#include "trt_yolov8_detector.h"

namespace trt_yolov8 {
    using namespace nvinfer1;
    void trt_yolov8_detector::serialize_engine(std::string& wts_name, std::string& engine_name, int& is_p, std::string& sub_type, float& gd,
                        float& gw, int& max_channels) {
        IBuilder* builder = createInferBuilder(gLogger);
        IBuilderConfig* config = builder->createBuilderConfig();
        IHostMemory* serialized_engine = nullptr;

        if (is_p == 6) {
            serialized_engine = buildEngineYolov8DetP6(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
        } else if (is_p == 2) {
            serialized_engine = buildEngineYolov8DetP2(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
        } else {
            serialized_engine = buildEngineYolov8Det(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
        }

        assert(serialized_engine);
        std::ofstream p(engine_name, std::ios::binary);
        if (!p) {
            std::cout << "could not open plan output file" << std::endl;
            assert(false);
        }
        p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());

        delete serialized_engine;
        delete config;
        delete builder;
    }

    void trt_yolov8_detector::deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine,
                            IExecutionContext** context) {
        std::ifstream file(engine_name, std::ios::binary);
        if (!file.good()) {
            std::cerr << "read " << engine_name << " error!" << std::endl;
            assert(false);
        }
        size_t size = 0;
        file.seekg(0, file.end);
        size = file.tellg();
        file.seekg(0, file.beg);
        char* serialized_engine = new char[size];
        assert(serialized_engine);
        file.read(serialized_engine, size);
        file.close();

        *runtime = createInferRuntime(gLogger);
        assert(*runtime);
        *engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
        assert(*engine);
        *context = (*engine)->createExecutionContext();
        assert(*context);
        delete[] serialized_engine;
    }

    void trt_yolov8_detector::prepare_buffer(ICudaEngine* engine, float** input_buffer_device, float** output_buffer_device,
                        float** output_buffer_host, float** decode_ptr_host, float** decode_ptr_device,
                        std::string cuda_post_process) {
        assert(engine->getNbBindings() == 2);
        // In order to bind the buffers, we need to know the names of the input and output tensors.
        // Note that indices are guaranteed to be less than IEngine::getNbBindings()
        const int inputIndex = engine->getBindingIndex(kInputTensorName);
        const int outputIndex = engine->getBindingIndex(kOutputTensorName);
        assert(inputIndex == 0);
        assert(outputIndex == 1);
        // Create GPU buffers on device
        CUDA_CHECK(cudaMalloc((void**)input_buffer_device, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
        CUDA_CHECK(cudaMalloc((void**)output_buffer_device, kBatchSize * kOutputSize * sizeof(float)));
        if (cuda_post_process == "c") {
            *output_buffer_host = new float[kBatchSize * kOutputSize];
        } else if (cuda_post_process == "g") {
            if (kBatchSize > 1) {
                std::cerr << "Do not yet support GPU post processing for multiple batches" << std::endl;
                exit(0);
            }
            // Allocate memory for decode_ptr_host and copy to device
            *decode_ptr_host = new float[1 + kMaxNumOutputBbox * bbox_element];
            CUDA_CHECK(cudaMalloc((void**)decode_ptr_device, sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element)));
        }
    }

    void trt_yolov8_detector::infer(IExecutionContext& context, cudaStream_t& stream, void** buffers, float* output, int batchsize,
            float* decode_ptr_host, float* decode_ptr_device, int model_bboxes, std::string cuda_post_process) {
        // infer on the batch asynchronously, and DMA output back to host
        auto start = std::chrono::system_clock::now();
        context.enqueue(batchsize, buffers, stream, nullptr);
        if (cuda_post_process == "c") {
            CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
                                    stream));
                                    /*
            auto end = std::chrono::system_clock::now();
            std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()
                    << "ms" << std::endl;*/
        } else if (cuda_post_process == "g") {
            CUDA_CHECK(
                    cudaMemsetAsync(decode_ptr_device, 0, sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element), stream));
            cuda_decode((float*)buffers[1], model_bboxes, kConfThresh, decode_ptr_device, kMaxNumOutputBbox, stream);
            cuda_nms(decode_ptr_device, kNmsThresh, kMaxNumOutputBbox, stream);  //cuda nms
            CUDA_CHECK(cudaMemcpyAsync(decode_ptr_host, decode_ptr_device,
                                    sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element), cudaMemcpyDeviceToHost,
                                    stream));
                                    /*
            auto end = std::chrono::system_clock::now();
            std::cout << "inference and gpu postprocess time: "
                    << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;*/
        }

