Update paddleseg doc

2025-10-20 07:24:42 +08:00 · 2023-02-08 03:12:50 +00:00
parent 576b2e3cb0
commit bc3a7ce8fe
40 changed files with 570 additions and 625 deletions
--- a/examples/vision/segmentation/paddleseg/kunlun/cpp/README_CN.md
+++ b/examples/vision/segmentation/paddleseg/kunlun/cpp/README_CN.md
@@ -1,42 +1,30 @@
 [English](README.md) | 简体中文
 # PaddleSeg C++部署示例

-本目录下提供`infer.cc`快速完成Unet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+本目录下提供`infer.cc`快速完成PP-LiteSeg在华为昇腾上部署的示例。

-在部署前，需确认以下两个步骤
+在部署前，需自行编译基于昆仑芯XPU的预测库，参考文档[昆仑芯XPU部署环境编译安装](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/kunlunxin.md)

- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
-
-【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
-
-以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
+>>**注意** **PP-Matting**、**PP-HumanMatting**的模型，请从[Matting模型部署](../../../matting)下载

 ```bash
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/paddleseg/ascend/cpp
+
 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=${PWD}/fastdeploy-linux-x64-x.x.x
+# 使用编译完成的FastDeploy库编译infer_demo
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-ascend
 make -j

-# 下载Unet模型文件和测试图片
-wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
-tar -xvf Unet_cityscapes_without_argmax_infer.tgz
+# 下载PP-LiteSeg模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
+tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
 wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

-
-# CPU推理
-./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 0
-# GPU推理
-./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
-# GPU上TensorRT推理
-./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
-# 昆仑芯XPU推理
-./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3
 # 华为昇腾推理
-./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 4
+./infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png
 ```

 运行完成可视化结果如下图所示
@@ -44,12 +32,6 @@ wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
 <img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
 </div>

-以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
-
-如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
-
 ## PaddleSeg C++接口

 ### PaddleSeg类
@@ -84,7 +66,7 @@ PaddleSegModel模型加载和初始化，其中model_file为导出的Paddle模
 > **参数**
 >
 > > * **im**: 输入图像，注意需为HWC，BGR格式
-> > * **result**: 分割结果，包括分割预测的标签以及标签对应的概率值, SegmentationResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+> > * **result**: 分割结果，包括分割预测的标签以及标签对应的概率值, SegmentationResult结构体说明参考[SegmentationResult结构体介绍](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result_CN.md)

 ### 类成员属性
 #### 预处理参数
@@ -95,7 +77,12 @@ PaddleSegModel模型加载和初始化，其中model_file为导出的Paddle模
 #### 后处理参数
 > > * **apply_softmax**(bool): 当模型导出时，并未指定`apply_softmax`参数，可通过此设置此参数为`true`，将预测的输出分割标签（label_map）对应的概率结果(score_map)做softmax归一化处理

- [模型介绍](../../)
+## 快速链接
+- [PaddleSeg模型介绍](../../)
 - [Python部署](../python)
- [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)
+
+## 常见问题
+- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result_CN.md)
+- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+- [PaddleSeg C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1segmentation.html)
+)
--- a/examples/vision/segmentation/paddleseg/kunlun/cpp/infer.cc
+++ b/examples/vision/segmentation/paddleseg/kunlun/cpp/infer.cc
@@ -20,34 +20,6 @@ const char sep = '\\';
 const char sep = '/';
 #endif

-void CpuInfer(const std::string& model_dir, const std::string& image_file) {
-  auto model_file = model_dir + sep + "model.pdmodel";
-  auto params_file = model_dir + sep + "model.pdiparams";
-  auto config_file = model_dir + sep + "deploy.yaml";
-  auto option = fastdeploy::RuntimeOption();
-  option.UseCpu();
-  auto model = fastdeploy::vision::segmentation::PaddleSegModel(
-      model_file, params_file, config_file, option);
-
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
-
-  auto im = cv::imread(image_file);
-
-  fastdeploy::vision::SegmentationResult res;
-  if (!model.Predict(im, &res)) {
-    std::cerr << "Failed to predict." << std::endl;
-    return;
-  }
-
-  std::cout << res.Str() << std::endl;
-  auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
-  cv::imwrite("vis_result.jpg", vis_im);
-  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
-}
-
 void KunlunXinInfer(const std::string& model_dir,
                    const std::string& image_file) {
  auto model_file = model_dir + sep + "model.pdmodel";
@@ -77,116 +49,14 @@ void KunlunXinInfer(const std::string& model_dir,
  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
 }

