From c8ed6d782a143e25972fc6797e044188e2d308c9 Mon Sep 17 00:00:00 2001
From: Francesco Gatti <gattifrancesco@hotmail.it>
Date: Mon, 1 Jun 2020 16:15:29 +0200
Subject: [PATCH] all test ok

---
 include/tkDNN/DarknetParser.h                        | 12 ++++++------
 include/tkDNN/DetectionNN.h                          | 12 ++++++------
 include/tkDNN/test.h                                 |  8 ++++----
 include/tkDNN/utils.h                                |  6 +++---
 tests/backbones/dla34/dla34.cpp                      |  8 ++++----
 tests/backbones/resnet101/resnet101.cpp              |  8 ++++----
 tests/centernet/dla34_cnet/dla34_cnet.cpp            |  8 ++++----
 tests/centernet/resnet101_cnet/resnet101_cnet.cpp    |  8 ++++----
 tests/imuodom/imuodom.cpp                            |  4 ++--
 tests/mnist/test_mnist.cpp                           |  8 ++++----
 tests/mnist/test_mnistRT.cpp                         |  8 ++++----
 .../bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp        |  8 ++++----
 tests/mobilenet/mobilenetv2ssd/mobilenetv2ssd.cpp    |  8 ++++----
 .../mobilenetv2ssd512/mobilenetv2ssd512.cpp          |  8 ++++----
 tests/simple/test_simple.cpp                         |  8 ++++----
 tests/test_rtinference/rtinference.cpp               |  4 ++--
 16 files changed, 63 insertions(+), 63 deletions(-)

diff --git a/include/tkDNN/DarknetParser.h b/include/tkDNN/DarknetParser.h
index a918a5b..cbeed48 100644
--- a/include/tkDNN/DarknetParser.h
+++ b/include/tkDNN/DarknetParser.h
@@ -124,7 +124,7 @@ namespace tk { namespace dnn {
     }
 
     tk::dnn::Network *darknetAddNet(darknetFields_t &fields) {
-        std::cout<<"Add Net: "<<fields.type<<"\n";
+        //std::cout<<"Add Net: "<<fields.type<<"\n";
         dataDim_t dim(1, fields.channels, fields.height, fields.width);
         return new tk::dnn::Network(dim);
     }
@@ -138,10 +138,10 @@ namespace tk { namespace dnn {
         if(f.pad == 1) {
             f.padding_x = f.padding_y = f.size_x /2;
         }
-        std::cout<<"Add layer: "<<f.type<<"\n";
+        //std::cout<<"Add layer: "<<f.type<<"\n";
         if(f.type == "convolutional") {
             std::string wgs = wgs_path + "/c" + std::to_string(netLayers.size()) + ".bin";
-            printf("%d (%d,%d) (%d,%d) (%d,%d) %s %d %d\n", f.filters, f.size_x, f.size_y, f.stride_x, f.stride_y, f.padding_x, f.padding_y, wgs.c_str(), f.batch_normalize, f.groups);
+            //printf("%d (%d,%d) (%d,%d) (%d,%d) %s %d %d\n", f.filters, f.size_x, f.size_y, f.stride_x, f.stride_y, f.padding_x, f.padding_y, wgs.c_str(), f.batch_normalize, f.groups);
             tk::dnn::Conv2d *l= new tk::dnn::Conv2d(net, f.filters, f.size_x, f.size_y, f.stride_x, 
                                 f.stride_y, f.padding_x, f.padding_y, wgs, f.batch_normalize, false, f.groups);
             netLayers.push_back(l);
@@ -163,7 +163,7 @@ namespace tk { namespace dnn {
             if(layerIdx < 0)
                 layerIdx = netLayers.size() + layerIdx; 
             if(layerIdx < 0 || layerIdx >= netLayers.size()) FatalError("impossible to shortcut\n");
-            std::cout<<"shortcut to "<<layerIdx<<" "<<netLayers[layerIdx]->getLayerName()<<"\n";
+            //std::cout<<"shortcut to "<<layerIdx<<" "<<netLayers[layerIdx]->getLayerName()<<"\n";
             netLayers.push_back(new tk::dnn::Shortcut(net, netLayers[layerIdx]));
 
