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https://github.com/PaddlePaddle/FastDeploy.git
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60a44f5af1
* 添加paddleclas模型 * 更新README_CN * 更新README_CN * 更新README * update get_model.sh * update get_models.sh * update paddleseg models * update paddle_seg models * update paddle_seg models * modified test resources * update benchmark_gpu_trt.sh * add paddle detection * add paddledetection to benchmark * modified benchmark cmakelists * update benchmark scripts * modified benchmark function calling --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
90 lines
3.5 KiB
C++
Executable File
90 lines
3.5 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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DEFINE_bool(no_nms, false, "Whether the model contains nms.");
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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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std::unordered_map<std::string, std::string> config_info;
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benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path,
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&config_info);
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std::string model_name, params_name, config_name;
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auto model_format = fastdeploy::ModelFormat::PADDLE;
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if (!UpdateModelResourceName(&model_name, ¶ms_name, &config_name,
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&model_format, config_info)) {
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return -1;
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}
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auto model_file = FLAGS_model + sep + model_name;
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auto params_file = FLAGS_model + sep + params_name;
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auto config_file = FLAGS_model + sep + config_name;
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if (config_info["backend"] == "paddle_trt") {
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option.paddle_infer_option.collect_trt_shape = true;
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}
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if (config_info["backend"] == "paddle_trt" ||
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config_info["backend"] == "trt") {
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option.trt_option.SetShape("image", {1, 3, 640, 640}, {1, 3, 640, 640},
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{1, 3, 640, 640});
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option.trt_option.SetShape("scale_factor", {1, 2}, {1, 2},
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{1, 2});
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}
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auto model_gfl = vision::detection::GFL(
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model_file, params_file, config_file, option, model_format);
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vision::DetectionResult res;
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if (config_info["precision_compare"] == "true") {
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// Run once at least
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model_gfl.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 det_result_path = "gfl_result.txt";
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benchmark::ResultManager::SaveDetectionResult(res, det_result_path);
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// Load result from <- disk.
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vision::DetectionResult res_loaded;
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benchmark::ResultManager::LoadDetectionResult(&res_loaded, det_result_path);
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// Calculate diff between two results.
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auto det_diff =
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benchmark::ResultManager::CalculateDiffStatis(res, res_loaded);
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std::cout << "Boxes diff: mean=" << det_diff.boxes.mean
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<< ", max=" << det_diff.boxes.max
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<< ", min=" << det_diff.boxes.min << std::endl;
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std::cout << "Label_ids diff: mean=" << det_diff.labels.mean
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<< ", max=" << det_diff.labels.max
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<< ", min=" << det_diff.labels.min << std::endl;
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}
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// Run profiling
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if (FLAGS_no_nms) {
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model_gfl.GetPostprocessor().ApplyNMS();
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}
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BENCHMARK_MODEL(model_gfl, model_gfl.Predict(im, &res))
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auto vis_im = vision::VisDetection(im, res);
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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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}
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