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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
111 lines
4.7 KiB
C++
111 lines
4.7 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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#pragma once
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#include "fastdeploy/fastdeploy_model.h"
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#include "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_preprocessor.h"
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#include "fastdeploy/vision/classification/ppshitu/ppshituv2_rec_postprocessor.h"
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namespace fastdeploy {
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namespace vision {
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namespace classification {
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/*! @brief PPShiTuV2Recognizer model object used when to load a PPShiTuV2Recognizer model exported by PP-ShiTuV2 Rec model.
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*/
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class FASTDEPLOY_DECL PPShiTuV2Recognizer : public FastDeployModel {
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public:
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/** \brief Set path of model file and configuration file, and the configuration of runtime
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*
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* \param[in] model_file Path of model file, e.g PPLCNet/inference.pdmodel
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* \param[in] params_file Path of parameter file, e.g PPLCNet/inference.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] config_file Path of configuration file for deployment, e.g PPLCNet/inference_cls.yml
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
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* \param[in] model_format Model format of the loaded model, default is Paddle format
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*/
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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 = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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/** \brief Clone a new PPShiTuV2Recognizer with less memory usage when multiple instances of the same model are created
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*
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* \return new PPShiTuV2Recognizer* type unique pointer
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*/
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virtual std::unique_ptr<PPShiTuV2Recognizer> Clone() const;
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/// Get model's name
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virtual std::string ModelName() const { return "PPShiTuV2Recognizer"; }
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/** \brief DEPRECATED Predict the feature vector result for an input image, remove at 1.0 version
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*
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* \param[in] im The input image data, comes from cv::imread()
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* \param[in] result The output feature vector result will be writen to this structure
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, ClassifyResult* result);
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/** \brief Predict the classification result for an input image
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*
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* \param[in] img The input image data, comes from cv::imread()
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* \param[in] result The output feature vector result
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool Predict(const cv::Mat& img, ClassifyResult* result);
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/** \brief Predict the feature vector results for a batch of input images
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*
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* \param[in] imgs, The input image list, each element comes from cv::imread()
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* \param[in] results The output feature vector(namely ClassifyResult.feature) result list
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool BatchPredict(const std::vector<cv::Mat>& imgs,
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std::vector<ClassifyResult>* results);
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/** \brief Predict the feature vector result for an input image
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*
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* \param[in] mat The input mat
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* \param[in] result The output feature vector result
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool Predict(const FDMat& mat, ClassifyResult* result);
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/** \brief Predict the feature vector results for a batch of input images
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*
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* \param[in] mats, The input mat list
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* \param[in] results The output feature vector result list
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool BatchPredict(const std::vector<FDMat>& mats,
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std::vector<ClassifyResult>* results);
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/// Get preprocessor reference of PPShiTuV2Recognizer
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virtual PPShiTuV2RecognizerPreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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/// Get postprocessor reference of PPShiTuV2Recognizer
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virtual PPShiTuV2RecognizerPostprocessor& GetPostprocessor() {
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return postprocessor_;
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}
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protected:
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bool Initialize();
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PPShiTuV2RecognizerPreprocessor preprocessor_;
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PPShiTuV2RecognizerPostprocessor postprocessor_;
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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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