mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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84 lines
3.2 KiB
C++
Executable File
84 lines
3.2 KiB
C++
Executable File
// 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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#pragma once
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#include <unordered_map>
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#include "gflags/gflags.h"
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#include "fastdeploy/benchmark/utils.h"
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#ifdef WIN32
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static const char sep = '\\';
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#else
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static const char sep = '/';
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#endif
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DEFINE_string(model, "", "Directory of the inference model.");
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DEFINE_string(image, "", "Path of the image file.");
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DEFINE_string(config_path, "config.txt", "Path of benchmark config.");
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static void PrintUsage() {
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std::cout << "Usage: infer_demo --model model_path --image img_path "
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"--config_path config.txt[Path of benchmark config.] "
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<< std::endl;
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std::cout << "Default value of device: cpu" << std::endl;
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std::cout << "Default value of backend: default" << std::endl;
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std::cout << "Default value of use_fp16: false" << std::endl;
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}
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static void PrintBenchmarkInfo(std::unordered_map<std::string,
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std::string> config_info) {
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#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
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// Get model name
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std::vector<std::string> model_names;
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fastdeploy::benchmark::Split(FLAGS_model, model_names, sep);
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if (model_names.empty()) {
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std::cout << "Directory of the inference model is invalid!!!" << std::endl;
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return;
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}
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// Save benchmark info
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std::stringstream ss;
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ss.precision(3);
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ss << "\n======= Model Info =======\n";
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ss << "model_name: " << model_names[model_names.size() - 1] << std::endl;
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ss << "profile_mode: " << config_info["profile_mode"] << std::endl;
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if (config_info["profile_mode"] == "runtime") {
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ss << "include_h2d_d2h: " << config_info["include_h2d_d2h"] << std::endl;
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}
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ss << "\n======= Backend Info =======\n";
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ss << "warmup: " << config_info["warmup"] << std::endl;
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ss << "repeats: " << config_info["repeat"] << std::endl;
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ss << "device: " << config_info["device"] << std::endl;
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if (config_info["device"] == "gpu") {
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ss << "device_id: " << config_info["device_id"] << std::endl;
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}
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ss << "use_fp16: " << config_info["use_fp16"] << std::endl;
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ss << "backend: " << config_info["backend"] << std::endl;
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if (config_info["device"] == "cpu") {
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ss << "cpu_thread_nums: " << config_info["cpu_thread_nums"] << std::endl;
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}
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ss << "collect_memory_info: "
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<< config_info["collect_memory_info"] << std::endl;
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if (config_info["collect_memory_info"] == "true") {
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ss << "sampling_interval: " << config_info["sampling_interval"]
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<< "ms" << std::endl;
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}
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std::cout << ss.str() << std::endl;
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// Save benchmark info
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult(ss.str(),
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config_info["result_path"]);
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#endif
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return;
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}
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