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9cd00ad4c5
* Refactoring code of YOLOv5Cls with new model type * fix reviewed problem * Normalize&HWC2CHW -> NormalizeAndPermute * remove cast()
77 lines
3.1 KiB
C++
Executable File
77 lines
3.1 KiB
C++
Executable File
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. //NOLINT
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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/contrib/yolov5cls/preprocessor.h"
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#include "fastdeploy/vision/classification/contrib/yolov5cls/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 YOLOv5Cls model object used when to load a YOLOv5Cls model exported by YOLOv5Cls.
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*/
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class FASTDEPLOY_DECL YOLOv5Cls : public FastDeployModel {
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public:
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/** \brief Set path of model file and the configuration of runtime.
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*
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* \param[in] model_file Path of model file, e.g ./yolov5cls.onnx
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* \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
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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 ONNX format
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*/
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YOLOv5Cls(const std::string& model_file, const std::string& params_file = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX);
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std::string ModelName() const { return "yolov5cls"; }
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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(), is a 3-D array with layout HWC, BGR format
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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(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 YOLOv5Cls
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virtual YOLOv5ClsPreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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/// Get postprocessor reference of YOLOv5Cls
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virtual YOLOv5ClsPostprocessor& 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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YOLOv5ClsPreprocessor preprocessor_;
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YOLOv5ClsPostprocessor 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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