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77cb9db6da
* [cmake] add faiss.cmake -> pp-shituv2 * [PP-ShiTuV2] Support PP-ShituV2-Det model * [PP-ShiTuV2] Support PP-ShiTuV2-Det model * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [Bug Fix] fix ppshitu_pybind error * [benchmark] Add ppshituv2-det c++ benchmark * [examples] Add PP-ShiTuV2 det & rec examples * [vision] Update vision classification result * [Bug Fix] fix trt shapes setting errors
73 lines
2.7 KiB
C++
73 lines
2.7 KiB
C++
// Copyright (c) 2023 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/manager.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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namespace classification {
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/*! @brief Preprocessor object for PP-ShiTuV2 Recognizer model.
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*/
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class FASTDEPLOY_DECL PPShiTuV2RecognizerPreprocessor : public ProcessorManager {
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public:
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/** \brief Create a preprocessor instance for PP-ShiTuV2 Recognizer model
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*
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* \param[in] config_file Path of configuration file for deployment, e.g PPLCNet/infer_cfg.yml
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*/
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explicit PPShiTuV2RecognizerPreprocessor(const std::string& config_file);
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/** \brief Implement the virtual function of ProcessorManager, Apply() is the
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* body of Run(). Apply() contains the main logic of preprocessing, Run() is
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* called by users to execute preprocessing
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*
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* \param[in] image_batch The input image batch
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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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virtual bool Apply(FDMatBatch* image_batch,
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std::vector<FDTensor>* outputs);
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/// This function will disable normalize in preprocessing step.
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void DisableNormalize();
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/// This function will disable hwc2chw in preprocessing step.
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void DisablePermute();
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/** \brief When the initial operator is Resize, and input image size is large,
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* maybe it's better to run resize on CPU, because the HostToDevice memcpy
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* is time consuming. Set this true to run the initial resize on CPU.
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*
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* \param[in] v ture or false
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*/
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void InitialResizeOnCpu(bool v) { initial_resize_on_cpu_ = v; }
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private:
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bool BuildPreprocessPipelineFromConfig();
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bool initialized_ = false;
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std::vector<std::shared_ptr<Processor>> processors_;
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// for recording the switch of hwc2chw
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bool disable_permute_ = false;
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// for recording the switch of normalize
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bool disable_normalize_ = false;
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// read config file
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std::string config_file_;
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bool initial_resize_on_cpu_ = false;
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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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