mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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2f8d9c9a57
* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32
71 lines
2.9 KiB
C++
Executable File
71 lines
2.9 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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namespace vision = fastdeploy::vision;
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namespace benchmark = fastdeploy::benchmark;
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int main(int argc, char* argv[]) {
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#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
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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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auto model_file = FLAGS_model + sep + "model.pdmodel";
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auto params_file = FLAGS_model + sep + "model.pdiparams";
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auto config_file = FLAGS_model + sep + "deploy.yaml";
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if (FLAGS_backend == "paddle_trt") {
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option.paddle_infer_option.collect_trt_shape = true;
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}
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if (FLAGS_backend == "paddle_trt" || FLAGS_backend == "trt") {
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option.trt_option.SetShape("x", {1, 3, 192, 192}, {1, 3, 192, 192},
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{1, 3, 192, 192});
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}
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auto model_ppseg = vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option);
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vision::SegmentationResult res;
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// Run once at least
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model_ppseg.Predict(im, &res);
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// 1. Test result diff
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std::cout << "=============== Test result diff =================\n";
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// Save result to -> disk.
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std::string seg_result_path = "ppseg_result.txt";
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benchmark::ResultManager::SaveSegmentationResult(res, seg_result_path);
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// Load result from <- disk.
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vision::SegmentationResult res_loaded;
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benchmark::ResultManager::LoadSegmentationResult(&res_loaded,
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seg_result_path);
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// Calculate diff between two results.
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auto seg_diff =
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benchmark::ResultManager::CalculateDiffStatis(res, res_loaded);
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std::cout << "Labels diff: mean=" << seg_diff.labels.mean
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<< ", max=" << seg_diff.labels.max
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<< ", min=" << seg_diff.labels.min << std::endl;
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if (res_loaded.contain_score_map) {
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std::cout << "Scores diff: mean=" << seg_diff.scores.mean
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<< ", max=" << seg_diff.scores.max
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<< ", min=" << seg_diff.scores.min << std::endl;
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}
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BENCHMARK_MODEL(model_ppseg, model_ppseg.Predict(im, &res))
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auto vis_im = vision::VisSegmentation(im, res, 0.5);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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#endif
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return 0;
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} |