         } else if(f.type == "upsample") {
@@ -177,7 +177,7 @@ namespace tk { namespace dnn {
                 if(layerIdx < 0)
                     layerIdx = netLayers.size() + layerIdx; 
                 if(layerIdx < 0 || layerIdx >= netLayers.size()) FatalError("impossible to route\n");
-                std::cout<<"Route to "<<layerIdx<<" "<<netLayers[layerIdx]->getLayerName()<<"\n";
+                //std::cout<<"Route to "<<layerIdx<<" "<<netLayers[layerIdx]->getLayerName()<<"\n";
                 layers.push_back(netLayers[layerIdx]);
             }
             netLayers.push_back(new tk::dnn::Route(net, layers.data(), layers.size()));
@@ -190,7 +190,7 @@ namespace tk { namespace dnn {
 
         } else if(f.type == "yolo") {
             std::string wgs = wgs_path + "/g" + std::to_string(netLayers.size()) + ".bin";
-            printf("%d %d %s %d %f\n", f.classes, f.num/f.n_mask, wgs.c_str(), f.n_mask, f.scale_xy);
+            //printf("%d %d %s %d %f\n", f.classes, f.num/f.n_mask, wgs.c_str(), f.n_mask, f.scale_xy);
             tk::dnn::Yolo *l = new tk::dnn::Yolo(net, f.classes, f.num/f.n_mask, wgs, f.n_mask, f.scale_xy);
             if(names.size() != f.classes)
                 FatalError("Mismatch between number of classes and names");
diff --git a/include/tkDNN/DetectionNN.h b/include/tkDNN/DetectionNN.h
index 144ded5..030cf8f 100644
--- a/include/tkDNN/DetectionNN.h
+++ b/include/tkDNN/DetectionNN.h
@@ -106,14 +106,14 @@ class DetectionNN {
             originalSize.clear();
             if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30); 
             {
-                TIMER_START
+                TKDNN_TSTART
                 for(int bi=0; bi<cur_batches;++bi){
                     if(!frames[bi].data)
                         FatalError("No image data feed to detection");
                     originalSize.push_back(frames[bi].size());
                     preprocess(frames[bi], bi);    
                 }
-                TIMER_STOP
+                TKDNN_TSTOP
                 if(save_times) *times<<t_ns<<";";
             }
 
@@ -122,9 +122,9 @@ class DetectionNN {
             dim.n = cur_batches;
             {
                 if(TKDNN_VERBOSE) dim.print();
-                TIMER_START
+                TKDNN_TSTART
                 netRT->infer(dim, input_d);
-                TIMER_STOP
+                TKDNN_TSTOP
                 if(TKDNN_VERBOSE) dim.print();
                 stats.push_back(t_ns);
                 if(save_times) *times<<t_ns<<";";
@@ -132,10 +132,10 @@ class DetectionNN {
 
             batchDetected.clear();
             {
-                TIMER_START
+                TKDNN_TSTART
                 for(int bi=0; bi<cur_batches;++bi)
                     postprocess(bi, mAP);
-                TIMER_STOP
+                TKDNN_TSTOP
                 if(save_times) *times<<t_ns<<"\n";
             }
         }      
diff --git a/include/tkDNN/test.h b/include/tkDNN/test.h
index 34dab59..be3d891 100644
--- a/include/tkDNN/test.h
+++ b/include/tkDNN/test.h
@@ -34,9 +34,9 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
     tk::dnn::dataDim_t dim1 =  net->input_dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net->infer(dim1, data);    
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();   
     }
     for(int i=0; i<outputs.size(); i++) cudnn_out[i] = outputs[i]->dstData;
@@ -45,9 +45,9 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
         tk::dnn::dataDim_t dim2 = net->input_dim;
         printCenteredTitle(" TENSORRT inference ", '=', 30); {
             dim2.print();
-            TIMER_START
+            TKDNN_TSTART
             netRT->infer(dim2, data);
-            TIMER_STOP
+            TKDNN_TSTOP
             dim2.print();
         }
         for(int i=0; i<outputs.size(); i++) rt_out[i] = (dnnType*)netRT->buffersRT[i+1];
diff --git a/include/tkDNN/utils.h b/include/tkDNN/utils.h
index cc9a4cd..f9f6ae7 100644
--- a/include/tkDNN/utils.h
+++ b/include/tkDNN/utils.h
@@ -39,15 +39,15 @@
 #define TKDNN_VERBOSE 0
 
