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https://github.com/PaddlePaddle/FastDeploy.git
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ee85a3cade
* [Benchmark] Init benchmark precision api * [Benchmark] Init benchmark precision api * [Benchmark] Add benchmark precision api * [Benchmark] Calculate the statis of diff * [Benchmark] Calculate the statis of diff * [Benchmark] Calculate the statis of diff * [Benchmark] Calculate the statis of diff * [Benchmark] Calculate the statis of diff * [Benchmark] Add SplitDataLine utils * [Benchmark] Add LexSortByXY func * [Benchmark] Add LexSortByXY func * [Benchmark] Add LexSortDetectionResultByXY func * [Benchmark] Add LexSortDetectionResultByXY func * [Benchmark] Add tensor diff presicion test * [Benchmark] fixed conflicts * [Benchmark] fixed calc tensor diff * fixed build bugs * fixed ci bugs when WITH_TESTING=ON
87 lines
3.6 KiB
C++
87 lines
3.6 KiB
C++
// Copyright (c) 2023 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 "flags.h"
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#include "macros.h"
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#include "option.h"
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namespace vision = fastdeploy::vision;
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namespace benchmark = fastdeploy::benchmark;
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int main(int argc, char* argv[]) {
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#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
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// Initialization
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auto option = fastdeploy::RuntimeOption();
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if (!CreateRuntimeOption(&option, argc, argv, true)) {
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return -1;
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}
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auto im = cv::imread(FLAGS_image);
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auto model_file = FLAGS_model + sep + "model.pdmodel";
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auto params_file = FLAGS_model + sep + "model.pdiparams";
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auto config_file = FLAGS_model + sep + "infer_cfg.yml";
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auto model_ppyolov8 = vision::detection::PaddleYOLOv8(model_file, params_file,
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config_file, option);
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vision::DetectionResult res;
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// Run once at least
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model_ppyolov8.Predict(im, &res);
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// 1. Test result diff
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std::cout << "=============== Test result diff =================\n";
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// Save result to -> disk.
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std::string det_result_path = "ppyolov8_result.txt";
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benchmark::ResultManager::SaveDetectionResult(res, det_result_path);
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// Load result from <- disk.
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vision::DetectionResult res_loaded;
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benchmark::ResultManager::LoadDetectionResult(&res_loaded, det_result_path);
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// Calculate diff between two results.
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auto det_diff =
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benchmark::ResultManager::CalculateDiffStatis(&res, &res_loaded);
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std::cout << "diff: mean=" << det_diff.mean << ",max=" << det_diff.max
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<< ",min=" << det_diff.min << std::endl;
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// 2. Test tensor diff
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std::cout << "=============== Test tensor diff =================\n";
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std::vector<vision::DetectionResult> bacth_res;
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std::vector<fastdeploy::FDTensor> input_tensors, output_tensors;
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std::vector<cv::Mat> imgs;
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imgs.push_back(im);
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std::vector<vision::FDMat> fd_images = vision::WrapMat(imgs);
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model_ppyolov8.GetPreprocessor().Run(&fd_images, &input_tensors);
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input_tensors[0].name = "image";
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input_tensors[1].name = "scale_factor";
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input_tensors[2].name = "im_shape";
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input_tensors.pop_back();
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model_ppyolov8.Infer(input_tensors, &output_tensors);
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model_ppyolov8.GetPostprocessor().Run(output_tensors, &bacth_res);
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// Save tensor to -> disk.
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auto& tensor_dump = output_tensors[0];
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std::string det_tensor_path = "ppyolov8_tensor.txt";
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benchmark::ResultManager::SaveFDTensor(tensor_dump, det_tensor_path);
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// Load tensor from <- disk.
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fastdeploy::FDTensor tensor_loaded;
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benchmark::ResultManager::LoadFDTensor(&tensor_loaded, det_tensor_path);
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// Calculate diff between two tensors.
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auto det_tensor_diff = benchmark::ResultManager::CalculateDiffStatis(
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&tensor_dump, &tensor_loaded);
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std::cout << "diff: mean=" << det_tensor_diff.mean
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<< ",max=" << det_tensor_diff.max << ",min=" << det_tensor_diff.min
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<< std::endl;
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// 3. Run profiling
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BENCHMARK_MODEL(model_ppyolov8, model_ppyolov8.Predict(im, &res))
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auto vis_im = vision::VisDetection(im, res);
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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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#endif
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return 0;
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} |