mmr/tkDNN
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opencv viz

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This commit is contained in:
Francesco Gatti
2017-08-10 16:22:17 +02:00
parent b75fa637cb
commit 266330009c
31 changed files with 211 additions and 152 deletions
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tests/mnist/test_mnist.cpp
+5 -5
View File
@@ -25,11 +25,11 @@ int main() {
tkDNN::NetworkRT netRT(&net);
// Load input
value_type *data;
value_type *input_h;
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
value_type *out_data, *out_data2;
dnnType *out_data, *out_data2;
std::cout<<"CUDNN inference:\n"; {
dim.print(); //print initial dimension
@@ -63,8 +63,8 @@ int main() {
/*
// Print real test
std::cout<<"\n==== CHECK RESULT ====\n";
value_type *out;
value_type *out_h;
dnnType *out;
dnnType *out_h;
readBinaryFile(output_bin, dim.tot(), &out_h, &out);
printDeviceVector(dim.tot(), out);
*/
tests/mnist/test_mnistRT.cpp
+4 -4
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@@ -39,8 +39,8 @@ int main() {
tkDNN::Softmax l7(&net);
// Load input
value_type *data;
value_type *input_h;
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
dim.print(); //print initial dimension
@@ -55,8 +55,8 @@ int main() {
// Print real test
std::cout<<"\n==== CHECK CUDNN RESULT ====\n";
value_type *out;
value_type *out_h;
dnnType *out;
dnnType *out_h;
readBinaryFile(output_bin, dim.tot(), &out_h, &out);
std::cout<<"Diff: "<<checkResult(dim.tot(), out, data)<<"\n";
tests/simple/test_simple.cpp
+4 -4
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@@ -21,8 +21,8 @@ int main() {
tkDNN::Activation l6(&net, CUDNN_ACTIVATION_RELU);
// Load input
value_type *data;
value_type *input_h;
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
printDeviceVector(dim.tot(), data);
@@ -39,8 +39,8 @@ int main() {
// Print real test
std::cout<<"\n==== CHECK RESULT ====\n";
value_type *out;
value_type *out_h;
dnnType *out;
dnnType *out_h;
readBinaryFile(output_bin, dim.tot(), &out_h, &out);
printDeviceVector(dim.tot(), out);
return 0;
tests/yolo-tiny/yolo-tiny.cpp
+5 -4
View File
@@ -52,8 +52,8 @@ int main() {
tkDNN::Region g14(&net, 80, 4, 5, 0.6f, g14_bin);
// Load input
value_type *data;
value_type *input_h;
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//print network model
@@ -62,7 +62,7 @@ int main() {
//convert network to tensorRT
tkDNN::NetworkRT netRT(&net);
value_type *out_data, *out_data2; // cudnn output, tensorRT output
dnnType *out_data, *out_data2; // cudnn output, tensorRT output
tkDNN::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); {
@@ -83,7 +83,7 @@ int main() {
}
printCenteredTitle(" CHECK RESULTS ", '=', 30);
value_type *out, *out_h;
dnnType *out, *out_h;
int out_dim = net.getOutputDim().tot();
readBinaryFile(output_bin, out_dim, &out_h, &out);
std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
@@ -92,5 +92,6 @@ int main() {
std::cout<<"\n\nDetected objects: \n";
g14.interpretData();
g14.showImageResult(input_h);
return 0;
}
tests/yolo/yolo.cpp
+5 -4
View File
@@ -100,8 +100,8 @@ int main() {
tkDNN::Region g31(&net, 80, 4, 5, 0.6f, g31_bin);
// Load input
value_type *data;
value_type *input_h;
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//print network model
@@ -110,7 +110,7 @@ int main() {
//convert network to tensorRT
tkDNN::NetworkRT netRT(&net);
value_type *out_data, *out_data2; // cudnn output, tensorRT output
dnnType *out_data, *out_data2; // cudnn output, tensorRT output
tkDNN::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); {
@@ -131,7 +131,7 @@ int main() {
}
printCenteredTitle(" CHECK RESULTS ", '=', 30);
value_type *out, *out_h;
dnnType *out, *out_h;
int out_dim = net.getOutputDim().tot();
readBinaryFile(output_bin, out_dim, &out_h, &out);
std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
@@ -140,5 +140,6 @@ int main() {
std::cout<<"\n\nDetected objects: \n";
g31.interpretData();
g31.showImageResult(input_h);
return 0;
}