Micaela Verucchi
119 lines
3.0 KiB
C++
119 lines
3.0 KiB
C++
#include<cassert>
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#include "../kernels.h"
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#define YOLORT_CLASSNAME_W 256
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class YoloRT : public IPlugin {
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public:
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YoloRT(int classes, int num, tk::dnn::Yolo *yolo = nullptr, int n_masks=3) {
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this->classes = classes;
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this->num = num;
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this->n_masks = n_masks;
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mask = new dnnType[n_masks];
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bias = new dnnType[num*n_masks*2];
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if(yolo != nullptr) {
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memcpy(mask, yolo->mask_h, sizeof(dnnType)*n_masks);
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memcpy(bias, yolo->bias_h, sizeof(dnnType)*num*n_masks*2);
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classesNames = yolo->classesNames;
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}
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}
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~YoloRT(){
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}
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int getNbOutputs() const override {
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return 1;
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}
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Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
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return inputs[0];
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}
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void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
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c = inputDims[0].d[0];
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h = inputDims[0].d[1];
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w = inputDims[0].d[2];
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}
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int initialize() override {
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return 0;
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}
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virtual void terminate() override {
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}
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virtual size_t getWorkspaceSize(int maxBatchSize) const override {
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return 0;
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}
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virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {
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dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
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dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
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checkCuda( cudaMemcpyAsync(dstData, srcData, batchSize*c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));
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for (int b = 0; b < batchSize; ++b){
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for(int n = 0; n < n_masks; ++n){
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int index = entry_index(b, n*w*h, 0, batchSize);
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activationLOGISTICForward(srcData + index, dstData + index, 2*w*h, stream);
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index = entry_index(b, n*w*h, 4, batchSize);
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activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*w*h, stream);
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}
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}
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//std::cout<<"YOLO END\n";
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return 0;
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}
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virtual size_t getSerializationSize() override {
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return 6*sizeof(int) + n_masks*sizeof(dnnType) + num*n_masks*2*sizeof(dnnType) + YOLORT_CLASSNAME_W*classes*sizeof(char);
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}
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virtual void serialize(void* buffer) override {
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char *buf = reinterpret_cast<char*>(buffer);
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tk::dnn::writeBUF(buf, classes);
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tk::dnn::writeBUF(buf, num);
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tk::dnn::writeBUF(buf, n_masks);
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tk::dnn::writeBUF(buf, c);
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tk::dnn::writeBUF(buf, h);
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tk::dnn::writeBUF(buf, w);
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for(int i=0; i<n_masks; i++)
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tk::dnn::writeBUF(buf, mask[i]);
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for(int i=0; i<n_masks*2*num; i++)
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tk::dnn::writeBUF(buf, bias[i]);
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// save classes names
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for(int i=0; i<classes; i++) {
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char tmp[YOLORT_CLASSNAME_W];
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strcpy(tmp, classesNames[i].c_str());
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for(int j=0; j<YOLORT_CLASSNAME_W; j++) {
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tk::dnn::writeBUF(buf, tmp[j]);
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}
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}
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}
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int c, h, w;
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int classes, num, n_masks;
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std::vector<std::string> classesNames;
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dnnType *mask;
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dnnType *bias;
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int entry_index(int batch, int location, int entry, int batchSize) {
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int n = location / (w*h);
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int loc = location % (w*h);
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return batch*c*h*w*batchSize + n*w*h*(4+classes+1) + entry*w*h + loc;
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}
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};
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