Files
deepstream_yolo/non_square/nvdsinfer_custom_impl_Yolo/yolo.h
Marcos Luciano 9dbb9edb7f Added non_square
Support for non square YOLO models.
2021-02-18 18:01:03 -03:00

100 lines
3.2 KiB
C++

/*
* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
* Edited by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#ifndef _YOLO_H_
#define _YOLO_H_
#include "layers/convolutional_layer.h"
#include "layers/dropout_layer.h"
#include "layers/shortcut_layer.h"
#include "layers/route_layer.h"
#include "layers/upsample_layer.h"
#include "layers/maxpool_layer.h"
#include "nvdsinfer_custom_impl.h"
struct NetworkInfo
{
std::string networkType;
std::string configFilePath;
std::string wtsFilePath;
std::string deviceType;
std::string inputBlobName;
};
struct TensorInfo
{
std::string blobName;
uint stride{0};
uint gridSizeY{0};
uint gridSizeX{0};
uint numClasses{0};
uint numBBoxes{0};
uint64_t volume{0};
std::vector<uint> masks;
std::vector<float> anchors;
int bindingIndex{-1};
float* hostBuffer{nullptr};
};
class Yolo : public IModelParser {
public:
Yolo(const NetworkInfo& networkInfo);
~Yolo() override;
bool hasFullDimsSupported() const override { return false; }
const char* getModelName() const override {
return m_ConfigFilePath.empty() ? m_NetworkType.c_str()
: m_ConfigFilePath.c_str();
}
NvDsInferStatus parseModel(nvinfer1::INetworkDefinition& network) override;
nvinfer1::ICudaEngine *createEngine (nvinfer1::IBuilder* builder);
protected:
const std::string m_NetworkType;
const std::string m_ConfigFilePath;
const std::string m_WtsFilePath;
const std::string m_DeviceType;
const std::string m_InputBlobName;
std::vector<TensorInfo> m_OutputTensors;
std::vector<std::vector<int>> m_OutputMasks;
std::vector<std::map<std::string, std::string>> m_ConfigBlocks;
uint m_InputH;
uint m_InputW;
uint m_InputC;
uint64_t m_InputSize;
std::vector<nvinfer1::Weights> m_TrtWeights;
private:
NvDsInferStatus buildYoloNetwork(
std::vector<float>& weights, nvinfer1::INetworkDefinition& network);
std::vector<std::map<std::string, std::string>> parseConfigFile(
const std::string cfgFilePath);
void parseConfigBlocks();
void destroyNetworkUtils();
};
#endif // _YOLO_H_