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[Docs] Pick seg fastdeploy docs from PaddleSeg (#1482)
* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
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DefTruth
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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[English](README.md) | 简体中文
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# PaddleSeg CPU-GPU C++部署示例
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本目录下提供`infer.cc`快速完成PP-LiteSeg在CPU/GPU，以及GPU上通过Paddle-TensorRT加速部署的示例。
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## 1. 说明
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PaddleSeg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Segmentation模型。
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## 2. 部署环境准备
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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。
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## 3. 部署模型准备
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在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)，如果你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)。
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## 4. 运行部署示例
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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)
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```bash
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# 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
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/semantic_segmentation/cpp-gpu/cpp
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# 如果您希望从PaddleSeg下载示例代码，请运行
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# git clone https://github.com/PaddlePaddle/PaddleSeg.git
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# # 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
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# # git checkout develop
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# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/cpp-gpu/cpp
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# 编译部署示例
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mkdir build && cd build
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# 下载PP-LiteSeg模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# 运行部署示例
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# CPU推理
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./infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 0
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# GPU推理
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./infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 1
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# GPU上Paddle-TensorRT推理
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./infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 2
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```
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运行完成可视化结果如下图所示
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
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</div>
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- 注意，以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考文档: [如何在Windows中使用FastDeploy C++ SDK](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/use_sdk_on_windows.md)
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- 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)
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## 6. 更多指南
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- [PaddleSeg C++ API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1segmentation.html)
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- [FastDeploy部署PaddleSeg模型概览](../../)
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- [Python部署](../python)
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## 7. 常见问题
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- [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)
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- [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)
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- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
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- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
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- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void CpuInfer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "deploy.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu();
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::SegmentationResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void GpuInfer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "deploy.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::SegmentationResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void TrtInfer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + sep + "model.pdmodel";
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auto params_file = model_dir + sep + "model.pdiparams";
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auto config_file = model_dir + sep + "deploy.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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// If use original Tensorrt, not Paddle-TensorRT,
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// comment the following two lines
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option.EnablePaddleToTrt();
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option.EnablePaddleTrtCollectShape();
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option.SetTrtInputShape("x", {1, 3, 256, 256}, {1, 3, 1024, 1024},
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{1, 3, 2048, 2048});
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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fastdeploy::vision::SegmentationResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisSegmentation(im, res, 0.5);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout
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<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
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"e.g ./infer_model ./ppseg_model_dir ./test.jpeg 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu; 2: run with gpu and use tensorrt backend; 3: run "
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"with kunlunxin."
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[3]) == 0) {
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CpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 1) {
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GpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2]);
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}
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return 0;
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}
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