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Update paddleseg doc

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This commit is contained in:
felixhjh
2023-02-08 03:12:50 +00:00
parent 576b2e3cb0
commit bc3a7ce8fe
40 changed files with 570 additions and 625 deletions
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136
examples/vision/segmentation/paddleseg/kunlun/cpp/infer.cc
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@@ -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;
}