Shelfnet works on tensorRT (shortcut need to be fixed)

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

include/tkDNN/Layer.h

@@ -225,8 +225,9 @@ class Activation : public Layer {
 public:
     int act_mode;
     float ceiling;
+    float slope;
 
-    Activation(Network *net, int act_mode, const float ceiling=0.0); 
+    Activation(Network *net, int act_mode, const float ceiling=0.0, const float slope=0.1); 
     virtual ~Activation();
     virtual layerType_t getLayerType() { 
         if(act_mode == CUDNN_ACTIVATION_CLIPPED_RELU)

include/tkDNN/NetworkRT.h

@@ -105,6 +105,7 @@ public:
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Route *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Flatten *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reshape *l);
+    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Resize *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reorg *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Region *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Shortcut *l);

include/tkDNN/kernels.h

@@ -4,7 +4,7 @@
 #include "utils.h"
 
 void activationELUForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
-void activationLEAKYForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
+void activationLEAKYForward(dnnType *srcData, dnnType *dstData, int size, float slope, cudaStream_t stream = cudaStream_t(0));
 void activationReLUCeilingForward(dnnType *srcData, dnnType *dstData, int size, const float ceiling, cudaStream_t stream = cudaStream_t(0));
 void activationLOGISTICForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));
 void activationSIGMOIDForward(dnnType *srcData, dnnType *dstData, int size, cudaStream_t stream = cudaStream_t(0));

include/tkDNN/pluginsRT/ActivationLeakyRT.h

@@ -4,9 +4,8 @@
 class ActivationLeakyRT : public IPlugin {
 
 public:
-	ActivationLeakyRT() {
+	ActivationLeakyRT(float s) {
-
+		slope = s;
-
 	}
 
 	~ActivationLeakyRT(){
@@ -42,19 +41,21 @@ public:
 	virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {
 
 		activationLEAKYForward((dnnType*)reinterpret_cast<const dnnType*>(inputs[0]), 
-											reinterpret_cast<dnnType*>(outputs[0]), batchSize*size, stream);
+											reinterpret_cast<dnnType*>(outputs[0]), batchSize*size, slope, stream);
 		return 0;
 	}
 
 
 	virtual size_t getSerializationSize() override {
-		return 1*sizeof(int);
+		return 1*sizeof(int) + 1*sizeof(float);
 	}
 
 	virtual void serialize(void* buffer) override {
 		char *buf = reinterpret_cast<char*>(buffer);
+		tk::dnn::writeBUF(buf, slope);
 		tk::dnn::writeBUF(buf, size);
 	}
 
 	int size;
+	float slope;
 };

src/Activation.cpp

@@ -5,11 +5,12 @@
 
 namespace tk { namespace dnn {
 
-Activation::Activation(Network *net, int act_mode, const float ceiling) : 
+Activation::Activation(Network *net, int act_mode, const float ceiling, const float slope) : 
     Layer(net) {
 
-    this->act_mode = act_mode;
+    this->act_mode  = act_mode;
-    this->ceiling = ceiling;
+    this->ceiling   = ceiling;
+    this->slope     = slope;
     checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );
 
     if(int(act_mode) < 100) {
@@ -46,8 +47,7 @@ Activation::~Activation() {
 
 dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
     if(act_mode == ACTIVATION_LEAKY) {
-        activationLEAKYForward(srcData, dstData, dim.tot());
+        activationLEAKYForward(srcData, dstData, dim.tot(), this->slope);
-
     }
     else if(act_mode == ACTIVATION_MISH) {
         activationMishForward(srcData, dstData, dim.tot());

src/NetworkRT.cpp

@@ -236,6 +236,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
         return convert_layer(input, (Flatten*) l);
     if(type == LAYER_RESHAPE)
         return convert_layer(input, (Reshape*) l);
+    if(type == LAYER_RESIZE)
+        return convert_layer(input, (Resize*) l);
     if(type == LAYER_REORG)
         return convert_layer(input, (Reorg*) l);
     if(type == LAYER_REGION)
@@ -389,13 +391,13 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Activation *l) {
         
