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b557dbc2d8
* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test Co-authored-by: Jason <jiangjiajun@baidu.com>
71 lines
2.9 KiB
C++
Executable File
71 lines
2.9 KiB
C++
Executable File
// 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/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All image 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 YOLOv5Cls model object used when to load a YOLOv5Cls model exported by YOLOv5
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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 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 yolov5cls/yolov5n-cls.onnx
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* \param[in] params_file Path of parameter file, 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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/// Get model's name
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virtual 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] 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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* \param[in] topk Returns the topk classification result with the highest predicted probability, the default is 1
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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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/// Preprocess image size, the default is (224, 224)
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std::vector<int> size;
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private:
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bool Initialize();
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/// Preprocess an input image, and set the preprocessed results to `outputs`
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bool Preprocess(Mat* mat, FDTensor* output,
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const std::vector<int>& size = {224, 224});
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/// Postprocess the inferenced results, and set the final result to `result`
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bool Postprocess(const FDTensor& infer_result, ClassifyResult* result,
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int topk = 1);
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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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