        CUDA_CHECK(cudaStreamSynchronize(stream));
    }

    void trt_yolov8_detector::detect(std::vector<cv::Mat> images, std::vector<std::vector<Detection>>& detections) {
        // Prepare cpu and gpu buffers
        float* device_buffers[2];
        float* output_buffer_host = nullptr;
        float* decode_ptr_host = nullptr;
        float* decode_ptr_device = nullptr;    

        prepare_buffer(engine, &device_buffers[0], &device_buffers[1], &output_buffer_host, &decode_ptr_host,
                    &decode_ptr_device, cuda_post_process);
        // batch predict
        for (size_t i = 0; i < images.size(); i += kBatchSize) {
            // Get a batch of images
            std::vector<cv::Mat> img_batch;
            for (size_t j = i; j < i + kBatchSize && j < images.size(); j++) {
                img_batch.push_back(images[j]);
            }
            // Preprocess
            cuda_batch_preprocess(img_batch, device_buffers[0], kInputW, kInputH, stream);
            // Run inference
            infer(*context, stream, (void**)device_buffers, output_buffer_host, kBatchSize, decode_ptr_host,
                decode_ptr_device, model_bboxes, cuda_post_process);
            std::vector<std::vector<Detection>> res_batch;
            if (cuda_post_process == "c") {
                // NMS
                batch_nms(res_batch, output_buffer_host, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);
            } else if (cuda_post_process == "g") {
                //Process gpu decode and nms results
                batch_process(res_batch, decode_ptr_host, img_batch.size(), bbox_element, img_batch);
            }

            // push back to return
            detections.insert(detections.end(), res_batch.begin(), res_batch.end());
        }

        CUDA_CHECK(cudaFree(device_buffers[0]));
        CUDA_CHECK(cudaFree(device_buffers[1]));
        CUDA_CHECK(cudaFree(decode_ptr_device));
        delete[] decode_ptr_host;
        delete[] output_buffer_host;
    }

    trt_yolov8_detector::trt_yolov8_detector(std::string model_path) {
        if (model_path.empty()) {
            return;
        }
        
        cudaSetDevice(kGpuId);
        // Deserialize the engine from file
        deserialize_engine(model_path, &runtime, &engine, &context);
        CUDA_CHECK(cudaStreamCreate(&stream));
        cuda_preprocess_init(kMaxInputImageSize);
        auto out_dims = engine->getBindingDimensions(1);
        model_bboxes = out_dims.d[0];
    }
    
    trt_yolov8_detector::~trt_yolov8_detector() {
        // Release stream and buffers
        cudaStreamDestroy(stream);
        cuda_preprocess_destroy();
        // Destroy the engine
        delete context;
        delete engine;
        delete runtime;
    }

    bool trt_yolov8_detector::wts_2_engine(std::string wts_name, std::string engine_name, std::string sub_type) {
        int is_p = 0;
        float gd = 0.0f, gw = 0.0f;
        int max_channels = 0;

        if (sub_type[0] == 'n') {          // yolov8n
            gd = 0.33;
            gw = 0.25;
            max_channels = 1024;
        } else if (sub_type[0] == 's') {   // yolov8s
            gd = 0.33;
            gw = 0.50;
            max_channels = 1024;
        } else if (sub_type[0] == 'm') {   // yolov8m
            gd = 0.67;
            gw = 0.75;
            max_channels = 576;
        } else if (sub_type[0] == 'l') {   // yolov8l
            gd = 1.0;
            gw = 1.0;
            max_channels = 512;
        } else if (sub_type[0] == 'x') {   // yolov8x
            gd = 1.0;
            gw = 1.25;
            max_channels = 640;
        } else {
            return false;  // not support
        }
        
        if (sub_type.size() == 2 && sub_type[1] == '6') {         // yolov8n6/yolov8s6/yolov8m6/yolov8l6/yolov8x6
            is_p = 6;
        } else if (sub_type.size() == 2 && sub_type[1] == '2') {  // yolov8n2/yolov8s2/yolov8m2/yolov8l2/yolov8x2
            is_p = 2;
        }

        serialize_engine(wts_name, engine_name, is_p, sub_type, gd, gw, max_channels);
        return true;
    }
}