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[Benchmark]Benchmark cpp for YOLOv5 (PaddlePaddle#1224)
* 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
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| PROJECT(infer_demo C CXX) | ||
| CMAKE_MINIMUM_REQUIRED (VERSION 3.10) | ||
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| # specify the decompress directory of FastDeploy SDK | ||
| option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.") | ||
| include(${FASTDEPLOY_INSTALL_DIR}/utils/gflags.cmake) | ||
| include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake) | ||
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| include_directories(${FASTDEPLOY_INCS}) | ||
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| add_executable(benchmark_yolov5 ${PROJECT_SOURCE_DIR}/benchmark_yolov5.cc) | ||
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| if(UNIX AND (NOT APPLE) AND (NOT ANDROID)) | ||
| target_link_libraries(benchmark_yolov5 ${FASTDEPLOY_LIBS} gflags pthread) | ||
| else() | ||
| target_link_libraries(benchmark_yolov5 ${FASTDEPLOY_LIBS} gflags) | ||
| endif() |
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| // 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. | ||
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| #include "fastdeploy/benchmark/utils.h" | ||
| #include "fastdeploy/vision.h" | ||
| #include "flags.h" | ||
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| bool RunModel(std::string model_file, std::string image_file, size_t warmup, | ||
| size_t repeats, size_t dump_period, std::string cpu_mem_file_name, | ||
| std::string gpu_mem_file_name) { | ||
| // Initialization | ||
| auto option = fastdeploy::RuntimeOption(); | ||
| if (!CreateRuntimeOption(&option)) { | ||
| PrintUsage(); | ||
| return false; | ||
| } | ||
| if (FLAGS_profile_mode == "runtime") { | ||
| option.EnableProfiling(FLAGS_include_h2d_d2h, repeats, warmup); | ||
| } | ||
| auto model = fastdeploy::vision::detection::YOLOv5(model_file, "", option); | ||
| if (!model.Initialized()) { | ||
| std::cerr << "Failed to initialize." << std::endl; | ||
| return false; | ||
| } | ||
| auto im = cv::imread(image_file); | ||
| // For Runtime | ||
| if (FLAGS_profile_mode == "runtime") { | ||
| fastdeploy::vision::DetectionResult res; | ||
| if (!model.Predict(im, &res)) { | ||
| std::cerr << "Failed to predict." << std::endl; | ||
| return false; | ||
| } | ||
| double profile_time = model.GetProfileTime() * 1000; | ||
| std::cout << "Runtime(ms): " << profile_time << "ms." << std::endl; | ||
| 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; | ||
| } else { | ||
| // For End2End | ||
| // Step1: warm up for warmup times | ||
| std::cout << "Warmup " << warmup << " times..." << std::endl; | ||
| for (int i = 0; i < warmup; i++) { | ||
| fastdeploy::vision::DetectionResult res; | ||
| if (!model.Predict(im, &res)) { | ||
| std::cerr << "Failed to predict." << std::endl; | ||
| return false; | ||
| } | ||
| } | ||
| std::vector<float> end2end_statis; | ||
| // Step2: repeat for repeats times | ||
| std::cout << "Counting time..." << std::endl; | ||
| fastdeploy::TimeCounter tc; | ||
| fastdeploy::vision::DetectionResult res; | ||
| for (int i = 0; i < repeats; i++) { | ||
| if (FLAGS_collect_memory_info && i % dump_period == 0) { | ||
| fastdeploy::benchmark::DumpCurrentCpuMemoryUsage(cpu_mem_file_name); | ||
| fastdeploy::benchmark::DumpCurrentGpuMemoryUsage(gpu_mem_file_name, | ||
| FLAGS_device_id); | ||
| } | ||
| tc.Start(); | ||
| if (!model.Predict(im, &res)) { | ||
| std::cerr << "Failed to predict." << std::endl; | ||
| return false; | ||
| } | ||
| tc.End(); | ||
| end2end_statis.push_back(tc.Duration() * 1000); | ||
| } | ||
| float end2end = std::accumulate(end2end_statis.end() - repeats, | ||
| end2end_statis.end(), 0.f) / | ||
| repeats; | ||
| std::cout << "End2End(ms): " << end2end << "ms." << std::endl; | ||
| 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; | ||
| } | ||
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| return true; | ||
| } | ||
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| int main(int argc, char* argv[]) { | ||
| google::ParseCommandLineFlags(&argc, &argv, true); | ||
| int repeats = FLAGS_repeat; | ||
| int warmup = FLAGS_warmup; | ||
| int dump_period = FLAGS_dump_period; | ||
| std::string cpu_mem_file_name = "result_cpu.txt"; | ||
