1.FastDeploy介绍

⚡️FastDeploy是一款全场景、易用灵活、极致高效的AI推理部署工具， 支持云边端部署。提供超过 🔥160+ Text，Vision， Speech和跨模态模型📦开箱即用的部署体验，并实现🔚端到端的推理性能优化，满足开发者多场景、多硬件、多平台的产业部署需求。

        使用FastDeploy可以简单高效的在X86 CPU、NVIDIA GPU、飞腾CPU、ARM CPU、Intel GPU、昆仑、昇腾、瑞芯微、晶晨、算能等10+款硬件上对PaddleSeg语义分割模型进行快速部署，并且支持Paddle Inference、Paddle Lite、TensorRT、OpenVINO、ONNXRuntime、RKNPU2、SOPHGO等多种推理后端。

2. 通过FastDeploy C++ 部署PaddleSeg模型

支持CpuInfer、GpuInfer、TrtInfer三种推理模式

// 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 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 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();
  // If use original Tensorrt, not Paddle-TensorRT,
  // comment the following two lines
  option.EnablePaddleToTrt();
  option.EnablePaddleTrtCollectShape();
  option.SetTrtInputShape("x", {1, 3, 256, 256}, {1, 3, 1024, 1024},
                          {1, 3, 2048, 2048});

  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[]) {
    std::string model_dir = "model\PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer";
   std::string image_file = "model\cityscapes_demo.png";

 
  // CpuInfer(argv[1], argv[2]);

   GpuInfer(model_dir, image_file);

 //  TrtInfer(argv[1], argv[2]);
  
  return 0;
}

推理结果可视化：

 3.待打包上传整个工程