 #if NV_TENSORRT_MAJOR < 6                
         // plugin version
-        IPlugin *plugin = new ActivationLeakyRT();
+        IPlugin *plugin = new ActivationLeakyRT(l->slope);
         IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
         checkNULL(lRT);
         return lRT;
 #else 
         IActivationLayer *lRT = networkRT->addActivation(*input, ActivationType::kLEAKY_RELU);
-        lRT->setAlpha(0.1);
+        lRT->setAlpha(l->slope);
         checkNULL(lRT);
         return lRT;
 #endif
@@ -469,13 +471,22 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Flatten *l) {
 ILayer* NetworkRT::convert_layer(ITensor *input, Reshape *l) {
     // std::cout<<"convert Reshape\n";
 
-    l->output_dim.print();
     IPlugin *plugin = new ReshapeRT(l->output_dim);
     IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
     checkNULL(lRT);
     return lRT;
 }
 
+ILayer* NetworkRT::convert_layer(ITensor *input, Resize *l) {
+    // std::cout<<"convert Resize\n";
+
+    IResizeLayer *lRT = networkRT->addResize(*input); //default is kNEAREST
+    checkNULL(lRT);
+    Dims d{};
+    lRT->setOutputDimensions(DimsCHW{l->output_dim.c, l->output_dim.h, l->output_dim.w});
+    return lRT;
+}
+
 ILayer* NetworkRT::convert_layer(ITensor *input, Reorg *l) {
     //std::cout<<"convert Reorg\n";
 
@@ -503,7 +514,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Shortcut *l) {
     
     ITensor *back_tens = tensors[l->backLayer];
 
-    if(l->backLayer->output_dim.c == l->output_dim.c)
+    if(false) //l->backLayer->output_dim.c == l->output_dim.c && !l->mul) FIXME
     {
         IElementWiseLayer *lRT = networkRT->addElementWise(*input, *back_tens, ElementWiseOperation::kSUM);
         checkNULL(lRT);
@@ -641,7 +652,7 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
     //std::cout<<name<<std::endl;
 
     if(name.find("ActivationLeaky") == 0) {
-        ActivationLeakyRT *a = new ActivationLeakyRT();
+        ActivationLeakyRT *a = new ActivationLeakyRT(readBUF<float>(buf));
         a->size = readBUF<int>(buf);
         return a;
     }

src/Shortcut.cpp

@@ -11,11 +11,9 @@ Shortcut::Shortcut(Network *net, Layer *backLayer, bool mul) : Layer(net) {
     this->mul = mul;
     checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
 
-    //FIXME
+    if( ( backLayer->output_dim.c != input_dim.c && mul ) ||
-    // if( /*backLayer->output_dim.c != input_dim.c ||*/
+        (( backLayer->output_dim.w != input_dim.w ||  backLayer->output_dim.h != input_dim.h ) && !mul ) )  
-    //     backLayer->output_dim.w != input_dim.w ||
+        FatalError("Shortcut dim missmatch");
-    //     backLayer->output_dim.h != input_dim.h   )
-    //     FatalError("Shortcut dim missmatch");
     
 }
 

src/kernels/activation_leaky.cu

@@ -1,7 +1,7 @@
 #include "kernels.h"
 
 __global__
-void activation_leaky(dnnType *input, dnnType *output, int size) {
+void activation_leaky(dnnType *input, dnnType *output, int size, float slope) {
 
     int i = blockDim.x*blockIdx.x + threadIdx.x;
 
@@ -9,7 +9,7 @@ void activation_leaky(dnnType *input, dnnType *output, int size) {
         if (input[i]>0)
             output[i] = input[i];
         else
-            output[i] = 0.01f*input[i]; //FIME!!
+            output[i] = slope*input[i];
     }
  }
 
@@ -17,12 +17,12 @@ void activation_leaky(dnnType *input, dnnType *output, int size) {
 /**
     ELU activation function
 */
-void activationLEAKYForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream)
+void activationLEAKYForward(dnnType* srcData, dnnType* dstData, int size, float slope, cudaStream_t stream)
 {
     int blocks = (size+255)/256;
     int threads = 256;
     
