mmr/tkDNN
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Mnist works at the moment with trt8,others like yolo4tiny and mobilenet generate the engine files but crash after throwing nvifer1::CudaRuntimeError and when demo is being run ,it doesnt deserialize properly and crashes

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This commit is contained in:
perseusdg
2021-08-29 03:18:58 +05:30
parent 3d8b1ac494
commit 2ffe07057e
12 changed files with 255 additions and 42 deletions
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src/Yolo3Detection.cpp
+35 -16
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@@ -3,7 +3,7 @@
namespace tk { namespace dnn {
bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes, const int n_batches, const float conf_thresh) {
bool Yolo3Detection::init(const std::string& tensor_path,const std::string& cfg_path,const std::string& name_path,const int n_classes, const int n_batches, const float conf_thresh) {
//convert network to tensorRT
std::cout<<(tensor_path).c_str()<<"\n";
@@ -14,28 +14,42 @@ bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes, c
tk::dnn::dataDim_t idim = netRT->input_dim;
idim.n = nBatches;
std::vector<int> yolosLine = noYolosLine(cfg_path);
noYolos = yolosLine;
int channels,height,width;
loadYoloInitInfo(channels,width,height,cfg_path);
if(netRT->pluginFactory->n_yolos < 2 ) {
if(yolosLine.size() < 2 ) {
FatalError("this is not yolo3");
}
for(int i=0; i<netRT->pluginFactory->n_yolos; i++) {
YoloRT *yRT = netRT->pluginFactory->yolos[i];
classes = yRT->classes;
num = yRT->num;
nMasks = yRT->n_masks;
for(int i=0; i<noYolos.size(); i++) {
std::vector<float> maskTemp,anchorsTemp;
std::vector<std::string> classNamesTemp;
int classes,nms_kind,coords,numTemp;
float nmsthresh;
loadYoloInfo(cfg_path,yolosLine[i],maskTemp,anchorsTemp,numTemp,classes,nmsthresh,nms_kind,coords);
classNamesTemp = darknetReadNames(name_path);
num = numTemp/maskTemp.size();
nMasks = maskTemp.size();
dnnType* maskTempF;
dnnType* biasTempF;
maskTempF = maskTemp.data();
biasTempF = anchorsTemp.data();
// make a yolo layer to interpret predictions
yolo[i] = new tk::dnn::Yolo(nullptr, classes, nMasks, ""); // yolo without input and bias
yolo[i]->mask_h = new dnnType[nMasks];
yolo[i]->bias_h = new dnnType[num*nMasks*2];
memcpy(yolo[i]->mask_h, yRT->mask, sizeof(dnnType)*nMasks);
memcpy(yolo[i]->bias_h, yRT->bias, sizeof(dnnType)*num*nMasks*2);
yolo[i]->input_dim = yolo[i]->output_dim = tk::dnn::dataDim_t(1, yRT->c, yRT->h, yRT->w);
yolo[i]->classesNames = yRT->classesNames;
yolo[i]->nms_thresh = yRT->nms_thresh;
yolo[i]->nsm_kind = (tk::dnn::Yolo::nmsKind_t) yRT->nms_kind;
yolo[i]->new_coords = yRT->new_coords;
memcpy(yolo[i]->mask_h, maskTempF, sizeof(dnnType)*nMasks);
memcpy(yolo[i]->bias_h, biasTempF, sizeof(dnnType)*num*nMasks*2);
yolo[i]->input_dim = yolo[i]->output_dim = tk::dnn::dataDim_t(1, channels, height, width);
yolo[i]->classesNames = classNamesTemp;
yolo[i]->nms_thresh = nmsthresh;
yolo[i]->nsm_kind = (tk::dnn::Yolo::nmsKind_t) nms_kind;
yolo[i]->new_coords = coords;
}
dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
@@ -94,10 +108,15 @@ void Yolo3Detection::preprocess(cv::Mat &frame, const int bi){
void Yolo3Detection::postprocess(const int bi, const bool mAP){
//get yolo outputs
if(noYolos.size() < 2){
FatalError("YOLOS WRONG!!");
}
std::vector<float *> rt_out;
//dnnType *rt_out[netRT->pluginFactory->n_yolos];
for(int i=0; i<netRT->pluginFactory->n_yolos; i++)
for(int i=0; i<noYolos.size(); i++)
rt_out.push_back((dnnType*)netRT->buffersRT[i+1] + netRT->buffersDIM[i+1].tot()*bi);
float x_ratio = float(originalSize[bi].width) / float(netRT->input_dim.w);
@@ -105,7 +124,7 @@ void Yolo3Detection::postprocess(const int bi, const bool mAP){
// compute dets
nDets = 0;
for(int i=0; i<netRT->pluginFactory->n_yolos; i++) {
for(int i=0; i<noYolos.size(); i++) {
yolo[i]->dstData = rt_out[i];
yolo[i]->computeDetections(dets, nDets, netRT->input_dim.w, netRT->input_dim.h, confThreshold, yolo[i]->new_coords);
}