[Docs] Pick paddleclas fastdeploy docs from PaddleClas (#1654)

* Adjust folders structures in paddleclas * remove useless files * Update sophgo * improve readme
2025-10-17 22:21:48 +08:00 · 2023-03-23 13:06:09 +08:00
parent ab65557121
commit c91e99b5f5
90 changed files with 2005 additions and 2584 deletions
--- a/examples/vision/classification/paddleclas/cpu-gpu/cpp/CMakeLists.txt
+++ b/examples/vision/classification/paddleclas/cpu-gpu/cpp/CMakeLists.txt
@@ -0,0 +1,14 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
--- a/examples/vision/classification/paddleclas/cpu-gpu/cpp/README.md
+++ b/examples/vision/classification/paddleclas/cpu-gpu/cpp/README.md
@@ -0,0 +1,106 @@
+# PaddleClas CPU-GPU C++部署示例
+
+本目录下提供`infer.cc`快速完成PaddleClas系列模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+## 1. 说明  
+PaddleClas支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型.
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
+
+## 3. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 4. 运行部署示例
+以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
+
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/classification/paddleclas/cpu-gpu/cpp
+
+# 如果您希望从PaddleClas下载示例代码，请运行
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
+git checkout develop
+cd PaddleClas/deploy/fastdeploy/cpu-gpu/cpp
+
+mkdir build
+cd build
+# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# 在CPU上使用Paddle Inference推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
+# 在CPU上使用OenVINO推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
+# 在CPU上使用ONNX Runtime推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2
+# 在CPU上使用Paddle Lite推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 3
+# 在GPU上使用Paddle Inference推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 4
+# 在GPU上使用Paddle TensorRT推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 5
+# 在GPU上使用ONNX Runtime推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 6
+# 在GPU上使用Nvidia TensorRT推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 7
+```
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+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)
+
+
+## 5. 部署示例选项说明  
+在我们使用`infer_demo`时, 输入了3个参数, 分别为分类模型, 预测图片, 与最后一位的数字选项.
+现在下表将解释最后一位数字选项的含义.
+|数字选项|含义|
+|:---:|:---:|
+|0| 在CPU上使用Paddle Inference推理 |
+|1| 在CPU上使用OenVINO推理 |
+|2| 在CPU上使用ONNX Runtime推理 |
+|3| 在CPU上使用Paddle Lite推理 |
+|4| 在GPU上使用Paddle Inference推理 |
+|5| 在GPU上使用Paddle TensorRT推理 |
+|6| 在GPU上使用ONNX Runtime推理 |
+|7| 在GPU上使用Nvidia TensorRT推理 |
+
+- 关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[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)
+
+## 6. 更多指南
+- [PaddleClas系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1classification.html)
+- [PaddleClas Python部署](../python)
+- [PaddleClas C 部署](../c)
+- [PaddleClas C# 部署](../csharp)
+
+## 7. 常见问题
+- PaddleClas能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[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)
+
+|硬件类型|支持的后端|
+|:---:|:---:|
+|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
+|ARM CPU| Paddle Lite |
+|飞腾 CPU| ONNX Runtime |
+|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
+
+- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
+- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
+- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
+- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
--- a/examples/vision/classification/paddleclas/cpu-gpu/cpp/infer.cc
+++ b/examples/vision/classification/paddleclas/cpu-gpu/cpp/infer.cc
@@ -0,0 +1,88 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void InitAndInfer(const std::string &model_dir, const std::string &image_file,
+                  const fastdeploy::RuntimeOption &option) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      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::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  // print res
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char *argv[]) {
+  if (argc < 4) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
+                 "e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
+              << std::endl;
+    return -1;
+  }
+
+  fastdeploy::RuntimeOption option;
+  int flag = std::atoi(argv[3]);
+
+  if (flag == 0) {
+    option.UseCpu();
+    option.UsePaddleBackend();  // Paddle Inference
+  } else if (flag == 1) {
+    option.UseCpu();
+    option.UseOpenVINOBackend();  // OpenVINO
+  } else if (flag == 2) {
+    option.UseCpu();
+    option.UseOrtBackend();  // ONNX Runtime
+  } else if (flag == 3) {
+    option.UseCpu();
+    option.UseLiteBackend();  // Paddle Lite
+  } else if (flag == 4) {
+    option.UseGpu();
+    option.UsePaddleBackend();  // Paddle Inference
+  } else if (flag == 5) {
+    option.UseGpu();
+    option.UsePaddleInferBackend();
+    option.paddle_infer_option.enable_trt = true;
+  } else if (flag == 6) {
+    option.UseGpu();
+    option.UseOrtBackend();  // ONNX Runtime
+  } else if (flag == 7) {
+    option.UseGpu();
+    option.UseTrtBackend();  // TensorRT
+  }
+
+  std::string model_dir = argv[1];
+  std::string image_dir = argv[2];
+  InitAndInfer(model_dir, image_dir, option);
+}