mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-24 00:53:22 +08:00
[RKNN2] Fix bugs (#851)
* 修复picodet格式 * * 修正错误文档 * 修复rknpu2 backend后端的部分错误 * 更新pphumanseg example格式 * * 更新pphumanseg example格式 * * 更新picodet example格式 * * 更新scrfd example格式 * * 更新ppseg rknpu2 python example中的错误 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 * * 修复代码格式问题 Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
@@ -15,83 +15,84 @@
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#include <string>
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#include "fastdeploy/vision.h"
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void InferHumanPPHumansegv2Lite(const std::string& device = "cpu");
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int main() {
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InferHumanPPHumansegv2Lite("npu");
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return 0;
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}
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fastdeploy::RuntimeOption GetOption(const std::string& device) {
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auto option = fastdeploy::RuntimeOption();
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if (device == "npu") {
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option.UseRKNPU2();
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} else {
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option.UseCpu();
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}
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return option;
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}
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fastdeploy::ModelFormat GetFormat(const std::string& device) {
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auto format = fastdeploy::ModelFormat::ONNX;
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if (device == "npu") {
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format = fastdeploy::ModelFormat::RKNN;
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} else {
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format = fastdeploy::ModelFormat::ONNX;
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}
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return format;
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}
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std::string GetModelPath(std::string& model_path, const std::string& device) {
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if (device == "npu") {
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model_path += "rknn";
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} else {
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model_path += "onnx";
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}
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return model_path;
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}
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void InferHumanPPHumansegv2Lite(const std::string& device) {
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std::string model_file =
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"./model/Portrait_PP_HumanSegV2_Lite_256x144_infer/"
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"Portrait_PP_HumanSegV2_Lite_256x144_infer_rk3588.";
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void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
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std::string model_file = model_dir + "/Portrait_PP_HumanSegV2_Lite_256x144_infer.onnx";
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std::string params_file;
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std::string config_file =
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"./model/Portrait_PP_HumanSegV2_Lite_256x144_infer/deploy.yaml";
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std::string config_file = model_dir + "/deploy.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu();
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auto format = fastdeploy::ModelFormat::ONNX;
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fastdeploy::RuntimeOption option = GetOption(device);
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fastdeploy::ModelFormat format = GetFormat(device);
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model_file = GetModelPath(model_file, device);
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option, format);
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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 image_file =
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"./images/portrait_heng.jpg";
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fastdeploy::TimeCounter tc;
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tc.Start();
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auto im = cv::imread(image_file);
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if (device == "npu") {
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model.GetPreprocessor().DisableNormalizeAndPermute();
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}
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fastdeploy::vision::SegmentationResult res;
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clock_t start = clock();
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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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clock_t end = clock();
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auto dur = (double)(end - start);
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printf("infer_human_pp_humansegv2_lite_npu use time:%f\n",
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(dur / CLOCKS_PER_SEC));
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisSegmentation(im, res);
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cv::imwrite("human_pp_humansegv2_lite_npu_result.jpg", vis_im);
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tc.End();
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tc.PrintInfo("PPSeg in ONNX");
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cv::imwrite("infer_onnx.jpg", vis_im);
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std::cout
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<< "Visualized result saved in ./human_pp_humansegv2_lite_npu_result.jpg"
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<< "Visualized result saved in ./infer_onnx.jpg"
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<< std::endl;
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}
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}
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void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
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std::string model_file = model_dir + "/Portrait_PP_HumanSegV2_Lite_256x144_infer_rk3588.rknn";
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std::string params_file;
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std::string config_file = model_dir + "/deploy.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseRKNPU2();
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auto format = fastdeploy::ModelFormat::RKNN;
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option, format);
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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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model.GetPreprocessor().DisableNormalizeAndPermute();
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fastdeploy::TimeCounter tc;
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tc.Start();
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auto im = cv::imread(image_file);
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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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auto vis_im = fastdeploy::vision::VisSegmentation(im, res);
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tc.End();
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tc.PrintInfo("PPSeg in RKNPU2");
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cv::imwrite("infer_rknn.jpg", vis_im);
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std::cout
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<< "Visualized result saved in ./infer_rknn.jpg"
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<< std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 3) {
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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 ./picodet_model_dir ./test.jpeg"
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<< std::endl;
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return -1;
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}
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RKNPU2Infer(argv[1], argv[2]);
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ONNXInfer(argv[1], argv[2]);
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return 0;
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}
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@@ -49,7 +49,7 @@ model = fd.vision.segmentation.PaddleSegModel(
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.RKNN)
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model.disable_normalize_and_permute()
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model.preprocessor.disable_normalize_and_permute()
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# 预测图片分割结果
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im = cv2.imread(args.image)
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