 // Simple Timer 
-#define TIMER_START timespec start, end;                               \
+#define TKDNN_TSTART timespec start, end;                               \
                     clock_gettime(CLOCK_MONOTONIC, &start);            
 
-#define TIMER_STOP_C(col, show)  clock_gettime(CLOCK_MONOTONIC, &end);       \
+#define TKDNN_TSTOP_C(col, show)  clock_gettime(CLOCK_MONOTONIC, &end);       \
     double t_ns = ((double)(end.tv_sec - start.tv_sec) * 1.0e9 +       \
                   (double)(end.tv_nsec - start.tv_nsec))/1.0e6;        \
     if(show) std::cout<<col<<"Time:"<<std::setw(16)<<t_ns<<" ms\n"<<COL_END; 
 
-#define TIMER_STOP TIMER_STOP_C(COL_CYANB, TKDNN_VERBOSE)
+#define TKDNN_TSTOP TKDNN_TSTOP_C(COL_CYANB, TKDNN_VERBOSE)
 
 /********************************************************
  * Prints the error message, and exits
diff --git a/tests/backbones/dla34/dla34.cpp b/tests/backbones/dla34/dla34.cpp
index 4440e44..f3e287d 100644
--- a/tests/backbones/dla34/dla34.cpp
+++ b/tests/backbones/dla34/dla34.cpp
@@ -312,9 +312,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
     cudnn_out = net.layers[net.num_layers-1]->dstData;
@@ -326,9 +326,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
     rt_out = (dnnType *)netRT.buffersRT[1];
diff --git a/tests/backbones/resnet101/resnet101.cpp b/tests/backbones/resnet101/resnet101.cpp
index 5bb6784..50d6c7e 100644
--- a/tests/backbones/resnet101/resnet101.cpp
+++ b/tests/backbones/resnet101/resnet101.cpp
@@ -301,9 +301,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
     cudnn_out = net.layers[net.num_layers-1]->dstData;
@@ -314,9 +314,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
     rt_out = (dnnType *)netRT.buffersRT[1];
diff --git a/tests/centernet/dla34_cnet/dla34_cnet.cpp b/tests/centernet/dla34_cnet/dla34_cnet.cpp
index 1461094..d62a203 100644
--- a/tests/centernet/dla34_cnet/dla34_cnet.cpp
+++ b/tests/centernet/dla34_cnet/dla34_cnet.cpp
@@ -486,9 +486,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
 
@@ -496,9 +496,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/centernet/resnet101_cnet/resnet101_cnet.cpp b/tests/centernet/resnet101_cnet/resnet101_cnet.cpp
index d48043b..7554923 100644
--- a/tests/centernet/resnet101_cnet/resnet101_cnet.cpp
+++ b/tests/centernet/resnet101_cnet/resnet101_cnet.cpp
@@ -361,9 +361,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
 
@@ -373,9 +373,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/imuodom/imuodom.cpp b/tests/imuodom/imuodom.cpp
index 24be585..b924760 100644
--- a/tests/imuodom/imuodom.cpp
+++ b/tests/imuodom/imuodom.cpp
@@ -46,10 +46,10 @@ int main() {
     int ret_cudnn = 0; 
     for(int i=0; i<N; i++) {
         std::cout<<"i: "<<i<<"\n";
-        //TIMER_START
+        //TKDNN_TSTART
         // Inference
         ImuNet.update(i0_h, i1_h, i2_h);
-        //TIMER_STOP
+        //TKDNN_TSTOP
 
         // log path
         path<<ImuNet.odomPOS(0)<<" "<<ImuNet.odomPOS(1)<<" "<< ImuNet.odomPOS(2)<<" ";
diff --git a/tests/mnist/test_mnist.cpp b/tests/mnist/test_mnist.cpp
index 5aa39bb..d537494 100644
--- a/tests/mnist/test_mnist.cpp
+++ b/tests/mnist/test_mnist.cpp
@@ -35,9 +35,9 @@ int main() {
 
     std::cout<<"CUDNN inference:\n"; {
         dim.print(); //print initial dimension  
-        TIMER_START
+        TKDNN_TSTART
         out_data = net.infer(dim, data);    
-        TIMER_STOP
+        TKDNN_TSTOP
         dim.print();   
     }
 
