mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
2dfda1db85
* 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 * rm pmap and use mem api * rm pmap and use mem api * add mem api * Add PrintBenchmarkInfo func * Add PrintBenchmarkInfo func * Add PrintBenchmarkInfo func * deal with comments --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
70 lines
4.5 KiB
C++
Executable File
70 lines
4.5 KiB
C++
Executable File
// 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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#pragma once
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#include "fastdeploy/benchmark/utils.h"
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#include "fastdeploy/utils/perf.h"
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#define BENCHMARK_MODEL(MODEL_NAME, BENCHMARK_FUNC) \
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{ \
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if (!MODEL_NAME.Initialized()) { \
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std::cerr << "Failed to initialize." << std::endl; \
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return 0; \
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} \
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auto __im__ = cv::imread(FLAGS_image); \
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fastdeploy::benchmark::ResourceUsageMonitor __resource_moniter__( \
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FLAGS_sampling_interval, FLAGS_device_id); \
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if (FLAGS_collect_memory_info) { \
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__resource_moniter__.Start(); \
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} \
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if (FLAGS_profile_mode == "runtime") { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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return 0; \
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} \
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double __profile_time__ = MODEL_NAME.GetProfileTime() * 1000; \
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std::cout << "Runtime(ms): " << __profile_time__ << "ms." << std::endl; \
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} else { \
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std::cout << "Warmup " << FLAGS_warmup << " times..." << std::endl; \
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for (int __i__ = 0; __i__ < FLAGS_warmup; __i__++) { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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return 0; \
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} \
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} \
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std::cout << "Counting time..." << std::endl; \
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std::cout << "Repeat " << FLAGS_repeat << " times..." << std::endl; \
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fastdeploy::TimeCounter __tc__; \
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__tc__.Start(); \
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for (int __i__ = 0; __i__ < FLAGS_repeat; __i__++) { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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return 0; \
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} \
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} \
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__tc__.End(); \
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double __end2end__ = __tc__.Duration() / FLAGS_repeat * 1000; \
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std::cout << "End2End(ms): " << __end2end__ << "ms." << std::endl; \
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} \
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if (FLAGS_collect_memory_info) { \
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float __cpu_mem__ = __resource_moniter__.GetMaxCpuMem(); \
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float __gpu_mem__ = __resource_moniter__.GetMaxGpuMem(); \
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float __gpu_util__ = __resource_moniter__.GetMaxGpuUtil(); \
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std::cout << "cpu_rss_mb: " << __cpu_mem__ << "MB." << std::endl; \
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std::cout << "gpu_rss_mb: " << __gpu_mem__ << "MB." << std::endl; \
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std::cout << "gpu_util: " << __gpu_util__ << std::endl; \
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__resource_moniter__.Stop(); \
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} \
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}
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