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
62 lines
2.4 KiB
C++
Executable File
62 lines
2.4 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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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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// Set max_batch_size 1 for best performance
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if (FLAGS_backend == "paddle_trt") {
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option.trt_option.max_batch_size = 1;
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}
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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 = vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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vision::ClassifyResult res;
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// Run once at least
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model_ppcls.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 cls_result_path = "ppcls_result.txt";
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benchmark::ResultManager::SaveClassifyResult(res, cls_result_path);
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// Load result from <- disk.
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vision::ClassifyResult res_loaded;
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benchmark::ResultManager::LoadClassifyResult(&res_loaded, cls_result_path);
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// Calculate diff between two results.
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auto cls_diff =
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benchmark::ResultManager::CalculateDiffStatis(res, res_loaded);
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std::cout << "Labels diff: mean=" << cls_diff.labels.mean
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<< ", max=" << cls_diff.labels.max
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<< ", min=" << cls_diff.labels.min << std::endl;
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std::cout << "Scores diff: mean=" << cls_diff.scores.mean
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<< ", max=" << cls_diff.scores.max
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<< ", min=" << cls_diff.scores.min << std::endl;
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BENCHMARK_MODEL(model_ppcls, model_ppcls.Predict(im, &res))
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#endif
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return 0;
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} |