| std::string gpu_mem_file_name = "result_gpu.txt"; | ||
| // Run model | ||
| if (RunModel(FLAGS_model, FLAGS_image, warmup, repeats, dump_period, | ||
| cpu_mem_file_name, gpu_mem_file_name) != true) { | ||
| exit(1); | ||
| } | ||
| if (FLAGS_collect_memory_info) { | ||
| float cpu_mem = fastdeploy::benchmark::GetCpuMemoryUsage(cpu_mem_file_name); | ||
| float gpu_mem = fastdeploy::benchmark::GetGpuMemoryUsage(gpu_mem_file_name); | ||
| std::cout << "cpu_rss_mb: " << cpu_mem << "MB." << std::endl; | ||
| std::cout << "gpu_rss_mb: " << gpu_mem << "MB." << std::endl; | ||
| } | ||
| return 0; | ||
| } |
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| // 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. | ||
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| #pragma once | ||
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| #include "gflags/gflags.h" | ||
| #include "fastdeploy/utils/perf.h" | ||
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| DEFINE_string(model, "", "Directory of the inference model."); | ||
| DEFINE_string(image, "", "Path of the image file."); | ||
| DEFINE_string(device, "cpu", | ||
| "Type of inference device, support 'cpu' or 'gpu'."); | ||
| DEFINE_int32(device_id, 0, "device(gpu) id."); | ||
| DEFINE_int32(warmup, 200, "Number of warmup for profiling."); | ||
| DEFINE_int32(repeat, 1000, "Number of repeats for profiling."); | ||
| DEFINE_string(profile_mode, "runtime", "runtime or end2end."); | ||
| DEFINE_string(backend, "default", | ||
| "The inference runtime backend, support: ['default', 'ort', " | ||
| "'paddle', 'ov', 'trt', 'paddle_trt']"); | ||
| DEFINE_int32(cpu_thread_nums, 8, "Set numbers of cpu thread."); | ||
| DEFINE_bool( | ||
| include_h2d_d2h, false, "Whether run profiling with h2d and d2h."); | ||
| DEFINE_bool( | ||
| use_fp16, false, | ||
| "Whether to use FP16 mode, only support 'trt' and 'paddle_trt' backend"); | ||
| DEFINE_bool( | ||
| collect_memory_info, false, "Whether to collect memory info"); | ||
| DEFINE_int32(dump_period, 100, "How often to collect memory info."); | ||
|
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| void PrintUsage() { | ||
| std::cout << "Usage: infer_demo --model model_path --image img_path --device " | ||
| "[cpu|gpu] --backend " | ||
| "[default|ort|paddle|ov|trt|paddle_trt] " | ||
| "--use_fp16 false" | ||
| << std::endl; | ||
| std::cout << "Default value of device: cpu" << std::endl; | ||
| std::cout << "Default value of backend: default" << std::endl; | ||
| std::cout << "Default value of use_fp16: false" << std::endl; | ||
| } | ||
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| bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) { | ||
| if (FLAGS_device == "gpu") { | ||
| option->UseGpu(); | ||
| if (FLAGS_backend == "ort") { | ||
| option->UseOrtBackend(); | ||
| } else if (FLAGS_backend == "paddle") { | ||
| option->UsePaddleInferBackend(); | ||
| } else if (FLAGS_backend == "trt" || FLAGS_backend == "paddle_trt") { | ||
| option->UseTrtBackend(); | ||
| option->SetTrtInputShape("input", {1, 3, 112, 112}); | ||
| if (FLAGS_backend == "paddle_trt") { | ||
| option->EnablePaddleToTrt(); | ||
| } | ||
| if (FLAGS_use_fp16) { | ||
| option->EnableTrtFP16(); | ||
| } | ||
| } else if (FLAGS_backend == "default") { | ||
| return true; | ||
| } else { | ||
| std::cout << "While inference with GPU, only support " | ||
| "default/ort/paddle/trt/paddle_trt now, " | ||
| << FLAGS_backend << " is not supported." << std::endl; | ||
| return false; | ||
| } | ||
| } else if (FLAGS_device == "cpu") { | ||
| option->SetCpuThreadNum(FLAGS_cpu_thread_nums); | ||
| if (FLAGS_backend == "ort") { | ||
| option->UseOrtBackend(); | ||
| } else if (FLAGS_backend == "ov") { | ||
| option->UseOpenVINOBackend(); | ||
| } else if (FLAGS_backend == "paddle") { | ||
| option->UsePaddleInferBackend(); | ||
| } else if (FLAGS_backend == "default") { | ||
| return true; | ||
| } else { | ||
| std::cout << "While inference with CPU, only support " | ||
| "default/ort/ov/paddle now, " | ||
| << FLAGS_backend << " is not supported." << std::endl; | ||
| return false; | ||
| } | ||
| } else { | ||
| std::cerr << "Only support device CPU/GPU now, " << FLAGS_device | ||
| << " is not supported." << std::endl; | ||
| return false; | ||
| } | ||
|
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| return true; | ||
| } |
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