-    activation_leaky<<<blocks, threads, 0, stream>>>(srcData, dstData, size);
+    activation_leaky<<<blocks, threads, 0, stream>>>(srcData, dstData, size, slope);
 }
 
 

tests/shelfnet/shelfnet.cpp

@@ -6,8 +6,6 @@
 #include "NetworkViz.h"
 
 
-const char *output_bin1 = "shelfnet/debug/classification_headers-5.bin";
-const char *output_bin2 = "shelfnet/debug/regression_headers-5.bin";
 const char *input_bin = "shelfnet/debug/input.bin";
 
 const char *backbone[] = {
@@ -95,14 +93,14 @@ int main()
 
     int bi = 0, di = 0, li = 0, ci = 0;
     new tk::dnn::Conv2d(&net, 64, 7, 7, 2, 2, 3, 3, backbone[bi++], true);
-    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
     tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
 
 
     
     for(int i=0; i<2; ++i){
         new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
-        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
         new tk::dnn::Shortcut(&net, last);
         last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
@@ -113,7 +111,7 @@ int main()
         int out_channel = pow(2,7+i);
         std::cout<<out_channel<<std::endl;
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
-        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
         new tk::dnn::Route(&net, &last, 1);
         new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
@@ -121,7 +119,7 @@ int main()
         last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
 
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
-        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
         
         new tk::dnn::Shortcut(&net, last);
@@ -133,7 +131,7 @@ int main()
         new tk::dnn::Route(&net, &features[i], 1);
         int out_channel = pow(2,6+i);
         new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
-        features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
     }
 
     //DECODER
@@ -142,7 +140,7 @@ int main()
     std::vector<tk::dnn::Layer*> up_out;
     //bottom
     new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
-    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
     new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
     new tk::dnn::Shortcut(&net, last);
     last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
@@ -153,7 +151,7 @@ int main()
         //up-conv
         std::cout<<out_channel<<std::endl;
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
-        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         
         new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
         new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, decoder[di++], true);
@@ -168,7 +166,7 @@ int main()
 
         //up-dense
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
-        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         up_out.push_back(last);
     }
 
@@ -176,7 +174,7 @@ int main()
 
     std::vector<tk::dnn::Layer*> down_out;
     new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
-    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
     new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
     new tk::dnn::Shortcut(&net, last);
     new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
@@ -186,7 +184,7 @@ int main()
         tk::dnn::Layer* l_last = new tk::dnn::Shortcut(&net, up_out[2-i]);
     
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
-        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
         new tk::dnn::Shortcut(&net, l_last);
         l_last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
@@ -197,7 +195,7 @@ int main()
     }
 
     new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
-    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
     new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
     new tk::dnn::Shortcut(&net, last);
     last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
@@ -208,7 +206,7 @@ int main()
         int out_channel = pow(2,7-i);
         //up-conv
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
-        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         
         new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
         new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, ladder[li++], true);
@@ -223,7 +221,7 @@ int main()
 
         // //up-dense
         new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
-        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         up_out.push_back(last);
     }
 
@@ -231,29 +229,26 @@ int main()
     // for(int i=2;i>=0;--i){
         // new tk::dnn::Route(&net, &up_out[i], 1);
         new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
-        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
         new tk::dnn::Conv2d (&net, 19, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
-        /*up_out[i] =*/ new tk::dnn::Resize(&net, 19, net.input_dim.h, net.input_dim.w, true);
+        // /*up_out[i] =*/ new tk::dnn::Resize(&net, 19, net.input_dim.h, net.input_dim.w, true);
     // }
 
-    new tk::dnn::Softmax(&net);
+    // new tk::dnn::Softmax(&net);
-    
-    const char *output_bin = "shelfnet/debug/fofmaf.bin";
-
     
+    const char *output_bin = "shelfnet/debug/conv_out-conv_out.bin";
     
