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https://github.com/PaddlePaddle/FastDeploy.git
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da94fc46cf
* 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 * fixed enable_paddle_to_trt * add log for paddle_trt * support ppcls benchmark * use new trt option api * update benchmark info * simplify benchmark.cc * simplify benchmark.cc * deal with comments --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
36 lines
1.4 KiB
C++
Executable File
36 lines
1.4 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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#include "flags.h"
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#include "macros.h"
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#include "option.h"
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int main(int argc, char* argv[]) {
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// Initialization
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auto option = fastdeploy::RuntimeOption();
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if (!CreateRuntimeOption(&option, argc, argv, true)) {
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return -1;
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}
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auto im = cv::imread(FLAGS_image);
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// Set max_batch_size 1 for best performance
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option.trt_option.max_batch_size = 1;
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auto model_file = FLAGS_model + sep + "inference.pdmodel";
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auto params_file = FLAGS_model + sep + "inference.pdiparams";
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auto config_file = FLAGS_model + sep + "inference_cls.yaml";
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auto model_ppcls = fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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fastdeploy::vision::ClassifyResult res;
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BENCHMARK_MODEL(model_ppcls, model_ppcls.Predict(im, &res))
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return 0;
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} |