mmr/tkDNN
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interpret

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Francesco Gatti
2017-08-22 01:32:59 -07:00
parent 5a52de17eb
commit 5595b8037b
8 changed files with 106 additions and 60 deletions
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tests/yolo/yolo.cpp
+9 -4
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@@ -97,7 +97,9 @@ int main() {
tkDNN::Conv2d c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
tkDNN::Activation a29(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c30(&net, 425, 1, 1, 1, 1, 0, 0, c30_bin, false);
tkDNN::Region g31(&net, 80, 4, 5, 0.6f, g31_bin);
tkDNN::Region g31(&net, 80, 4, 5);
tkDNN::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.6f, g31_bin);
// Load input
dnnType *data;
@@ -120,7 +122,7 @@ int main() {
TIMER_STOP
dim1.print();
}
tkDNN::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
@@ -139,7 +141,10 @@ int main() {
std::cout<<"CUDNN vs TRT "; checkResult(out_dim, out_data, out_data2);
std::cout<<"\n\nDetected objects: \n";
g31.interpretData();
g31.showImageResult(input_h);
dnnType *output_h = new dnnType[rI.output_dim.tot()];
checkCuda(cudaMemcpy(output_h, out_data2,
rI.output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost));
rI.interpretData(output_h);
rI.showImageResult(input_h);
return 0;
}