/*
 * 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 "NvInferPlugin.h"
#include "nvdsinfer_custom_impl.h"

#include "layers/convolutional_layer.h"
#include "layers/deconvolutional_layer.h"
#include "layers/batchnorm_layer.h"
#include "layers/implicit_layer.h"
#include "layers/channels_layer.h"
#include "layers/shortcut_layer.h"
#include "layers/sam_layer.h"
#include "layers/route_layer.h"
#include "layers/upsample_layer.h"
#include "layers/pooling_layer.h"
#include "layers/reorg_layer.h"

#if NV_TENSORRT_MAJOR >= 8
#define INT int32_t
#else
#define INT int

static class Logger : public nvinfer1::ILogger {
  void log(nvinfer1::ILogger::Severity severity, const char* msg) noexcept override {
    if (severity <= nvinfer1::ILogger::Severity::kWARNING)
      std::cout << msg << std::endl;
  }
} logger;
#endif

struct NetworkInfo
{
  std::string inputBlobName;
  std::string networkType;
  std::string modelName;
  std::string onnxWtsFilePath;
  std::string darknetWtsFilePath;
  std::string darknetCfgFilePath;
  uint batchSize;
  int implicitBatch;
  std::string int8CalibPath;
  std::string deviceType;
  uint numDetectedClasses;
  int clusterMode;
  std::string networkMode;
  float scaleFactor;
  const float* offsets;
  uint workspaceSize;
};

struct TensorInfo
{
  std::string blobName;
  uint gridSizeX {0};
  uint gridSizeY {0};
  uint numBBoxes {0};
  float scaleXY;
  std::vector<float> anchors;
  std::vector<int> mask;
};

class Yolo : public IModelParser {
  public:
    Yolo(const NetworkInfo& networkInfo);

    ~Yolo() override;

    bool hasFullDimsSupported() const override { return false; }

    const char* getModelName() const override {
      return m_NetworkType == "onnx" ? m_OnnxWtsFilePath.substr(0, m_OnnxWtsFilePath.find(".onnx")).c_str() :
          m_DarknetCfgFilePath.substr(0, m_DarknetCfgFilePath.find(".cfg")).c_str();
    }

    NvDsInferStatus parseModel(nvinfer1::INetworkDefinition& network) override;

#if NV_TENSORRT_MAJOR >= 8
    nvinfer1::ICudaEngine* createEngine(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config);
#else
    nvinfer1::ICudaEngine* createEngine(nvinfer1::IBuilder* builder);
#endif

  protected:
    const std::string m_InputBlobName;
    const std::string m_NetworkType;
    const std::string m_ModelName;
    const std::string m_OnnxWtsFilePath;
    const std::string m_DarknetWtsFilePath;
    const std::string m_DarknetCfgFilePath;
    const uint m_BatchSize;
    const int m_ImplicitBatch;
    const std::string m_Int8CalibPath;
    const std::string m_DeviceType;
    const uint m_NumDetectedClasses;
    const int m_ClusterMode;
    const std::string m_NetworkMode;
    const float m_ScaleFactor;
    const float* m_Offsets;
    const uint m_WorkspaceSize;

    uint m_InputC;
    uint m_InputH;
    uint m_InputW;
    uint64_t m_InputSize;
    uint m_NumClasses;
    uint m_LetterBox;
    uint m_NewCoords;
    uint m_YoloCount;

    std::vector<TensorInfo> m_YoloTensors;
    std::vector<std::map<std::string, std::string>> m_ConfigBlocks;
    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_