apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2026-04-24 17:49:42 +08:00
Code Issues Actions 13 Packages Projects Releases Wiki Activity

Update PaddleClas and PaddleSeg doc (#108)

Browse Source 
* 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

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-08-12 20:39:06 +08:00
committed by GitHub
parent b6247238f5
commit 77d0422709
3 changed files with 67 additions and 53 deletions
Download Patch File Download Diff File Expand all files Collapse all files
examples/vision/classification/paddleclas/cpp/infer.cc
+38 -23
View File
@@ -13,80 +13,95 @@
// limitations under the License.
#include "fastdeploy/vision.h"
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif
void CpuInfer(const std::string& model_file, const std::string& image_file) {
auto model = fastdeploy::vision::detection::YOLOv7(model_file);
void CpuInfer(const std::string& model_dir, const std::string& image_file) {
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 option = fastdeploy::RuntimeOption();
option.UseCpu();
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);
auto im_bak = im.clone();
fastdeploy::vision::DetectionResult res;
fastdeploy::vision::ClassifyResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
// print res
std::cout << res.Str() << std::endl;
}
void GpuInfer(const std::string& model_file, const std::string& image_file) {
void GpuInfer(const std::string& model_dir, const std::string& image_file) {
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 option = fastdeploy::RuntimeOption();
option.UseGpu();
auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
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);
auto im_bak = im.clone();
fastdeploy::vision::DetectionResult res;
fastdeploy::vision::ClassifyResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
// print res
std::cout << res.Str() << std::endl;
}
void TrtInfer(const std::string& model_file, const std::string& image_file) {
void TrtInfer(const std::string& model_dir, const std::string& image_file) {
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 option = fastdeploy::RuntimeOption();
option.UseGpu();
option.UseTrtBackend();
option.SetTrtInputShape("images", {1, 3, 640, 640});
auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
option.SetTrtInputShape("inputs", {1, 3, 224, 224}, {1, 3, 224, 224},
{1, 3, 224, 224});
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);
auto im_bak = im.clone();
fastdeploy::vision::DetectionResult res;
fastdeploy::vision::ClassifyResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
// 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_model ./yolov7.onnx ./test.jpeg 0"
"e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
<< 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."