   int kpts_imgs = 0;
   std::vector<double> keypoint_t = {0, 0, 0};
   double midtimecost = 0;
@@ -243,7 +241,7 @@ void PredictImage(const std::vector<std::string> all_img_paths,
 
           std::chrono::duration<float> midtimediff =
               keypoint_crop_time - keypoint_start_time;
-          midtimecost += double(midtimediff.count() * 1000);
+          midtimecost += static_cast<double>(midtimediff.count() * 1000);
 
           if (imgs_kpts.size() == RT_Config["batch_size_keypoint"].as<int>() ||
               ((i == imsize - 1) && !imgs_kpts.empty())) {
@@ -275,8 +273,8 @@ void PredictImage(const std::vector<std::string> all_img_paths,
         std::string kpts_savepath =
             output_path + "keypoint_" +
             image_file_path.substr(image_file_path.find_last_of('/') + 1);
-        cv::Mat kpts_vis_img =
-            VisualizeKptsResult(im, result_kpts, colormap_kpts, keypoint->get_threshold());
+        cv::Mat kpts_vis_img = VisualizeKptsResult(
+            im, result_kpts, colormap_kpts, keypoint->get_threshold());
         cv::imwrite(kpts_savepath, kpts_vis_img, compression_params);
         printf("Visualized output saved as %s\n", kpts_savepath.c_str());
       } else {
@@ -298,23 +296,22 @@ void PredictImage(const std::vector<std::string> all_img_paths,
   PrintBenchmarkLog(det_t, all_img_paths.size());
   if (keypoint) {
     PrintKptsBenchmarkLog(keypoint_t, kpts_imgs);
-    PrintTotalIimeLog((det_t[0] + det_t[1] + det_t[2]) / all_img_paths.size(),
-                    (keypoint_t[0] + keypoint_t[1] + keypoint_t[2]) / all_img_paths.size(),
-                    midtimecost / all_img_paths.size());
+    PrintTotalIimeLog(
+        (det_t[0] + det_t[1] + det_t[2]) / all_img_paths.size(),
+        (keypoint_t[0] + keypoint_t[1] + keypoint_t[2]) / all_img_paths.size(),
+        midtimecost / all_img_paths.size());
   }
-
 }
 
 int main(int argc, char** argv) {
-  std::cout << "Usage: " << argv[0]
-            << " [config_path](option) [image_dir](option)\n";
+  std::cout << "Usage: " << argv[0] << " [config_path] [image_dir](option)\n";
   if (argc < 2) {
     std::cout << "Usage: ./main det_runtime_config.json" << std::endl;
     return -1;
   }
   std::string config_path = argv[1];
   std::string img_path = "";
-    
+
   if (argc >= 3) {
     img_path = argv[2];
   }