tkDNN/include/tkDNN/pluginsRT/YoloRT.h

#include<cassert>
#include "../kernels.h"

#define YOLORT_CLASSNAME_W 256

class YoloRT : public IPlugin {



public:
	YoloRT(int classes, int num, tk::dnn::Yolo *yolo = nullptr, int n_masks=3, float scale_xy=1, float nms_thresh=0.45, int nms_kind=0, int new_coords=0) {

		this->classes = classes;
		this->num = num;
		this->n_masks = n_masks;
		this->scaleXY = scale_xy;
		this->nms_thresh = nms_thresh;
		this->nms_kind = nms_kind;
		this->new_coords = new_coords;

        mask = new dnnType[n_masks];
        bias = new dnnType[num*n_masks*2];
        if(yolo != nullptr) {
            memcpy(mask, yolo->mask_h, sizeof(dnnType)*n_masks);
            memcpy(bias, yolo->bias_h, sizeof(dnnType)*num*n_masks*2);
			classesNames = yolo->classesNames;
        }
	}

	~YoloRT(){

	}

	int getNbOutputs() const override {
		return 1;
	}

	Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
		return inputs[0];
	}

	void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
		c = inputDims[0].d[0];
		h = inputDims[0].d[1];
		w = inputDims[0].d[2];
	}

	int initialize() override {

		return 0;
	}

	virtual void terminate() override {
	}

	virtual size_t getWorkspaceSize(int maxBatchSize) const override {
		return 0;
	}

	virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {

		dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
		dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);

		checkCuda( cudaMemcpyAsync(dstData, srcData, batchSize*c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));


        for (int b = 0; b < batchSize; ++b){
            for(int n = 0; n < n_masks; ++n){
                int index = entry_index(b, n*w*h, 0);
                if (new_coords == 1){
                    if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
                }
                else{
                    activationLOGISTICForward(srcData + index, dstData + index, 2*w*h, stream); //x,y

                    if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);

                    index = entry_index(b, n*w*h, 4);
                    activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*w*h, stream);
                }
            }
        }

		//std::cout<<"YOLO END\n";
		return 0;
	}


	virtual size_t getSerializationSize() override {
		return 8*sizeof(int) + 2*sizeof(float)+ n_masks*sizeof(dnnType) + num*n_masks*2*sizeof(dnnType) + YOLORT_CLASSNAME_W*classes*sizeof(char);
	}

	virtual void serialize(void* buffer) override {
		char *buf = reinterpret_cast<char*>(buffer),*a=buf;
		tk::dnn::writeBUF(buf, classes); 	//std::cout << "Classes :" << classes << std::endl;
		tk::dnn::writeBUF(buf, num); 		//std::cout << "Num : " << num << std::endl;
		tk::dnn::writeBUF(buf, n_masks); 	//std::cout << "N_Masks" << n_masks << std::endl;
		tk::dnn::writeBUF(buf, scaleXY); 	//std::cout << "ScaleXY :" << scaleXY << std::endl;
		tk::dnn::writeBUF(buf, nms_thresh); //std::cout << "nms_thresh :" << nms_thresh << std::endl;
		tk::dnn::writeBUF(buf, nms_kind); 	//std::cout << "nms_kind : " << nms_kind << std::endl;
		tk::dnn::writeBUF(buf, new_coords); //std::cout << "new_coords : " << new_coords << std::endl;
		tk::dnn::writeBUF(buf, c); 			//std::cout << "C : " << c << std::endl;
		tk::dnn::writeBUF(buf, h); 			//std::cout << "H : " << h << std::endl;
		tk::dnn::writeBUF(buf, w); 			//std::cout << "C : " << c << std::endl;
		for (int i = 0; i < n_masks; i++)
		{
			tk::dnn::writeBUF(buf, mask[i]); //std::cout << "mask[i] : " << mask[i] << std::endl;
		}
		for (int i = 0; i < n_masks * 2 * num; i++)
		{
			tk::dnn::writeBUF(buf, bias[i]); //std::cout << "bias[i] : " << bias[i] << std::endl;
		}

		// save classes names
		for(int i=0; i<classes; i++) {
			char tmp[YOLORT_CLASSNAME_W];
			strcpy(tmp, classesNames[i].c_str());
			for(int j=0; j<YOLORT_CLASSNAME_W; j++) {
				tk::dnn::writeBUF(buf, tmp[j]);
			}
		}
		assert(buf == a + getSerializationSize());
	}

	int c, h, w;
    int classes, num, n_masks;
	float scaleXY;
	float nms_thresh;
	int nms_kind;
	int new_coords;
	std::vector<std::string> classesNames;

    dnnType *mask;
    dnnType *bias;

	int entry_index(int batch, int location, int entry) {
		int n =   location / (w*h);
		int loc = location % (w*h);
		return batch*c*h*w + n*w*h*(4+classes+1) + entry*w*h + loc;
	}

};