mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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84 lines
3.2 KiB
C++
84 lines
3.2 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/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 segmentation model APIs are defined inside this namespace
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*
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*/
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namespace segmentation {
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/*! @brief PaddleSeg serials model object used when to load a PaddleSeg model exported by PaddleSeg repository
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*/
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class FASTDEPLOY_DECL PaddleSegModel : 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 unet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g unet/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 unet/deploy.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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PaddleSegModel(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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std::string ModelName() const { return "PaddleSeg"; }
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/** \brief Predict the segmentation 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 segmentation result will be writen to this structure
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* \return true if the segmentation prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, SegmentationResult* result);
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/** \brief Whether applying softmax operator in the postprocess, default value is false
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*/
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bool apply_softmax = false;
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/** \brief For PP-HumanSeg model, set true if the input image is vertical image(height > width), default value is false
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*/
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bool is_vertical_screen = false;
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private:
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bool Initialize();
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bool BuildPreprocessPipelineFromConfig();
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bool Preprocess(Mat* mat, FDTensor* outputs);
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bool Postprocess(FDTensor* infer_result, SegmentationResult* result,
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const std::map<std::string, std::array<int, 2>>& im_info);
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bool is_with_softmax = false;
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bool is_with_argmax = true;
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std::vector<std::shared_ptr<Processor>> processors_;
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std::string config_file_;
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};
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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