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darknet parser to be tested on yolo3

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This commit is contained in:
Francesco Gatti
2020-05-30 19:38:26 +02:00
parent 298487d5c5 e5e6654b1d
commit 0ae96a6bc4
2 changed files with 123 additions and 77 deletions
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tests/yolo3/yolo3.cpp
+18 -38
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@@ -11,72 +11,52 @@ int main() {
tk::dnn::Network *net = tk::dnn::darknetParser("../tests/yolo3/yolov3.cfg", "yolo3/layers");
net->print();
/*
int classes = 80;
tk::dnn::Yolo *yolo [3];
#include "models/Yolo3.h"
std::vector<tk::dnn::Yolo*> yolo;
for(int i=0; i<net->num_layers; i++) {
if(net->layers[i]->getLayerType() == tk::dnn::layerType_t::LAYER_YOLO)
yolo.push_back((tk::dnn::Yolo*)net->layers[i]);
}
// 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"};
}
std::string input_bin = bin_path + "/layers/input.bin";
std::vector<std::string> output_bins = {
bin_path + "/debug/layer82_out.bin",
bin_path + "/debug/layer94_out.bin",
bin_path + "/debug/layer106_out.bin"
};
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//print network model
net.print();
readBinaryFile(input_bin, net->input_dim.tot(), &input_h, &data);
//convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3"));
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
tk::dnn::dataDim_t dim1 = net->input_dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); {
dim1.print();
TIMER_START
net.infer(dim1, data);
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;
tk::dnn::dataDim_t dim2 = net->input_dim;
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
TIMER_START
@@ -100,5 +80,5 @@ int main() {
ret_cudnn_tensorrt |= checkResult(odim, cudnn_out[i], rt_out[i]) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
}
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
*/
}