@@ -49,9 +49,9 @@ int main() {
 
     std::cout<<"TENSORRT inference:\n"; {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         out_data2 = netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/mnist/test_mnistRT.cpp b/tests/mnist/test_mnistRT.cpp
index be1dc08..1b4e9c3 100644
--- a/tests/mnist/test_mnistRT.cpp
+++ b/tests/mnist/test_mnistRT.cpp
@@ -49,9 +49,9 @@ int main() {
     
     // Inference
     {
-        TIMER_START
+        TKDNN_TSTART
         data = net.infer(dim, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim.print();   
     }
 
@@ -157,9 +157,9 @@ int main() {
     {
         checkCuda(cudaMemcpyAsync(buffers[inputIndex], input_h, 1 * 28*28* sizeof(float), cudaMemcpyHostToDevice, stream));
         cudaStreamSynchronize(stream);  //want to test only the inference time
-        TIMER_START
+        TKDNN_TSTART
         context->enqueue(1, buffers, stream, nullptr);
-        TIMER_STOP
+        TKDNN_TSTOP
         checkCuda(cudaMemcpyAsync(output, buffers[outputIndex],10*sizeof(float), cudaMemcpyDeviceToHost, stream));
         cudaStreamSynchronize(stream);
     }
diff --git a/tests/mobilenet/bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp b/tests/mobilenet/bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp
index 1549983..c3c6472 100644
--- a/tests/mobilenet/bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp
+++ b/tests/mobilenet/bdd-mobilenetv2ssd/bdd-mobilenetv2ssd.cpp
@@ -477,9 +477,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
 
@@ -492,9 +492,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/mobilenet/mobilenetv2ssd/mobilenetv2ssd.cpp b/tests/mobilenet/mobilenetv2ssd/mobilenetv2ssd.cpp
index 787341f..58463a4 100644
--- a/tests/mobilenet/mobilenetv2ssd/mobilenetv2ssd.cpp
+++ b/tests/mobilenet/mobilenetv2ssd/mobilenetv2ssd.cpp
@@ -477,9 +477,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
 
@@ -492,9 +492,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/mobilenet/mobilenetv2ssd512/mobilenetv2ssd512.cpp b/tests/mobilenet/mobilenetv2ssd512/mobilenetv2ssd512.cpp
index 8886232..54b00c1 100644
--- a/tests/mobilenet/mobilenetv2ssd512/mobilenetv2ssd512.cpp
+++ b/tests/mobilenet/mobilenetv2ssd512/mobilenetv2ssd512.cpp
@@ -476,9 +476,9 @@ int main()
     printCenteredTitle(" CUDNN inference ", '=', 30);
     {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
 
@@ -491,9 +491,9 @@ int main()
     printCenteredTitle(" TENSORRT inference ", '=', 30);
     {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/simple/test_simple.cpp b/tests/simple/test_simple.cpp
index b2b0441..10b0d2f 100644
--- a/tests/simple/test_simple.cpp
+++ b/tests/simple/test_simple.cpp
@@ -38,18 +38,18 @@ int main() {
     tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
-        TIMER_START
+        TKDNN_TSTART
         out_data = net.infer(dim1, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim1.print();
     }
 
     tk::dnn::dataDim_t dim2 = dim;
     printCenteredTitle(" TENSORRT inference ", '=', 30); {
         dim2.print();
-        TIMER_START
+        TKDNN_TSTART
         out_data2 = netRT.infer(dim2, data);
-        TIMER_STOP
+        TKDNN_TSTOP
         dim2.print();
     }
 
diff --git a/tests/test_rtinference/rtinference.cpp b/tests/test_rtinference/rtinference.cpp
index 4b6b21f..a629168 100644
--- a/tests/test_rtinference/rtinference.cpp
+++ b/tests/test_rtinference/rtinference.cpp
@@ -42,9 +42,9 @@ int main(int argc, char *argv[]) {
         checkCuda(cudaMemcpy(input_d, input, idim.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
 
         tk::dnn::dataDim_t dim = idim;
-        TIMER_START
+        TKDNN_TSTART
         netRT.infer(dim, input_d);
-        TIMER_STOP
+        TKDNN_TSTOP
         total_time+= t_ns;
 
         // control output