mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
6c31198342
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST Co-authored-by: Jason <jiangjiajun@baidu.com>
97 lines
2.9 KiB
C++
97 lines
2.9 KiB
C++
// Copyright (c) 2022 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 <iostream>
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#include <string>
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#include "fastdeploy/vision.h"
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void InferHumanPPHumansegv2Lite(const std::string& device = "cpu");
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int main() {
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InferHumanPPHumansegv2Lite("npu");
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return 0;
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}
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fastdeploy::RuntimeOption GetOption(const std::string& device) {
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auto option = fastdeploy::RuntimeOption();
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if (device == "npu") {
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option.UseRKNPU2();
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} else {
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option.UseCpu();
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}
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return option;
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}
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fastdeploy::ModelFormat GetFormat(const std::string& device) {
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auto format = fastdeploy::ModelFormat::ONNX;
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if (device == "npu") {
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format = fastdeploy::ModelFormat::RKNN;
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} else {
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format = fastdeploy::ModelFormat::ONNX;
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}
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return format;
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}
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std::string GetModelPath(std::string& model_path, const std::string& device) {
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if (device == "npu") {
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model_path += "rknn";
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} else {
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model_path += "onnx";
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}
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return model_path;
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}
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void InferHumanPPHumansegv2Lite(const std::string& device) {
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std::string model_file =
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"./model/Portrait_PP_HumanSegV2_Lite_256x144_infer/"
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"Portrait_PP_HumanSegV2_Lite_256x144_infer_rk3588.";
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std::string params_file;
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std::string config_file =
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"./model/Portrait_PP_HumanSegV2_Lite_256x144_infer/deploy.yaml";
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fastdeploy::RuntimeOption option = GetOption(device);
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fastdeploy::ModelFormat format = GetFormat(device);
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model_file = GetModelPath(model_file, device);
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auto model = fastdeploy::vision::segmentation::PaddleSegModel(
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model_file, params_file, config_file, option, format);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto image_file =
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"./images/portrait_heng.jpg";
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auto im = cv::imread(image_file);
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if (device == "npu") {
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model.GetPreprocessor().DisableNormalizeAndPermute();
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}
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fastdeploy::vision::SegmentationResult res;
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clock_t start = clock();
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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clock_t end = clock();
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auto dur = (double)(end - start);
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printf("infer_human_pp_humansegv2_lite_npu use time:%f\n",
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(dur / CLOCKS_PER_SEC));
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisSegmentation(im, res);
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cv::imwrite("human_pp_humansegv2_lite_npu_result.jpg", vis_im);
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std::cout
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<< "Visualized result saved in ./human_pp_humansegv2_lite_npu_result.jpg"
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<< std::endl;
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} |