Micaela Verucchi
96 lines
3.8 KiB
C++
96 lines
3.8 KiB
C++
#include<iostream>
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#include<vector>
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#include "tkdnn.h"
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
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tk::dnn::Network net(dim);
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// create yolo3 model
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std::string bin_path = "../tests/yolo3";
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downloadWeightsifDoNotExist("../tests/yolo3/layers/input.bin", bin_path, "https://cloud.hipert.unimore.it/s/jPXmHyptpLoNdNR/download");
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int classes = 80;
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tk::dnn::Yolo *yolo [3];
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#include "models/Yolo3.h"
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// fill classes names
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for(int i=0; i<3; i++) {
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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"};
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}
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// Load input
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dnnType *data;
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dnnType *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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//print network model
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net.print();
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//convert network to tensorRT
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tk::dnn::NetworkRT netRT(&net, "yolo3.rt");
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// the network have 3 outputs
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tk::dnn::dataDim_t out_dim[3];
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for(int i=0; i<3; i++) out_dim[i] = yolo[i]->output_dim;
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dnnType *cudnn_out[3], *rt_out[3];
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tk::dnn::dataDim_t dim1 = dim; //input dim
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printCenteredTitle(" CUDNN inference ", '=', 30); {
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dim1.print();
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TIMER_START
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net.infer(dim1, data);
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TIMER_STOP
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dim1.print();
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}
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for(int i=0; i<3; i++) cudnn_out[i] = yolo[i]->dstData;
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printCenteredTitle(" compute detections ", '=', 30);
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TIMER_START
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int ndets = 0;
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tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
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for(int i=0; i<3; i++) yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
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tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
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for(int j=0; j<ndets; j++) {
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tk::dnn::Yolo::box b = dets[j].bbox;
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int x0 = (b.x-b.w/2.);
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int x1 = (b.x+b.w/2.);
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int y0 = (b.y-b.h/2.);
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int y1 = (b.y+b.h/2.);
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int cl = 0;
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for(int c = 0; c < classes; ++c){
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float prob = dets[j].prob[c];
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if(prob > 0)
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cl = c;
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}
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std::cout<<cl<<": "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
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}
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TIMER_STOP
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tk::dnn::dataDim_t dim2 = dim;
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printCenteredTitle(" TENSORRT inference ", '=', 30); {
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dim2.print();
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TIMER_START
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netRT.infer(dim2, data);
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TIMER_STOP
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dim2.print();
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}
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for(int i=0; i<3; i++) rt_out[i] = (dnnType*)netRT.buffersRT[i+1];
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for(int i=0; i<3; i++) {
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printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
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dnnType *out, *out_h;
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int odim = out_dim[i].tot();
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readBinaryFile(output_bins[i], odim, &out_h, &out);
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std::cout<<"CUDNN vs correct"; checkResult(odim, cudnn_out[i], out);
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std::cout<<"TRT vs correct"; checkResult(odim, rt_out[i], out);
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std::cout<<"CUDNN vs TRT "; checkResult(odim, cudnn_out[i], rt_out[i]);
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}
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return 0;
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}
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