mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
90 lines
3.8 KiB
C++
90 lines
3.8 KiB
C++
// 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_string(trt_shape, "1,3,512,512:1,3,512,512:1,3,512,512",
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"Set min/opt/max shape for trt/paddle_trt backend."
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"eg:--trt_shape 1,3,512,512:1,3,512,512:1,3,512,512");
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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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std::vector<std::vector<int32_t>> trt_shapes =
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benchmark::ResultManager::GetInputShapes(FLAGS_trt_shape);
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option.trt_option.SetShape("x", trt_shapes[0], trt_shapes[1],
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trt_shapes[2]);
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}
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auto model_ppmatting = vision::matting::PPMatting(
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model_file, params_file, config_file, option, model_format);
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vision::MattingResult res;
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if (config_info["precision_compare"] == "true") {
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// Run once at least
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model_ppmatting.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 matting_result_path = "ppmatting_result.txt";
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benchmark::ResultManager::SaveMattingResult(res, matting_result_path);
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// Load result from <- disk.
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vision::MattingResult res_loaded;
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benchmark::ResultManager::LoadMattingResult(&res_loaded,
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matting_result_path);
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// Calculate diff between two results.
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auto matting_diff =
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benchmark::ResultManager::CalculateDiffStatis(res, res_loaded);
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std::cout << "Alpha diff: mean=" << matting_diff.alpha.mean
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<< ", max=" << matting_diff.alpha.max
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<< ", min=" << matting_diff.alpha.min << std::endl;
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if (res_loaded.contain_foreground) {
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std::cout << "Foreground diff: mean=" << matting_diff.foreground.mean
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<< ", max=" << matting_diff.foreground.max
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<< ", min=" << matting_diff.foreground.min << std::endl;
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}
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}
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BENCHMARK_MODEL(model_ppmatting, model_ppmatting.Predict(&im, &res))
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auto vis_im = vision::VisMatting(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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} |