Add TKDNN_MODE variable to the name of the network.

This commit permits to obtain different .rt files for different
precision optimizations of the same network.

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>

tests/csresnext50-panet-spp/csresnext50-panet-spp.cpp

@@ -475,7 +475,7 @@ int main()
     net.print();
 
     // //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "csresnext50-panet-spp.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("csresnext50-panet-spp"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/dla34/dla34.cpp

@@ -301,7 +301,7 @@ int main()
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "dla34.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34"));
 
     
     tk::dnn::dataDim_t out_dim;

tests/dla34_cnet/dla34_cnet.cpp

@@ -477,7 +477,7 @@ int main()
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "dla34_cnet.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_cnet"));
 
     tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30);

tests/mnist/test_mnist.cpp

@@ -22,7 +22,7 @@ int main() {
     tk::dnn::Dense      l6(&net, 10, d3_bin);
     tk::dnn::Softmax    l7(&net);
 
-    tk::dnn::NetworkRT netRT(&net, "mnist.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("mnist"));
 
     // Load input
     dnnType *data;

tests/mobilenetv2ssd/mobilenetv2ssd.cpp

@@ -467,7 +467,7 @@ int main()
     net.print();
 
     // convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "mobilenetv2ssd.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("mobilenetv2ssd"));
 
     tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30);

tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp

@@ -466,7 +466,7 @@ int main()
     net.print();
 
     // convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "mobilenetv2ssd512.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("mobilenetv2ssd512"));
 
     tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30);

tests/resnet101/resnet101.cpp

@@ -290,7 +290,7 @@ int main()
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "resnet101.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("resnet101"));
 
     
     tk::dnn::dataDim_t out_dim;

tests/resnet101_cnet/resnet101_cnet.cpp

@@ -350,7 +350,7 @@ int main()
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "resnet101_cnet.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("resnet101_cnet"));
 
     
     tk::dnn::dataDim_t dim1 = dim; //input dim

tests/simple/test_simple.cpp

@@ -32,7 +32,7 @@ int main() {
     std::cout<<"\n";
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "simple.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("simple"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo/yolo.cpp

@@ -112,7 +112,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo3/yolo3.cpp

@@ -31,7 +31,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/yolo3_512/yolo3_512.cpp

@@ -31,7 +31,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_512.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_512"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/yolo3_512tp/yolo3_512tp.cpp

@@ -29,7 +29,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_512tp.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_512tp"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/yolo3_berkeley/yolo3_berkeley.cpp

@@ -31,7 +31,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_berkeley.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_berkeley"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/yolo3_coco4/yolo3_coco4.cpp

@@ -24,7 +24,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_coco4.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_coco4"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/yolo3_flir/yolo3_flir.cpp

@@ -31,7 +31,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_flir.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_flir"));
 
     // the network have 3 outputs
     tk::dnn::dataDim_t out_dim[3];

tests/yolo3_tiny/yolo3_tiny.cpp

@@ -92,7 +92,7 @@ int main() {
     net.print();
 
     // convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_tiny.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_tiny"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo3_tiny512/yolo3_tiny512.cpp

@@ -89,7 +89,7 @@ int main() {
     net.print();
 
     // convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_tiny512.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_tiny512"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo3_tiny512tp/yolo3_tiny512tp.cpp

@@ -89,7 +89,7 @@ int main() {
     net.print();
 
     // convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo3_tiny512tp.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo3_tiny512tp"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo_224/yolo_224.cpp

@@ -110,7 +110,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo_224.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo_224"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo_berkeley/yolo_berkeley.cpp

@@ -110,7 +110,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo_berkeley.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo_berkeley"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo_relu/yolo_relu.cpp

@@ -110,7 +110,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo_relu.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo_relu"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo_tiny/yolo_tiny.cpp

@@ -62,7 +62,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo_tiny.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo_tiny"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 

tests/yolo_voc/yolo_voc.cpp

@@ -112,7 +112,7 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tk::dnn::NetworkRT netRT(&net, "yolo_voc.rt");
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("yolo_voc"));
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output