80 lines
3.4 KiB
C++
80 lines
3.4 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/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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// The namespace shoulde be
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// fastdeploy::vision::classification (fastdeploy::vision::${task})
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namespace fastdeploy {
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namespace vision {
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/** \brief All object 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 Torchvision ResNet series model
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*/
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class FASTDEPLOY_DECL ResNet : 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 ./resnet50.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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ResNet(const std::string& model_file,
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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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virtual std::string ModelName() const { return "ResNet"; }
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/** \brief Predict for the input "im", the result will be saved in "result".
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*
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* \param[in] im 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 Saving the inference result.
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* \param[in] topk The length of return values, e.g., if topk==2, the result will include the 2 most possible class label for input image.
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*/
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virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
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/*! @brief
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Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {224, 224}
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*/
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std::vector<int> size;
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/*! @brief
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Mean parameters for normalize, size should be the the same as channels, default mean_vals = {0.485f, 0.456f, 0.406f}
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*/
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std::vector<float> mean_vals;
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/*! @brief
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Std parameters for normalize, size should be the the same as channels, default std_vals = {0.229f, 0.224f, 0.225f}
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*/
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std::vector<float> std_vals;
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private:
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/*! @brief Initialize for ResNet model, assign values to the global variables and call InitRuntime()
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*/
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bool Initialize();
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/// PreProcessing for the input "mat", the result will be saved in "outputs".
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bool Preprocess(Mat* mat, FDTensor* outputs);
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/*! @brief PostProcessing for the input "infer_result", the result will be saved in "result".
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*/
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bool Postprocess(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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