     // Load input
     dnnType *data;
     dnnType *input_h;
     readBinaryFile(input_bin, dim.tot(), &input_h, &data);
     std::cout<<"Input:"<<std::endl;
-    // printDeviceVector(64, data, true);
 
     //print network model
     net.print();
 
     // // convert network to tensorRT
-    // tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet"));
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet"));
 
     tk::dnn::dataDim_t dim1 = dim; //input dim
     dnnType *cudnn_out = nullptr;
@@ -266,67 +261,35 @@ int main()
         dim1.print();
     }
 
-    // tk::dnn::dataDim_t out_dim1 = conf5[0]->output_dim;
+    tk::dnn::dataDim_t dim2 = dim;
-    // dnnType *cudnn_out2 = loc5[0]->dstData;
+    printCenteredTitle(" TENSORRT inference ", '=', 30);
-    // tk::dnn::dataDim_t out_dim2 = loc5[0]->output_dim;
+    {
+        dim2.print();
+        TKDNN_TSTART
+        netRT.infer(dim2, data);
+        TKDNN_TSTOP
+        dim2.print();
+    }
 
-    // tk::dnn::dataDim_t dim2 = dim;
+    dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
-    // printCenteredTitle(" TENSORRT inference ", '=', 30);
-    // {
-    //     dim2.print();
-    //     TKDNN_TSTART
-    //     netRT.infer(dim2, data);
-    //     TKDNN_TSTOP
-    //     dim2.print();
-    // }
 
-    // dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
+    printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
-    // dnnType *rt_out2 = (dnnType *)netRT.buffersRT[2];
-    // dnnType *rt_out3 = (dnnType *)netRT.buffersRT[3];
-    // dnnType *rt_out4 = (dnnType *)netRT.buffersRT[4];
-
-    printCenteredTitle(std::string(" RESNET CHECK RESULTS ").c_str(), '=', 30);
     dnnType *out1, *out1_h;
     int odim1 = dim1.tot();
     readBinaryFile(output_bin, odim1, &out1_h, &out1);
 
-    printDeviceVector(64, out1);
+    int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
-
-    // dnnType *out2, *out2_h;
-    // int odim2 = out_dim2.tot();
-    // readBinaryFile(output_bin2, odim2, &out2_h, &out2);
-    // int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
-
     std::cout << "CUDNN vs correct" << std::endl;
-    checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
+    ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
 
-    // std::cout << "TRT   vs correct" << std::endl;
+    std::cout << "TRT   vs correct" << std::endl;
-    // checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
+    ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
-    // ret_tensorrt |= checkResult(odim2, rt_out2, out2) == 0 ? 0 : ERROR_TENSORRT;
 
-    // std::cout << "CUDNN vs TRT    " << std::endl;
+    std::cout << "CUDNN vs TRT    " << std::endl;
-    // ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out1, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
+    ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
-    // ret_cudnn_tensorrt |= checkResult(odim2, cudnn_out2, rt_out2) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
-
-    // std::cout << "---------------------------------------------------" << std::endl;
-    // std::cout << "Confidence CUDNN" << std::endl;
-    // printDeviceVector(64, conf->dstData, true);
-    // std::cout << "Locations CUDNN" << std::endl;
-    // printDeviceVector(64, loc->dstData, true);
-    // std::cout << "---------------------------------------------------" << std::endl;
-
-    // std::cout << "Confidence tensorRT" << std::endl;
-    // printDeviceVector(64, rt_out3, true);
-    // std::cout << "Locations tensorRT" << std::endl;
-    // printDeviceVector(64, rt_out4, true);
-    // std::cout << "---------------------------------------------------" << std::endl;
-
-    // std::cout << "CUDNN vs TRT    " << std::endl;
-    // ret_cudnn_tensorrt |= checkResult(conf->output_dim.tot(), conf->dstData, rt_out3) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
-    // ret_cudnn_tensorrt |= checkResult(loc->output_dim.tot(), loc->dstData, rt_out4) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
-
-    // return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
     
     cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
     cv::imwrite("test.png", viz);
+
+    return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
 }