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https://github.com/PaddlePaddle/FastDeploy.git
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a36f5d3396
* cuda normalize and permute, cuda concat * add use cuda option for preprocessor * ppyoloe use cuda normalize * ppseg use cuda normalize * add proclib cuda in processor base * ppcls add use cuda preprocess api * ppcls preprocessor set gpu id * fix pybind * refine ppcls preprocessing use gpu logic * fdtensor device id is -1 by default * refine assert message Co-authored-by: heliqi <1101791222@qq.com>
59 lines
2.0 KiB
C++
59 lines
2.0 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/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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namespace classification {
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/*! @brief Preprocessor object for PaddleClas serials model.
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*/
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class FASTDEPLOY_DECL PaddleClasPreprocessor {
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public:
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/** \brief Create a preprocessor instance for PaddleClas serials model
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*
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* \param[in] config_file Path of configuration file for deployment, e.g resnet/infer_cfg.yml
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*/
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explicit PaddleClasPreprocessor(const std::string& config_file);
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/** \brief Process the input image and prepare input tensors for runtime
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*
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* \param[in] images The input image data list, all the elements are returned by cv::imread()
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* \param[in] outputs The output tensors which will feed in runtime
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* \return true if the preprocess successed, otherwise false
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*/
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bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
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/** \brief Use GPU to run preprocessing
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*
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* \param[in] gpu_id GPU device id
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*/
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void UseGpu(int gpu_id = -1);
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private:
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bool BuildPreprocessPipelineFromConfig(const std::string& config_file);
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std::vector<std::shared_ptr<Processor>> processors_;
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bool initialized_ = false;
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bool use_cuda_ = false;
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// GPU device id
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int device_id_ = -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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