109 lines
3.8 KiB
C++
109 lines
3.8 KiB
C++
/*
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* Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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* Edited by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*/
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#include <algorithm>
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#include "nvdsinfer_custom_impl.h"
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#include "nvdsinfer_context.h"
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#include "yolo.h"
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#define USE_CUDA_ENGINE_GET_API 1
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static bool
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getYoloNetworkInfo(NetworkInfo& networkInfo, const NvDsInferContextInitParams* initParams)
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{
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std::string yoloCfg = initParams->customNetworkConfigFilePath;
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std::string yoloType;
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std::transform(yoloCfg.begin(), yoloCfg.end(), yoloCfg.begin(), [] (uint8_t c) {
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return std::tolower(c);
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});
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yoloType = yoloCfg.substr(0, yoloCfg.find(".cfg"));
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networkInfo.inputBlobName = "input";
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networkInfo.networkType = yoloType;
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networkInfo.configFilePath = initParams->customNetworkConfigFilePath;
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networkInfo.wtsFilePath = initParams->modelFilePath;
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networkInfo.int8CalibPath = initParams->int8CalibrationFilePath;
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networkInfo.deviceType = (initParams->useDLA ? "kDLA" : "kGPU");
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networkInfo.numDetectedClasses = initParams->numDetectedClasses;
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networkInfo.clusterMode = initParams->clusterMode;
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if (initParams->networkMode == 0)
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networkInfo.networkMode = "FP32";
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else if (initParams->networkMode == 1)
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networkInfo.networkMode = "INT8";
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else if (initParams->networkMode == 2)
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networkInfo.networkMode = "FP16";
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if (networkInfo.configFilePath.empty() || networkInfo.wtsFilePath.empty()) {
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std::cerr << "YOLO config file or weights file is not specified\n" << std::endl;
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return false;
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}
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if (!fileExists(networkInfo.configFilePath) || !fileExists(networkInfo.wtsFilePath)) {
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std::cerr << "YOLO config file or weights file is not exist\n" << std::endl;
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return false;
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}
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return true;
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}
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#if !USE_CUDA_ENGINE_GET_API
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IModelParser*
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NvDsInferCreateModelParser(const NvDsInferContextInitParams* initParams)
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{
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NetworkInfo networkInfo;
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if (!getYoloNetworkInfo(networkInfo, initParams))
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return nullptr;
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return new Yolo(networkInfo);
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}
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#else
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extern "C" bool
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NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder* const builder, nvinfer1::IBuilderConfig* const builderConfig,
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const NvDsInferContextInitParams* const initParams, nvinfer1::DataType dataType, nvinfer1::ICudaEngine*& cudaEngine);
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extern "C" bool
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NvDsInferYoloCudaEngineGet(nvinfer1::IBuilder* const builder, nvinfer1::IBuilderConfig* const builderConfig,
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const NvDsInferContextInitParams* const initParams, nvinfer1::DataType dataType, nvinfer1::ICudaEngine*& cudaEngine)
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{
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NetworkInfo networkInfo;
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if (!getYoloNetworkInfo(networkInfo, initParams))
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return false;
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Yolo yolo(networkInfo);
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cudaEngine = yolo.createEngine(builder, builderConfig);
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if (cudaEngine == nullptr) {
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std::cerr << "Failed to build CUDA engine on " << networkInfo.configFilePath << std::endl;
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return false;
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}
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return true;
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}
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#endif
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