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77cb9db6da
* [cmake] add faiss.cmake -> pp-shituv2 * [PP-ShiTuV2] Support PP-ShituV2-Det model * [PP-ShiTuV2] Support PP-ShiTuV2-Det model * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [Bug Fix] fix ppshitu_pybind error * [benchmark] Add ppshituv2-det c++ benchmark * [examples] Add PP-ShiTuV2 det & rec examples * [vision] Update vision classification result * [Bug Fix] fix trt shapes setting errors
122 lines
4.2 KiB
C++
122 lines
4.2 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 "fastdeploy/vision/classification/ppshitu/ppshituv2_rec.h"
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#include "fastdeploy/utils/unique_ptr.h"
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namespace fastdeploy {
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namespace vision {
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namespace classification {
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PPShiTuV2Recognizer::PPShiTuV2Recognizer(const std::string& model_file,
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const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format)
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: preprocessor_(config_file) {
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if (model_format == ModelFormat::PADDLE) {
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valid_cpu_backends = {Backend::OPENVINO, Backend::PDINFER, Backend::ORT,
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Backend::LITE};
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valid_gpu_backends = {Backend::ORT, Backend::PDINFER, Backend::TRT};
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valid_timvx_backends = {Backend::LITE};
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valid_ascend_backends = {Backend::LITE};
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valid_kunlunxin_backends = {Backend::LITE};
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valid_ipu_backends = {Backend::PDINFER};
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valid_directml_backends = {Backend::ORT};
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} else if (model_format == ModelFormat::SOPHGO) {
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valid_sophgonpu_backends = {Backend::SOPHGOTPU};
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} else {
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valid_cpu_backends = {Backend::ORT, Backend::OPENVINO};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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valid_rknpu_backends = {Backend::RKNPU2};
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valid_directml_backends = {Backend::ORT};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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std::unique_ptr<PPShiTuV2Recognizer> PPShiTuV2Recognizer::Clone() const {
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std::unique_ptr<PPShiTuV2Recognizer> clone_model =
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utils::make_unique<PPShiTuV2Recognizer>(PPShiTuV2Recognizer(*this));
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clone_model->SetRuntime(clone_model->CloneRuntime());
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return clone_model;
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}
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bool PPShiTuV2Recognizer::Initialize() {
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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bool PPShiTuV2Recognizer::Predict(cv::Mat* im, ClassifyResult* result) {
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if (!Predict(*im, result)) {
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return false;
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}
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return true;
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}
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bool PPShiTuV2Recognizer::Predict(const cv::Mat& im, ClassifyResult* result) {
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FDMat mat = WrapMat(im);
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return Predict(mat, result);
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}
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bool PPShiTuV2Recognizer::BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<ClassifyResult>* results) {
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std::vector<FDMat> mats = WrapMat(images);
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return BatchPredict(mats, results);
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}
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bool PPShiTuV2Recognizer::Predict(const FDMat& mat, ClassifyResult* result) {
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std::vector<ClassifyResult> results;
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std::vector<FDMat> mats = {mat};
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if (!BatchPredict(mats, &results)) {
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return false;
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}
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*result = std::move(results[0]);
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return true;
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}
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bool PPShiTuV2Recognizer::BatchPredict(const std::vector<FDMat>& mats,
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std::vector<ClassifyResult>* results) {
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std::vector<FDMat> fd_mats = mats;
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if (!preprocessor_.Run(&fd_mats, &reused_input_tensors_)) {
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FDERROR << "Failed to preprocess the input image." << std::endl;
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return false;
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}
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reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, results)) {
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FDERROR << "Failed to postprocess the inference results by runtime."
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<< std::endl;
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return false;
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}
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return true;
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}
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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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