更新example 和模型转换代码

examples/vision/keypointdetection/tiny_pose/rknpu2/cpp/README.md

@@ -15,9 +15,7 @@
 ```bash
 mkdir build
 cd build
-# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
-tar xvf fastdeploy-linux-x64-x.x.x.tgz
 cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
 make -j
 
@@ -27,8 +25,8 @@ tar -xvf PP_TinyPose_256x192_infer.tgz
 wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg
 
 
-# CPU推理
+# NPU推理
-./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg
+sudo ./infer_tinypose_demo ./PP_TinyPose_256x192_infer ./hrnet_demo.jpg
 ```
 
 运行完成可视化结果如下图所示
@@ -79,7 +77,7 @@ PPTinyPose模型加载和初始化，其中model_file为导出的Paddle模型格
 #### 后处理参数
 > > * **use_dark**(bool): 是否使用DARK进行后处理[参考论文](https://arxiv.org/abs/1910.06278)
 
-- [模型介绍](../../)
+- [模型介绍](../../../)
-- [Python部署](../python)
+- [Python部署](../../python)
-- [视觉模型预测结果](../../../../../docs/api/vision_results/)
+- [视觉模型预测结果](../../../../../../docs/api/vision_results/)
-- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
+- [如何切换模型推理后端引擎](../../../../../../docs/cn/faq/how_to_change_backend.md)

examples/vision/keypointdetection/tiny_pose/rknpu2/cpp/pptinypose_infer.cc

@@ -17,13 +17,14 @@
 void RKNPU2Infer(const std::string& tinypose_model_dir,
                  const std::string& image_file) {
   auto tinypose_model_file =
-      tinypose_model_dir + "/picodet_s_416_coco_lcnet_rk3588.rknn";
+      tinypose_model_dir + "/PP_TinyPose_256x192_infer_rk3588_unquantized.rknn";
   auto tinypose_params_file = "";
-  auto tinypose_config_file = tinypose_model_dir + "infer_cfg.yml";
+  auto tinypose_config_file = tinypose_model_dir + "/infer_cfg.yml";
   auto option = fastdeploy::RuntimeOption();
   option.UseRKNPU2();
   auto tinypose_model = fastdeploy::vision::keypointdetection::PPTinyPose(
-      tinypose_model_file, tinypose_params_file, tinypose_config_file, option);
+      tinypose_model_file, tinypose_params_file, tinypose_config_file, option,
+      fastdeploy::RKNN);
 
   if (!tinypose_model.Initialized()) {
     std::cerr << "TinyPose Model Failed to initialize." << std::endl;
@@ -51,20 +52,14 @@ void RKNPU2Infer(const std::string& tinypose_model_dir,
 }
 
 int main(int argc, char* argv[]) {
-  if (argc < 4) {
+  if (argc < 3) {
     std::cout << "Usage: infer_demo path/to/pptinypose_model_dir path/to/image "
                  "run_option, "
-                 "e.g ./infer_model ./pptinypose_model_dir ./test.jpeg 0"
+                 "e.g ./infer_model ./pptinypose_model_dir ./test.jpeg"
-              << std::endl;
-    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
-                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run "
-                 "with kunlunxin."
               << std::endl;
     return -1;
   }
 
-  if (std::atoi(argv[3]) == 0) {
   RKNPU2Infer(argv[1], argv[2]);
-  }
   return 0;
 }

fastdeploy/vision/keypointdet/pptinypose/pptinypose.cc

@@ -139,6 +139,18 @@ bool PPTinyPose::Postprocess(std::vector<FDTensor>& infer_result,
            "Only support batch = 1 in FastDeploy now.");
   result->Clear();
 
+  if (infer_result.size() == 1) {
+    FDTensor result_copy = infer_result[0];
+    std::cout << "Reshape result_copy!" << std::endl;
+    result_copy.Reshape({result_copy.shape[0], result_copy.shape[1],
+                         result_copy.shape[2] * result_copy.shape[3]});
+    std::cout << "Resize infer_result!" << std::endl;
+    infer_result.resize(2);
+    std::cout << "Do ArgMax!" << std::endl;
+    function::ArgMax(result_copy,&infer_result[1],-1);
+    std::cout << "Done!" << std::endl;
+  }
+
   // Calculate output length
   int outdata_size =
       std::accumulate(infer_result[0].shape.begin(),