mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
b62912ff9c
* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32 * Add diff compare for OCR * Add diff compare for OCR * rm ppocr pipeline * Add yolov5 diff compare * Add yolov5 diff compare * deal with comments * deal with comments * fixed bug * fixed bug * fixed thread nums * Add Failed log * optimizer x86 pipeline
112 lines
7.7 KiB
C++
Executable File
112 lines
7.7 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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std::unordered_map<std::string, std::string> __config_info__; \
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fastdeploy::benchmark::ResultManager::LoadBenchmarkConfig( \
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FLAGS_config_path, &__config_info__); \
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std::stringstream __ss__; \
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__ss__.precision(6); \
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fastdeploy::benchmark::ResourceUsageMonitor __resource_moniter__( \
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std::stoi(__config_info__["sampling_interval"]), \
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std::stoi(__config_info__["device_id"])); \
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if (__config_info__["collect_memory_info"] == "true") { \
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__resource_moniter__.Start(); \
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} \
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if (__config_info__["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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__ss__ << "Runtime(ms): Failed" << std::endl; \
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if (__config_info__["collect_memory_info"] == "true") { \
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__ss__ << "cpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_util: Failed" << std::endl; \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult( \
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__ss__.str(), __config_info__["result_path"]); \
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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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__ss__ << "Runtime(ms): " << __profile_time__ << "ms." << std::endl; \
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} else { \
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std::cout << "Warmup " \
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<< __config_info__["warmup"] \
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<< " times..." << std::endl; \
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int __warmup__ = std::stoi(__config_info__["warmup"]); \
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for (int __i__ = 0; __i__ < __warmup__; __i__++) { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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__ss__ << "End2End(ms): Failed" << std::endl; \
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if (__config_info__["collect_memory_info"] == "true") { \
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__ss__ << "cpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_util: Failed" << std::endl; \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult( \
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__ss__.str(), __config_info__["result_path"]); \
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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 " \
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<< __config_info__["repeat"] \
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<< " times..." << std::endl; \
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fastdeploy::TimeCounter __tc__; \
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__tc__.Start(); \
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int __repeat__ = std::stoi(__config_info__["repeat"]); \
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for (int __i__ = 0; __i__ < __repeat__; __i__++) { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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__ss__ << "End2End(ms): Failed" << std::endl; \
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if (__config_info__["collect_memory_info"] == "true") { \
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__ss__ << "cpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_util: Failed" << std::endl; \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult( \
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__ss__.str(), __config_info__["result_path"]); \
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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() / __repeat__ * 1000; \
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std::cout << "End2End(ms): " << __end2end__ << "ms." << std::endl; \
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__ss__ << "End2End(ms): " << __end2end__ << "ms." << std::endl; \
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} \
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if (__config_info__["collect_memory_info"] == "true") { \
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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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__ss__ << "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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__ss__ << "gpu_rss_mb: " << __gpu_mem__ << "MB." << std::endl; \
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std::cout << "gpu_util: " << __gpu_util__ << std::endl; \
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__ss__ << "gpu_util: " << __gpu_util__ << "MB." << std::endl; \
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__resource_moniter__.Stop(); \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult(__ss__.str(), \
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__config_info__["result_path"]); \
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}
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