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https://github.com/PaddlePaddle/FastDeploy.git
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95beb2bbf6
* RKNPU2 Backend兼容其他模型的量化 fd_tensor正式移除zp和scale的量化参数 * 更新FP32返回值的RKYOLO * 更新rkyolov5支持fp32格式 * 更新rkyolov5支持fp32格式 * 更新YOLOv5速度文档 Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
56 lines
1.7 KiB
C++
56 lines
1.7 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision.h"
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void RKNPU2Infer(const std::string& model_file, const std::string& image_file) {
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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::detection::RKYOLOV5(
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model_file, option,format);
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auto im = cv::imread(image_file);
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fastdeploy::vision::DetectionResult res;
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fastdeploy::TimeCounter tc;
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tc.Start();
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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::VisDetection(im, res,0.5);
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tc.End();
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tc.PrintInfo("RKYOLOV5 in RKNN");
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std::cout << res.Str() << std::endl;
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << 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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return 0;
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}
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