mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
Add PaddleSeg doc and infer.cc demo (#114)
* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg * Add segmentation doc * Add PaddleClas infer.py * Update PaddleClas infer.py * Delete .infer.py.swp * Add PaddleClas infer.cc * Update README.md * Update README.md * Update README.md * Update infer.py * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleSeg doc and infer.cc demo * Update README.md * Update README.md * Update README.md Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
@@ -14,34 +14,45 @@
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#include "fastdeploy/vision.h"
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void CpuInfer(const std::string& model_file, const std::string& params_file,
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const std::string& config_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu() auto model =
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fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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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 model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file);
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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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auto im_bak = im.clone();
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fastdeploy::vision::ClassifyResult res;
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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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// print res
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res.Str();
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auto vis_im = fastdeploy::vision::Visualize::VisSegmentation(im_bak, res);
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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_file, const std::string& params_file,
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const std::string& config_file, const std::string& image_file) {
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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::classification::PaddleClasModel(
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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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@@ -49,25 +60,30 @@ void GpuInfer(const std::string& model_file, const std::string& params_file,
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::ClassifyResult res;
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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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// print res
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res.Str();
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auto vis_im = fastdeploy::vision::Visualize::VisSegmentation(im_bak, res);
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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_file, const std::string& params_file,
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const std::string& config_file, const std::string& image_file) {
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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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option.SetTrtInputShape("inputs", [ 1, 3, 224, 224 ], [ 1, 3, 224, 224 ],
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[ 1, 3, 224, 224 ]);
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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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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@@ -75,40 +91,37 @@ void TrtInfer(const std::string& model_file, const std::string& params_file,
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::ClassifyResult res;
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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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// print res
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res.Str();
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auto vis_im = fastdeploy::vision::Visualize::VisSegmentation(im_bak, res);
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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 << "Usage: infer_demo path/to/model path/to/image run_option, "
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"e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
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<< std::endl;
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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."
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<< std::endl;
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return -1;
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}
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std::string model_file =
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argv[1] + "/" + "model.pdmodel" std::string params_file =
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argv[1] + "/" + "model.pdiparams" std::string config_file =
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argv[1] + "/" + "inference_cls.yaml" std::string image_file =
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argv[2] if (std::atoi(argv[3]) == 0) {
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CpuInfer(model_file, params_file, config_file, image_file);
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}
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else if (std::atoi(argv[3]) == 1) {
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GpuInfer(model_file, params_file, config_file, image_file);
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}
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else if (std::atoi(argv[3]) == 2) {
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TrtInfer(model_file, params_file, config_file, image_file);
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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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