Batchnorm eps fix, works on jetpack 4.3

README.md

@@ -3,9 +3,10 @@ tkDNN is a Deep Neural Network library built with cuDNN primitives specifically
 The main scope is to do high performance inference on already trained models.
 
 this branch actually work on every NVIDIA GPU that support the dependencies:
-* CUDA 9
-* CUDNN 7.105
-* TENSORRT 4.02
+* CUDA 10.0
+* CUDNN 7.603
+* TENSORRT 6.01
+* OPENCV 4.1
 
 ## Dependencies
 

include/Layer.h

@@ -27,6 +27,8 @@ enum layerType_t
     LAYER_YOLO
 };
 
+#define TKDNN_BN_MIN_EPSILON 1e-5
+
 /** 
     Simple layer Father class
 */

src/Conv2d.cpp

@@ -120,11 +120,11 @@ dnnType *Conv2d::infer(dataDim_t &dim, dnnType *srcData)
         float one = 1;
         float zero = 0;
         cudnnBatchNormalizationForwardInference(net->cudnnHandle,
-                                                CUDNN_BATCHNORM_SPATIAL, &one, &zero,
-                                                dstTensorDesc, dstData, dstTensorDesc,
-                                                dstData, biasTensorDesc, //same tensor descriptor as bias
-                                                scales_d, bias_d, mean_d, variance_d,
-                                                CUDNN_BN_MIN_EPSILON);
+            CUDNN_BATCHNORM_SPATIAL, &one, &zero, 
+            dstTensorDesc, dstData, dstTensorDesc, 
+            dstData, biasTensorDesc, //same tensor descriptor as bias 
+            scales_d, bias_d, mean_d, variance_d, 
+            TKDNN_BN_MIN_EPSILON);
     }
     //update data dimensions
     dim = output_dim;

src/LayerWgs.cpp

@@ -34,7 +34,7 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
         seek += outputs;
         readBinaryFile(weights_path.c_str(), outputs, &variance_h, &variance_d, seek);
 
-        float eps = CUDNN_BN_MIN_EPSILON;
+        float eps = TKDNN_BN_MIN_EPSILON;
 
         power_h = new dnnType[outputs];
         for (int i = 0; i < outputs; i++)

src/Network.cpp

@@ -21,10 +21,6 @@ Network::Network(dataDim_t input_dim)
               << ", CUDNN v" << cu_ver << ")\n";
     dataType = CUDNN_DATA_FLOAT;
     tensorFormat = CUDNN_TENSOR_NCHW;
-
-    checkCUDNN(cudnnCreate(&cudnnHandle));
-    checkERROR(cublasCreate(&cublasHandle));
-
     num_layers = 0;
 
     fp16 = false;
@@ -39,11 +35,16 @@ Network::Network(dataDim_t input_dim)
             fp16 = true;
         }
     }
+   
+    if(fp16)
+        std::cout<<COL_REDB<<"!! FP16 INERENCE ENABLED !!"<<COL_END<<"\n";
+    if(dla)
+        std::cout<<COL_GREENB<<"!! DLA INERENCE ENABLED !!"<<COL_END<<"\n";
+
+
+    checkCUDNN( cudnnCreate(&cudnnHandle) );
+    checkERROR( cublasCreate(&cublasHandle) );
 
-    if (fp16)
-        std::cout << COL_REDB << "!! FP16 INERENCE ENABLED !!" << COL_END << "\n";
-    if (dla)
-        std::cout << COL_GREENB << "!! DLA INERENCE ENABLED !!" << COL_END << "\n";
 }
 
 Network::~Network()