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[Doc] Update RKYOLO Docs (PaddlePaddle#1330)
* 更新docs * 修正docs错误 * 更新docs * 更新python example脚本和ppyoloe转换脚本 * 更新PaddleDetection文档 * 更新文档 * 更新文档 * 更新文档 * 更新文档 * 更新文档 * 更新RKYOLO系列模型文档 * 更新PaddleDetection python example
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examples/vision/detection/paddledetection/rknpu2/cpp/infer_ppyoloe_demo.cc
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| // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #include "fastdeploy/vision.h" | ||
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| void ONNXInfer(const std::string& model_dir, const std::string& image_file) { | ||
| std::string model_file = model_dir + "/yolov8_n_500e_coco.onnx"; | ||
| std::string params_file; | ||
| std::string config_file = model_dir + "/infer_cfg.yml"; | ||
| auto option = fastdeploy::RuntimeOption(); | ||
| option.UseCpu(); | ||
| auto format = fastdeploy::ModelFormat::ONNX; | ||
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| auto model = fastdeploy::vision::detection::PPYOLOE( | ||
| model_file, params_file, config_file, option, format); | ||
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| fastdeploy::TimeCounter tc; | ||
| tc.Start(); | ||
| auto im = cv::imread(image_file); | ||
| fastdeploy::vision::DetectionResult res; | ||
| if (!model.Predict(im, &res)) { | ||
| std::cerr << "Failed to predict." << std::endl; | ||
| return; | ||
| } | ||
| auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5); | ||
| tc.End(); | ||
| tc.PrintInfo("PPDet in ONNX"); | ||
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| std::cout << res.Str() << std::endl; | ||
| cv::imwrite("infer_onnx.jpg", vis_im); | ||
| std::cout << "Visualized result saved in ./infer_onnx.jpg" << std::endl; | ||
| } | ||
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| void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) { | ||
| auto model_file = model_dir + "/ppyoloe_plus_crn_s_80e_coco_rk3588_quantized.rknn"; | ||
| auto params_file = ""; | ||
| auto config_file = model_dir + "/infer_cfg.yml"; | ||
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| auto option = fastdeploy::RuntimeOption(); | ||
| option.UseRKNPU2(); | ||
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| auto format = fastdeploy::ModelFormat::RKNN; | ||
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| auto model = fastdeploy::vision::detection::PPYOLOE( | ||
| model_file, params_file, config_file, option, format); | ||
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| model.GetPreprocessor().DisablePermute(); | ||
| model.GetPreprocessor().DisableNormalize(); | ||
| model.GetPostprocessor().ApplyDecodeAndNMS(); | ||
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| auto im = cv::imread(image_file); | ||
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| fastdeploy::vision::DetectionResult res; | ||
| fastdeploy::TimeCounter tc; | ||
| tc.Start(); | ||
| if (!model.Predict(&im, &res)) { | ||
| std::cerr << "Failed to predict." << std::endl; | ||
| return; | ||
| } | ||
| tc.End(); | ||
| tc.PrintInfo("PPDet in RKNPU2"); | ||
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| std::cout << res.Str() << std::endl; | ||
| 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; | ||
| } | ||
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| int main(int argc, char* argv[]) { | ||
| 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; | ||
| } | ||
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| 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; | ||
| } |
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