-void GpuInfer(const std::string& model_dir, const std::string& image_file) {
-  auto model_file = model_dir + sep + "model.pdmodel";
-  auto params_file = model_dir + sep + "model.pdiparams";
-  auto config_file = model_dir + sep + "deploy.yaml";
-
-  auto option = fastdeploy::RuntimeOption();
-  option.UseGpu();
-  auto model = fastdeploy::vision::segmentation::PaddleSegModel(
-      model_file, params_file, config_file, option);
-
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
-
-  auto im = cv::imread(image_file);
-
-  fastdeploy::vision::SegmentationResult res;
-  if (!model.Predict(im, &res)) {
-    std::cerr << "Failed to predict." << std::endl;
-    return;
-  }
-
-  std::cout << res.Str() << std::endl;
-  auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
-  cv::imwrite("vis_result.jpg", vis_im);
-  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
-}
-
-void TrtInfer(const std::string& model_dir, const std::string& image_file) {
-  auto model_file = model_dir + sep + "model.pdmodel";
-  auto params_file = model_dir + sep + "model.pdiparams";
-  auto config_file = model_dir + sep + "deploy.yaml";
-
-  auto option = fastdeploy::RuntimeOption();
-  option.UseGpu();
-  option.UseTrtBackend();
-  auto model = fastdeploy::vision::segmentation::PaddleSegModel(
-      model_file, params_file, config_file, option);
-
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
-
-  auto im = cv::imread(image_file);
-
-  fastdeploy::vision::SegmentationResult res;
-  if (!model.Predict(im, &res)) {
-    std::cerr << "Failed to predict." << std::endl;
-    return;
-  }
-
-  std::cout << res.Str() << std::endl;
-  auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
-  cv::imwrite("vis_result.jpg", vis_im);
-  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
-}
-
-void AscendInfer(const std::string& model_dir, const std::string& image_file) {
-  auto model_file = model_dir + sep + "model.pdmodel";
-  auto params_file = model_dir + sep + "model.pdiparams";
-  auto config_file = model_dir + sep + "deploy.yaml";
-  auto option = fastdeploy::RuntimeOption();
-  option.UseAscend();
-  auto model = fastdeploy::vision::segmentation::PaddleSegModel(
-      model_file, params_file, config_file, option);
-
-  if (!model.Initialized()) {
-    std::cerr << "Failed to initialize." << std::endl;
-    return;
-  }
-
-  auto im = cv::imread(image_file);
-
-  fastdeploy::vision::SegmentationResult res;
-  if (!model.Predict(im, &res)) {
-    std::cerr << "Failed to predict." << std::endl;
-    return;
-  }
-
-  std::cout << res.Str() << std::endl;
-  auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
-  cv::imwrite("vis_result.jpg", vis_im);
-  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
-}
-
 int main(int argc, char* argv[]) {
-  if (argc < 4) {
+  if (argc < 3) {
    std::cout
        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
-           "e.g ./infer_model ./ppseg_model_dir ./test.jpeg 0"
+           "e.g ./infer_model ./ppseg_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) {
-    CpuInfer(argv[1], argv[2]);
-  } else if (std::atoi(argv[3]) == 1) {
-    GpuInfer(argv[1], argv[2]);
-  } else if (std::atoi(argv[3]) == 2) {
-    TrtInfer(argv[1], argv[2]);
-  } else if (std::atoi(argv[3]) == 3) {
-    KunlunXinInfer(argv[1], argv[2]);
-  } else if (std::atoi(argv[3]) == 4) {
-    AscendInfer(argv[1], argv[2]);
-  }
+  KunlunXinInfer(argv[1], argv[2]);
  return 0;
 }