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[Model] Add YOLOV8 For RKNPU2 (#1153)

Browse Source 
* 更新ppdet

* 更新ppdet

* 更新ppdet

* 更新ppdet

* 更新ppdet

* 新增ppdet_decode

* 更新多batch支持

* 更新多batch支持

* 更新多batch支持

* 更新注释内容

* 尝试解决pybind问题

* 尝试解决pybind的问题

* 尝试解决pybind的问题

* 重构代码

* 重构代码

* 重构代码

* 按照要求修改

* 更新Picodet文档

* 更新Picodet文档，更新yolov8文档

* 修改picodet 以及 yolov8 example

* 更新Picodet模型转换脚本

* 更新example代码

* 更新yolov8量化代码

* 修复部分bug
加入pybind

* 修复pybind

* 修复pybind错误的问题

* 更新说明文档

* 更新说明文档
This commit is contained in:
Zheng-Bicheng
2023-01-16 22:33:02 +08:00
committed by GitHub
parent 66240a6f66
commit f441ffe56b
12 changed files with 290 additions and 189 deletions
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27
examples/vision/detection/paddledetection/rknpu2/cpp/infer_picodet.cc
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@@ -13,8 +13,8 @@
// limitations under the License.
#include <iostream>
#include <string>
#include "fastdeploy/vision.h"
#include <sys/time.h>
void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
std::string model_file = model_dir + "/picodet_s_416_coco_lcnet.onnx";
@@ -25,7 +25,7 @@ void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
auto format = fastdeploy::ModelFormat::ONNX;
auto model = fastdeploy::vision::detection::PicoDet(
model_file, params_file, config_file,option,format);
model_file, params_file, config_file, option, format);
fastdeploy::TimeCounter tc;
tc.Start();
@@ -35,14 +35,12 @@ void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
std::cerr << "Failed to predict." << std::endl;
return;
}
auto vis_im = fastdeploy::vision::VisDetection(im, res,0.5);
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
tc.End();
tc.PrintInfo("PPDet in ONNX");
cv::imwrite("infer_onnx.jpg", vis_im);
std::cout
<< "Visualized result saved in ./infer_onnx.jpg"
<< std::endl;
std::cout << "Visualized result saved in ./infer_onnx.jpg" << std::endl;
}
void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
@@ -56,8 +54,10 @@ void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
auto format = fastdeploy::ModelFormat::RKNN;
auto model = fastdeploy::vision::detection::PicoDet(
model_file, params_file, config_file,option,format);
model_file, params_file, config_file, option, format);
model.GetPreprocessor().DisablePermute();
model.GetPreprocessor().DisableNormalize();
model.GetPostprocessor().ApplyDecodeAndNMS();
auto im = cv::imread(image_file);
@@ -73,21 +73,24 @@ void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
tc.PrintInfo("PPDet in RKNPU2");
std::cout << res.Str() << std::endl;
auto vis_im = fastdeploy::vision::VisDetection(im, res,0.5);
auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
cv::imwrite("infer_rknpu2.jpg", vis_im);
std::cout << "Visualized result saved in ./infer_rknpu2.jpg" << std::endl;
}
int main(int argc, char* argv[]) {
if (argc < 3) {
if (argc < 4) {
std::cout
<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
"e.g ./infer_model ./picodet_model_dir ./test.jpeg"
<< std::endl;
return -1;
}
RKNPU2Infer(argv[1], argv[2]);
//ONNXInfer(argv[1], argv[2]);
if (std::atoi(argv[3]) == 0) {
ONNXInfer(argv[1], argv[2]);
} else if (std::atoi(argv[3]) == 1) {
RKNPU2Infer(argv[1], argv[2]);
}
return 0;
}