mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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3589c0fa94
* Refactor the PaddleClas module * fix bug * remove debug code * clean unused code * support pybind * Update fd_tensor.h * Update fd_tensor.cc * temporary revert python api * fix ci error * fix code style problem
104 lines
4.2 KiB
C++
104 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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#pragma once
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#include "fastdeploy/fastdeploy_model.h"
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#include "fastdeploy/vision/classification/ppcls/preprocessor.h"
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#include "fastdeploy/vision/classification/ppcls/postprocessor.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All classification model APIs are defined inside this namespace
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*
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*/
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namespace classification {
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/*! @brief PaddleClas serials model object used when to load a PaddleClas model exported by PaddleClas repository
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*/
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class FASTDEPLOY_DECL PaddleClasModel : 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 resnet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g resnet/model.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 resnet/infer_cfg.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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PaddleClasModel(const std::string& model_file, 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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/// Get model's name
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virtual std::string ModelName() const { return "PaddleClas/Model"; }
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/** \brief DEPRECATED Predict the classification 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 classification 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, int topk = 1);
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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 classification 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 classification 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 classification 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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/// Get preprocessor reference of PaddleClasModel
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virtual PaddleClasPreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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/// Get postprocessor reference of PaddleClasModel
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virtual PaddleClasPostprocessor& 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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PaddleClasPreprocessor preprocessor_;
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PaddleClasPostprocessor postprocessor_;
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};
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typedef PaddleClasModel PPLCNet;
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typedef PaddleClasModel PPLCNetv2;
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typedef PaddleClasModel EfficientNet;
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typedef PaddleClasModel GhostNet;
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typedef PaddleClasModel MobileNetv1;
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typedef PaddleClasModel MobileNetv2;
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typedef PaddleClasModel MobileNetv3;
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typedef PaddleClasModel ShuffleNetv2;
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typedef PaddleClasModel SqueezeNet;
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typedef PaddleClasModel Inceptionv3;
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typedef PaddleClasModel PPHGNet;
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typedef PaddleClasModel ResNet50vd;
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typedef PaddleClasModel SwinTransformer;
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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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