Micaela Verucchi
100 lines
4.0 KiB
C++
100 lines
4.0 KiB
C++
#include<iostream>
|
|
#include<vector>
|
|
#include "tkdnn.h"
|
|
|
|
int main() {
|
|
|
|
// Network layout
|
|
tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
|
|
tk::dnn::Network net(dim);
|
|
|
|
// create yolo3 model
|
|
std::string bin_path = "yolo3";
|
|
downloadWeightsifDoNotExist("yolo3/layers/input.bin", bin_path, "https://cloud.hipert.unimore.it/s/jPXmHyptpLoNdNR/download");
|
|
int classes = 80;
|
|
tk::dnn::Yolo *yolo [3];
|
|
#include "models/Yolo3.h"
|
|
|
|
|
|
|
|
// fill classes names
|
|
for(int i=0; i<3; i++) {
|
|
yolo[i]->classesNames = {"person" , "bicycle" , "car" , "motorbike" , "aeroplane" , "bus" , "train" , "truck" , "boat" , "traffic light" , "fire hydrant" , "stop sign" , "parking meter" , "bench" , "bird" , "cat" , "dog" , "horse" , "sheep" , "cow" , "elephant" , "bear" , "zebra" , "giraffe" , "backpack" , "umbrella" , "handbag" , "tie" , "suitcase" , "frisbee" , "skis" , "snowboard" , "sports ball" , "kite" , "baseball bat" , "baseball glove" , "skateboard" , "surfboard" , "tennis racket" , "bottle" , "wine glass" , "cup" , "fork" , "knife" , "spoon" , "bowl" , "banana" , "apple" , "sandwich" , "orange" , "broccoli" , "carrot" , "hot dog" , "pizza" , "donut" , "cake" , "chair" , "sofa" , "pottedplant" , "bed" , "diningtable" , "toilet" , "tvmonitor" , "laptop" , "mouse" , "remote" , "keyboard" , "cell phone" , "microwave" , "oven" , "toaster" , "sink" , "refrigerator" , "book" , "clock" , "vase" , "scissors" , "teddy bear" , "hair drier" , "toothbrush"};
|
|
}
|
|
|
|
// Load input
|
|
dnnType *data;
|
|
dnnType *input_h;
|
|
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
|
|
|
//print network model
|
|
net.print();
|
|
|
|
//convert network to tensorRT
|
|
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3"));
|
|
|
|
// the network have 3 outputs
|
|
tk::dnn::dataDim_t out_dim[3];
|
|
for(int i=0; i<3; i++) out_dim[i] = yolo[i]->output_dim;
|
|
dnnType *cudnn_out[3], *rt_out[3];
|
|
|
|
tk::dnn::dataDim_t dim1 = dim; //input dim
|
|
printCenteredTitle(" CUDNN inference ", '=', 30); {
|
|
dim1.print();
|
|
TIMER_START
|
|
net.infer(dim1, data);
|
|
TIMER_STOP
|
|
dim1.print();
|
|
}
|
|
for(int i=0; i<3; i++) cudnn_out[i] = yolo[i]->dstData;
|
|
|
|
printCenteredTitle(" compute detections ", '=', 30);
|
|
TIMER_START
|
|
int ndets = 0;
|
|
tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
|
|
for(int i=0; i<3; i++) yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
|
|
tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
|
|
|
|
for(int j=0; j<ndets; j++) {
|
|
tk::dnn::Yolo::box b = dets[j].bbox;
|
|
int x0 = (b.x-b.w/2.);
|
|
int x1 = (b.x+b.w/2.);
|
|
int y0 = (b.y-b.h/2.);
|
|
int y1 = (b.y+b.h/2.);
|
|
|
|
int cl = 0;
|
|
for(int c = 0; c < classes; ++c){
|
|
float prob = dets[j].prob[c];
|
|
if(prob > 0)
|
|
cl = c;
|
|
}
|
|
std::cout<<cl<<": "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
|
|
}
|
|
TIMER_STOP
|
|
|
|
tk::dnn::dataDim_t dim2 = dim;
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30); {
|
|
dim2.print();
|
|
TIMER_START
|
|
netRT.infer(dim2, data);
|
|
TIMER_STOP
|
|
dim2.print();
|
|
}
|
|
for(int i=0; i<3; i++) rt_out[i] = (dnnType*)netRT.buffersRT[i+1];
|
|
|
|
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
|
|
for(int i=0; i<3; i++) {
|
|
printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
|
|
dnnType *out, *out_h;
|
|
int odim = out_dim[i].tot();
|
|
readBinaryFile(output_bins[i], odim, &out_h, &out);
|
|
std::cout<<"CUDNN vs correct";
|
|
ret_cudnn |= checkResult(odim, cudnn_out[i], out) == 0 ? 0: ERROR_CUDNN;
|
|
std::cout<<"TRT vs correct";
|
|
ret_tensorrt |= checkResult(odim, rt_out[i], out) == 0 ? 0 : ERROR_TENSORRT;
|
|
std::cout<<"CUDNN vs TRT ";
|
|
ret_cudnn_tensorrt |= checkResult(odim, cudnn_out[i], rt_out[i]) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
|
|
}
|
|
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
|
|
}
|