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https://github.com/PaddlePaddle/FastDeploy.git
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6d0261e9e4
* [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] add structurev2_layout_preprocessor * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [pybind] add pp-structurev2-layout model pybind * [pybind] add pp-structurev2-layout model pybind * [Bug Fix] fixed code style * [examples] add pp-structurev2-layout c++ examples * [PP-StructureV2] add python example and docs * [benchmark] add pp-structurev2-layout benchmark support
93 lines
3.8 KiB
C++
93 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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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, false)) {
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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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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, 800, 608}, {1, 3, 800, 608},
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{1, 3, 800, 608});
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}
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auto layout_model = vision::ocr::StructureV2Layout(model_file, params_file,
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option, model_format);
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// 5 for publaynet, 10 for cdla
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layout_model.GetPostprocessor().SetNumClass(5);
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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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layout_model.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 layout_result_path = "layout_result.txt";
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benchmark::ResultManager::SaveDetectionResult(res, layout_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,
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layout_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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BENCHMARK_MODEL(layout_model, layout_model.Predict(im, &res))
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std::vector<std::string> labels = {"text", "title", "list", "table",
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"figure"};
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if (layout_model.GetPostprocessor().GetNumClass() == 10) {
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labels = {"text", "title", "figure", "figure_caption",
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"table", "table_caption", "header", "footer",
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"reference", "equation"};
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}
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auto vis_im =
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vision::VisDetection(im, res, labels, 0.3, 2, .5f, {255, 0, 0}, 2);
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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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} |