Files
FastDeploy/benchmark/cpp/benchmark_yolov5.cc
WJJ1995 47b1d27fbb [Benchmark] Add macros for benchmark (#1301)
* add GPL lisence

* add GPL-3.0 lisence

* add GPL-3.0 lisence

* add GPL-3.0 lisence

* support yolov8

* add pybind for yolov8

* add yolov8 readme

* add cpp benchmark

* add cpu and gpu mem

* public part split

* add runtime mode

* fixed bugs

* add cpu_thread_nums

* deal with comments

* deal with comments

* deal with comments

* rm useless code

* add FASTDEPLOY_DECL

* add FASTDEPLOY_DECL

* fixed for windows

* mv rss to pss

* mv rss to pss

* Update utils.cc

* use thread to collect mem

* Add ResourceUsageMonitor

* rm useless code

* fixed bug

* fixed typo

* update ResourceUsageMonitor

* fixed bug

* fixed bug

* add note for ResourceUsageMonitor

* deal with comments

* add macros

* deal with comments

* deal with comments

* deal with comments

* re-lint

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2023-02-13 16:12:54 +08:00

37 lines
1.3 KiB
C++

// Copyright (c) 2023 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.
#include "flags.h"
#include "macros.h"
#include "option.h"
int main(int argc, char* argv[]) {
google::ParseCommandLineFlags(&argc, &argv, true);
auto im = cv::imread(FLAGS_image);
// Initialization
auto option = fastdeploy::RuntimeOption();
if (!CreateRuntimeOption(&option)) {
PrintUsage();
return false;
}
auto model_yolov5 =
fastdeploy::vision::detection::YOLOv5(FLAGS_model, "", option);
fastdeploy::vision::DetectionResult res;
BENCHMARK_MODEL(model_yolov5, model_yolov5.Predict(im, &res))
auto vis_im = fastdeploy::vision::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
return 0;
}